TY - GEN A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - Ontology Based Decision Making for Process Control T2 - 2019 AIChE Annual Meeting N2 - In the context of Industry 4.0, engineering systems and manufacturing processes are becoming increasingly complex, combining the physical world of the processing units with the cyber world of the wireless sensing and communication networks, big data analytics, ubiquitous computing and other elements that the Industrial Internet of Things technologies. The fields of ontology, knowledge management and decision-making systems have matured significantly in the recent years and their integration with the cyber-physical system (CPS) facilitates and improves the effectiveness of decision-support systems (DSS) [1]. Yet, this comes with an increase in the system’s complexity and a need for deployment of intelligent systems for process systems engineering (PSE) applications, that should adapt to the continuously new requirements of Industry 4.0. Considering the large number of devices existent in a CPS, distributed methods are required to transfer the computational load from centralised to local (decentralised) controllers. This has led to the motivation of applying multi-agent systems (MASs) methodologies as a solution to distributed control as a computational paradigm [2]. An agent can be defined as an entity placed in an environment that can sense different parameters used to make a decision based on the goals of the entity. A MAS is a computerised system composed of multiple interacting agents exploited to solve a problem. Their salient features, which include efficiency, low cost, flexibility, and reliability, make it an effective solution for solving tasks [3]. Usually, DSS adopt a rule-based or logic-based representation scheme [4]. For this reason, ontologies have attracted the attention of the PSE community as a convenient means for knowledge representation [1, 5]. An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts, and it serves as a library of knowledge to efficiently build intelligent systems and as a shared vocabulary for communication between interacting human and/or software agents [6]. In this paper, a multi-agent cooperative-based model predictive control (MPC) system for monitoring and control of a chemical process is proposed. The system uses ontology to formally represent the system knowledge at process, communication and decision-making level. The application of the proposed framework is discussed for a chemical process that produces iso-octane. A cooperative MPC is implemented to achieve the control of the plant. This protocol is defined using a simple algorithm to reach an agreement regarding the state of a number of N agents [7]. The monitoring feature is defined by means of a MAS, consisting of follower agents (FAs), a coordinator agent (CA) and a monitor agent (MoA), that is integrated with the MPC. The MAS has two main tasks: a) decide optimal connectivity between the distributed MPCs for safer and better operation; and b) monitor the system and detect any deviation in the behaviour. The addition of the MAS makes the cooperative MPC controller more efficient by taking advantage of the communication between the various elements of the CPS. Using the knowledge form the ontology and the agents’ sharing capabilities, the system can detect faster any deviation compared to standard operation. The framework can easily be adapted for other control approaches by very simple modifications in the structure and objectives. A practical demonstration in a pilot plant environment is envisaged for the future. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/145e-ontology-based-decision-making-process-control SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Yusuf, Ifrah A1 - Dorneanu, Bogdan A1 - Avignone-Rossa, Claudio A1 - Arellano-García, Harvey T1 - Synthesis and Characterization of Hydrochars Produced By Hydrothermal Carbonization of Banana Peels T2 - 2019 AIChE Annual Meeting N2 - Hydrothermal carbonisation (HTC) is a thermochemical process which imitates the natural coalification of biomass. If the natural process requires some hundred to some million years, depending on the type of coal produced, HTC needs less than half a day for the transformation of biomass into materials quite similar to brown coal [1].The organic feedstock is reacted with water at mild temperature (130-300 0C) compared to other thermochemical processes such as pyrolysis, gasification, or flash carbonisation, and under autogenous pressures (10-96 bar). The result is a homogeneous carbon-rich solid, a high-strength process liquid, and a gaseous product mainly consisting of CO2 [2]. Compared to the biomass feedstock, biochar possesses a higher heating value and higher carbon content, has a lower ash content, more surface oxygen-containing groups, and it can lead to lower emissions of greenhouse gases [3]. The difference in chemical composition of the final products depend on the reaction mechanisms that occur during HTC, which include hydrolysis, dehydration, decarboxylation, aromatization and re-condensation. Although these processes generally occur in this order, they do not operate in a successive manner; instead they occur simultaneously during HTC and are interconnected with each other [4]. The main purpose of this work is to evaluate the technological feasibility of converting banana peel residues in useful products using the HTC, towards a localised production strategy to harness the value of the waste for improving the livelihoods of rural agricultural communities. A study of the prevailing reactions, their rates and products from banana peel processing through HTC is used to support the optimisation of the reactor design. The products’ yield is influenced by factors such as temperature, feed solid content, the nature of the biomass, and residence time. A detailed characterisation of all the products obtained from HTC is conducted. Considerable effort is needed to comprehend their stability and quality and thereby the ongoing process reactions and upgrading needs. Characterisation methods, such as GC/MS NMR, and HPLC for product analysis are critical to understand the nature of the reactive species influencing product quality and yield. Additionally, as efficient separation from an aqueous phase increases the yield of useful products, the separation of the main products and water is investigated. Furthermore, the feasibility of the recycle and re-use of the process water is analysed. The improvement and reuse of the hydrochar are appealing for applications such as solid fuel, pre-cursor for activated carbon, adsorbent, soil amendment or carbon sequestering biochar. Moreover, the use of HTC to convert banana peels into products such as hydrochar or bio-oil will enable the local rural communities create value from something they are discarding as waste. The hydrochar, processed into pellet form to increase its bulk density in order to reduce storage and transportation costs, can be directly used as a solid fuel that can be burned for energy. This is particularly effective for small and medium farms dispersed over extended areas, due to the significant reduction of expenses and environmental impact. The hydrochar can be added to soil to enhance the effects of the fertilisers, by reducing the amount of fertiliser lost through surface run-off. In addition, it increases the amount of water that can be retained by sandy soils, with a low available water capacity. The use of banana peels will produce highly effective sorbent hydrochars to be used for heavy metals removal from water. Moreover, our results suggest that the liquid fraction obtained from the hydrothermal processing of banana peel is a good feedstock for Microbial Fuel Cells. The hydrochar obtained in the process has shown to present several properties, such as the removal of various types of pollutants from contaminated waters. Therefore, the integration of HTC to convert banana peels into hydrochar and the utilization of the liquid by-product as feedstock for bioelectrochemical system (BES) technology enables full utilization of an otherwise recalcitrant waste. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/397d-synthesis-and-characterization-hydrochars-produced-hydrothermal-carbonization-banana-peels SN - 978-0-8169-1112-7 ER - TY - GEN A1 - De Mel, Ishanki A1 - Mechleri, Evgenia A1 - Demis, Panagiotis A1 - Dorneanu, Bogdan A1 - Klymenko, Oleksiy A1 - Arellano-García, Harvey T1 - A Methodology for Global Sensitivity Analysis for the Operation of Distributed Energy Systems Using a Two-Stage Approach T2 - 2019 AIChE Annual Meeting N2 - Optimisation-based models are often employed for the design and operation of distributed energy systems (DES). A two-stage approach often involves the optimisation of the design of a distributed energy system for a specified location or scale, and the subsequent optimisation of the operational model based on the structure recommended by the design model. The structure includes what types of generation and storage technologies should be used in the operation, related capacities and sizes, and potential locations. Often, both design and operational models are deterministic in nature, as either past or fictitious data is fed into the models to minimise an objective function such as the total cost or environmental impact due to carbon emissions. Consequently, the operational models encounter challenges when real-time data is fed, as time-variant input variables such as electricity demand, heating demand and solar insolation can be deemed uncertain. These variables could have unexpected and significant impacts on the total costs involved with the operation of distributed energy systems, leading to sub-optimality or even infeasibilities. Identifying these input variables, quantifying their uncertainties (which are then described in the models), and evaluating the influence of these variables on the outputs can lead to the design of more robust models. Such models can then be used to design and operate optimal distributed energy systems. This paper presents a novel methodology for using global sensitivity analysis (GSA) on an operational optimisation-based model of a distributed energy system. The operational model also utilises Model Predictive Control (MPC) rolling horizon concepts (as done by [1]) to determine hourly total operational costs. The paper also addresses how some challenges and limitations encountered in the operational model can be attributed to the deterministic design model on which the structure of the operational model has been based. Furthermore, the research explores how the design can be improved to support more robust operation. Another novel aspect of this paper highlights the use of the optimisation tool GAMS alongside SobolGSA, a global sensitivity analysis software [2]. This software uses the variance-based Sobol method to generate N samples and perform global sensitivity analysis, allowing users to understand how variations in the inputs can influence the outputs, whilst accounting for the different combinations of the uncertain parameters without varying one uncertain parameter at a time. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/373ah-methodology-global-sensitivity-analysis-operation-distributed-energy-systems-using-two-stage SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Mechleri, Evgenia A1 - Shafiei, Zarif A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Klymenko, Oleksiy T1 - A Blockchain Model for Residential Distributed Energy Resources Networks T2 - 2019 AIChE Annual Meeting N2 - The energy production landscape is reshaped by distributed energy resources (DERs) – photovoltaic (PV) panels, combined heat and power (CHP), wind turbines (WT), fuel cells or battery storage systems, to name just a few [1]. Microgrids, collections of units or DERs that are locally controlled, close to the consumption point and cooperating with each other and the centralised grid [2], allow for the reduction in energy losses compared to traditional generation due to the close proximity to end users. Due to its volatility, the integration of this non-controllable generation poses severe challenges to the current energy system and ensuring a reliable balance of energy becomes an increasingly demanding task [3]. The optimal design and scheduling of the DERs and subsequent microgrid is of high importance in order to increase the reliability and determine their effectiveness in reducing losses, emissions and costs compared to conventional generation so that they may be implemented at faster rates to reduce global emissions and fossil fuel usage. In distributed energy systems, individual users typically have flexible tariffs while they also have the capability not only to use, but also to store and trade electric power. Direct transactions schemes can save money for end users, generate revenues for producers, reduce transmission losses and promote the use of renewable energy [4]. But it must be a robust, efficient and low-cost trading system to handle the rapid changes of information and value in the system. The blockchain technology can fulfil these requirements by enabling the implementation of optimal energy management strategies through distributed databases. Since its introduction as the underlying technology of Bitcoin, the blockchain technology has emerged from its use as a verification mechanism for cryptocurrencies and heads to a broader field of applications. Blockchain-based systems are basically a combination of a distributed ledger, a decentralised consensus mechanism, and cryptographic security measures [5]. More precisely, it allows the resolution of conflicts and dismantles information asymmetries by providing transparent and valid records of past transactions that cannot be altered retrospectively [6]. With the help of specific algorithms and applications, multiple operations can be performed automatically on the blockchain, using this information together with information from the Internet or the real world (e.g. on whether, energy pricing, etc.). Furthermore, smart contracts can be implemented between the nodes of the microgrid. This paper introduces a model for the implementation of a blockchain and smart contracts into the scheduling of a residential DER network. The blockchain is implemented in terms of energy rather than voltages [7], to allow for the decentralised operation of the microgrid without a centralised microgrid aggregator. Thus, the model will minimise only the operational cost. Furthermore, the DER network model is improved by the addition of more detailed transmission losses and costs within the microgrid and between the microgrid and the national grid. The resulted energy flows are stored and information on the availability/demand are exchanged between the network nodes. To appropriately compensate the DER operators in the microgrid for their services and to charge the consumers for withdrawals, nodal clearing prices are determined and implemented through smart contracts. The resulting MILP model minimises the overall investment and operating costs of the system. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/373ai-blockchain-model-residential-distributed-energy-resources-networks SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Hajizeinalibioki, Sahar A1 - Sebastia-Saez, Daniel A1 - Klymenko, Oleksiy A1 - Arellano-García, Harvey T1 - Investigation of Two-Phase Flow Characteristics in a Fractal-Branching Microchannel T2 - AIChE Annual Meeting, November 10, 2019 to November 15, 2019 N2 - Inspiration from nature to solve advanced engineering problems has attracted the interests of engineers, designers and scientists. Biomimetics is to imitate and apply the elements, systems and mechanisms from nature to solve technological challenges as stated by Gleich et al. (2009). They also added that one of nature’s solution which is being explored is fractal shapes. Fractal shapes appeared in a variety of cases such as snowflakes, blood vessels and plant root systems in nature. Fractal shapes consistently appear in situations which require mass or heat transfer throughout a large space. The optimal spreading and transfer throughout the space characteristics of fractal shapes, making them a practical solution to design more efficient heat and mass transfer devices. Fractal shapes were first employed to improve fluid mechanics designs by West et al. (1997) to minimise the workflow for bulk fluid transportation through a network of branching tubes. On the other hand, two-phase flow in microscale channels has great applicability due to its diverse range of applications. As expressed by Serizawa et al. (2002), modern and advanced technologies such as micro-electro-mechanical systems, chemical process engineering, medical engineering and electronic cooling utilise multiphase flow in microchannels. This work aims to investigate the application of nature-inspired fractal geometries as multiphase microscale flow passage using CFD analysis. ANSYS Fluent software has been utilised to investigate the flow characteristics numerically in order to improve the pressure drop and heat transfer. Also, this question will be raised whether two-phase flow patterns in fractal microchannels are different from straight channels or not. Y1 - 2019 UR - https://aiche.confex.com/aiche/2019/meetingapp.cgi/Paper/577654 SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Merino, Manuel A1 - Carrasco, Laura A1 - Dorneanu, Bogdan A1 - Manrique, Jose A1 - Menzhausen, Robert A1 - Arellano-García, Harvey T1 - Control Strategies for a Vapour Compression Refrigeration System Used in Mango Exports: An Alternative to Traditional on-Off Controllers T2 - AIChE Annual Meeting N2 - ropical fruits are important products on the global market. The change to a healthier nutrition, the development of new products and great availability led to a rise in their consumption during the last decade [1-2]. Due to their perishable nature they are stored at lower temperature. To achieve a rapid and efficient decrease in product temperature, refrigeration systems are employed, using vapor compression refrigeration plants. They consist of four main components: the compressor, the condenser, the expansion valve and the evaporator. Within the system a refrigerant is circulating. Though designed to satisfy maximum load, these plants usually work at part-load for much of their life, generally regulated by on/off cycles of the compressor, working at nominal frequency of 50 Hz [3]. The high cost involved in developing cold storage or controlled atmosphere storage is a pressing problem in several developing countries [4]. This contribution presents development and comparison of various strategies for the control