TY - GEN A1 - Miah, Sayeef A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-García, Harvey T1 - Multi-Objective Design Optimisation of a Distributed Energy System through 3E (economic, environmental and exergy) Analysis T2 - 2020 Virtual AIChE Annual Meeting, November 20, 2020 N2 - To facilitate the commitments of reducing greenhouse gas emissions will require the utilisation of renewable energy resources, as well as shifting away from a centralised generation. Distributed energy systems (DESs) are a promising alternative to conventional centralised layouts. Thus, there is a need for the development of models able to optimally design DES which show savings in cost as well as having a low carbon impact. Current literature focuses on the design optimisation of a DES through economical and environmental cost minimisation [1-4]. However, these two criteria alone do not show the complete picture and do not satisfy the long-term sustainability priorities. The inclusion of exergy analysis allows for the satisfaction of this criteria through the rational use of energy resources. The use of exergy analysis within DESs was first studied by [5], with a multiobjective approach whereby cost and exergy efficiency are considered. The novelty of this paper is twofold. The first is the investigation of exergy DES design optimisation through a multiobjective approach whilst considering the economic and environmental cost, thus making this work the first to simultaneously minimise three objective functions in the context of DES. Y1 - 2020 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/340o-multi-objective-design-optimisation-distributed-energy-system-through-3e-economic-environmental SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Ruan, Hang A1 - Xiao, Pei A1 - Gao, Yang A1 - Arellano-García, Harvey T1 - Stepping Towards the Industrial Sixth Sense T2 - AIChE Annual Meeting, November 20, 2020 N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems, by the addition of elements such as smart sensors, Internet of Things, big data analytics or cloud computing. Modern engineering systems and manufacturing processes are operating in highly dynamic environments, and exhibiting scale, structure and behaviour complexity. Under these conditions, plant operators find it extremely difficult to manage all the information available, infer the desired conditions of the plant and take timely decisions to handle abnormal operation1. Human beings acquire information from the surroundings through sensory receptors for vision, sound, smell, touch, and taste, the Five Senses. The sensory stimulus is converted to electrical signals as nerve impulse data communicated with the brain. When one or more senses fail, the humans are able to re-establish communication and improve the other senses to protect from incoming dangers. Furthermore, a mechanism of ‘reasoning’ has been developed during evolution, which enable analysis of present data and generation of a vision of the future, which might be called the Sixth Sense. As industrial processes are already equipped with five senses: ‘hearing’ from acoustic sensors, ‘smelling’ from gas and liquid sensors, ‘seeing’ from camera, ‘touching’ from vibration sensors and ‘tasting’ from composition monitors, the Sixth Sense could be achieved by forming a sensing network which is self-adaptive and self-repairing, carrying out deep-thinking analysis with even limited data, and predicting the sequence of events via integrated system modelling. This contribution introduces the development of an intelligent monitoring and control framework for chemical process, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Y1 - 2020 UR - https://www.aiche.org/academy/videos/conference-presentations/stepping-towards-industrial-sixth-sense SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-García, Harvey T1 - Towards smart distributed energy systems T2 - Jahrestreffen der ProcessNet-Fachgemeinschaften "Prozess-, Apparate- und Anlagentechnik" (PAAT) N2 - A distributed energy resource (DER) system is an energy generation system located in the vicinity of the end users, simultaneously providing electricity, cooling and heating to meet the demands of the local users1. Unlike conventional, centralized energy supply, a DER system typically employs a wide range of technologies such as photovoltaics, wind turbines, gas turbines, biomass boilers, combined heating and power, absorption chillers, etc. In addition, energy storage technologies (batteries, hot/cold water storage) are available as well. DER systems can potentially play a vital role as the energy sector faces unprecedented challenges to reduce emissions by increasing energy generation using renewable and low-carbon energy resources. Different designs of the DER systems could lead to different performance in reducing the costs, the environmental impact or the use of primary energy. Hence, optimal design and management of complex DER systems