@misc{KetabchiRamirezReinaDorneanuetal., author = {Ketabchi, Elham and Ramirez Reina, Tomas and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {An identification approach to a reaction network for an ABE catalytic upgrade}, series = {Computer Aided Chemical Engineering}, volume = {50}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-323-88506-5.50102-9}, pages = {643 -- 648}, abstract = {This contribution presents a kinetic study for the identification of the complex reaction mechanism occurring during the ABE upgrading, and the development of a kinetic model. Employing graph theory analysis, a directed bipartite graph is constructed to reduce the complexity of the reaction network, and the reaction rate constants and reaction orders are calculated using the initial rate method, followed by the calculation of the activation energy and frequency factor for an Arrhenius-type law. Subsequently, using general mass balancing a proposed mathematical model is produced to determine the apparent reaction rates, which are successfully in line with the experimental results.}, language = {en} } @misc{ClarkeDorneanuMechlerietal., author = {Clarke, Fiona and Dorneanu, Bogdan and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Optimal design of heating and cooling pipeline networks for residential distributed energy resource systems}, series = {Energy}, volume = {235}, journal = {Energy}, issn = {1873-6785}, doi = {10.1016/j.energy.2021.121430}, abstract = {This paper presents a mixed integer linear programming model for the optimal design of a distributed energy resource (DER) system that meets electricity, heating, cooling and domestic hot water demands of a neighbourhood. The objective is the optimal selection of the system components among different technologies, as well as the optimal design of the heat pipeline network to allow heat exchange between different nodes in the neighbourhood. More specifically, this work focuses on the design, interaction and operation of the pipeline network, assuming the operation and maintenance costs. Furthermore, thermal and cold storage, transfer of thermal energy, and pipelines for transfer of cold and hot water to meet domestic hot water demands are additions to previously published models. The application of the final model is investigated for a case-study of a neighbourhood of five houses located in the UK. The scalability of the model is tested by also applying the model to a neighbourhood of ten and twenty houses, respectively. Enabling exchange of thermal power between the neighbours, including for storage purposes, and using separate hot and cold pipeline networks reduces the cost and the environmental impact of the resulting DER.}, language = {en} } @misc{SidnellDorneanuMechlerietal., author = {Sidnell, Tim and Dorneanu, Bogdan and Mechleri, Evgenia and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Effects of Dynamic Pricing on the Design and Operation of Distributed Energy Resource Networks}, series = {Processes}, volume = {9}, journal = {Processes}, number = {8}, issn = {2227-9717}, doi = {https://doi.org/10.3390/pr9081306}, abstract = {This paper presents a framework for the use of variable pricing to control electricity im-ported/exported to/from both fixed and unfixed residential distributed energy resource (DER) network designs. The framework shows that networks utilizing much of their own energy, and importing little from the national grid, are barely affected by dynamic import pricing, but are encouraged to sell more by dynamic export pricing. An increase in CO2 emissions per kWh of energy produced is observed for dynamic import and export, against a baseline configuration utilizing constant pricing. This is due to feed-in tariffs (FITs) that encourage CHP generation over lower-carbon technologies. Furthermore, batteries are shown to be expensive in systems receiving income from FITs and grid exports, but for the cases when they sell to/buy from the grid using dynamic pricing, their use in the networks becomes more economical. Keywords: distributed energy resource (DER); dynamic pricing; mixed-integer linear programming (MILP); renewable heat incentive (RHI); feed-in tariff (FIT); electricity storage in batteries.}, language = {en} } @misc{SidnellClarkeDorneanuetal., author = {Sidnell, Tim and Clarke, Fiona and Dorneanu, Bogdan and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Optimal design and operation of distributed energy resources systems for residential neighbourhoods}, series = {Smart Energy}, volume = {4}, journal = {Smart Energy}, issn = {2666-9552}, doi = {10.1016/j.segy.2021.100049}, abstract = {Different designs of distributed energy resources (DER) systems could lead to different performance in reducing cost, environmental impact or use of primary energy in residential networks. Hence, optimal design and management are important tasks to promote diffusion against the centralised grid. However, current operational models for such systems do not adequately analyse their complexity. This paper presents the results of a mixed-integer linear programming (MILP) model of distributed energy systems in the residential sector which builds up on previous work in this field. A superstructure optimisation model for design and operation of DER systems is obtained, providing a more holistic overview of such systems by including the following novel elements: a) Design and utilisation of a network with integrated heating/cooling pipelines and microgrid connections between neighbourhoods; b) Exploration of use of feed-in tariffs (FITs), renewable heat incentives (RHIs) and the ability to buy/sell from/to the national grid. It is shown that the (DER network mitigates around 30-40\% of the CO2 emissions per household, compared with "traditional generation". Money from FITs, RHIs and sales to the grid, as well as reduced grid purchases, make DER networks far more economical, and even profitable, compared to the traditional energy consumption.