of a refrigeration plant used for fruit cooling. The starting point is a model of the plant which is able to simulate both the chamber and the fruit temperature. The model is based on energy balances for each section of the refrigeration system and the fruits. Y1 - 2020 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/340c-control-strategies-vapour-compression-refrigeration-system-used-mango-exports-alternative SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Miah, Sayeef A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-García, Harvey T1 - Multi-Objective Design Optimisation of a Distributed Energy System through 3E (economic, environmental and exergy) Analysis T2 - 2020 Virtual AIChE Annual Meeting, November 20, 2020 N2 - To facilitate the commitments of reducing greenhouse gas emissions will require the utilisation of renewable energy resources, as well as shifting away from a centralised generation. Distributed energy systems (DESs) are a promising alternative to conventional centralised layouts. Thus, there is a need for the development of models able to optimally design DES which show savings in cost as well as having a low carbon impact. Current literature focuses on the design optimisation of a DES through economical and environmental cost minimisation [1-4]. However, these two criteria alone do not show the complete picture and do not satisfy the long-term sustainability priorities. The inclusion of exergy analysis allows for the satisfaction of this criteria through the rational use of energy resources. The use of exergy analysis within DESs was first studied by [5], with a multiobjective approach whereby cost and exergy efficiency are considered. The novelty of this paper is twofold. The first is the investigation of exergy DES design optimisation through a multiobjective approach whilst considering the economic and environmental cost, thus making this work the first to simultaneously minimise three objective functions in the context of DES. Y1 - 2020 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/340o-multi-objective-design-optimisation-distributed-energy-system-through-3e-economic-environmental SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Ruan, Hang A1 - Xiao, Pei A1 - Gao, Yang A1 - Arellano-García, Harvey T1 - Stepping Towards the Industrial Sixth Sense T2 - AIChE Annual Meeting, November 20, 2020 N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems, by the addition of elements such as smart sensors, Internet of Things, big data analytics or cloud computing. Modern engineering systems and manufacturing processes are operating in highly dynamic environments, and exhibiting scale, structure and behaviour complexity. Under these conditions, plant operators find it extremely difficult to manage all the information available, infer the desired conditions of the plant and take timely decisions to handle abnormal operation1. Human beings acquire information from the surroundings through sensory receptors for vision, sound, smell, touch, and taste, the Five Senses. The sensory stimulus is converted to electrical signals as nerve impulse data communicated with the brain. When one or more senses fail, the humans are able to re-establish communication and improve the other senses to protect from incoming dangers. Furthermore, a mechanism of ‘reasoning’ has been developed during evolution, which enable analysis of present data and generation of a vision of the future, which might be called the Sixth Sense. As industrial processes are already equipped with five senses: ‘hearing’ from acoustic sensors, ‘smelling’ from gas and liquid sensors, ‘seeing’ from camera, ‘touching’ from vibration sensors and ‘tasting’ from composition monitors, the Sixth Sense could be achieved by forming a sensing network which is self-adaptive and self-repairing, carrying out deep-thinking analysis with even limited data, and predicting the sequence of events via integrated system modelling. This contribution introduces the development of an intelligent monitoring and control framework for chemical process, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Y1 - 2020 UR - https://www.aiche.org/academy/videos/conference-presentations/stepping-towards-industrial-sixth-sense SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-García, Harvey T1 - Towards smart distributed energy systems T2 - Jahrestreffen der ProcessNet-Fachgemeinschaften "Prozess-, Apparate- und Anlagentechnik" (PAAT) N2 - A distributed energy resource (DER) system is an energy generation system located in the vicinity of the end users, simultaneously providing electricity, cooling and heating to meet the demands of the local users1. Unlike conventional, centralized energy supply, a DER system typically employs a wide range of technologies such as photovoltaics, wind turbines, gas turbines, biomass boilers, combined heating and power, absorption chillers, etc. In addition, energy storage technologies (batteries, hot/cold water storage) are available as well. DER systems can potentially play a vital role as the energy sector faces unprecedented challenges to reduce emissions by increasing energy generation using renewable and low-carbon energy resources. Different designs of the DER systems could lead to different performance in reducing the costs, the environmental impact or the use of primary energy. Hence, optimal design and management of complex DER systems are important tasks to promote their diffusion against the centralized grid. However, current operational models for DERs do not adequately analyse the complexity of such systems. This contribution presents a set of models for the optimal design and operation of residential DER systems, which build up on previous work in this field, and aims to provide a more holistic overview of such systems. For each node in the DER system, there is an option of installing the following ten technologies: wind turbines, photovoltaics arrays, combined heating and power units, absorption chillers, air-conditioning units, gas boilers, biomass boilers, gas heaters, batteries and thermal storage. Only one of each item may be installed in each home. There is also the option to connect a house to another via a combined hot and cold water pipeline and/or a microgrid cable, to share thermal and electrical energy, respectively. Due to increased availability of government incentives such as the feed-in tariffs (FIT) and renewable heat incentives (RHI) payments, these are included in the model as well. The increased penetration of Internet of Things technology and their potential to better control and optimize DER systems enable its use to help stabilize national grids. To this end, the models include the use of dynamic pricing, a strategy in which national grids publish in real time variable prices for electricity within given time periods. Furthermore, as current literature’s focus on economic and environmental cost minimization, which do not satisfy long-term sustainability priorities through the rational use of energy resources, the introduction of a third criteria, exergy, is investigated. A third novelty of this contribution is the consideration of a multi-objective optimization which simultaneously includes the three objectives: the economic, the environmental and exergetic criteria in the design and operation of the residential DER system. Additionally, a novel methodology to analyse the effect of uncertain input variables on the total daily cost of the DER operational models. The methodology combines the operational model with model predictive control to predict the current state of the model. A subset of the model inputs (i.e., electricity demand, heating demand, and insolation) are considered uncertain. Global sensitivity analysis is conducted to quantify and understand how the uncertain variables influence, both individually and through interactions, the total daily cost. Finally, the implementation of blockchain technology and smart contracts within optimally designed and scheduled DER systems is investigated, to assess the advantages of smart technologies on the efficiency of residential DERs. Challenges, limitations and suggestions for improving the overall design and operation are also discussed in detail. All models are developed as mixed-integer linear programming models implemented and solved in GAMS, and show significant reduction of costs for all considered criteria when compared to the centralised grid and the classical approach towards the modelling of DER systems. Y1 - 2023 UR - https://www.researchgate.net/publication/388185482_Towards_smart_distributed_energy_systems ER - TY - GEN A1 - Gonzalez-Arias, Judith A1 - Gonzalez-Castano, Miriam A1 - Arellano-García, Harvey T1 - Utilization of CO2-Rich Residues for Syngas Production: Strategies for Catalyst Design T2 - AIChE Annual Meeting, November 15, 2021 N2 - Compared to a Reverse Water Gas Shift (RWGS) process carried out under ideal conditions, the valorization of CO2-rich residues involve additional challenges. Indeed, for an ideal RWGS reaction unit, the CO2 methanation reaction and the constitution of carbon deposits via Boudouard reaction are the main side reactions to take into consideration. For CO2-rich residues derived from biomass treatment and heavy metal industries, the presence of CH4 and CO species (among others) constitute an, although often disregarded, much complex panorama where side reactions like CO methanation, dry reforming of methane, the forward Water Gas Shift reaction and the decomposition of CO and CH4 resulting in carbon deposits, are occurring to some extent within the catalytic reactor. This work aimed at designing advanced catalytic systems capable of converting the CO2/CO/CH4 feedstocks into syngas mixtures. Thus, with the RWGS reaction considered as the major process, this work focusses on the side reactions involving CO/CH4 species. In this context, a series Cu-MnOx/Al2O3 spinel derived catalysts were optimized for syngas production in presence of CO and CH4 fractions. Once the optimal active phase was determined, the optimal Cu contents and the impact of the support nature (Al2O3, SiO2-Al2O3 and CeO2-Al2O3) was evaluated for the valorization of realistic CO2-rich feedstocks. Remarkably, the obtained outcomes underline operative strategies for developing catalytic systems with advanced implementation potential. For that aim, the catalyst design should present, along with an active and selective phase for RWGS reaction, superior cooking resistances, activities towards methane reforming and low tendencies towards the forward WGS reaction. Further developments should tackle difficult tasks like improving the RWGS reaction rate while inhibiting the forwards WGS reaction as well as improving the CH4 conversion to CO without affecting the process selectivity. Strategies towards advancing catalytic systems capable of operating under variable conditions also arise as appealing routes. Y1 - 2021 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/661v-utilization-co2-rich-residues-syngas-production-strategies-catalyst-design UR - https://plan.core-apps.com/aiche2021/event/30d89249d0653ff1de80a79e11b79a16 SN - 978-0-8169-1116-5 ER - TY - GEN A1 - Arellano-García, Harvey A1 - El Bari, Hassan A1 - Kalibe Fanezoune, Casimir A1 - Dorneanu, Bogdan A1 - Majozi, Thokozani A1 - Elhenawy, Yasser A1 - Bayssi, Oussama A1 - Hirt, Ayoub A1 - Peixinho, Jorge A1 - Dhahak, Asma A1 - Gadalla, Mamdouh A. A1 - Khashaba, Nourhan H. A1 - Ashour, Fatma T1 - Catalytic Fast Pyrolysis of Lignocellulosic Biomass: Recent Advances and Comprehensive Overview T2 - Journal of Analytical and Applied Pyrolysis N2 - Using biomass as a renewable resource to produce biofuels and high-value chemicals through fast pyrolysis offers significant application value and wide market possibilities, especially in light of the current energy and environmental constraints. Bio-oil from fast-pyrolysis has various conveniences over raw biomass, including simpler transportation and storage and a higher energy density. The catalytic fast pyrolysis (CFP) is a complex technology which is affected by several parameters, mainly the biomass type, composition, and the interaction between components, process operation, catalysts, reactor types, and production scale or pre-treatment techniques. Nevertheless, due to its complicated makeup, high water and oxygen presence, low heating value, unstable nature, elevated viscosity, corrosiveness, and insolubility within conventional fuels, crude bio-oil has drawbacks. In this context, catalysts are added to reactor to decrease activation energy, substitute the output composition, and create valuable compounds and higher-grade fuels. The study aim is to explore the suitability of lignocellulosic biomasses as an alternative feedstock in CFP for the optimization of bio-oil production. Furthermore, we provide an up-to-date review of the challenges in bio-oil production from CFP, including the factors and parameters that affect its production and the effect of used catalysis on its quality and yield. In addition, this work describes the advanced upgrading methods and applications used for products from CFP, the modeling and simulation of the CFP process, and the application of life cycle assessment. The complicated fluid dynamics and heat transfer mechanisms that take place during the pyrolysis process have been better understood due to the use of CFD modeling in studies on biomass fast pyrolysis. Zeolites have been reported for their superior performance in bio-oil upgrading. Indeed, Zeolites as catalyses have demonstrated significant catalytic effects in boosting dehydration and cracking process, resulting in the production of final liquid products with elevated H/C ratios and small C/O ratios. Combining ex-situ and in-situ catalytic pyrolysis can leverage the benefits of both approaches. Recent studies recommend more and more the development of pyrolysis-based bio-refinery processes where these approaches are combined in an optimal way, considering sustainable and circular approaches. KW - Catalytic fast pyrolysis KW - Lignocellulosic Biomass KW - Bio-Oil KW - Modelling Y1 - 2024 U6 - https://doi.org/10.1016/j.jaap.2024.106390 SN - 0165-2370 VL - Vol. 178 ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Akhtar, Farid T1 - Development of Ni-doped A-site lanthanides-based perovskite-type oxide catalysts for CO2 methanation by auto-combustion method T2 - RSC Advances N2 - Engineering the interfacial interaction between the active metal element and support material is a promising strategy for improving the performance of catalysts toward CO2 methanation. Herein, the Ni-doped rare-earth metal-based A-site substituted perovskite-type oxide catalysts (Ni/AMnO3; A = Sm, La, Nd, Ce, Pr) were synthesized by auto-combustion method, thoroughly characterized, and evaluated for CO2 methanation reaction. The XRD analysis confirmed the perovskite structure and the formation of nano-size particles with crystallite sizes ranging from 18 to 47 nm. The Ni/CeMnO3 catalyst exhibited a higher CO2 conversion rate of 6.6 × 10−5 molCO2 gcat−1 s−1 and high selectivity towards CH4 formation due to the surface composition of the active sites and capability to activate CO2 molecules under redox property adopted associative and dissociative mechanisms. The higher activity of the catalyst could be attributed to the strong metal–support interface, available active sites, surface basicity, and higher surface area. XRD analysis of spent catalysts showed enlarged crystallite size, indicating particle aggregation during the reaction; nevertheless, the cerium-containing catalyst displayed the least increase, demonstrating resilience, structural stability, and potential for CO2 methanation reaction. KW - perovskite KW - CO2 methanation KW - lanthanide KW - auto-combustion method Y1 - 2024 UR - https://pubs.rsc.org/en/content/articlelanding/2024/ra/d4ra02106a U6 - https://doi.org/10.1039/d4ra02106a VL - 2024 IS - 14 SP - 20240 EP - 20253 ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation T2 - Book of Abstracts zur Jahrestagung der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik, 04. – 06. März 2024 Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractDechemaFluidverfahrenstechnik_Catalysts.pdf PB - Ruhr Universität CY - Bochum ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_Poster_DECHEMA2024_Fluidverfahrenstechnik.pdf CY - Bochum ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Schmidt, Heiko A1 - Arellano-García, Harvey T1 - Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation T2 - Journal of Physics: Conference Series N2 - Progress in the modeling of structured catalysts is crucial for enhancing efficiency and scalability in industrial applications. Extensive research has investigated reactive flows over catalyst surfaces, covering chemical kinetics analysis and (direct) numerical simulations of the complete fluid flow in fixed-bed or structured catalysts. Nonetheless, this comes at a high computational cost. This study focuses on the homogeneous modeling of structured catalysts utilizing volume-averaging theory (VAT) as a more efficient method for representing the behaviour of such systems. We discuss modeling strategies for both 1-D and 3-D simulations. For steady 1-D flow simulations, we assess the influence of simplified gas chemical kinetics versus detailed surface chemistry, comparing with experimental data from the literature for a CO2 methanation processes. We also simulate 3-D flows of a steam reforming process, previously studied in the literature, using models which rely on different assumptions regarding the nature of the porous catalyst. Our findings reveal significant discrepancies based on different modeling assumptions, underscoring the necessity for accurate modeling of permeability and diffusivity tensors in homogeneous models. Y1 - 2024 U6 - https://doi.org/10.1088/1742-6596/2899/1/012004 SN - 1742-6596 VL - 2899/2024 ER - TY - GEN A1 - Medina Méndez, Juan Alí A1 - Dorneanu, Bogdan A1 - Schmidt, Heiko A1 - Arellano-García, Harvey T1 - Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation T2 - Book of Abstracts XXVI Fluid Mechanics Conference (FMC 2024), Warsaw, Poland, September 10-13, 2024 Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractFMC26_Catalysts.pdf PB - University of Technology CY - Warsaw ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Novel parametric gradient calculation method for multistage systems with generalized constraints T2 - 2024 AIChE Annual Meeting N2 - Sensitivity and gradient evaluations are essential for understanding the variability of a system subject to changes in input parameters, aiding in applications such as optimization, control, or decision-making processes (Castillo et al., 2008; Logsdon