are important tasks to promote their diffusion against the centralized grid. However, current operational models for DERs do not adequately analyse the complexity of such systems. This contribution presents a set of models for the optimal design and operation of residential DER systems, which build up on previous work in this field, and aims to provide a more holistic overview of such systems. For each node in the DER system, there is an option of installing the following ten technologies: wind turbines, photovoltaics arrays, combined heating and power units, absorption chillers, air-conditioning units, gas boilers, biomass boilers, gas heaters, batteries and thermal storage. Only one of each item may be installed in each home. There is also the option to connect a house to another via a combined hot and cold water pipeline and/or a microgrid cable, to share thermal and electrical energy, respectively. Due to increased availability of government incentives such as the feed-in tariffs (FIT) and renewable heat incentives (RHI) payments, these are included in the model as well. The increased penetration of Internet of Things technology and their potential to better control and optimize DER systems enable its use to help stabilize national grids. To this end, the models include the use of dynamic pricing, a strategy in which national grids publish in real time variable prices for electricity within given time periods. Furthermore, as current literature’s focus on economic and environmental cost minimization, which do not satisfy long-term sustainability priorities through the rational use of energy resources, the introduction of a third criteria, exergy, is investigated. A third novelty of this contribution is the consideration of a multi-objective optimization which simultaneously includes the three objectives: the economic, the environmental and exergetic criteria in the design and operation of the residential DER system. Additionally, a novel methodology to analyse the effect of uncertain input variables on the total daily cost of the DER operational models. The methodology combines the operational model with model predictive control to predict the current state of the model. A subset of the model inputs (i.e., electricity demand, heating demand, and insolation) are considered uncertain. Global sensitivity analysis is conducted to quantify and understand how the uncertain variables influence, both individually and through interactions, the total daily cost. Finally, the implementation of blockchain technology and smart contracts within optimally designed and scheduled DER systems is investigated, to assess the advantages of smart technologies on the efficiency of residential DERs. Challenges, limitations and suggestions for improving the overall design and operation are also discussed in detail. All models are developed as mixed-integer linear programming models implemented and solved in GAMS, and show significant reduction of costs for all considered criteria when compared to the centralised grid and the classical approach towards the modelling of DER systems. Y1 - 2023 UR - https://www.researchgate.net/publication/388185482_Towards_smart_distributed_energy_systems ER - TY - GEN A1 - Gonzalez-Arias, Judith A1 - Gonzalez-Castano, Miriam A1 - Arellano-García, Harvey T1 - Utilization of CO2-Rich Residues for Syngas Production: Strategies for Catalyst Design T2 - AIChE Annual Meeting, November 15, 2021 N2 - Compared to a Reverse Water Gas Shift (RWGS) process carried out under ideal conditions, the valorization of CO2-rich residues involve additional challenges. Indeed, for an ideal RWGS reaction unit, the CO2 methanation reaction and the constitution of carbon deposits via Boudouard reaction are the main side reactions to take into consideration. For CO2-rich residues derived from biomass treatment and heavy metal industries, the presence of CH4 and CO species (among others) constitute an, although often disregarded, much complex panorama where side reactions like CO methanation, dry reforming of methane, the forward Water Gas Shift reaction and the decomposition of CO and CH4 resulting in carbon deposits, are occurring to some extent within the catalytic reactor. This work aimed at designing advanced catalytic systems capable of converting the CO2/CO/CH4 feedstocks into syngas mixtures. Thus, with the RWGS reaction considered as the major process, this work focusses on the side reactions involving CO/CH4 species. In this context, a series Cu-MnOx/Al2O3 spinel derived catalysts were optimized for syngas production in presence of CO and CH4 fractions. Once the optimal active phase was determined, the optimal Cu contents and the impact of the support nature (Al2O3, SiO2-Al2O3 and CeO2-Al2O3) was evaluated for the valorization of realistic CO2-rich feedstocks. Remarkably, the obtained outcomes underline operative strategies for developing catalytic systems with advanced implementation potential. For