}, language = {en} } @misc{DeMelDemisDorneanuetal., author = {De Mel, Ishanki and Demis, Panagiotis and Dorneanu, Bogdan and Klymenko, Oleksiy and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Global Sensitivity Analysis for Design and Operation of Distributed Energy Systems}, series = {Computer Aided Chemical Engineering}, volume = {48}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-12-823377-1.50254-8}, pages = {1519 -- 1524}, abstract = {Distributed Energy Systems (DES) are set to play a vital role in achieving emission targets and meeting higher global energy demand by 2050. However, implementing these systems has been challenging, particularly due to uncertainties in local energy demand and renewable energy generation, which imply uncertain operational costs. In this work we are implementing a Mixed-Integer Linear Programming (MILP) model for the operation of a DES, and analysing impacts of uncertainties in electricity demand, heating demand and solar irradiance on the main model output, the total daily operational cost, using Global Sensitivity Analysis (GSA). Representative data from a case study involving nine residential areas at the University of Surrey are used to test the model for the winter season. Distribution models for uncertain variables, obtained through statistical analysis of raw data, are presented. Design results show reduced costs and emissions, whilst GSA results show that heating demand has the largest influence on the variance of total daily operational cost. Challenges and design limitations are also discussed. Overall, the methodology can be easily applied to improve DES design and operation.}, language = {en} } @misc{RuanDorneanuArellanoGarciaetal., author = {Ruan, Hang and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Xiao, Pei and Zhang, Li}, title = {Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes}, series = {IEEE Access}, volume = {10}, journal = {IEEE Access}, issn = {2169-3536}, doi = {10.1109/ACCESS.2022.3144333}, pages = {10867 -- 10879}, abstract = {This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.}, language = {en} } @incollection{DorneanuArellanoGarciaRuanetal., author = {Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Ruan, Hang and Mohamed, Abdelrahim and Xiao, Pei and Heshmat, Mohamed and Gao, Yang}, title = {Towards fault detection and self-healing of chemical processes over wireless sensor networks}, series = {Industry 4.0 - Shaping The Future of The Digital World}, booktitle = {Industry 4.0 - Shaping The Future of The Digital World}, edition = {1st edition}, publisher = {CRC Press}, address = {London, United Kingdom}, isbn = {9780367823085}, doi = {10.1201/9780367823085-02}, pages = {9 -- 14}, abstract = {This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring.}, language = {en} } @incollection{DorneanuArellanoGarciaHeshmatetal., author = {Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Heshmat, Mohamed and Gao, Yang}, title = {A framework for intelligent monitoring and control of chemical processes with multi-agent systems}, series = {Industry 4.0 - Shaping The Future of The Digital World}, booktitle = {Industry 4.0 - Shaping The Future of The Digital World}, edition = {1st edition}, publisher = {CRC Press}, address = {London, United Kingdom}, isbn = {9780367823085}, doi = {10.1201/9780367823085-04}, pages = {18 -- 23}, abstract = {Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems that require intelligent methods to support the operators in taking decisions for better and safer operation. In this paper, a multi-agent cooperative-based model predictive system for monitoring and control of a chemical process is proposed. This system uses ontology to formally represent the system knowledge. By integrating the cooperative-based model predictive controller with the multi-agent system, the control can be improved, and the process can be converted into a self-adaptive system.}, language = {en} } @misc{YentumiDorneanuArellanoGarcia, author = {Yentumi, Richard and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Optimal Operation of an Industrial Natural Gas Fired Natural Draft Heater}, series = {Chemical Engineering Journal Advances}, volume = {11}, journal = {Chemical Engineering Journal Advances}, issn = {2666-8211}, doi = {10.1016/j.ceja.2022.100354}, abstract = {In this work, a custom dynamic mathematical model of an industrial vertical-cylindrical type natural gas fired natural draft heater is developed using gPROMS® ProcessBuilder®. The integrated model comprises sub-models for each of the distinct sections of the fired heater which are connected by mass and energy flows. The temperature profiles of the tubular coils and the process fluid, a heat transfer fluid (HTF) are modelled using the distributed parameter system (DPS) in the axial direction (1D). The flue gas temperature in each section is modelled using the lumped parameter approach. Published empirical methods and correlations are used for estimating some unknown model parameters. The resulting model is a system of partial differential-algebraic equations (PDAEs) and serves as a basis for conducting an optimisation study to aid decision-making and to identify the best operating conditions within the specified constraints that minimise the daily operational costs. Through process simulation studies, the model predictions are adjusted to closely approximate collected actual plant data. It is demonstrated through the optimisation study that significant reduction in fuel gas consumption can be achieved compared to the current operating consumption levels. The developed models can be extended for use by other hydrocarbon processing plant operators with slight modifications, by specifying geometric parameters, HTF thermophysical properties, fuel gas composition and properties, among others.