and Biegler, 1989, Horn and Tsai, 1967). Various approaches are available for the gradient evaluation in the simulation of large-scale steady-state systems, utilizing techniques such as automatic differentiation, sensitivity analysis, optimization or machine learning (Amaran et al., 2016). The term large-scale refers to problems with a substantial number of design variables, structural state variables, or constraint functions, or a combination thereof, necessitating significant high-performance parallel computing resources to solve within a reasonable timeframe (Kennedy and Martins, 2014). However, the evaluation of gradients in large-scale multistage systems simulation poses significant challenges due to computational complexity, numerical instability, scalability issues, and the limitations of the traditional differentiation techniques. Additionally, model complexity, sensitivity to noise, and data requirements of machine learning-based approaches further amplify these challenges. Overcoming these obstacles necessitates the development of efficient, scalable and robust gradient evaluation techniques that can effectively handle the characteristics of large-scale systems while offering reliable insights for a wide array of applications. This contribution focuses on re-examining and advancing the evaluation of parametric sensitivities within the context of simulating highly complex, hierarchical multiscale modular systems of very large size. The models being analyzed may necessitate sensitivity evaluations concerning their response to parametric inputs. These evaluations serve not only to test and verify their robustness, but also to integrate them into modular structures within a comprehensive optimization framework. Such an optimization framework aims to enhance system performance based on selected criteria, while simultaneously adhering to essential optimality constraints. While gradient-free optimization methods have been successfully applied to important design problems, their applications typically involve no more than O(102) design variables, and these methods exhibit very poor scalability with the dimensionality of the design variables (Kennedy and Martins, 2014). For large-scale, high-fidelity applications, gradient-based methods are deemed more suitable, although the challenges related to computational time and accuracy need to be addressed. To address these challenges, the use of either sensitivities or appropriately generalized adjoint equations for efficient calculation of constraint and objective functions gradients for generalized multistage systems, irrespective of whether they are dynamic in nature or they are steady-state. The proposed approach adopts a generalized modular strategy suitable for any type of system, starting from a traditional sensitivity-based calculations initially, and subsequently developing a novel generalized adjoint-based method. The resulting algorithm comprises a sequence of forward and backward sweep computational steps, which are entirely equivalent, and serve as a generalization of the adjoint-based calculation methods for gradients of constraints. These methods find application in various numerical analysis computations related to dynamical systems, including optimal control problems. It has to be noted that the model is regarded as a general modular representation of any coupled system, without making a distinction between dynamic or steady-state systems. In this context, a dynamic system is perceived as having state profiles as private internal variables, while interacting with its external environment through the input of initial conditions and parameter values. Its output consists of final conditions or any internal trajectory points that require reporting to the external environment during dynamic simulation. The proposed strategy using a novel adjoint scheme generalizes this approach to any multistage system model, of which the stages need not be of dynamic nature, such as in the use of adjoint equations in optimal control of multistage Differential- Algebraic Equation (DAE) systems (Morison and Sargent, 1986). The choice between the use of the adjoint- and the sensitivity-based approach depends on the balance between the number of constraints/functions requiring gradient evaluation, and the number of states in the underlying dynamical system. The adjoint-based approach may be advantageous when dealing with a smaller number of constraints than state variables that require gradient evaluation, whereas the sensitivity-based approach could be more computationally efficient for a larger number of constraints than state variables in the modular treatment of the underlying dynamic system. The simulation of a multistage system is demonstrated using an example consisting of steady-state feedforward blocks, employing both the sensitivity- and the proposed adjoint-based approach. The results obtained reveal that the numerical values derived from the gradient evaluation are identical for both methods. Therefore, it can be concluded that the newly introduced approach for general multistage sequential systems is entirely non-restrictive. This indicates its effectiveness and applicability, offering flexibility and robustness in gradient evaluation for such systems. Y1 - 2024 UR - https://www.researchgate.net/publication/388185581_Novel_parametric_gradient_calculation_method_for_multistage_systems_with_generalized_constraints ER - TY - GEN A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Santos da Silva, Jefferson A1 - Santos Mascarenhas, Artur Jose A1 - Valverde Pontes, Karen A1 - Arellano-García, Harvey T1 - Advancements in CO2 methanation: customized heterogeneous Ni-Perovskite catalyst for sustainable SNG production T2 - Annual Meeting on Reaction Engineering and Electrochemical Processes 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388185658_Advancements_in_CO2_methanation_Customized_Heterogeneous_Ni-Perovskite_Catalyst_for_Sustainable_SNG_Production ER - TY - GEN A1 - Mappas, Vassileios A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-García, Harvey T1 - A unified modular framework for modeling multiphase reactors T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143505_A_unified_modular_framework_for_modeling_multiphase_reactors ER - TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143384_Towards_efficient_material_design_Use_of_Machine_Learning_to_predict_chemical_reactions_and_retrosynthesis ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Optimal design of a thermochemical hydrogen storage and release system via the reversible redox of iron oxide/iron T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143925_Thermochemical_Hydrogen_Storage_via_the_Reversible_Reduction_and_Oxidation_of_Metal_Oxides ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Vassiladis, Vassilios S. A1 - Arellano-García, Harvey T1 - A novel approach to staggered training of deep learning networks T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143628_A_novel_approach_to_staggered_training_of_deep_learning_networks ER - TY - GEN A1 - Safdar, Muddasar A1 - Safdar, Mutahar A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Process intensification by additive manufacturing strategies for power-to-X conversion application: Case studies T2 - 16th International Conference on Gas–Liquid and Gas–Liquid–Solid Reactor Engineering Y1 - 2024 UR - https://www.researchgate.net/publication/388185473_Process_Intensification_by_Additive_Manufacturing_Strategies_for_Power-to-X_Conversion_Application_Case_Studies ER - TY - GEN A1 - Jafari, Mitra A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Sustainable aviation fuel production through Fischer-Tropsch synthesis and hydrocracking integration using Co bifunctional catalysts: Support effects T2 - 18th International Congress on Catalysis N2 - Considering the increasing demand for clean and sustainable aviation fuel, in this study, cobalt bifunctional catalysts are used to convert syngas from biomass to aviation fuel. Y1 - 2024 UR - https://www.researchgate.net/publication/388109868_Sustainable_aviation_fuel_production_through_Fischer-Tropsch_synthesis_and_hydrocracking_integration_using_Co_bifunctional_catalysts_Support_effects ER - TY - GEN A1 - Alves Amorim, Ana Paula A1 - Valverde Pontes, Karen A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimizing microgrid design and operation : a decision-making framework for residential distributed energy systems in Brazil T2 - Chemical Engineering Research and Design N2 - This paper explores the optimization of microgrid design and operation for residential distributed energy systems in Brazil, addressing the growing demand for sustainable energy in the context of climate change. A decision-making framework based on Mixed-Integer Nonlinear Programming (MINLP) is proposed to integrate distributed energy resources (DERs) such as solar, wind, and biogas. Key challenges include managing the variability of renewable resources and complying with local regulations, while also addressing gaps in literature, particularly the impact of time-dependent efficiency profiles on energy sharing within microgrids. By employing innovative analyses and clustering techniques, the research optimizes microgrid configurations, accounting for seasonal demand fluctuations and the influence of incentive policies on system feasibility. The findings reveal that incorporating a time-dependent efficiency model can reduce total costs by 45 %. This reduction underscores the importance of accurate efficiency predictions, as the model captures variations in energy generation and utilization efficiency over time, improving system optimization. Additionally, the findings reveal that a well-structured optimization model can meet 100 % of electricity and hot water demands across all scenarios, with customized incentives playing a crucial role in reducing costs and promoting sustainability. Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S0263876224007123?via%3Dihub U6 - https://doi.org/https://doi.org/10.1016/j.cherd.2024.12.033 SN - 0263-8762 VL - 214 (2025) IS - February 2025 SP - 251 EP - 268 PB - Elsevier ER - TY - GEN A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Towards Machine Learning-driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis T2 - Chemical Engineering Transactions N2 - Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing the catalyst design and operating conditions to maximize the desired C8-C16 jet fuel range is a challenging task. This study introduces the application of a machine learning (ML) framework to guide the design of Co/Fe-supported FTS catalysts and operating conditions for enhanced fuel selectivity. A comprehensive dataset was constructed with 21 input features spanning catalyst structure, preparation method, activation procedure, and FTS operating parameters. The random forest ML algorithm was evaluated for predicting CO conversion and C8-C16 selectivity using this dataset. Feature engineering identified the most significant descriptors influencing performance. A principal component analysis reduced the dataset dimensionality prior to ML modelling. The random forest algorithm achieved high prediction accuracy for the conversion of CO (R2 = 0.92) and C8-C16 selectivity (R2 = 0.90). In addition to confirming the known effects of operating conditions, key roles of Co/Fe-supported properties were elucidated. This ML framework provides a powerful tool for the rational design of FTS catalysts and operating windows to maximize jet fuel productivity Y1 - 2024 UR - https://www.cetjournal.it/cet/24/114/098.pdf U6 - https://doi.org/10.3303/CET24114098 SN - 2283-9216 VL - 114 SP - 583 EP - 588 ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Multiphase Catalytic Reactors: a Modular Approach T2 - Chemical Engineering Transactions N2 - Currently, state-of-the-art approaches to simulating the behaviour of trickle-bed reactors (TBRs) have focused solely on methods requiring high computational time and are unable to tackle systems with a large number of particles. In this work, a modular methodology based on a Lagrangian approach to TBR modelling is presented, which overcomes these drawbacks by implementing a simulation framework where different modules are interconnected and relevant information is transferred between them. The novelty of this framework stems from its adaptable configuration and its modular and unified setup, enabling it to accommodate both local and global multiscale events. The proposed methodology includes modules for the packing generation, liquid flow simulation, and of reaction system modelling within the reactor. To illustrate the procedure, a case study is discussed while demonstrating the potential of the presented approach. The results were validated against data obtained from a purpose-built experimental setup showing good agreement. The main advantages of this approach lie in its efficiency, the interrelation between different modules, and its ability to capture a wide range of information and phenomena. Y1 - 2024 UR - https://www.cetjournal.it/cet/24/114/097.pdf U6 - https://doi.org/10.3303/CET24114097 SN - 2283-9216 VL - 114 SP - 577 EP - 582 ER - TY - CHAP A1 - Jafar Khan, Maria A1 - Safdar, Muddasar A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - Methods of indirect conversion of CO2 to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - The promptly increasing CO2 concentration in the atmosphere causes a major climate change, requiring effective way of its mitigation. The indirect conversion of CO2 to methanol via syngas is a promising strategy to control greenhouse gas emissions and produce valuable feedstock's and chemicals. This chapter focuses on different indirect CO2 conversion methods to methanol, multistep processes that involve capturing of carbon dioxide, intermediate formation syngas, type of catalyst used, and then hydrogenation to methanol. Indirect conversion of CO2 involves two steps, the production of syngas which is known as a mixture of carbon monoxide and hydrogen followed by methanol integration and catalyst-based hydrogenation of CO2. The economic feasibility, the effectiveness of different methods, development, and optimization of catalysts along with reaction conditions are thoroughly discussed in this chapter. The chapter concluded with the direction of suitable methods to convert carbon dioxide into methanol along with the future research development in the methodology to reduce greenhouse emissions and advance the production of sustainable chemicals. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00155-5 VL - 2024 PB - Elsevier ER - TY - CHAP A1 - Shafiee, Parisa A1 - Arellano-Garcia, Harvey T1 - Photocatalysts in CO2 direct conversion to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - The escalating global industrialization has led to fossil fuel scarcity and environmental deterioration, with CO2 levels rising significantly. To combat climate change, researchers are focusing on renewable energy and carbon capture technologies. This chapter reviews recent progress on photocatalytic conversion of CO2 to methanol, a promising approach for greenhouse gas reduction and sustainable energy production. Methanol, a versatile chemical feedstock and potential renewable fuel, can be synthesized from CO2 using solar energy and semiconductor photocatalysts. This chapter covers the fundamentals, mechanisms, materials development, photocatalysts design strategies, and preparation processes for this technology. Despite challenges in achieving high efficiency, CO2 photocatalytic reduction to methanol offers an attractive green alternative to traditional fossil-based methanol production. This comprehensive overview consolidates the current research landscape, providing insights to guide future advancements towards scalable and economically viable CO2 photocatalytic methanol synthesis. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00121-X VL - 2024 ER - TY - CHAP A1 - Shafiee, Parisa A1 - Arellano-Garcia, Harvey T1 - Heterogeneous and Homogeneous Catalysts in CO2 Direct Conversion to Methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - Catalytic conversion of CO2 into valuable products offers a promising solution to mitigate climate change by closing the carbon cycle. However, activating the thermodynamically stable and kinetically inert CO2 molecule remains a significant scientific challenge. This chapter focuses on homogeneous and heterogeneous catalysts for the direct conversion of CO2 to methanol, a valuable chemical feedstock and potential fuel. It introduces the importance of this process for reducing carbon emissions and outlines the chapter's objectives. The fundamentals of heterogeneous catalysis and catalyst design principles for methanol synthesis from CO2 are discussed. Various types of heterogeneous catalysts are examined, along with the mechanisms involved in CO2 activation and hydrogenation to methanol. Strategies to enhance catalyst selectivity, product distribution, and performance are explored, as well as challenges and future research directions. This comprehensive chapter serves as a guide to understanding the pivotal role of heterogeneous catalysts in the direct catalytic conversion of CO2 to methanol. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00119-1 VL - 2024 PB - Elsevier ER - TY - CHAP A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - CO2 sources and features for direct CO2 conversion to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - In recent years, global concern over climate change caused by the accumulation of atmospheric CO2 has intensified. While various technologies for capturing CO2 have been proposed, utilizing captured CO2 from power plants is gaining popularity due to the concerns about the safety and effectiveness of underground and ocean storage methods. This article explores several techniques for utilizing CO2 from exhaust gases emitted by power plants. It provides a comprehensive review of current and emerging technologies worldwide that aim to harness CO2 for beneficial purposes. The conversion of CO2 into chemicals and energy products represents a promising approach to not only mitigate CO2 emissions but also enhance economic value. However, since CO2 lacks hydrogen, which is essential for many chemical processes, the development of clean, sustainable, and cost-effective hydrogen sources is crucial. This chapter delves into the literature surrounding the production of biofuels derived from microalgae cultivated using captured CO2, the conversion of CO2 combined with hydrogen into various chemicals, specially methanol and the exploration of sustainable hydrogen sources. These efforts collectively underscore the potential of CO2 utilization as a pivotal strategy in the battle against climate change and for fostering sustainable industrial practices. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00127-0 VL - 2024 ER - TY - CHAP A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - Shift from syngas to CO2 for methanol production T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - This chapter delves into diverse methodologies for converting carbon dioxide (CO2) into methanol, employing homogeneous and heterogeneous catalysts through hydrogenation, photochemical, electrochemical, and photo-electrochemical techniques. Given the significant contribution of CO2 to global warming, utilizing it for fuel and chemical production stands as a sustainable approach to environmental conservation. However, due to high stability and low reactivity of CO2, the development of appropriate methods and catalysts is crucial for breaking its bonds to yield valuable chemicals like methanol. Also, in this chapter various methods and their mechanisms for CO2 conversion to methanol are described. Finally, new types of catalyst and their characteristics for CO2 hydrogenation to methanol are introduced and discussed in detail. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00126-9 VL - 2024 ER - TY - CHAP A1 - Shafiee, Parisa A1 - Arellano-Garcia, Harvey T1 - Electrocatalysts in CO2 direct conversion to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - By now, atmospheric CO2 levels necessitate new ways of reducing them more than ever with increasing global warming and climate change. Converting CO2 into valuable products like methanol fuel presents a solution by reducing atmospheric CO2 while creating economic opportunities. This chapter reviews the state-of-the-art in electrocatalytic CO2-to-methanol conversion technologies, covering basic electrocatalysis principles, electrocatalyst materials, design strategies, performance optimization, mechanistic pathways, catalyst compositions, technological hurdles, and potential solutions. It also examines environmental impacts, economic aspects, and scaling up possibilities, aiming to provide a comprehensive technological, economic and environmental overview of this sustainable energy solution for climate change mitigation, highlighting the critical role of innovation in addressing global challenges for a more sustainable future. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00120-8 VL - 2024 ER - TY - GEN A1 - Mappas, Vassileios A1 - Dorneanu, Bogdan A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Multistage optimal control and nonlinear programming formulation for automated control loop selection T2 - Computer Aided Chemical Engineering N2 - Control loop design, as well as controller tuning, constitute the pillars of process control to achieve design specifications and smooth process operation, and to meet predefined performance criteria. Currently, state-of-the-art approaches have focused on methods that yield only the pairings between input and output methods, and are not able to incorporate path and end-point constraints. This work introduces a novel strategy based on the multistage optimal control formulation of the control loop selection problem. This approach overcomes the drawbacks of traditional methods by producing an automated integrated solution for the task of control loop design. Furthermore, it obviates the need for any form of combinatorial optimization and incorporating path and terminal constraints. The results show that the proposed solution framework produces the same control loops as in the case of traditional approaches, however the inclusion of path and end-point constraints improves the performance of the control profiles. Y1 - 2024 U6 - https://doi.org/10.1016/B978-0-443-28824-1.50327-6 SN - 1570-7946 VL - 53 SP - 1957 EP - 1962 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Keykha, Mina A1 - Arellano-García, Harvey T1 - Assessment of parameter uncertainty in the maintenance scheduling of reverse osmosis networks via a multistage optimal control reformulation T2 - Computer Aided Chemical Engineering N2 - In this work, the influence of uncertain parameters on the maintenance scheduling of Reverse Osmosis Networks (RONs) is explored. Based on a foundation of successful applications in various maintenance optimization domains, this paper extends the methodology to the domain of RON regeneration actions planning, highlighting its adaptability to diverse areas of dynamic processes with planning uncertainty. Traditional approaches in membrane cleaning scheduling have predominantly relied on MixedInteger Nonlinear Programming (MINLP), often leading to combinatorial problems that fail to capture the dynamic nature of the system. As part of this study, a novel approach based on the Multistage Integer Nonlinear Optimal Control Problem (MSINOCP) formulation is used to automate and optimize membrane cleaning scheduling without requiring combinatorial optimization. To evaluate the consequences of parameter uncertainty, 26 scenarios are considered in which the cost of the energy unit is considered as variable based on a random distribution, and these results are compared to a scenario where a fixed cost parameter is assumed. The findings show that when the cost of energy is considered as an uncertain parameter, the optimization process requires more frequent cleaning measures. Y1 - 2024 U6 - https://doi.org/10.1016/B978-0-443-28824-1.50326-4 SN - 1570-7946 VL - 53 SP - 1951 EP - 1956 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Zhang, Sushen A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Optimizing deep neural networks through hierarchical multiscale parameter tuning T2 - Computer Aided Chemical Engineering N2 - Deep neural networks (DNNs) are frequently employed for information extraction in big data applications across various domains; however, their application in real-time industrial systems is hindered by constraints such as limited computational, storage capacity, energy availability, and time constraints. This contribution introduces the development of a novel hierarchical multiscale framework for the training of DNNs that incorporates neural sensitivity analysis for the automatic and selective training of neurons evaluated to be the most effective. This alternative training methodology generates local minima that closely match or surpass those achieved by traditional approaches, such as the backpropagation method, utilizing identical starting points for comparative purposes. Y1 - 2024 U6 - https://doi.org/10.1016/B978-0-443-28824-1.50155-1 SN - 1570-7946 VL - 53 SP - 925 EP - 930 ER - TY - GEN A1 - Vassiliadis, Vassilios S. A1 - Mappas, Vassileios A1 - Espaas, Tomas A. A1 - Dorneanu, Bogdan A1 - Isafiade, Adeniyi A1 - Möller, Klaus A1 - Arellano-García, Harvey T1 - Reloading process systems engineering within chemical engineering T2 - Chemical Engineering Research and Design N2 - Established as a sub-discipline of Chemical Engineering in the 1960s by the late Professor R.W.H. Sargent at Imperial College London, Process Systems Engineering (PSE) has played a significant role in advancing the field, positioning it as a leading engineering discipline in the contemporary technological landscape. Rooted in Applied Mathematics and Computing, PSE aligns with the key components driving advancements in our modern, information-centric era. Sargent’s visionary foresight anticipated the evolution of early computational tools into fundamental elements for future technological and scientific breakthroughs, all while maintaining a central focus on Chemical Engineering. This paper aims to present concise and concrete ideas for propelling PSE into a new era of progress. The objective is twofold: to preserve PSE’s extensive and diverse knowledge base and to reposition it more prominently within modern Chemical Engineering, while also establishing robust connections with other data-driven engineering and applied science domains that play important roles in industrial and technological advancements. Rather than merely reacting to contemporary challenges, this article seeks to proactively create opportunities to lead the future of Chemical Engineering across its vital contributions in education, research, technology transfer, and business creation, fully leveraging its inherent multidisciplinarity and versatile character. Y1 - 2024 UR - https://www.sciencedirect.com/science/article/pii/S0263876224004568?via%3Dihub U6 - https://doi.org/10.1016/j.cherd.2024.07.066 VL - 209 SP - 380 EP - 398 ER - TY - GEN A1 - Quinlan, Laura A1 - Brooks, Talia A1 - Ghaemi, Nasrin A1 - Arellano-García, Harvey A1 - Irandoost, Maryam A1 - Sharifianjazi, Fariborz A1 - Amini Horri, Bahman T1 - Synthesis and characterisation of nanocrystalline CoxFe1−xGDC powders as a functional anode material for the solid oxide fuel cell T2 - Materials N2 - The necessity for high operational temperatures presents a considerable obstacle to the commercial viability of solid oxide fuel cells (SOFCs). The introduction of active co-dopant ions to polycrystalline solid structures can directly impact the physiochemical and electrical properties of the resulting composites including crystallite size, lattice parameters, ionic and electronic conductivity, sinterability, and mechanical strength. This study proposes cobalt–iron-substituted gadolinium-doped ceria (CoxFe1-xGDC) as an innovative, nickel-free anode composite for developing ceramic fuel cells. A new co-precipitation technique using ammonium tartrate as the precipitant in a multi-cationic solution with Co2+, Gd3+, Fe3+, and Ce3+ ions was utilized. The physicochemical and morphological characteristics of the synthesized samples were systematically analysed using a comprehensive set of techniques, including DSC/TGA for a thermal analysis, XRD for a crystallographic analysis, SEM/EDX for a morphological and elemental analysis, FT-IR for a chemical bonding analysis, and Raman spectroscopy for a vibrational analysis. The morphological analysis, SEM, showed the formation of nanoparticles (≤15 nm), which corresponded well with the crystal size determined by the XRD analysis, which was within the range of ≤10 nm. The fabrication of single SOFC bilayers occurred within an electrolyte-supported structure, with the use of the GDC as the electrolyte layer and the CoO–Fe2O3/GDC composite as the anode. SEM imaging and the EIS analysis were utilized to examine the fabricated symmetrical cells. KW - Co KW - SOFC anode KW - solid oxide fuel cell KW - electrical conductivity KW - SOFC materials Y1 - 2024 U6 - https://doi.org/10.3390/ma17153864 SN - 1996-1944 VL - 17 IS - 15 PB - MDPI ER - TY - GEN A1 - Sohail, Norman A1 - Riedel, Ramona A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques T2 - Membranes N2 - The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling. KW - Membrane bioreactor (MBR) KW - quorum sensing (QS) KW - quorum quenching (QQ) KW - moving bed biofilm reactor (MBBR) KW - moving bed biofilm membrane reactor (MBBMR) KW - model-based anti-fouling strategies Y1 - 2023 UR - https://www.mdpi.com/2077-0375/13/2/217 U6 - https://doi.org/10.3390/membranes13020217 SN - 2077-0375 VL - 13 IS - 2 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-García, Harvey T1 - BasMo - An interactive approach to modelling of trickle bed reactors T2 - Jahrestreffen der "Prozess-, Apparate- und Anlagentechnik", 21.–22. November 2022, Frankfurt am Main N2 - The trickle bed reactor (TBR), in which gas and liquid flow downward through a packed bed to undergo chemical reactions, is a frequently used solution for industrial multiphase exothermic catalytic reactions (e.g., hydrogenation, oxidation, etc.) due to flexibility and simplicity of operation and large annual throughput (Tan et al., 2021). They have significant advantages with respect to other solutions, but they also show complex behaviour, with uncertainties in catalyst heterogeneity, packing, fluid flow, and transport parameters, resulting in its modelling being highly challenging (Azarpour et al., 2021). In this contribution, the development of an interactive toolbox for the simulation of TBRs, based on the work of Schwidder & Schnitzlein (2012) is introduced. The implementation uses a modular and flexible setup, mirroring the multiscale nature of the phenomena tacking place in the reactor, from large scale of the reactor to the medium and low scale of the particle bed, fluid flow, as well as fluid-solid and fluid-fluid interactions, including chemical reactions. The toolbox enables implementation of complex geometries of the catalyst particles, enabled by a novel representation of the surface mesh. Validation using experimental data shows that the model is able to reliably predict the performance of the catalytic TBR. Y1 - 2022 UR - https://dechema.de/PAAT2022_Themen/_/_1_Programm_PAAT_2022_ezl.pdf ER - TY - GEN A1 - Straub, Adrian A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Towards a novel concept for solid energy storage T2 - Computer Aided Chemical Engineering N2 - In this contribution, the model-based development of a novel process concept for the storage and release of ammonia in solids is proposed. The concept is validated by means of the Aspen Plus® process simulator. As a promising prospect, Hexaaminenickel(II) chloride is selected. After a preparative stage, the process can cycle between the storage and release of energy. The process is split in a reaction and a separation section, in such a way that the same equipment is used for both storage and release steps. Sensitivity analysis and design parameter optimization are used to determine key process parameters. The operation ranges from standard conditions (25 °C and 1 atm) to temperatures not higher than 120 °C. Moreover, the simulation results show that it is possible to store over 50% of the base material in form of ammonia, equivalent to almost 10 wt.% hydrogen, placing the concept within the specific system targets set by the U.S. Department of Energy. KW - Process design KW - Process modelling KW - Aspen Plus Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50472-8 SN - 1570-7946 VL - Vol. 52 SP - 2965 EP - 2970 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Miah, Sayeef A1 - Mechleri, Evgenia A1 - Arellano-García, Harvey T1 - Multiobjective optimization of distributed energy systems design through 3E (economic, environmental and exergy) analysis T2 - Computer Aided Chemical Engineering N2 - Distributed energy systems (DES) are promising alternative to conventional centralized generation, with multiple financial incentives in many parts of the world. Current approaches focus on the design optimization of a DES through economic and environmental cost minimization. However, these two criteria alone do not satisfy long-term sustainability priorities of the system. The novelty of this paper is the simultaneous investigation of economic, environmental and exergetic criteria in the modelling of DES through the two most commonly used solution methodologies for solving multi-objective optimization problems – the weighted sum and the epsilon-constraint methods. Out of the set of Pareto optimal solutions, a best-compromised solution is chosen using the fuzzy-based method. Numerical results reveal reduction of around 93% and 89-91% in environmental and primary exergy input, respectively. KW - Multiobjective optimization KW - Distributed energy systems KW - Exergy KW - Mixed-integer linear programming KW - Fuzzy-based methods Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50473-X SN - 1570-7946 VL - Vol. 52 SP - 2971 EP - 2976 ER - TY - GEN A1 - Alves Amorim, Ana Paula A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-García, Harvey T1 - A framework for decision-making to encourage utilization of residential distributed energy systems in Brazil T2 - Computer Aided Chemical Engineering N2 - The Distributed Energy Systems (DES) or microgrid arose from the need to reduce greenhouse gases (GHG) emitted into the atmosphere by burning fossil fuels to generate energy. Reduction of energy losses, reconfiguration of the protection system and reduction of costs, and optimizing the configuration of these systems is recommended. Despite new research in literature, there is still a lack of optimization models that address the Brazilian reality. Therefore, the objective of this work is to introduce a decision-making framework for the design and operation of residential DES that takes into account the particularities of Brazil, based on mixed-integer programming models. The applicability of the framework is tested on a case study of a residential DES of 5 houses, located in Salvador, and used to compare scenarios pre- and post-COVID-19. The results show significant reduction in total annual cost and GHG emissions versus the base case without DES. This indicates that, although the country has a mostly “clean” energy matrix due to the use of hydroelectric plants, DES can enable improvement in residential electricity generation. KW - Distributeed energy systems KW - Microgrid KW - Mixed-integer non-linear programming KW - Net metering Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50481-9 SN - 1570-7946 VL - Vol. 52 SP - 3019 EP - 3024 ER - TY - GEN A1 - Tarifa, Pilar A1 - Gonzalez-Castano, Miriam A1 - Cazana, Fernando A1 - Monzon, Antonio A1 - Arellano-García, Harvey T1 - Hydrophobic RWGS catalysts: valorization of CO2-rich streams in presence of CO/H2O T2 - Catalysis Today N2 - Nowadays, the majority of the Reverse Water Gas Shift (RWGS) studies assume somehow model feedstock (diluted CO2/H2) for syngas production. Nonetheless, biogas streams contain certain amounts of CO/H2O which will decrease the obtained CO2 conversion values by promoting the forward WGS reaction. Since the rate limiting step for the WGS reaction concerns the water splitting, this work proposes the use of hydrophobic RWGS catalysts as an effective strategy for the valorization of CO2-rich feedstock in presence of H2O and CO. Over Fe-Mg catalysts, the different hydrophilicities attained over pristine, N- and B-doped carbonaceous supports accounted for the impact on the activity of the catalyst in presence of CO/H2O. Overall, the higher CO productivity (4.12 μmol/(min·m2)) attained by Fe-Mg/CDC in presence of 20% of H2O relates to hindered water adsorption and unveil the use of hydrophobic surfaces as a suitable approach for avoiding costly pre-conditioning units for the valorization of CO2-rich streams based on RWGS processes in presence of CO/H2O. Y1 - 2023 U6 - https://doi.org/10.1016/j.cattod.2023.114276 SN - 1873-4308 VL - Vol. 423 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Masham, Elliot A1 - Keykha, Mina A1 - Mechleri, Evgenia A1 - Cole, Rosanna A1 - Arellano-García, Harvey T1 - Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models