that aim, the catalyst design should present, along with an active and selective phase for RWGS reaction, superior cooking resistances, activities towards methane reforming and low tendencies towards the forward WGS reaction. Further developments should tackle difficult tasks like improving the RWGS reaction rate while inhibiting the forwards WGS reaction as well as improving the CH4 conversion to CO without affecting the process selectivity. Strategies towards advancing catalytic systems capable of operating under variable conditions also arise as appealing routes. Y1 - 2021 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/661v-utilization-co2-rich-residues-syngas-production-strategies-catalyst-design UR - https://plan.core-apps.com/aiche2021/event/30d89249d0653ff1de80a79e11b79a16 SN - 978-0-8169-1116-5 ER - TY - GEN A1 - Arellano-García, Harvey A1 - El Bari, Hassan A1 - Kalibe Fanezoune, Casimir A1 - Dorneanu, Bogdan A1 - Majozi, Thokozani A1 - Elhenawy, Yasser A1 - Bayssi, Oussama A1 - Hirt, Ayoub A1 - Peixinho, Jorge A1 - Dhahak, Asma A1 - Gadalla, Mamdouh A. A1 - Khashaba, Nourhan H. A1 - Ashour, Fatma T1 - Catalytic Fast Pyrolysis of Lignocellulosic Biomass: Recent Advances and Comprehensive Overview T2 - Journal of Analytical and Applied Pyrolysis N2 - Using biomass as a renewable resource to produce biofuels and high-value chemicals through fast pyrolysis offers significant application value and wide market possibilities, especially in light of the current energy and environmental constraints. Bio-oil from fast-pyrolysis has various conveniences over raw biomass, including simpler transportation and storage and a higher energy density. The catalytic fast pyrolysis (CFP) is a complex technology which is affected by several parameters, mainly the biomass type, composition, and the interaction between components, process operation, catalysts, reactor types, and production scale or pre-treatment techniques. Nevertheless, due to its complicated makeup, high water and oxygen presence, low heating value, unstable nature, elevated viscosity, corrosiveness, and insolubility within conventional fuels, crude bio-oil has drawbacks. In this context, catalysts are added to reactor to decrease activation energy, substitute the output composition, and create valuable compounds and higher-grade fuels. The study aim is to explore the suitability of lignocellulosic biomasses as an alternative feedstock in CFP for the optimization of bio-oil production. Furthermore, we provide an up-to-date review of the challenges in bio-oil production from CFP, including the factors and parameters that affect its production and the effect of used catalysis on its quality and yield. In addition, this work describes the advanced upgrading methods and applications used for products from CFP, the modeling and simulation of the CFP process, and the application of life cycle assessment. The complicated fluid dynamics and heat transfer mechanisms that take place during the pyrolysis process have been better understood due to the use of CFD modeling in studies on biomass fast pyrolysis. Zeolites have been reported for their superior performance in bio-oil upgrading. Indeed, Zeolites as catalyses have demonstrated significant catalytic effects in boosting dehydration and cracking process, resulting in the production of final liquid products with elevated H/C ratios and small C/O ratios. Combining ex-situ and in-situ catalytic pyrolysis can leverage the benefits of both approaches. Recent studies recommend more and more the development of pyrolysis-based bio-refinery processes where these approaches are combined in an optimal way, considering sustainable and circular approaches. KW - Catalytic fast pyrolysis KW - Lignocellulosic Biomass KW - Bio-Oil KW - Modelling Y1 - 2024 U6 - https://doi.org/10.1016/j.jaap.2024.106390 SN - 0165-2370 VL - Vol. 178 ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Akhtar, Farid T1 - Development of Ni-doped A-site lanthanides-based perovskite-type oxide catalysts for CO2 methanation by auto-combustion method T2 - RSC Advances N2 - Engineering the interfacial interaction between the active metal element and support material is a promising strategy for improving the performance of catalysts toward CO2 methanation. Herein, the Ni-doped rare-earth metal-based A-site substituted perovskite-type oxide catalysts (Ni/AMnO3; A = Sm, La, Nd, Ce, Pr) were synthesized by auto-combustion method, thoroughly characterized, and evaluated for CO2 methanation reaction. The XRD analysis confirmed the perovskite structure and the formation of nano-size particles with crystallite sizes ranging from 18 to 47 nm. The Ni/CeMnO3 catalyst exhibited a higher CO2 conversion rate of 6.6 × 10−5 molCO2 gcat−1 s−1 and high selectivity towards CH4 formation due to the surface composition of the active sites and capability to activate CO2 molecules under redox property adopted associative and dissociative mechanisms. The