}, language = {en} } @misc{DeMelDemisDorneanuetal., author = {De Mel, Ishanki and Demis, Panagiotis and Dorneanu, Bogdan and Klymenko, Oleksiy and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Global sensitivity analysis for design and operation of distributed energy systems: A two-stage approach}, series = {Sustainable Energy Technologies and Assessments}, volume = {56}, journal = {Sustainable Energy Technologies and Assessments}, issn = {2213-1388}, doi = {10.1016/j.seta.2023.103064}, abstract = {Distributed Energy Systems (DES) can play a vital role as the energy sector faces unprecedented changes to reduce carbon emissions by increasing renewable and low-carbon energy generation. However, current operational DES models do not adequately reflect the influence of uncertain inputs on operational outputs, resulting in poor planning and performance. This paper details a methodology to analyse the effects of uncertain model inputs on the primary output, the total daily cost, of an operational model of a DES. Global Sensitivity Analysis (GSA) is used to quantify these effects, both individually and through interactions, on the variability of the output. A Mixed-Integer Linear Programming model for the DES design is presented, followed by the operational model, which incorporates Rolling Horizon Model Predictive Control. A subset of model inputs, which include electricity and heating demand, and solar irradiance, is treated as uncertain using data from a case study. Results show reductions of minimum 25\% in the total annualised cost compared to a traditional design that purchases electricity from the centralised grid and meets heating demand using boilers. In terms of carbon emissions, the savings are much smaller, although the dependency on the national grid is drastically reduced. Limitations and suggestions for improving the overall DES design and operation are also discussed in detail, highlighting the importance of incorporating GSA into the DES framework.}, language = {en} } @misc{DorneanuArellanoGarciaHeshmatetal., author = {Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Heshmat, Mohamed and Gao, Yang}, title = {Ontology Based Decision Making for Process Control}, series = {2019 AIChE Annual Meeting}, journal = {2019 AIChE Annual Meeting}, isbn = {978-0-8169-1112-7}, abstract = {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{\^a}€™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{\^a}€™ 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.}, language = {en} } @misc{YusufDorneanuAvignoneRossaetal., author = {Yusuf, Ifrah and Dorneanu, Bogdan and Avignone-Rossa, Claudio and Arellano-Garc{\´i}a, Harvey}, title = {Synthesis and Characterization of Hydrochars Produced By Hydrothermal Carbonization of Banana Peels}, series = {2019 AIChE Annual Meeting}, journal = {2019 AIChE Annual Meeting}, isbn = {978-0-8169-1112-7}, abstract = {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{\^a}€™ 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.}, language = {en} } @misc{DeMelMechleriDemisetal., author = {De Mel, Ishanki and Mechleri, Evgenia and Demis, Panagiotis and Dorneanu, Bogdan and Klymenko, Oleksiy and Arellano-Garc{\´i}a, Harvey}, title = {A Methodology for Global Sensitivity Analysis for the Operation of Distributed Energy Systems Using a Two-Stage Approach}, series = {2019 AIChE Annual Meeting}, journal = {2019 AIChE Annual Meeting}, isbn = {978-0-8169-1112-7}, abstract = {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.}, language = {en} } @misc{MechleriShafieiDorneanuetal., author = {Mechleri, Evgenia and Shafiei, Zarif and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Klymenko, Oleksiy}, title = {A Blockchain Model for Residential Distributed Energy Resources Networks}, series = {2019 AIChE Annual Meeting}, journal = {2019 AIChE Annual Meeting}, isbn = {978-0-8169-1112-7}, abstract = {The energy production landscape is reshaped by distributed energy resources (DERs) {\^a}€" 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.}, language = {en} } @misc{MerinoCarrascoDorneanuetal., author = {Merino, Manuel and Carrasco, Laura and Dorneanu, Bogdan and Manrique, Jose and Menzhausen, Robert and Arellano-Garc{\´i}a, Harvey}, title = {Control Strategies for a Vapour Compression Refrigeration System Used in Mango Exports: An Alternative to Traditional on-Off Controllers}, series = {AIChE Annual Meeting}, journal = {AIChE Annual Meeting}, isbn = {978-0-8169-1114-1}, abstract = {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.}, language = {en} } @misc{MiahDorneanuMechlerietal., author = {Miah, Sayeef and Dorneanu, Bogdan and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Multi-Objective Design Optimisation of a Distributed Energy System through 3E (economic, environmental and exergy) Analysis}, series = {2020 Virtual AIChE Annual Meeting, November 20, 2020}, journal = {2020 Virtual AIChE Annual Meeting, November 20, 2020}, isbn = {978-0-8169-1114-1}, abstract = {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.}, language = {en} } @misc{DorneanuHeshmatMohamedetal., author = {Dorneanu, Bogdan and Heshmat, Mohamed and Mohamed, Abdelrahim and Ruan, Hang and Xiao, Pei and Gao, Yang and Arellano-Garc{\´i}a, Harvey}, title = {Stepping Towards the Industrial Sixth Sense}, series = {AIChE Annual Meeting, November 20, 2020}, journal = {AIChE Annual Meeting, November 20, 2020}, isbn = {978-0-8169-1114-1}, abstract = {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.}, language = {en} } @misc{DorneanuMechleriArellanoGarcia, author = {Dorneanu, Bogdan and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Towards smart distributed energy systems}, series = {Jahrestreffen der ProcessNet-Fachgemeinschaften "Prozess-, Apparate- und Anlagentechnik" (PAAT)}, journal = {Jahrestreffen der ProcessNet-Fachgemeinschaften "Prozess-, Apparate- und Anlagentechnik" (PAAT)}, pages = {1}, abstract = {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.