T2 - Supply Chain Analytics N2 - Traditional food supply chains are often centralised and global in nature, entailing substantial resource consumption. However, in the face of growing demand for sustainability, this strategy faces significant challenges. Adoption of localised supply chains is deemed a more sustainable option, yet its efficacy requires verification. Supply chain analytics methodologies provide invaluable tools to guide decisions regarding inventory management, demand forecasting and distribution optimisation. These solutions not only enhance facilitate operational efficiency, but also pave the way for cost reduction, further aligning with sustainability objectives. This research introduces a novel decision-making approach anchored in mixed integer linear programming (MILP) and neighbourhood flow models defined in cellular automata to compare the environmental benefits and vulnerability to disruption of these two chain configurations. Additionally, a comprehensive cost analysis is integrated to assess the economic feasibility of incorporating layout changes that enhance supply chain sustainability. The proposed framework is applied on an ice cream supply chain across England over a one-year timeframe. The findings indicate the superiority of the localised configuration in terms of economic benefits, leading to savings exceeding £ 1 million, alongside important reductions in environmental impact. However, in terms of resilience, the traditional configuration remains superior in three out of the four examined scenarios. KW - supply chain management KW - flow configuration model KW - ice cream KW - Mixed-integer linear programming Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2949863523000420 U6 - https://doi.org/10.1016/j.sca.2023.100043 VL - Vol. 4 ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Aquino, Gabrielle S. A1 - Silva, Jefferson S. A1 - Paff, Jessica Sophie A1 - Valverde Pontes, Karen A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Mascarenhas, Artur José T1 - Sustainable hydrogen production via biogas reforming over NiO-MxOy - Al2O3 catalysts (M = Na, K, Ca and Mg) T2 - 22 Congreso Brasileiro de Catalise N2 - A sustainable way to generate hydrogen is through dry biogas reforming, which uses methane gas and carbon dioxide to produce hydrogen. This study reveals partial results of the dry reforming of biogas in NiO-MxOy-Al2O3 catalysts (M=Na, K, Ca and Mg). The CO2 conversion varied between 79% and 94%, the CH4 conversion between 58% and 75%, the H2/CO ratio between 0.98 and 1.15 and the H2 yield between 37% and 45%. These values ​​surpass literary references and the industrial catalyst, highlighting the promise of these materials for sustainable hydrogen production. The catalyst with Ca stood out due to its higher surface basicity, exhibiting the best conversion results and yield in H2. Y1 - 2023 UR - https://submissao.cbcat.sbcat.org.br/index.php/2023-cbcat/article/view/409 ER - TY - GEN A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Jafari, Mitra A1 - Shashank Bhat, Sharvendu A1 - Akhtar, Farid A1 - Arellano-García, Harvey T1 - Dry Reforming of Methane for the Syngas Production Catalyzed by Ni-doped Perovskites T2 - 15Th European Congress on Katakysis EUROPACAT2023 N2 - different perovskite-type supports considering ABO3 (such as A= Al, La with B=Ce and A=Mg, Mn with B=Zr) were prepared via the sol-gel method. Ni metal loading of 10 wt.% was deposited on prepared perovskite supports via the impregnation method. The catalysts were characterized using XRD and FTIR techniques. The DRM activity was carried out in a tubular reactor as described in our previous study [5]. The catalytic performance was assessed in the temperature range of 500–700 ◦C, CH4/CO2 = 1/1 and under GHSV of 12,000 h–1. Among the prepared catalysts, Ni-doped perovskite combination (i.e. A=Mg with B=Zr)O3-δ exhibited higher (CH4, CO2) conversion ca. (69, 59) percent and syngas yield of ca. (H2/CO =0.72) at 700 oC. This indicates that the magnesium zirconate perovskite catalyst established strong interfacial metal-support interaction, redox properties and surface basic sites that linked with good performance of the catalyst during DRM process. KW - Dry reforming of methane (DRM) KW - Ni-Perovskites KW - Syngas production KW - Greenhouse gases (GHGs) Y1 - 2023 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Use of Multistage Optimal Control Principles for Novel Design and Implementation of Classical Controllers T2 - AIChE Annual Meeting N2 - Classical controllers, such as Proportional-Integral (PI) and Proportional-Integral Derivative (PID) controllers, are the most long-established and widely used in industry. Various methods for tuning these types of controllers exist (Ziegler et al., 1942; Blondin et al., 2018; Do et al., 2021), and up to this point, there is no fruitful avenue to improve their performance. In this contribution, a new approach for PI and PID controller implementation, based on a Multistage Optimal Control (MSOCP) approach is introduced. Our approach incorporates path and end-point constraints during its controller tuning phase, as well as parameter and disturbance uncertainty. The proposed framework is applied for different case studies and is able to reject any disturbances introduced to the examined systems, with or without uncertainty, satisfies end-point constraints and exhibits quicker response for switching steady states, compared to classical methods. Other aspects of controller design and incorporation within industrial process models, as related to using rigorous optimization methodologies and implementations, will further be highlighted within the context of the PI and PID controllers. Y1 - 2021 UR - https://aiche.confex.com/aiche/2021/meetingapp.cgi/Paper/630692 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Automated Control Loop Selection Via Multistage Optimal Control Formulation and Nonlinear Programming T2 - Chemical Engineering Research and Design N2 - In this work, a novel approach based on the multistage optimal control formulation of the control loop selection problem is introduced. Currently, state-of-the-art approaches for controller loop design have been focused on data that yield only the pairings between input-output variables, and are not able to incorporate path and end-point constraints. Thus, they only produce the optimal loops for control purposes, without the simultaneous consideration of their optimal tuning. This formulation overcomes these drawbacks by producing an automated integrated solution for the task of control loop design, which also obviates the need for any form of combinatorial optimisastion to be used. To illustrate the procedure, as well as the advantages of the proposed scheme, different practical case studies are discussed and the results compared with those obtained with standard controller loop selection methods and their tuning. The results of the proposed approach show improved performance over previous methodologies found in the literature. Furthermore, the framework is extended to the selection of the control loops that must obey path and end-point constraints imposed by the underlying dynamical process. This task is usually difficult for classical methods, which violate them or exhibit underdamped response in some cases. KW - control loop selection KW - controller tuning KW - feasible path approach KW - multistage integer nonlinear optimal control problem (MSINOCP) KW - dynamic constraints Y1 - 2023 U6 - https://doi.org/10.1016/j.cherd.2023.05.041 SN - 1744-3563 VL - 195 SP - 76 EP - 95 ER - TY - GEN A1 - Jafari, Mitra A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Gonzalez-Castaño, Miriam A1 - Arellano-García, Harvey T1 - Green and sustainable fuel from syngas via the Fischer-Tropsch synthesis process: Bifunctional cobalt-based catalysts T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This paper reviews and compares state-of-the-art cobalt-based catalysts and catalytic systems used to produce green and sustainable fuels using FTS. Being focused on comparing the effect of the catalyst formulation and synthesis method, the reactor type and operating parameters, as well as the quality of the obtained fuels, the aim is to identify the research gaps between these relevant research areas concerning production of green and sustainable fuels. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=15565&additions_conferenceschedule_action=detail&additions_conferenceschedule_controller=paperList&pid=44228&hash=be231d3139d7d89da32b1610b7a0d1af3770c06640f246348e3ca8cfa7dd324a ER - TY - GEN A1 - Plattfaut, Julia A1 - Suckow, Matthias A1 - Klepel, Olaf A1 - Erlitz, Marcel A1 - Arellano-García, Harvey T1 - Modellierung und Simulation der templatgestützten Synthese von porösen Kohlenstoffgerüsten mittels COMSOL Multiphysics T2 - Chemie Ingenieur Technik N2 - Mithilfe einer templatgestützten Synthese wurden poröse Kohlenstoffgerüste unter Verwendung von Silicagel als Templat hergestellt. Die chemische Gasphaseninfiltration (CVI) wurde hierbei als Synthese verwendet. Unter Variation verschiedener Reaktionsparameter zur Optimierung der Kohlenstoffabscheidung wurde dieser Prozess mathematisch modelliert and simuliert. Dabei konnten die experimentellen Ergebnisse gut mit den Modellen nachgebildet werden. Die zusätzliche Beschreibung der laminaren Strömung verbessert die Übereinstimmung deutlich. Y1 - 2023 U6 - https://doi.org/10.1002/cite.202300014 SN - 1522-2640 VL - 96(2024) IS - 3 SP - 318 EP - 328 ER - TY - GEN A1 - Zhang, Sushen A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Hierarchical multi-scale parametric optimization of deep neural networks T2 - Applied Intelligence N2 - Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. KW - Deep neural networks KW - Hierarchical multi-scale search KW - Scaling factor KW - Sensitivity analysis KW - Finite difference KW - Automatic differentiation Y1 - 2023 U6 - https://doi.org/10.1007/s10489-023-04745-8 SN - 1573-7497 VL - 53 IS - 21 SP - 24963 EP - 24990 ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Akhtar, Farid T1 - Synthesis and Characterizations of Ni-doped Perovskite-Type Oxides for Effective CO2 methanation T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This work proposes Ni metal supported over rare earth-based emerging perovskite-type oxides as potential catalysts for the CO2 methanation. Presence of oxygen vacancies in perovskite-like materials enable them to exhibit higher catalytic activity. Furthermore, to tune the surface basicity, metal-support interaction and to enhance the activation of CO2, rare earth metals (La, Ce, etc.) are considered best candidates. Moreover, different perovskite-type supports (AxMnxO3, A= La, Ce) based on A-side substitution of rare earth metals were prepared with Ni metal loading of 10 wt.% via impregnation method. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=13659&v=List&do=15&day=all&ses=9628# U6 - https://doi.org/10.5281/zenodo.10376612 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Quantum annealing for global optimization in Chemical Engineering T2 - Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main N2 - Classical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binary optimization (QUBO) problems. In this contribution, a novel approach is introduced to transform relevant problems in Chemical Engineering into QUBO at two distinct levels of granularity. Subsequently, these problem systems are embedded within virtual quantum machines employing two different architectures. Additionally, a comparative analysis is performed, wherein the same problem is solved utilizing both classical global optimization methods based on metaheuristics and a hypothetical quantum annealer. The findings indicate that annealing-based solving methods exhibit the most potential, indicating their applicability to the transformed formulation Chemical Engineering problems. Y1 - 2023 UR - https://dechema.de/PAAT2023_Prg/_/__Progr_PAAT_2023_final.pdf SP - 15 ER - TY - GEN A1 - Markowski, Jens A1 - Arellano-García, Harvey A1 - Meißner, André A1 - Acker, Jörg T1 - Comparative studies on the quality of recovered secondary graphites from the recycling of lithium-ion traction batteries T2 - Sustainable Minerals N2 - Automotive technology is increasingly determined by drives based on electric motors in combination with batteries. The lithium-ion traction battery is a storage medium that combines high electrical efficiency with compact dimensions and relatively low weight. For the recycling of the cathode coatings (esp. Ni, Mn, Co) and peripheral battery components a variety of recycling options already exist. The graphite coating of the anodes has hardly been the focus of research activities to date. State of the art is currently the melting of the complete Copper-anode foils including graphite coating, whereby the graphite contributes only as a carbon carrier to the recycling of the copper. Separation and reuse of the very high-quality graphite on an industrial scale has not yet taken place. At the BTU, a methodology has been developed, with which recovered anode graphites from traction batteries can be comprehensively characterised chemically and mechanically-physically. On this basis, targeted preparation for secondary applications is possible. The secondary graphites achieve a quality that allows them to be reused as second-use anode material and for other applications. KW - Graphitrecycling KW - Li-Ionen-Traction Batteries Y1 - 2023 UR - https://www.ceecthefuture.org/resource-center/comparative-studies-on-the-quality-of-recovered-secondary-graphites-from-the-recycling-of-lithium-ion-traction-batteries PB - Mining Engineering CY - Falmouth (UK) ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Lewis, Allana A1 - Radacsi, Norbert A1 - Fan, Xianfeng A1 - Huang, Yi T1 - Superhydrophobic ZIF-67 with exceptional hydrostability T2 - Materials Today Advances N2 - In this work, cosolvent-stabilized superhydrophobic, highly hydrostable ZIF-67 was synthesized at room temperature using a facile, one-pot hydrothermal synthesis route, and the effect of cosolvent concentration on ZIF-67 crystal structure properties and hydrostability was studied systematically. The underlying mechanism for the cosolvent-supported hydrostability improvement was also proposed. Furthermore, the influence of hydrotreatment on the resultant ZIF-67s' catalytic performance was studied in the ‘Sabatier reaction’ for CO2 to synthetic natural gas (CH4) conversion. KW - ZIF-67 KW - Superhydrophobicity KW - Hydrostability KW - Cosolvent-stabilization KW - CO2 methanation Y1 - 2023 U6 - https://doi.org/10.1016/j.mtadv.2023.100448 SN - 2590-0498 VL - Vol. 20 ER - TY - GEN A1 - Gonzalez-Castãno, Miriam A1 - Morales, Carlos A1 - Navarro de Miguel, Juan Carlos A1 - Boelte, Jens-H. A1 - Klepel, Olaf A1 - Flege, Jan Ingo A1 - Arellano-García, Harvey T1 - Are Ni/ and Ni5Fe1/biochar catalysts suitable for synthetic natural gas production? A comparison with γ-Al2O3 supported catalysts T2 - Green Energy & Environment N2 - Among challenges implicit in the transition to the post–fossil fuel energetic model, the finite amount of resources available for the technological implementation of CO2 revalorizing processes arises as a central issue. The development of fully renewable catalytic systems with easier metal recovery strategies would promote the viability and sustainability of synthetic natural gas production circular routes. Taking Ni and NiFe catalysts supported over γ-Al2O3 oxide as reference materials, this work evaluates the potentiality of Ni and NiFe supported biochar catalysts for CO2 methanation. The development of competitive biochar catalysts was found dependent on the creation of basic sites on the catalyst surface. Displaying lower Turn Over Frequencies than Ni/Al catalyst, the absence of basic sites achieved over Ni/C catalyst was related to the depleted catalyst performances. For NiFe catalysts, analogous Ni5Fe1 alloys were constituted over both alumina and biochar supports. The highest specific activity of the catalyst series, exhibited by the NiFe/C catalyst, was related to the development of surface basic sites along with weaker NiFe–C interactions, which resulted in increased Ni0:NiO surface populations under reaction conditions. In summary, the present work establishes biochar supports as a competitive material to consider within the future low-carbon energetic panorama. KW - Biochar catalysts KW - Carbon catalysts KW - Ni catalysts KW - NiFe alloy KW - Bimetallic catalysts KW - Synthetic natural gas KW - CO2 methanation Y1 - 2023 U6 - https://doi.org/10.1016/j.gee.2021.05.007 SN - 2468-0257 VL - 8 IS - 3 SP - 744 EP - 756 ER - TY - CHAP A1 - González-Castaño, Miriam A1 - Tarifa, Pilar A1 - Monzon, Antonio A1 - Arellano-García, Harvey T1 - Valorization of unconventional CO2-rich feedstock via Reverse Water Gas Shift reaction T2 - Circular Economy Processes for CO2 Capture and Utilization : Strategies and Case Studies N2 - The implementation of novel CO2 valorization technologies is one of the most promising approaches towards the achievement of sustainable energy models. This chapter highlights the importance of carbon capture and utilization technologies and proposes novel approaches for the valorization of CO2-rich feedstock derived from thermochemical biomass conversion through the production of syngas mixtures via the Reverse Water Gas Shift reaction. After, this classification of the different types of nonconventional gases and biomass-treatment processes, we have also revised the fundamentals of the Reverse Water Gas Shift reaction and the impact of species commonly present in CO2-rich streams on the performance of the catalytic systems are also reviewed. Finally, a catalytic bi-functionalization approach that ensures larger CO productivity from simulated biomass-derived CO2-rich feedstock is demonstrated. KW - Reverse Water Gas Shift KW - Valorization of CO2 KW - Syngas KW - Catalysts Y1 - 2024 SN - 9780323956697 U6 - https://doi.org/10.1016/B978-0-323-95668-0.00001-1 SP - 307 EP - 323 PB - Woodhead Publishing ER - TY - GEN A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Paff, Jessica Sophie A1 - Arellano-García, Harvey T1 - Structural formability of perovskite ABO3 oxide system synthesized via autocombustion technique, ruled by geometric factors T2 - 18th International Congress on Catalysis Y1 - 2024 UR - https://www.researchgate.net/publication/388109875_Structural_formability_of_perovskite_ABO3_oxide_system_synthesized_via_auto-_combustion_technique_ruled_by_geometric_factors ER - TY - GEN A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Machine learning application in kinetic studies: A review T2 - 18th International Congress on Catalysis N2 - Machine learning (ML) brings new opportunities in the field of heterogenous catalysis and reaction engineering. Here, the advancements brought by ML in the field of kinetic studies are reviewed. Y1 - 2024 UR - https://www.researchgate.net/publication/388109595_Machine_learning_application_in_kinetic_studies_A_review ER - TY - GEN A1 - Shafiee, Parisa A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Novel approaches for preparation of 3D Ni-Al2O3 core with zeolite shell catalysts for dry reforming of methane T2 - 18th International Congress on Catalysis Y1 - 2024 UR - https://www.researchgate.net/publication/388109598_Novel_Approaches_for_Preparation_of_3D_Ni-Al2O3_Core_with_Zeolite_Shell_Catalysts_for_Dry_Reforming_of_Methane ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Silva de Aquino, Gabrielle A1 - Santos da Silva, Jefferson A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Valverde Pontes, Karen A1 - Santos Mascarenhas, Artur José T1 - Estudo do efeito do suporte em catalisadores de Ni preparados pelo método da combustão aplicados na reforma a seco do biogás para produção de hidrogênio sustentável T2 - 63rd Brazilian Chemistry Congress Y1 - 2024 UR - https://www.researchgate.net/publication/388109502_Estudo_do_efeito_do_suporte_em_catalisadores_de_Ni_preparados_pelo_metodo_da_combustao_aplicados_na_reforma_a_seco_do_biogas_para_producao_de_hidrogenio_sustentavel ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Farhadi, Maryam A1 - Arellano-García, Harvey T1 - Conceptual design of a reactive distillation column for the catalytic upgrade of ABE T2 - Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik Y1 - 2024 UR - https://www.researchgate.net/publication/388143625_Conceptual_design_of_a_reactive_distillation_column_for_the_catalytic_upgrade_of_ABE ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - A second-order linesearch procedure within Newton’s method for highly nonlinear steady-state systems simulation T2 - 2024 AIChE Annual Meeting N2 - Linesearch, a crucial component of Newton’s method, ensures global convergence, guaranteeing convergence to a local solution from any starting point while satisfying all simultaneous nonlinear equations (Bellavia and Morini, 2003). Despite Newton’s method being considered established both theoretically and algorithmically, leaving little room for further improvements, this contribution focuses on enhancing the linesearch procedure and revealing significant advancements over existing methods. Specifically, this study aims to incorporate second-order information in a computationally efficient manner to improve the performance of the linesearch procedure, especially for highly nonlinear equation systems. Nonlinearity, particularly near the starting point, can substantially hinder algorithmic efficiency, necessitating frequent step reductions at the expense of function evaluations and major iterations involving Jacobian evaluations and factorizations (Gill and Zhang, 2024). The proposed approach leverages a a higher-order Taylor series expansion around the operating point of a major iteration in Newton’s algorithm, coupled with a custom Jacobian vector product finite difference scheme. This combination requires only one additional Jacobian evaluation to construct a locally accurate fourth-degree polynomial approximating the merit function along the search direction. In addition to the theoretical advancements, this contribution provides computational evidence supporting the claim that for highly nonlinear systems, significant computational savings and enhanced solution procedure stability can be achieved. Utilizing a Python implementation, linear subsets of equations are treated separately to boost the efficiency of function and Jacobian evaluations, aligning with standard practices in professional software development. While Python may not be a high-performance language, its suitability for rapid algorithm prototyping and validation precedes potential transfer to higher-performance languages like C++. Moreover, given Newton’s method central roles in various iterative solution tools, such as its repeated use within a Differential-Algebraic Equations (DAEs) integrators and potentially Partial Differential-Algebraic Equations (PDAEs) solvers, the significance of this work extends even further. Future research endeavors will explore these areas, building upon the foundations laid by this study. Y1 - 2024 UR - https://aiche.confex.com/aiche/2024/meetingapp.cgi/Paper/689676 ER - TY - GEN A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) T2 - Chemical Engineering Journal Advances N2 - Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 %), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 %). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity. KW - Jet fuels (C8-C16) KW - Machine learning (ML) KW - Fischer-Tropsch synthesis (FTS) KW - Operational conditions KW - Catalyst preparation Y1 - 2025 U6 - https://doi.org/10.1016/j.ceja.2024.100702 VL - 21 (2025) PB - Elsevier ER - TY - GEN A1 - Shezad, Nasir A1 - Safdar, Muddasar A1 - Arellano-Garcia, Harvey A1 - Tai, Cheuk-Wai A1 - Chen, Shaojiang A1 - Seo, Dong-Kyun A1 - You, Shujie A1 - Vomiero, Alberto A1 - Akhtar, Farid T1 - Deciphering the role of APTES in tuning the metal support interaction of NiO nanolayers over hierarchical zeolite 13X for CO2 methanation T2 - Carbon Capture Science & Technology N2 - The development of robust nickel catalysts on porous substrates offers great potential for converting carbon dioxide (CO2) into methane, thereby helping to address the global warming and sustainability challenges. This study investigates the dispersion and stability of Ni nanolayers by grafting bifunctional groups over the hierarchical zeolite 13X (h13X) support using (3-aminopropyl)triethoxysilane (APTES). The Ni nanolayers, with a thickness of 1.5-7 nm, were deposited around the edges of h13X and analyzed using STEM imaging. A clear shift in the binding energies was observed by XPS analysis, substantiating the enhanced metalsupport interaction (MSI) between NiO and h13X. The influence of reaction temperature on APTES incorporation into h13X was revealed by H2-TPR and CO2-TPD, with notable variations in the reducibility and surface basicity profiles of the catalysts. The optimized catalyst exhibited CO2 conversion of 61% with CH4 selectivity of 97% under GHSV of 60,000 mlgCat-1h-1 at 400 oC and 1 bar and demonstrated robust stability over a period of 150 h without discernible degradation. The enhanced performance could be attributed to the strengthened MSI and reduced size of Ni nanolayers over h13X. These findings highlight the development of robust heterogeneous catalysts by changing the surface chemistry of support material for various catalytic applications. KW - CO2 methanation KW - Catalyst stability KW - Metal-support interaction KW - APTES functionalization KW - Nickel nanolayers KW - Hierarchical zeolite Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S2772656825000636 U6 - https://doi.org/10.1016/j.ccst.2025.100424 VL - 15 SP - 1 EP - 11 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Mappas, Vasileios A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios A1 - Arellano-Garcia, Harvey T1 - Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept T2 - Chemical engineering research and design N2 - Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP’s bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly’s classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, providing foundational insights towards leveraging QA in Process Systems Engineering. KW - Quantum annealing KW - Bilinear programming KW - Pooling/blending problem KW - Quadratic unconstrained binary optimization (QUBO) Y1 - 2025 U6 - https://doi.org/10.1016/j.cherd.2025.08.031 SN - 1744-3563 VL - 221 SP - 560 EP - 576 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Multiphase systems, particularly trickle bed reactors (TBRs), are critical in various industrial applications and widely employed in catalytic processes such as hydrogenation and oxidation due to their high surface area, low operational and minimal catalyst loss. Despite advancements in modelling techniques, accurately capturing the complex multiphysics and multiscale phenomena remains challenging. Conventional approaches, relying on empirical correlations or Computational Fluid Dynamics (CFD) simulations, often fall short due to high computational demands, limited accuracy, and constraints on the number of catalytic particles that can be effectively simulated [3]. To address these limitations, this contribution presents a new framework tailored for the design and analysis of multiphase systems operating in the low-interaction regimes. This approach is based on the local structure of the packed bed and employs a Lagrangian approach, where flow dynamics within the reactor is represented by various discrete elements. The framework's modular and flexible setup enables the incorporation of multiscale information of both local and global levels, allowing for the additions of new modules or features to enhance modelling fidelity. Y1 - 2025 UR - www.researchgate.net/publication/388846704_Capturing_multiscale_phenomena_in_trickle_bed_reactors_A_flexible_framework_for_flow_and_reaction_analysis ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Kim, Jin-Kuk A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Industrial sectors contribute substantially to global CO2 emissions, emphasizing the need for low-carbon, reliable energy supplies to meet operational demands. Achieving net-zero emissions in industrial processes involves transitioning from fossil fuels to renewable energy sources. However, the intermittent nature of renewables poses challenges to energy reliability and resilience, particularly in utility systems. This contribution addresses industrial decarbonisation and sustainable hydrogen production by developing a comprehensive design and optimization framework for integrating renewable energy systems into industrial operations. This framework incorporates energy storage and grid connections to improve flexibility and stability and is evaluated through two case studies. Both case studies analyse the operational and configurational changes necessary for renewable-powered hydrogen production, estimating the cost of hydrogen or CO2 avoidance cost to analyse economic viability. These insights provide guidelines for sustainable and economically viable energy management in industrial and hydrogen production sectors, supporting broader global energy transition goals. Y1 - 2025 UR - https://www.researchgate.net/publication/388846958_Pathways_to_industrial_decarbonization_Renewable_energy_integration_and_electrified_hydrogen_production ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Safdar, Muddasar A1 - Helle, Sven A1 - Fischer, Thomas A1 - Arellano-García, Harvey T1 - Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst T2 - Chemie - Ingenieur - Technik : CIT N2 - Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. This study investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts under polychromatic UV–VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites (NiMn2O4, LaMnO3, NdMnO3, and their nickel-doped variants) were synthesized via a facile co-precipitation technique and characterized using X-ray diffraction (XRD), UV–VIS diffuse reflectance spectroscopy (UV–VIS-DRS), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), N2-physisorption, and microwave plasma atomic emission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradation efficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C–C bond cleavage. KW - Lanthanoids KW - Perovskites KW - PFAS KW - PFOA KW - Photocatalysis Y1 - 2025 U6 - https://doi.org/10.1002/cite.70027 SN - 1522-2640 SP - 1 EP - 11 PB - Wiley-VCH GmbH CY - Weinheim ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron T2 - Systems and control transactions N2 - In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. These findings contribute to advancing energy-efficient and scalable hydrogen storage technologies. KW - Hydrogen KW - Hydrogen fuel cells KW - Energy storage KW - Modelling and simulations KW - Optimisation KW - Thermochemical storage KW - Green hydrogen Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.121492 SN - 2818-4734 VL - 4 SP - 631 EP - 636 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Solving complex combinatorial optimization problems using quantum annealing approaches T2 - Systems and control transactions N2 - Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer's architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly's pooling and blending problem is examined while demonstrating the potential of the proposed approach. The results indicate that the succinct formulation exhibited higher success rate during the embedding procedure for the different examined architectures, and the quantum annealing solver exhibited the best performance among the various solvers investigated. This highlights the potential of the approach for solving this type of problems with the rapid development and improvement of quantum hardware and expanding it to more complex chemical engineering optimization systems. KW - Optimization KW - Scheduling KW - Algorithms KW - Quantum Computing KW - Quantum Annealing Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.188358 SN - 2818-4734 VL - 4 SP - 1561 EP - 1566 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Shafiee, Parisa A1 - Jafari, Mitra A1 - Schowarte, Julia A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis T2 - Systems and control transactions N2 - Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization. KW - Catalysis KW - Machine learning KW - Modelling KW - Optimization KW - Alternative fuels KW - Environment KW - FischerTropsch synthesis KW - Photocatalysis Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.135551 SN - 2818-4734 VL - 4 SP - 1866 EP - 1871 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Kim, Jin-Kuk T1 - Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems T2 - Systems and control transactions N2 - Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined with renewable-based electrification. The proposed optimization model evaluates the economic trade-offs of integrating carbon capture, renewable energy, and energy storage. By applying this approach, systematic design guidelines are developed for the transition of a conventional steady-state utility system toward renewable energy integration, ensuring economically viable and sustainable energy management in process industries. KW - Renewable energy KW - CO2 capture KW - Industrial utility operation KW - Cost optimization KW - Process integration Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.107403 SN - 2818-4734 VL - 4 SP - 1175 EP - 1180 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-Garcia, Harvey T1 - An efficient and unified modeling framework for trickle bed reactors : a modular approach T2 - Chemie - Ingenieur - Technik : CIT N2 - Trickle bed reactors (TBRs) involve complex and multiscale dynamics that challenge their design, modeling, and optimization. Current approaches often suffer from high computational cost and limited scalability, restricting their applicability in large-scale cases. This work introduces a modular, computationally efficient framework to address these issues by systematically capturing key transport and reaction phenomena. Furthermore, it provides a critical review of existing modeling strategies for TBRs, outlining their strengths and limitations and highlighting opportunities for enhancement through modularization. By offering a structured and scalable approach, the proposed framework improves predictive capabilities and supports the development of optimized and adaptable reactor designs. KW - Catalytic multiphase reactors KW - Modeling KW - Trickle bed reactors Y1 - 2025 U6 - https://doi.org/10.1002/cite.70035 SN - 1522-2640 VL - 97 IS - 11-12 SP - 1110 EP - 1126 PB - Wiley CY - Weinheim ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Santos da Silva, Jefferson A1 - De Aquino, Gabrielle A1 - Dos Santos, Mauricio A1 - Cruz, Fernanda A1 - Fiuza-Junior, Raildo A. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Pontes, Karen A1 - Mascarenhas, Artur T1 - Influência do Suporte em Catalisadores de Ni Obtidos Pelo Método da Combustão na Reforma a Seco do Biogás para Produção de Hidrogênio Sustentável T2 - 23º CBCAT : Congresso Brasileiro de Catalise T2 - 23rd Brazilian Congress of Catalysis N2 - Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biogás. As caracterizações revelaram variações na dispersão metálica, área metálica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior área metálica e melhor dispersão de Ni, favorecendo altas conversões de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e estável por 8 horas de reação. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resistência ao coque. O NiO-CeO₂, embora ativo no início, desativou com o tempo devido à deposição de coque (6,4%). A análise pós-reação mostrou baixa formação de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resistência dos catalisadores na reforma a seco do biogás. Palavras-chave: Hidrogênio sustentável; Reforma a seco do biogás; Catalisadores de NiO; efeito do suporte ABSTRACT-This study evaluates NiO-based catalysts supported on MgO, ZrO₂, Al₂O₃, La₂O₃, and CeO₂ for the dry reforming of biogas. Characterization of the samples revealed differences in metal dispersion, metallic area, and surface morphology. NiO-Al₂O₃ and NiO-CeO₂ show higher metallic areas and better Ni dispersion, leading to higher CH₄ and CO₂ conversions and good H₂ yield. NiO-Al₂O₃ is the most efficient and stable catalyst over 8 hours of reaction. NiO-La₂O₃ shows a gradual increase in activity and good coke resistance. Conversely, NiO-CeO₂, despite high initial activity, deactivates over time due to coke deposition (6.4%). Post-reaction analysis confirmed low coke formation for most catalysts. The results indicate that the choice of support directly affects catalyst activity, stability, and resistance. KW - Hidrogêniosustentável KW - Reforma a secodo biogás KW - Catalisadores de NiO KW - Efeito dosuporte KW - Sustainable hydrogen KW - Biogas dry reforming KW - NiO Catalysts KW - Supportrole Y1 - 2025 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203/303 VL - 1 IS - 1 SP - 1 EP - 6 ER - TY - GEN A1 - Mbuya, Christel Olivier Lenge A1 - Pawar, Kunal A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Okoye Chine, Chike George A1 - Tarifa, Pilar A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems T2 - Journal of CO2 utilization N2 - The dry reforming of methane (DRM) is a promising route for converting greenhouse gases such as methane (CH4) and carbon dioxide (CO2) into valuable syngas, hydrogen (H2) and carbon monoxide (CO). However, traditional nickel (Ni)-based catalysts suffer from rapid deactivation due to carbon deposition and sintering, especially when supported on low thermal conductivity materials. In this work, a novel post-synthesis microwave irradiation (MIR) treatment is introduced to systematically optimize the performance of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts for DRM. Unlike previous studies that have used MIR during reaction or with different supports, this approach tunes the metal – support interactions and textural properties of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts by varying the MIR exposure time after catalyst synthesis. MIR post-treatment (10–25 s) increased the CH4 conversion to 65 % and the CO2 conversions to 62 % for Ni–β–SiC catalysts and improved the H₂/CO ratio to 0.80, with stable performance over 20 h. For Ni–Ti–Cβ–SiC, MIR (10–20 s) maintained CH4 conversion up to 60 % and CO2 conversion to 58 % over 20 h, while the untreated catalyst, though initially higher, deactivated rapidly. Excessive MIR (30 s) reduced performance for both catalyst types, underscoring the need for optimal exposure time. These findings demonstrate post-synthesis MIR provides a tuneable approach for enhancing both the activity and durability of Ni/SiC – based DRM catalysts through controlled modification of metal – support interactions. This work offers new insights for the design of robust catalysts aimed at greenhouse gas utilization and sustainable syngas production, with activity and stability enhancements linked to controlled changes in metal – support interactions. KW - Carbon dioxide KW - Dry reforming KW - Methane KW - Microwave irradiation KW - Ni Silicon carbide catalysts Y1 - 2025 U6 - https://doi.org/10.1016/j.jcou.2025.103270 SN - 2212-9839 VL - 102 SP - 1 EP - 9 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Santos da Silva, Jefferson A1 - Silva de Aquino, Gabrielle A1 - Vaz dos Santos Rios, João Gabriel A1 - Brandão dos Santos, Maurício A1 - Teixeira Cruz, Fernanda A1 - Alves Fiuza-Junio, Raildo A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Valverde Pontes, Karen A1 - Santos Mascarenhas, Artur José T1 - Effect of support on Ni catalysts prepared by the combustion method applied in the dry reforming of biogas for production of sustainable hydrogen T2 - International journal of hydrogen energy N2 - This work investigated Ni catalysts on different supports (MgO, ZrO2, NiAl2O4, CeO2 and La2O3) prepared by the combustion method aiming for sustainable hydrogen production via simulated biogas dry reforming. The Ni/NiAl2O4 catalyst stood out among the materials due to its high Ni dispersion, low crystallite size and strong metal-support interaction, being stable for 8 h of reaction with high H2 yield and low coke deposition. The Ni/CeO2 catalyst showed good catalytic activity, but with high coke deposition (11.7 %). The Ni/La2O3 catalyst showed an increase over the reaction time, due to the dynamic reconstruction of the surface. The Ni/MgO and Ni/ZrO2 catalysts did not present satisfactory performance when compared to the other catalysts, due to the low Ni dispersion and high crystallite size. The Ni/NiAl2O4 catalyst is very promising, due to the high production of H2, low coke deposition, thermal stability, but new studies on durability and economic viability are necessary. KW - Sustainable hydrogen production KW - Biogas dry reforming KW - Ni-supported catalysts KW - Support effect KW - Combustion synthesis method Y1 - 2026 U6 - https://doi.org/10.1016/j.ijhydene.2025.153150 SN - 1879-3487 VL - 204 SP - 1 EP - 25 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Lee, Joohwa A1 - Park, Haryn A1 - Dorneanu, Bogdan A1 - Kim, Jin-Kuk A1 - Arellano-Garcia, Harvey T1 - Decarbonized hydrogen production : integrating renewable energy into electrified SMR process with CO₂ capture T2 - Systems and control transactions N2 - Electrified steam methane reforming has emerged as a promising technology for electrifying the hydrogen production process industries. Unlike conventional fossil fuel-based steam methane reforming, the electrified steam methane reforming process relies exclusively on electrical heating, eliminating the need for fossil fuel combustion. Beyond that, however, significant amounts of electricity required for the electrified process should be imported from the renewable energy-based system rather than fossil fuel-based grid electricity to have an environmental advantage over the conventional process. This study suggests a framework for integrating renewable energy systems into the electrified process for decarbonized hydrogen production. Considering the variability of renewable energy, wind and solar power are supplemented by battery storage, to facilitate a stable electricity supply to the electrified hydrogen production process. A Mixed-Integer Linear Programming (MILP) model is developed to optimally size and operate both the renewable system and potential grid imports. Case studies under various carbon tax scenarios, using historical weather data from a region in Germany, are conducted, followed by a techno-economic assessment to estimate the Cost of Hydrogen (COH). The results show that higher carbon taxes and reduced capital costs for wind, solar, and storage technologies significantly increase the share of renewable-based electricity. These findings highlight the importance of more stringent carbon taxation and improvements in the technology readiness level (TRL) of renewable energy are critical for accelerating large-scale, clean hydrogen production and industrial decarbonization. KW - Hydrogen KW - Renewable energy KW - Electrification Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.152295 SN - 2818-4734 VL - 4 SP - 613 EP - 618 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Arellano-Garcia, Harvey T1 - A novel approach to gradient evaluation and efficient deep learning : a hybrid method T2 - Systems and control transactions N2 - Deep learning faces significant challenges in efficiently training large-scale models. These issues are closely linked, as efficient training often depends on precise and computationally feasible gradient calculations. This work introduces innovative methodologies to improve deep learning network (DLN) training in complex systems. A novel approach to DLN training is proposed by adapting the block coordinate descent (BCD) method, which optimizes individual layers sequentially. This is combined with traditional batch-based training to create a hybrid method that harnesses the strengths of both techniques. Additionally, the study explores Iterated Control Random Search (ICRS) for initializing parameters and applies quasi-Newton methods like L-BFGS with restricted iterations to enhance optimization. By tackling DLN training efficiency, this contribution offers a comprehensive framework to address key challenges in modern machine learning. The proposed methods improve scalability and effectiveness, especially for handling complex real-world problems. Examples from Process Systems Engineering illustrate how these advancements can directly enhance the training of large-scale systems. KW - Artificial intelligence KW - Machine learning KW - Numerical methods Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.120349 SN - 2818-4734 VL - 4 SP - 1872 EP - 1877 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Shezad, Nasir A1 - Samikannu, Ajaikumar A1 - Safdar, Muddasar A1 - Arellano-Garcia, Harvey A1 - Mikkola, Jyri-Pekka A1 - Seo, Dong-Kyun A1 - Akhtar, Farid T1 - Nickel supported over hierarchical zeolite 13X catalysts for enhanced conversion of carbon dioxide into methane T2 - International journal of energy research N2 - Catalytic conversion of carbon dioxide (CO2) into useful chemicals such as methane (CH4) is a promising carbon utilization method that effectively mitigates CO2 and partially meets energy needs. The characteristics of commonly used nickel (Ni) supported meso/microporous catalysts for CO2 methanation can be tailored by tuning the structural properties of the support and adding promoters. This work investigated the Ni supported over hierarchical zeolite 13X (h13X) and incorporated with different promoters (Mg, Ca, Ce, and La) developed using the wet-impregnation method. The catalysts were thoroughly characterized using SEM, EDS, XRD, H2-TPR, CO2-TPD, thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), and N2 sorption and desorption techniques and evaluated for CO2 methanation. The impact of promoters on the characteristics of the catalysts was observed with improved surface basicity in CO2-TPD and metal-support interaction in H2-TPR analysis. Among the promoted catalysts, the NiLa/h13X catalyst exhibited the highest catalytic activity with a maximum conversion of 76% and CH4 selectivity of 98.5% at 400°C and 20 bar at GHSV of 60,000 mL gcat−1 h−1, respectively. Regarding stability, the Mg-promoted catalyst exhibited better stability during 24 h of reaction than other catalysts, demonstrating better resilience against deactivation. The enhanced performance of the NiLa/h13X catalyst could be credited to the increased surface basicity, high surface area, and dispersion. This study highlights the potential of hierarchical porous zeolites for CO2 methanation and other heterogeneous reactions. KW - Carbon dioxide KW - Catalyst KW - Hierarchical zeolite KW - Methane KW - Nickel KW - Promoters Y1 - 2025 U6 - https://doi.org/10.1155/er/4728304 SN - 1099-114X VL - 2025 SP - 1 EP - 14 PB - Wiley CY - Hoboken, NJ ER - TY - GEN A1 - Safdar, Muddasar A1 - Sher, Farooq A1 - Arellano-Garcia, Harvey T1 - Perovskite materials for catalytic CO₂ valorisation : structural characteristics, synthesis and lattice substitutions for gas-phase reactions T2 - Journal of environmental chemical engineering N2 - Perovskites are emerging materials that are being extensively investigated for converting greenhouse gases (GHGs) through thermochemical processes due to their versatile properties. Given their distinct physical and chemical characteristics and their unique structure (ABO3, general formula), they are desirable candidates for designing state-of-the-art catalytic systems. For instance, they can be prepared with modified oxygen vacancies, enhanced redox potential, and tailored nanoparticle formulations for use in various catalytic gas-phase CO2 conversion processes, thereby facilitating the formation of valuable, renewable raw materials such as fuels and chemicals. This comprehensive review explains the perovskite structures, including their crystallographic properties, standard synthesis methods, recent advancements in A, B, and X-site substitutions, and their effectiveness in upgrading CO2 to produce valuable commodities via different synthetic routes in gas-phase reactions via methanation, reverse water gas shift reaction (rWGS), and dry reforming of methane (DRM). To achieve a sustainable clean energy supply, application-oriented, efficient, and advanced catalytic systems that support the necessary reaction conditions and serve as the most active and selective catalysts are reported in each synthetic gas-phase production section. This study highlights current advancements and optimised research efforts to design potential catalytic materials that meet future requirements for developing efficient decarbonised energy systems. The proposed synthesis methods are the most effective techniques for conserving time and energy. They can also yield favourable morphology and allow manipulation of nanoparticle size, both of which are essential for designing innovative catalysts. To address concerns about CO2 emissions harming the environment, this study focuses on adaptable, sustainable gas-phase reaction methods with diverse industrial applications. The primary emphasis is on effective, robust perovskite-based catalysts that enable the efficient conversion of CO2 into value-added chemicals and fuels, thereby supporting low-carbon energy and chemical technologies. This review delineates explicit correlations among synthesis, structure, properties, performance, and stability by relating perovskite lattice design, defect chemistry, and compositional flexibility to catalytic activity, selectivity, and durability in heterogeneous catalytic reactions. KW - Perovskites KW - Advanced materials KW - Clean energy KW - CO2 utilization KW - Substitutional sites KW - Catalytic applications Y1 - 2026 U6 - https://doi.org/10.1016/j.jece.2026.121473 SN - 2213-3437 VL - 14 IS - 2 SP - 1 EP - 35 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Safdar, Muddasar A1 - Helle, Sven A1 - Fischer, Thomas A1 - Arellano-Garcia, Harvey T1 - Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst T2 - Chemie Ingenieur Technik N2 - Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. Thisstudy investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts underpolychromatic UV–VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites(NiMn2 O 4 , LaMnO 3 , NdMnO 3 , and their nickel-doped variants) were synthesized via a facile co-precipitation techniqueand characterized using X-ray diffraction (XRD), UV–VIS diffuse reflectance spectroscopy (UV–VIS-DRS), scanning elec-tron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), N2 -physisorption, and microwave plasma atomicemission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradationefficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C–C bond cleavage. KW - Lanthanoids KW - Perovskites KW - PFAS KW - PFOA KW - Photocatalysis Y1 - 2026 U6 - https://doi.org/10.1002/cite.70027 SN - 1522-2640 VL - 98 IS - 1-2 SP - 7 EP - 17 PB - Wiley-VCH CY - Weinheim ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A comprehensive modelling approach to enhance performance and scalability of iron-oxide based hydrogen storage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Hydrogen storage remains a major bottleneck in realizing a competitive hydrogen economy due to the energy intensity and economic limitations of existing solutions [1]. This contribution addresses these barriers through a model-driven optimization framework for a solid-state thermochemical hydrogen storage (TCS) based on reversible redox cycling of iron oxide/iron. While iron-based TCS offers inherent safety and scalability advantages, key limitations persist, including high reduction temperatures (requiring significant energy input), suboptimal energy storage density, and sluggish redox kinetics [2]. Kinetic parameters for the reduction and oxidation reactions were systematically derived through isothermal thermogravimetric analysis (TGA) coupled with kinetic model regression. A first-principles dynamic model of a fixed-bed reactor was developed, integrating mass, energy, and momentum balances, and validated against experimental data from a lab-scale apparatus. The experimental system featured precision gas flow control, an electric furnace reactor, rapid air-cooled condensation, molecular sieve dehydration, and online effluent analysis via flow meters and gas chromatography. Dynamic simulations were carried out to investigate the reactor's transient behaviour under variations in critical parameters, including H2/H2O partial pressures, reaction temperatures, and gas flow rates. These studies revealed trade-offs between energy efficiency (favoured by lower temperatures), and reaction rates (enhanced at higher temperatures), while identifying key bottlenecks in redox cycling. The model further demonstrated how optimizing feed composition and flow dynamics mitigates kinetic degradation during charge/discharge cycles. REFERENCES [1] Elberry A.M. et al. KW - Hydrogen KW - Green hydrogen KW - Storage KW - Thermochemical storage KW - Process modelling KW - Optimisation Y1 - 2025 UR - https://www.researchgate.net/publication/399753698_A_comprehensive_modelling_approach_to_enhance_performance_and_scalability_of_iron-oxide_based_hydrogen_storage_systems SP - 1 EP - 5 CY - Frankfurt am Main ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Adjoint methods for fast sensitivity analysis in nonlinear multistage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Parametric sensitivity analysis is critical for optimization, control, and decision-making in engineering systems, enabling precise understanding of system responses to changes in parameters [1]. In the case of large-scale multistage systems, characterized by interconnected components, high-dimensional parameter spaces, and nonlinear constraints, traditional gradient evaluation methods often face significant challenges in scalability, computational efficiency, and accuracy [2]. This contribution introduces a novel framework for evaluating parametric gradients tailored specifically for generally constrained multistage systems, which leverages adjoint-based techniques [3] to compute exact gradients efficiently, addressing the inherent complexity of these systems. This reduces the number of simulations required by direct numerical differentiation or finite difference methods. The framework accommodates continuous real-value parameters and is designed to handle high-dimensional spaces typical of multistage systems. The proposed methodology is validated through case studies involving large-scale modular systems with nonlinear constraints. Results demonstrate substantial improvements in computational efficiency and gradient accuracy compared to conventional techniques. These advancements enable optimization algorithms to converge more quickly and reliably while navigating complex solution spaces effectively. By facilitating accurate sensitivity analysis, the framework enhances the exploration of design alternatives and increases the likelihood of identifying globally optimal solutions. KW - Parametric sensitivities KW - Adjoint equations KW - Multistage systems KW - Gradient evaluation KW - Nonlinear optimization KW - Process systems engineering Y1 - 2025 UR - https://www.researchgate.net/publication/399753602_Adjoint_methods_for_fast_sensitivity_analysis_in_nonlinear_multistage_systems SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Mbuya, Christel-Olivier Lenge A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Structured FeMnK catalysts for aviation fuel production via Fischer-Tropsch synthesis : a channel geometry study T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Fischer–Tropsch synthesis (FTS) is a promising route for sustainable aviation fuel production but is often limited by heat and mass transfer constraints in fixed-bed reactors. This study investigates the role of structured catalyst geometry in intensifying FTS performance using FeMnK catalysts selectively tailored for aviation-range hydrocarbons. The catalyst was