higher activity of the catalyst could be attributed to the strong metal–support interface, available active sites, surface basicity, and higher surface area. XRD analysis of spent catalysts showed enlarged crystallite size, indicating particle aggregation during the reaction; nevertheless, the cerium-containing catalyst displayed the least increase, demonstrating resilience, structural stability, and potential for CO2 methanation reaction. KW - perovskite KW - CO2 methanation KW - lanthanide KW - auto-combustion method Y1 - 2024 UR - https://pubs.rsc.org/en/content/articlelanding/2024/ra/d4ra02106a U6 - https://doi.org/10.1039/d4ra02106a VL - 2024 IS - 14 SP - 20240 EP - 20253 ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation T2 - Book of Abstracts zur Jahrestagung der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik, 04. – 06. März 2024 Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractDechemaFluidverfahrenstechnik_Catalysts.pdf PB - Ruhr Universität CY - Bochum ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_Poster_DECHEMA2024_Fluidverfahrenstechnik.pdf CY - Bochum ER - TY - GEN A1 - Medina Méndez, Juan Ali A1 - Dorneanu, Bogdan A1 - Schmidt, Heiko A1 - Arellano-García, Harvey T1 - Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation T2 - Journal of Physics: Conference Series N2 - Progress in the modeling of structured catalysts is crucial for enhancing efficiency and scalability in industrial applications. Extensive research has investigated reactive flows over catalyst surfaces, covering chemical kinetics analysis and (direct) numerical simulations of the complete fluid flow in fixed-bed or structured catalysts. Nonetheless, this comes at a high computational cost. This study focuses on the homogeneous modeling of structured catalysts utilizing volume-averaging theory (VAT) as a more efficient method for representing the behaviour of such systems. We discuss modeling strategies for both 1-D and 3-D simulations. For steady 1-D flow simulations, we assess the influence of simplified gas chemical kinetics versus detailed surface chemistry, comparing with experimental data from the literature for a CO2 methanation processes. We also simulate 3-D flows of a steam reforming process, previously studied in the literature, using models which rely on different assumptions regarding the nature of the porous catalyst. Our findings reveal significant discrepancies based on different modeling assumptions, underscoring the necessity for accurate modeling of permeability and diffusivity tensors in homogeneous models. Y1 - 2024 U6 - https://doi.org/10.1088/1742-6596/2899/1/012004 SN - 1742-6596 VL - 2899/2024 ER - TY - GEN A1 - Medina Méndez, Juan Alí A1 - Dorneanu, Bogdan A1 - Schmidt, Heiko A1 - Arellano-García, Harvey T1 - Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation T2 - Book of Abstracts XXVI Fluid Mechanics Conference (FMC 2024), Warsaw, Poland, September 10-13, 2024 Y1 - 2024 UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractFMC26_Catalysts.pdf PB - University of Technology CY - Warsaw ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Novel parametric gradient calculation method for multistage systems with generalized constraints T2 - 2024 AIChE Annual Meeting N2 - Sensitivity and gradient evaluations are essential for understanding the variability of a system subject to changes in input parameters, aiding in applications such as optimization, control, or decision-making processes (Castillo et al., 2008; Logsdon and Biegler, 1989, Horn and Tsai, 1967). Various approaches are available for the gradient evaluation in the simulation of large-scale steady-state systems, utilizing techniques such as automatic differentiation, sensitivity analysis, optimization or machine learning (Amaran et al., 2016). The term large-scale refers to problems with a substantial number of design variables, structural state variables, or constraint functions, or a combination thereof, necessitating significant high-performance parallel computing resources to solve within a reasonable timeframe (Kennedy and Martins, 2014). However, the evaluation of gradients in large-scale multistage systems simulation poses significant challenges due to computational complexity, numerical instability, scalability issues, and the limitations of the traditional differentiation techniques. Additionally, model complexity, sensitivity to noise, and data requirements of machine learning-based approaches further amplify these challenges. Overcoming these obstacles necessitates the development of efficient, scalable and robust gradient evaluation techniques that can effectively handle the characteristics of large-scale systems while offering reliable insights for a wide array of applications. This contribution focuses on re-examining and advancing the evaluation of parametric sensitivities within the context of simulating highly complex, hierarchical multiscale modular systems of very large