}, language = {en} } @misc{ArellanoGarciaElBariKalibeFanezouneetal., author = {Arellano-Garc{\´i}a, Harvey and El Bari, Hassan and Kalibe Fanezoune, Casimir and Dorneanu, Bogdan and Majozi, Thokozani and Elhenawy, Yasser and Bayssi, Oussama and Hirt, Ayoub and Peixinho, Jorge and Dhahak, Asma and Gadalla, Mamdouh A. and Khashaba, Nourhan H. and Ashour, Fatma}, title = {Catalytic Fast Pyrolysis of Lignocellulosic Biomass: Recent Advances and Comprehensive Overview}, series = {Journal of Analytical and Applied Pyrolysis}, volume = {Vol. 178}, journal = {Journal of Analytical and Applied Pyrolysis}, issn = {0165-2370}, doi = {10.1016/j.jaap.2024.106390}, abstract = {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.}, language = {en} } @misc{MedinaMendezDorneanuArellanoGarcia, author = {Medina M{\´e}ndez, Juan Ali and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation}, series = {Book of Abstracts zur Jahrestagung der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik, 04. - 06. M{\"a}rz 2024}, journal = {Book of Abstracts zur Jahrestagung der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik, 04. - 06. M{\"a}rz 2024}, publisher = {Ruhr Universit{\"a}t}, address = {Bochum}, pages = {2}, language = {en} } @misc{MedinaMendezDorneanuArellanoGarcia, author = {Medina M{\´e}ndez, Juan Ali and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation}, address = {Bochum}, pages = {1}, language = {en} } @misc{MedinaMendezDorneanuSchmidtetal., author = {Medina M{\´e}ndez, Juan Ali and Dorneanu, Bogdan and Schmidt, Heiko and Arellano-Garc{\´i}a, Harvey}, title = {Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation}, series = {Journal of Physics: Conference Series}, volume = {2899/2024}, journal = {Journal of Physics: Conference Series}, issn = {1742-6596}, doi = {10.1088/1742-6596/2899/1/012004}, pages = {8}, abstract = {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.}, language = {en} } @misc{MedinaMendezDorneanuSchmidtetal., author = {Medina M{\´e}ndez, Juan Al{\´i} and Dorneanu, Bogdan and Schmidt, Heiko and Arellano-Garc{\´i}a, Harvey}, title = {Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation}, series = {Book of Abstracts XXVI Fluid Mechanics Conference (FMC 2024), Warsaw, Poland, September 10-13, 2024}, journal = {Book of Abstracts XXVI Fluid Mechanics Conference (FMC 2024), Warsaw, Poland, September 10-13, 2024}, publisher = {University of Technology}, address = {Warsaw}, pages = {2}, language = {en} } @misc{DorneanuMappasVassiliadisetal., author = {Dorneanu, Bogdan and Mappas, Vassileios and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Novel parametric gradient calculation method for multistage systems with generalized constraints}, series = {2024 AIChE Annual Meeting}, journal = {2024 AIChE Annual Meeting}, pages = {3}, abstract = {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.}, language = {en} } @misc{SafdarDorneanuSantosdaSilvaetal., author = {Safdar, Muddasar and Dorneanu, Bogdan and Santos da Silva, Jefferson and Santos Mascarenhas, Artur Jose and Valverde Pontes, Karen and Arellano-Garc{\´i}a, Harvey}, title = {Advancements in CO2 methanation: customized heterogeneous Ni-Perovskite catalyst for sustainable SNG production}, series = {Annual Meeting on Reaction Engineering and Electrochemical Processes 2024}, journal = {Annual Meeting on Reaction Engineering and Electrochemical Processes 2024}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vassileios and Dorneanu, Bogdan and Heinzelmann, Norbert and Schnitzlein, Klaus and Arellano-Garc{\´i}a, Harvey}, title = {A unified modular framework for modeling multiphase reactors}, series = {Annual Meeting of Process Engineering and Materials Technology 2024}, journal = {Annual Meeting of Process Engineering and Materials Technology 2024}, language = {en} } @misc{JafariShafieeDorneanuetal., author = {Jafari, Mitra and Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis}, series = {Annual Meeting of Process Engineering and Materials Technology 2024}, journal = {Annual Meeting of Process Engineering and Materials Technology 2024}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Optimal design of a thermochemical hydrogen storage and release system via the reversible redox of iron oxide/iron}, series = {Annual Meeting of Process Engineering and Materials Technology 2024}, journal = {Annual Meeting of Process Engineering and Materials Technology 2024}, language = {en} } @misc{DorneanuVassiladisArellanoGarcia, author = {Dorneanu, Bogdan and Vassiladis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {A novel approach to staggered training of deep learning networks}, series = {Annual Meeting of Process Engineering and Materials Technology 2024}, journal = {Annual Meeting of Process Engineering and Materials Technology 2024}, language = {en} } @misc{SafdarSafdarDorneanuetal., author = {Safdar, Muddasar and Safdar, Mutahar and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Process intensification by additive manufacturing strategies for power-to-X conversion application: Case studies}, series = {16th International Conference on Gas-Liquid and Gas-Liquid-Solid Reactor Engineering}, journal = {16th International Conference on Gas-Liquid and Gas-Liquid-Solid Reactor Engineering}, language = {en} } @misc{JafariMbuyaDorneanuetal., author = {Jafari, Mitra and Mbuya, Christel-Olivier Lenge and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Sustainable aviation fuel production through Fischer-Tropsch synthesis and hydrocracking integration using Co bifunctional catalysts: Support effects}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, abstract = {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.