synthesized via organic combustion and further modified by microwave irradiation, with structural properties characterized by X-ray diffraction. Aluminum honeycomb monoliths with square, circular, and triangular channel geometries were designed and fabricated by 3D printing, followed by catalyst deposition through dip-coating. High-pressure FTS experiments conducted at 30 bar and 300 °C demonstrate that channel geometry significantly influences conversion and selectivity by affecting flow dynamics and reactant mixing. The results highlight the potential of geometry-optimized structured catalysts to enhance FTS efficiency and support advanced reactor designs for sustainable aviation fuel production. Y1 - 2025 UR - https://www.researchgate.net/publication/391697232_Structured_FeMnK_catalysts_for_aviation_fuel_production_via_Fischer-Tropsch_synthesis_A_channel_geometry_study SP - 1 EP - 3 ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Combinatorial optimization problems : a quantum-based approach T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Classical computing faces challenges in global optimization (GO) when addressing non-convex problems, where the number of solutions grows exponentially with problem size 1. Quantum computing emerges as a potential solution. Gate-based quantum computing offers a broader range of applicability due to its ability to implement general quantum algorithms, in contrast to quantum annealing which is more specialized 2,3. This contribution explores the application of gate-based quantum computing to solving combinatorial optimization (CO) problems, specifically focusing on overcoming the limitations of classical computing. The proposed approach utilizes gate-based quantum computers, using IBM's hardware and software as an example, and the quantum approximate optimization algorithm (QAOA) for solving the reformulated problem using the Qiskit toolbox. The methodology includes translating the quadratic unconstrainted optimization problem (QUBO) into an Ising Hamiltonian, constructing the QAOA ansatz circuit, and optimizing its parameters. Furthermore, real quantum device architectures are employed to solve the optimized QUBO formulations. To demonstrate the capabilities of the approach, Haverly's pooling-blending problem is selected as a case study. Through the application of different discretization strategies, resolution levels, and circuit architectures, a comparative analysis of solver performance is conducted. The resulting QUBO formulations, efficiently embedded and solved on real quantum devices, underscore the potential of gate-based quantum computing as a promising solution approach for complex CO challenges. Y1 - 2025 UR - https://www.researchgate.net/publication/399789138_Combinatorial_Optimization_Problems_A_quantum-based_approach N1 - Sammelmail an Dorneanu. 27.01.2026 TR SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Safdar, Muddasar A1 - Schowarte, Julia A1 - Arellano-Garcia, Harvey T1 - Sustainable production of synthetic natural gas : CO2 methanation on 3D-printed structured Ni-based perovskite catalysts T2 - Annual Meeting on Reaction Engineering 2025 N2 - This study explores the thermo-catalytic conversion of captured CO₂ with renewable H₂ to produce synthetic natural gas (SNG) using Ni-based perovskite-type oxides (Ni-PTOs) as cost-effective and thermally stable catalysts in Power-to-Gas (PtG) applications. Structured monoliths improved the catalytic performance under reaction conditions. Use of 3D-printed geometries enhanced thermal/mechanical stability and process efficiency. Structured designs outperformed conventional powder-based configurations, indicating promise for scalable and sustainable SNG production. KW - CO₂ methanation KW - Power-to-Gas KW - Ni-PTOs catalysts KW - 3D-printed structures Y1 - 2025 UR - https://www.researchgate.net/publication/392195919_Sustainable_production_of_synthetic_natural_gas_CO2_methanation_on_3D-printed_structured_Ni-based_perovskite_catalysts SP - 1 EP - 4 CY - Würzburg ER - TY - GEN A1 - Jafari, Mitra A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME T2 - 58. Jahrestreffen Deutscher Katalytiker N2 - Dimethyl ether (DME, methoxymethane) is a clean-burning fuel and a promising alternative to conventional fossil fuels, especially in transportation and power generation. Its production from methanol through dehydration offers a viable pathway toward energy sustainability, not only because of its environmental benefits but also due to the high purity of the resulting products and the efficient conversion rate of methanol [1]. However, optimizing this process requires understanding the intricate dependencies among reaction parameters, including temperature, pressure, catalyst type, and feedstock composition [2]. Machine learning offers transformative potential in this context by identifying complex, non-linear interactions among variables and providing predictive insights that can improve reaction efficiency, yield, and product quality. Through predictive modeling, machine learning can significantly reduce the need for experimental trial-and-error by identifying optimal reaction conditions quickly, thereby decreasing costs, enhancing scalability, and supporting continuous, real-time process optimization [3, 4]. In this study, first a dataset is generated including different descriptors like catalyst formulation, pretreatment, characteristics, activation, and reaction conditions. This dataset is then preprocessed by encoding, imputation, and normalization to make it ready for modelling, followed by data analysis to identify patterns and dependencies. Different models, including Gradient Boosting Regressor, XGBoost, LightGBM, and neural networks, are applied to predict methanol conversion and DME yield based on input variables. The models were evaluated through cross-validation, achieving highaccuracy and underscoring the potential of data-driven optimization in enhancing DME production. These steps are illustrated in Figure 1. Finally, the prediction accuracy of each model is investigated, and the best algorithm is selected. The effect of different descriptors on the respond have also been assessed to find out the most effective parameters on the catalyst performance. The best model is then used to predict DME yield and optimize the catalyst and reaction parameters. Y1 - 2025 UR - https://www.researchgate.net/publication/390271407_Towards_Digitalization_of_Catalysis_Design_and_Reaction_Engineering_Data-Driven_Insights_into_Methanol_to_DME SP - 1 EP - 2 ER - TY - GEN A1 - Hamdan, Mustapha A1 - Hamdan, Malak A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Modular high-temperature thermal energy storage for industrial decarbonisation using a particle-based heat battery T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Industrial heat contributes over 9 Gt of annual CO₂ emissions, with high-grade requirements (>1000°C) posing exceptional decarbonization challenges [1]. This contribution present the two-loop (2LP) Heat Battery, a particle-based thermal energy storage system delivering dispatchable zero-carbon heat and electricity at temperatures up to 1600°C. The modular design employs a dual-loop recirculating bed of advanced ceramic particles, achieving 98% round-trip efficiency through controlled particle metering and high surface area heat transfer. Unlike bulk thermal storage systems that exhibit thermocline-induced temperature decay [2], the 2LP architecture maintains steady-state outlet temperatures during 24-hour discharge cycles. Key innovations include a volumetric energy density of 1280 kWh/m 3 (surpassing molten salts, lithium-ion batteries, and refractory brick systems) and thermal output density exceeding 1MWth/m 3. The technology reduces levelised cost of storage from €20/kWh (conventional molten salt) to below €3/kWh while supporting ultra-efficient supercritical CO2 Brayton cycles (thermal-to-electric efficiency >50%). System performance exceeds EU SET Plan targets, achieving 98% electro-thermal round-trip efficiency and 90% combined heat and power efficiency. This scalable solution addresses critical gaps in industrial electrification, enabling grid congestion mitigation and providing a cost-effective pathway to decarbonize hard-to-abate sectors like steel and cement production. The 2LP Heat Battery demonstrates technical and economic viability to support EU Net Zero objectives through high-temperature electrification. Y1 - 2025 UR - https://www.researchgate.net/publication/399753836_Modular_High-Temperature_Thermal_Energy_Storage_for_Industrial_Decarbonisation_Using_a_Particle-Based_Heat_Battery SP - 1 EP - 3 CY - Frankfurt am Main ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Modeling multiphase reactors with complex particle geometries T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Trickle bed reactors (TBRs) are the backbone of the catalytic multiphase reactors in industrial processes, owning to their simple design, flexible controllability and large surface area. One of the key aspects for designing TBRs is the flow simulation inside the reactor and the hydrodynamics phenomena that take place during its operation. Literature offers various methods for simulating the behaviour and the performance of TBRs based on empirical methods or Computational Fluid Dynamics (CFD) simulations leading to inaccurate results, high computational burden, case studies with small catalytic beds or considering only spherical particles particles 1. To overcome these drawbacks, a modular and flexible toolbox for the modelling and study of TBRs is proposed which is adapted to the local structure of the catalytic bed 2. To improve the contact point calculation and extend to more complex geometries (i.e., cylinders, Raschig rings, trilobes), an approach based on liquid element tracking (LET) is applied, where the particle's surface is discretised over a finite number of triangles. Therefore, a pointwise sequence of the fluid over individual partial surfaces, based on the applied forces, is implemented for the liquid flow path estimation. The benefits of this procedure lie in its effectiveness, modular interconnection, and robust capability to represent a diverse range of phenomena for simulating flow patterns during TBRs operation in the low-interaction regime. Furthermore, the required computational time is significantly reduced compared to CFD simulations and a large number of particles can be introduced in the examined case study. The new particle representation is successfully implemented and the results are in good agreement with the static holdup prediction and radial flow distribution based on the previous contact point model, based solely on geometric calculations of the distance between the spheres and the liquid-solid interactions. References [1] Fathiganjehlou, A., et al. (2024). Multi-scale pore network modeling of a reactive packed bed. Y1 - 2025 UR - https://www.researchgate.net/publication/399788961_Multiphase_catalytic_reactors_a_modular_approach SP - 1 EP - 4 CY - Frankfurt am Main ER - TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Advancing catalyst design with machine learning : insights from FTS and DME production T2 - Annual Meeting on Reaction Engineering 2025 N2 - This contribution presents a ML-based framework to optimize heterogeneous catalysts, with a primary focus on the targeted application for FTS and DME production. However, the framework is designed to be generic, with applicability to a broader range of heterogeneous catalytic processes. The study analyzes a variety of catalyst parameters, including composition, pretreatment, and operating conditions, and employs advanced ML techniques such as regression models, ensemble learning, and neural networks to model the relationships between these factors and reaction outcomes. Hyperparameter optimization and performance evaluation using metrics like R², RMSE, MSE, and AIC further improve the accuracy and robustness of the models. In addition, the study provides a comparative analysis of applying this ML framework to both FTS and DME processes, highlighting the similarities and differences in optimizing catalyst performance and operating conditions. The study aims to identify the most influential parameters that drive catalyst performance and to assess the predictive power of each model. By enhancing understanding of how catalyst properties and operating conditions influence the efficiency of FTS and DME production, this work provides valuable insights into more efficient and sustainable catalytic process design. Y1 - 2025 UR - https://www.researchgate.net/publication/392137173_Advancing_catalyst_design_with_Machine_Learning_Insights_from_FTS_and_DME_production SP - 1 EP - 6 ER - TY - GEN A1 - Jafari, Mitra A1 - Abadi, Amirreza A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Fischer-Tropsch synthesis and hydrocracking process integration : a study on mesoporosity modification and acidity optimization T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Machine learning (ML) is employed to identify the key parameters influencing CO conversion and C5+ selectivity. Insights from both the literature review and ML analysis highlight pore volume and acidity as the most critical factors. Therefore, this study focuses on developing cobalt/beta zeolite catalysts with tailored mesoporosity and acidity to enhance catalytic performance. Specifically, the goal is to optimize the acidity of the zeolite by determining the ideal concentration of NH4+ during the ion-exchange step. Y1 - 2025 UR - https://www.researchgate.net/publication/391564315_Fischer-Tropsch_Synthesis_and_Hydrocracking_Process_Integration_A_Study_on_Mesoporosity_Modification_and_Acidity_Optimization SP - 1 EP - 3 ER - TY - GEN A1 - Riedel, Ramona A1 - Schowarte, Julia A1 - Semisch, Laura A1 - Gonzalez Castano, Miriam A1 - Ivanova, Svetlana A1 - Martienssen, Marion A1 - Arellano-Garcia, Harvey T1 - Improving the photocatalytic degradation of EDTMP : effect of doped NPs (Na, Y, and K) into the lattice of modified Au/TiO2 nano-catalysts T2 - Chemical engineering journal N2 - This study presents the photocatalytic degradation of the aminophosphonate ethylenediaminetetra(methylenephosphonic acid) (EDTMP) with a range of different doped nanoparticles (NP). The photocatalysts were based on TiO2 benchmark P25 and gold (Au) doped either with sodium (Na), potassium (K) or yttrium (Y). The synthesized photocatalysts were characterized via TEM, XRF, XRD, UV-DRS (band gap estimation) and N2-physisorption. Photocatalytic pre-screening at pH values of 3, 7 and 10 indicated highest o-PO4 release of EDTMP at pH 7 and 10 for NP either doped with K or Y. The results of LC/MS analysis showed that the NPs doped with 5 % Y (Au2/Y5/P25) resulted in the fastest degradation of EDTMP. The target compound was completely degraded within 60 min, 4 times faster than photochemical treatment of unadulterated EDTMP. Importantly, also the transformation products were accelerated by the photocatalytic treatment with Au2/P25 either doped with 5 % Y or 10 % K. The results of scavenger experiments indicated that the enhanced photocatalytic degradation of EDTMP is primarily attributable to the presence of hydroxyl radicals in the bulk and to a lesser extent to •O2− and electron-holes (h+) at the surface of the catalysts. The study demonstrates that the catalytic efficiency of TiO2 nanocomposites is significantly influenced by the choice of dopants, which affect particle size, band gap, and photocatalytic activity. Yttrium at low concentrations (i.e., 5 wt% Y) doping emerged as particularly effective, enhancing both the visible light absorption and h+ separation, leading to superior photocatalytic performance in the degradation of EDTMP. The Au content also plays a crucial role in enhancing the photocatalytic efficiency. However, the combination of Au and Na doping was found to be less effective for this photocatalysis in aqueous media, potentially due to larger particle sizes and insufficient dopant contents. In conclusion, the findings emphasise the necessity of optimising both the selection of dopants and the design of catalysts in order to enhance photocatalytic applications. KW - EDTMP KW - Nanoparticles KW - Phosphonate KW - TiO2 KW - Yttrium Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S1385894725009088?via%3Dihub U6 - https://doi.org/10.1016/j.cej.2025.160109 VL - 506 ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Helle, Sven A1 - Martienssen, Marion A1 - Arellano-Garcia, Harvey T1 - Synergistic enhancement of PFOA and 6:2-FTAB photodegradation using Au/Y-doped TiO₂ nanocatalysts T2 - Chemical engineering journal advances N2 - Efficient degradation of perfluoroalkyl substances (PFAS) requires photocatalysts capable of promoting strong C-F bond cleavage and selective interfacial charge transfer. In this proof-of-concept-study, a dual-doped TiO2 nanophotocatalyst (Au2/Y5/P25) was synthesized by combining gold (Au) nanoparticles and yttrium (Y) dopants to enhance charge separation and reactive oxygen species (ROS) generation. Structural characterization supported Au deposition on the TiO2 surface and Y incorporation into the lattice, accompanied by a slight band-gap narrowing. Under UV irradiation in aqueous solution (unbuffered pH 5.8, room temperature) the nanophotocatalyst exhibited distinct degradation pathways for 1000 µg L−1 of two representative PFAS, perfluorooctanoic acid (PFOA) and Capstone B (6:2 FTAB), reflecting environmentally prevalent groups. PFOA underwent 99 % degradation within 100 min via a stepwise CF2-cleavage mechanism, generating a sequence of perfluorocarboxylic acids down to perfluorobutanoic acid (PFBA), consistent with enhanced electron-hole separation. In contrast, Capstone B showed rapid, single-step S-N bond cleavage to 6:2 perfluorooctanesulfonic acid (6:2 PFOS), primarily driven by hole- and •OH-mediated oxidation under oxygen-rich conditions. This process achieved 96 % degradation within 20 min but did not proceed to further defluorination, indicating oxidative limitations. Dissolved oxygen analysis revealed efficient electron utilization and sustained oxidative turnover without excessive oxygen depletion. The findings demonstrate that Au/Y co-doping promotes selective PFAS activation, enabling rapid precursor oxidation while exposing the kinetic limits of secondary C-F bond cleavage. These discoveries offer new insights into the design of plasmonic-rare-earth-modified TiO2 photocatalysts for efficient PFAS degradation through interface-driven oxidation pathways. KW - 6:2-FTAB KW - Nanoparticles KW - PFAS KW - PFOA KW - Photocatalysis KW - TiO₂ Y1 - 2026 U6 - https://doi.org/10.1016/j.ceja.2026.101121 SN - 2666-8211 VL - 26 SP - 1 EP - 12 PB - Elsevier BV CY - Amsterdam ER -