}, language = {en} } @misc{AlvesAmorimValverdePontesDorneanuetal., author = {Alves Amorim, Ana Paula and Valverde Pontes, Karen and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Optimizing microgrid design and operation : a decision-making framework for residential distributed energy systems in Brazil}, series = {Chemical Engineering Research and Design}, volume = {214 (2025)}, journal = {Chemical Engineering Research and Design}, number = {February 2025}, publisher = {Elsevier}, issn = {0263-8762}, doi = {https://doi.org/10.1016/j.cherd.2024.12.033}, pages = {251 -- 268}, abstract = {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.}, language = {en} } @misc{ShafieeDorneanuArellanoGarcia, author = {Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Towards Machine Learning-driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis}, series = {Chemical Engineering Transactions}, volume = {114}, journal = {Chemical Engineering Transactions}, issn = {2283-9216}, doi = {10.3303/CET24114098}, pages = {583 -- 588}, abstract = {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}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Multiphase Catalytic Reactors: a Modular Approach}, series = {Chemical Engineering Transactions}, volume = {114}, journal = {Chemical Engineering Transactions}, issn = {2283-9216}, doi = {10.3303/CET24114097}, pages = {577 -- 582}, abstract = {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.}, language = {en} } @misc{MappasDorneanuVassiliadisetal., author = {Mappas, Vassileios and Dorneanu, Bogdan and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Multistage optimal control and nonlinear programming formulation for automated control loop selection}, series = {Computer Aided Chemical Engineering}, volume = {53}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-28824-1.50327-6}, pages = {1957 -- 1962}, abstract = {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.}, language = {en} } @misc{DorneanuKeykhaArellanoGarcia, author = {Dorneanu, Bogdan and Keykha, Mina and Arellano-Garc{\´i}a, Harvey}, title = {Assessment of parameter uncertainty in the maintenance scheduling of reverse osmosis networks via a multistage optimal control reformulation}, series = {Computer Aided Chemical Engineering}, volume = {53}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-28824-1.50326-4}, pages = {1951 -- 1956}, abstract = {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.}, language = {en} } @misc{DorneanuZhangVassiliadisetal., author = {Dorneanu, Bogdan and Zhang, Sushen and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Optimizing deep neural networks through hierarchical multiscale parameter tuning}, series = {Computer Aided Chemical Engineering}, volume = {53}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-28824-1.50155-1}, pages = {925 -- 930}, abstract = {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.}, language = {en} } @misc{VassiliadisMappasEspaasetal., author = {Vassiliadis, Vassilios S. and Mappas, Vassileios and Espaas, Tomas A. and Dorneanu, Bogdan and Isafiade, Adeniyi and M{\"o}ller, Klaus and Arellano-Garc{\´i}a, Harvey}, title = {Reloading process systems engineering within chemical engineering}, series = {Chemical Engineering Research and Design}, volume = {209}, journal = {Chemical Engineering Research and Design}, doi = {10.1016/j.cherd.2024.07.066}, pages = {380 -- 398}, abstract = {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.}, language = {en} } @misc{SohailRiedelDorneanuetal., author = {Sohail, Norman and Riedel, Ramona and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques}, series = {Membranes}, volume = {13}, journal = {Membranes}, number = {2}, issn = {2077-0375}, doi = {10.3390/membranes13020217}, abstract = {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.}, language = {en} } @misc{DorneanuHeinzelmannSchnitzleinetal., author = {Dorneanu, Bogdan and Heinzelmann, Norbert and Schnitzlein, Klaus and Arellano-Garc{\´i}a, Harvey}, title = {BasMo - An interactive approach to modelling of trickle bed reactors}, series = {Jahrestreffen der "Prozess-, Apparate- und Anlagentechnik", 21.-22. November 2022, Frankfurt am Main}, journal = {Jahrestreffen der "Prozess-, Apparate- und Anlagentechnik", 21.-22. November 2022, Frankfurt am Main}, abstract = {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.}, language = {en} } @misc{StraubDorneanuArellanoGarcia, author = {Straub, Adrian and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Towards a novel concept for solid energy storage}, series = {Computer Aided Chemical Engineering}, volume = {Vol. 52}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-15274-0.50472-8}, pages = {2965 -- 2970}, abstract = {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.}, language = {en} } @misc{DorneanuMiahMechlerietal., author = {Dorneanu, Bogdan and Miah, Sayeef and Mechleri, Evgenia and Arellano-Garc{\´i}a, Harvey}, title = {Multiobjective optimization of distributed energy systems design through 3E (economic, environmental and exergy) analysis}, series = {Computer Aided Chemical Engineering}, volume = {Vol. 52}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-15274-0.50473-X}, pages = {2971 -- 2976}, abstract = {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.}, language = {en} } @misc{AlvesAmorimDorneanuValverdePontesetal., author = {Alves Amorim, Ana Paula and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garc{\´i}a, Harvey}, title = {A framework for decision-making to encourage utilization of residential distributed energy systems in Brazil}, series = {Computer Aided Chemical Engineering}, volume = {Vol. 52}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-15274-0.50481-9}, pages = {3019 -- 3024}, abstract = {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.}, language = {en} } @misc{DorneanuMashamKeykhaetal., author = {Dorneanu, Bogdan and Masham, Elliot and Keykha, Mina and Mechleri, Evgenia and Cole, Rosanna and Arellano-Garc{\´i}a, Harvey}, title = {Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models}, series = {Supply Chain Analytics}, volume = {Vol. 4}, journal = {Supply Chain Analytics}, doi = {10.1016/j.sca.2023.100043}, abstract = {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 \pounds 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.}, language = {en} } @misc{CunhaCordeiroSafdarAquinoetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Aquino, Gabrielle S. and Silva, Jefferson S. and Paff, Jessica Sophie and Valverde Pontes, Karen and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Mascarenhas, Artur Jos{\´e}}, title = {Sustainable hydrogen production via biogas reforming over NiO-MxOy - Al2O3 catalysts (M = Na, K, Ca and Mg)}, series = {22 Congreso Brasileiro de Catalise}, journal = {22 Congreso Brasileiro de Catalise}, abstract = {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.}, language = {en} } @misc{SafdarShezadDorneanuetal., author = {Safdar, Muddasar and Shezad, Nasir and Dorneanu, Bogdan and Jafari, Mitra and Shashank Bhat, Sharvendu and Akhtar, Farid and Arellano-Garc{\´i}a, Harvey}, title = {Dry Reforming of Methane for the Syngas Production Catalyzed by Ni-doped Perovskites}, series = {15Th European Congress on Katakysis EUROPACAT2023}, journal = {15Th European Congress on Katakysis EUROPACAT2023}, abstract = {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.}, language = {en} } @misc{MappasVassiliadisDorneanuetal., author = {Mappas, Vassileios and Vassiliadis, Vassilios S. and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Use of Multistage Optimal Control Principles for Novel Design and Implementation of Classical Controllers}, series = {AIChE Annual Meeting}, journal = {AIChE Annual Meeting}, abstract = {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.}, language = {en} } @misc{MappasVassiliadisDorneanuetal., author = {Mappas, Vassileios and Vassiliadis, Vassilios S. and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Automated Control Loop Selection Via Multistage Optimal Control Formulation and Nonlinear Programming}, series = {Chemical Engineering Research and Design}, volume = {195}, journal = {Chemical Engineering Research and Design}, issn = {1744-3563}, doi = {10.1016/j.cherd.2023.05.041}, pages = {76 -- 95}, abstract = {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.}, language = {en} } @misc{JafariSafdarDorneanuetal., author = {Jafari, Mitra and Safdar, Muddasar and Dorneanu, Bogdan and Gonzalez-Casta{\~n}o, Miriam and Arellano-Garc{\´i}a, Harvey}, title = {Green and sustainable fuel from syngas via the Fischer-Tropsch synthesis process: Bifunctional cobalt-based catalysts}, series = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, journal = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, abstract = {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.}, language = {en} } @misc{ZhangVassiliadisDorneanuetal., author = {Zhang, Sushen and Vassiliadis, Vassilios S. and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Hierarchical multi-scale parametric optimization of deep neural networks}, series = {Applied Intelligence}, volume = {53}, journal = {Applied Intelligence}, number = {21}, issn = {1573-7497}, doi = {10.1007/s10489-023-04745-8}, pages = {24963 -- 24990}, abstract = {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.}, language = {en} } @misc{ArellanoGarciaSafdarShezadetal., author = {Arellano-Garc{\´i}a, Harvey and Safdar, Muddasar and Shezad, Nasir and Dorneanu, Bogdan and Akhtar, Farid}, title = {Synthesis and Characterizations of Ni-doped Perovskite-Type Oxides for Effective CO2 methanation}, series = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, journal = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, doi = {10.5281/zenodo.10376612}, pages = {2}, abstract = {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.}, language = {en} } @misc{DorneanuNolascoVassiliadisetal., author = {Dorneanu, Bogdan and Nolasco, Eduardo and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Quantum annealing for global optimization in Chemical Engineering}, series = {Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main}, journal = {Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main}, pages = {15}, abstract = {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.}, language = {en} } @misc{SafdarDorneanuPaffetal., author = {Safdar, Muddasar and Dorneanu, Bogdan and Paff, Jessica Sophie and Arellano-Garc{\´i}a, Harvey}, title = {Structural formability of perovskite ABO3 oxide system synthesized via autocombustion technique, ruled by geometric factors}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, language = {en} } @misc{JafariDorneanuArellanoGarcia, author = {Jafari, Mitra and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Machine learning application in kinetic studies: A review}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, abstract = {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.}, language = {en} } @misc{ShafieeMbuyaDorneanuetal., author = {Shafiee, Parisa and Mbuya, Christel-Olivier Lenge and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Novel approaches for preparation of 3D Ni-Al2O3 core with zeolite shell catalysts for dry reforming of methane}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, language = {en} } @misc{CunhaCordeiroSilvadeAquinoSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Silva de Aquino, Gabrielle and Santos da Silva, Jefferson and Safdar, Muddasar and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Valverde Pontes, Karen and Santos Mascarenhas, Artur Jos{\´e}}, title = {Estudo do efeito do suporte em catalisadores de Ni preparados pelo m{\´e}todo da combust{\~a}o aplicados na reforma a seco do biog{\´a}s para produ{\c{c}}{\~a}o de hidrog{\^e}nio sustent{\´a}vel}, series = {63rd Brazilian Chemistry Congress}, journal = {63rd Brazilian Chemistry Congress}, pages = {11}, language = {pt} } @misc{DorneanuFarhadiArellanoGarcia, author = {Dorneanu, Bogdan and Farhadi, Maryam and Arellano-Garc{\´i}a, Harvey}, title = {Conceptual design of a reactive distillation column for the catalytic upgrade of ABE}, series = {Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik}, journal = {Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik}, pages = {2}, language = {en} } @misc{DorneanuMappasVassiliadisetal., author = {Dorneanu, Bogdan and Mappas, Vassileios and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {A second-order linesearch procedure within Newton's method for highly nonlinear steady-state systems simulation}, series = {2024 AIChE Annual Meeting}, journal = {2024 AIChE Annual Meeting}, abstract = {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.}, language = {en} } @misc{ShafieeDorneanuArellanoGarcia, author = {Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16)}, series = {Chemical Engineering Journal Advances}, volume = {21 (2025)}, journal = {Chemical Engineering Journal Advances}, publisher = {Elsevier}, doi = {10.1016/j.ceja.2024.100702}, pages = {25}, abstract = {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.}, language = {en} } @misc{MappasDorneanuNolascoetal., author = {Mappas, Vasileios and Dorneanu, Bogdan and Nolasco, Eduardo and Vassiliadis, Vassilios and Arellano-Garcia, Harvey}, title = {Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept}, series = {Chemical engineering research and design}, volume = {221}, journal = {Chemical engineering research and design}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1744-3563}, doi = {10.1016/j.cherd.2025.08.031}, pages = {560 -- 576}, abstract = {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.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, abstract = {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.}, language = {en} } @misc{ParkLeeKimetal., author = {Park, Haryn and Lee, Joowha and Kim, Jin-Kuk and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, pages = {1}, abstract = {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.}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.121492}, pages = {631 -- 636}, abstract = {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.}, language = {en} } @misc{MappasDorneanuArellanoGarcia, author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Solving complex combinatorial optimization problems using quantum annealing approaches}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.188358}, pages = {1561 -- 1566}, abstract = {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.}, language = {en} } @misc{ShafieeJafariSchowarteetal., author = {Shafiee, Parisa and Jafari, Mitra and Schowarte, Julia and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.135551}, pages = {1866 -- 1871}, abstract = {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.}, language = {en} } @misc{ParkLeeDorneanuetal., author = {Park, Haryn and Lee, Joowha and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Kim, Jin-Kuk}, title = {Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.107403}, pages = {1175 -- 1180}, abstract = {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.}, language = {en} } @misc{JafariDorneanuArelanoGarcia, author = {Jafari, Mitra and Dorneanu, Bogdan and Arelano-Garcia, Harvey}, title = {Machine learning-enhanced Fischer-Tropsch synthesis : optimizing catalysts and process conditions for efficient fuel production}, series = {Chemie - Ingenieur - Technik : CIT}, volume = {97}, journal = {Chemie - Ingenieur - Technik : CIT}, number = {11-12}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70030}, pages = {1085 -- 1093}, abstract = {Fischer-Tropsch synthesis (FTS) offers a promising route for producing clean, renewable fuels. Yet, designing efficient catalysts and determining optimal process conditions remain major hurdles. Machine learning (ML) provides powerful means to address these challenges. Despite their potential, metal/zeolite catalysts are scarcely studied in ML-driven FTS research. This work applies an ML-based framework to model and optimize metal/zeolite catalysts for liquid fuel synthesis via FTS. Supervised learning methods reveal key structure-performance correlations, whereas multi-objective optimization identifies ideal catalyst and process parameters. The top solution is benchmarked against nearest experimental data. Results show CatBoost as the best-performing model, with Pt-Co/Beta treated with NaOH and NH4+ emerging as the optimal catalyst.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Schnitzlein, Klaus and Arellano-Garcia, Harvey}, title = {An efficient and unified modeling framework for trickle bed reactors : a modular approach}, series = {Chemie - Ingenieur - Technik : CIT}, volume = {97}, journal = {Chemie - Ingenieur - Technik : CIT}, number = {11-12}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70035}, pages = {1110 -- 1126}, abstract = {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.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and De Aquino, Gabrielle and Dos Santos, Mauricio and Cruz, Fernanda and Fiuza-Junior, Raildo A. and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Pontes, Karen and Mascarenhas, Artur}, title = {Influ{\^e}ncia do Suporte em Catalisadores de Ni Obtidos Pelo M{\´e}todo da Combust{\~a}o na Reforma a Seco do Biog{\´a}s para Produ{\c{c}}{\~a}o de Hidrog{\^e}nio Sustent{\´a}vel}, series = {23º CBCAT : Congresso Brasileiro de Catalise}, volume = {1}, journal = {23º CBCAT : Congresso Brasileiro de Catalise}, number = {1}, pages = {1 -- 6}, abstract = {Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biog{\´a}s. As caracteriza{\c{c}}{\~o}es revelaram varia{\c{c}}{\~o}es na dispers{\~a}o met{\´a}lica, {\´a}rea met{\´a}lica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior {\´a}rea met{\´a}lica e melhor dispers{\~a}o de Ni, favorecendo altas convers{\~o}es de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e est{\´a}vel por 8 horas de rea{\c{c}}{\~a}o. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resist{\^e}ncia ao coque. O NiO-CeO₂, embora ativo no in{\´i}cio, desativou com o tempo devido {\`a} deposi{\c{c}}{\~a}o de coque (6,4\%). A an{\´a}lise p{\´o}s-rea{\c{c}}{\~a}o mostrou baixa forma{\c{c}}{\~a}o de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resist{\^e}ncia dos catalisadores na reforma a seco do biog{\´a}s. Palavras-chave: Hidrog{\^e}nio sustent{\´a}vel; Reforma a seco do biog{\´a}s; 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.}, language = {pt} } @misc{MbuyaPawarJafarietal., author = {Mbuya, Christel Olivier Lenge and Pawar, Kunal and Jafari, Mitra and Shafiee, Parisa and Okoye Chine, Chike George and Tarifa, Pilar and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems}, series = {Journal of CO2 utilization}, volume = {102}, journal = {Journal of CO2 utilization}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2212-9839}, doi = {10.1016/j.jcou.2025.103270}, pages = {1 -- 9}, abstract = {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.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and Silva de Aquino, Gabrielle and Vaz dos Santos Rios, Jo{\~a}o Gabriel and Brand{\~a}o dos Santos, Maur{\´i}cio and Teixeira Cruz, Fernanda and Alves Fiuza-Junio, Raildo and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Valverde Pontes, Karen and Santos Mascarenhas, Artur Jos{\´e}}, title = {Effect of support on Ni catalysts prepared by the combustion method applied in the dry reforming of biogas for production of sustainable hydrogen}, series = {International journal of hydrogen energy}, volume = {204}, journal = {International journal of hydrogen energy}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1879-3487}, doi = {10.1016/j.ijhydene.2025.153150}, pages = {1 -- 25}, abstract = {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.}, language = {en} } @misc{LeeParkDorneanuetal., author = {Lee, Joohwa and Park, Haryn and Dorneanu, Bogdan and Kim, Jin-Kuk and Arellano-Garcia, Harvey}, title = {Decarbonized hydrogen production : integrating renewable energy into electrified SMR process with CO₂ capture}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.152295}, pages = {613 -- 618}, abstract = {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.}, language = {en} } @misc{DorneanuMappasArellanoGarcia, author = {Dorneanu, Bogdan and Mappas, Vasileios K. and Arellano-Garcia, Harvey}, title = {A novel approach to gradient evaluation and efficient deep learning : a hybrid method}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.120349}, pages = {1872 -- 1877}, abstract = {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.}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {A comprehensive modelling approach to enhance performance and scalability of iron-oxide based hydrogen storage systems}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 5}, abstract = {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.}, language = {en} } @misc{DorneanuMappasVassiliadisetal., author = {Dorneanu, Bogdan and Mappas, Vasileios K. and Vassiliadis, Vassilios S. and Arellano-Garcia, Harvey}, title = {Adjoint methods for fast sensitivity analysis in nonlinear multistage systems}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @misc{MbuyaJafariDorneanuetal., author = {Mbuya, Christel-Olivier Lenge and Jafari, Mitra and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Structured FeMnK catalysts for aviation fuel production via Fischer-Tropsch synthesis : a channel geometry study}, series = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, journal = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, pages = {1 -- 3}, abstract = {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.}, language = {en} } @misc{MappasDorneanuArellanoGarcia, author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Combinatorial optimization problems : a quantum-based approach}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @misc{JafariSantosdaSilvaDorneanuetal., author = {Jafari, Mitra and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME}, series = {58. Jahrestreffen Deutscher Katalytiker}, journal = {58. Jahrestreffen Deutscher Katalytiker}, pages = {1 -- 2}, abstract = {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.}, language = {en} } @misc{HamdanHamdanDorneanuetal., author = {Hamdan, Mustapha and Hamdan, Malak and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Modular high-temperature thermal energy storage for industrial decarbonisation using a particle-based heat battery}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 3}, abstract = {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.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Modeling multiphase reactors with complex particle geometries}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 4}, abstract = {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.}, language = {en} } @misc{JafariShafieeSantosdaSilvaetal., author = {Jafari, Mitra and Shafiee, Parisa and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Advancing catalyst design with machine learning : insights from FTS and DME production}, series = {Annual Meeting on Reaction Engineering 2025}, journal = {Annual Meeting on Reaction Engineering 2025}, pages = {1 -- 6}, abstract = {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.}, language = {en} } @misc{JafariAbadiMbuyaetal., author = {Jafari, Mitra and Abadi, Amirreza and Mbuya, Christel-Olivier Lenge and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Fischer-Tropsch synthesis and hydrocracking process integration : a study on mesoporosity modification and acidity optimization}, series = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, journal = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, pages = {1 -- 3}, abstract = {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.}, language = {en} }