size. The models being analyzed may necessitate sensitivity evaluations concerning their response to parametric inputs. These evaluations serve not only to test and verify their robustness, but also to integrate them into modular structures within a comprehensive optimization framework. Such an optimization framework aims to enhance system performance based on selected criteria, while simultaneously adhering to essential optimality constraints. While gradient-free optimization methods have been successfully applied to important design problems, their applications typically involve no more than O(102) design variables, and these methods exhibit very poor scalability with the dimensionality of the design variables (Kennedy and Martins, 2014). For large-scale, high-fidelity applications, gradient-based methods are deemed more suitable, although the challenges related to computational time and accuracy need to be addressed. To address these challenges, the use of either sensitivities or appropriately generalized adjoint equations for efficient calculation of constraint and objective functions gradients for generalized multistage systems, irrespective of whether they are dynamic in nature or they are steady-state. The proposed approach adopts a generalized modular strategy suitable for any type of system, starting from a traditional sensitivity-based calculations initially, and subsequently developing a novel generalized adjoint-based method. The resulting algorithm comprises a sequence of forward and backward sweep computational steps, which are entirely equivalent, and serve as a generalization of the adjoint-based calculation methods for gradients of constraints. These methods find application in various numerical analysis computations related to dynamical systems, including optimal control problems. It has to be noted that the model is regarded as a general modular representation of any coupled system, without making a distinction between dynamic or steady-state systems. In this context, a dynamic system is perceived as having state profiles as private internal variables, while interacting with its external environment through the input of initial conditions and parameter values. Its output consists of final conditions or any internal trajectory points that require reporting to the external environment during dynamic simulation. The proposed strategy using a novel adjoint scheme generalizes this approach to any multistage system model, of which the stages need not be of dynamic nature, such as in the use of adjoint equations in optimal control of multistage Differential- Algebraic Equation (DAE) systems (Morison and Sargent, 1986). The choice between the use of the adjoint- and the sensitivity-based approach depends on the balance between the number of constraints/functions requiring gradient evaluation, and the number of states in the underlying dynamical system. The adjoint-based approach may be advantageous when dealing with a smaller number of constraints than state variables that require gradient evaluation, whereas the sensitivity-based approach could be more computationally efficient for a larger number of constraints than state variables in the modular treatment of the underlying dynamic system. The simulation of a multistage system is demonstrated using an example consisting of steady-state feedforward blocks, employing both the sensitivity- and the proposed adjoint-based approach. The results obtained reveal that the numerical values derived from the gradient evaluation are identical for both methods. Therefore, it can be concluded that the newly introduced approach for general multistage sequential systems is entirely non-restrictive. This indicates its effectiveness and applicability, offering flexibility and robustness in gradient evaluation for such systems. Y1 - 2024 UR - https://www.researchgate.net/publication/388185581_Novel_parametric_gradient_calculation_method_for_multistage_systems_with_generalized_constraints ER - TY - GEN A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Santos da Silva, Jefferson A1 - Santos Mascarenhas, Artur Jose A1 - Valverde Pontes, Karen A1 - Arellano-García, Harvey T1 - Advancements in CO2 methanation: customized heterogeneous Ni-Perovskite catalyst for sustainable SNG production T2 - Annual Meeting on Reaction Engineering and Electrochemical Processes 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388185658_Advancements_in_CO2_methanation_Customized_Heterogeneous_Ni-Perovskite_Catalyst_for_Sustainable_SNG_Production ER - TY - GEN A1 - Mappas, Vassileios A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-García, Harvey T1 - A unified modular framework for modeling multiphase reactors T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143505_A_unified_modular_framework_for_modeling_multiphase_reactors ER - TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143384_Towards_efficient_material_design_Use_of_Machine_Learning_to_predict_chemical_reactions_and_retrosynthesis ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Optimal design of a thermochemical hydrogen storage and release system via the reversible redox of iron oxide/iron T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143925_Thermochemical_Hydrogen_Storage_via_the_Reversible_Reduction_and_Oxidation_of_Metal_Oxides ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Vassiladis, Vassilios S. A1 - Arellano-García, Harvey T1 - A novel approach to staggered training of deep learning networks T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143628_A_novel_approach_to_staggered_training_of_deep_learning_networks ER - TY - GEN A1 - Safdar, Muddasar A1 - Safdar, Mutahar A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Process intensification by additive manufacturing strategies for power-to-X conversion application: Case studies T2 - 16th International Conference on Gas–Liquid and Gas–Liquid–Solid Reactor Engineering Y1 - 2024 UR - https://www.researchgate.net/publication/388185473_Process_Intensification_by_Additive_Manufacturing_Strategies_for_Power-to-X_Conversion_Application_Case_Studies ER - TY - GEN A1 - Jafari, Mitra A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Sustainable aviation fuel production through Fischer-Tropsch synthesis and hydrocracking integration using Co bifunctional catalysts: Support effects T2 - 18th International Congress on Catalysis N2 - Considering the increasing demand for clean and sustainable aviation fuel, in this study, cobalt bifunctional catalysts are used to convert syngas from biomass to aviation fuel. Y1 - 2024 UR - https://www.researchgate.net/publication/388109868_Sustainable_aviation_fuel_production_through_Fischer-Tropsch_synthesis_and_hydrocracking_integration_using_Co_bifunctional_catalysts_Support_effects ER - TY - GEN A1 - Alves Amorim, Ana Paula A1 - Valverde Pontes, Karen A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimizing microgrid design and operation : a decision-making framework for residential distributed energy systems in Brazil T2 - Chemical Engineering Research and Design N2 - This paper explores the optimization of microgrid design and operation for residential distributed energy systems in Brazil, addressing the growing demand for sustainable energy in the context of climate change. A decision-making framework based on Mixed-Integer Nonlinear Programming (MINLP) is proposed to integrate distributed energy resources (DERs) such as solar, wind, and biogas. Key challenges include managing the variability of renewable resources and complying with local regulations, while also addressing gaps in literature, particularly the impact of time-dependent efficiency profiles on energy sharing within microgrids. By employing innovative analyses and clustering techniques, the research optimizes microgrid configurations, accounting for seasonal demand fluctuations and the influence of incentive policies on system feasibility. The findings reveal that incorporating a time-dependent efficiency model can reduce total costs by 45 %. This reduction underscores the importance of accurate efficiency predictions, as the model captures variations in energy generation and utilization efficiency over time, improving system optimization. Additionally, the findings reveal that a well-structured optimization model can meet 100 % of electricity and hot water demands across all scenarios, with customized incentives playing a crucial role in reducing costs and promoting sustainability. Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S0263876224007123?via%3Dihub U6 - https://doi.org/https://doi.org/10.1016/j.cherd.2024.12.033 SN - 0263-8762 VL - 214 (2025) IS - February 2025 SP - 251 EP - 268 PB - Elsevier ER - TY - GEN A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Towards Machine Learning-driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis T2 - Chemical Engineering Transactions N2 - Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing the catalyst design and operating conditions to maximize the desired C8-C16 jet fuel range is a challenging task. This study introduces the application of a machine learning (ML) framework to guide the design of Co/Fe-supported FTS catalysts and operating conditions for enhanced fuel selectivity. A comprehensive dataset was constructed with 21 input features spanning catalyst structure, preparation method, activation procedure, and FTS operating parameters. The random forest ML algorithm was evaluated for predicting CO conversion and C8-C16 selectivity using this dataset. Feature engineering identified the most significant descriptors influencing performance. A principal component analysis reduced the dataset dimensionality prior to ML modelling. The random forest algorithm achieved high prediction accuracy for the conversion of CO (R2 = 0.92) and C8-C16 selectivity (R2 = 0.90). In addition to confirming the known effects of operating conditions, key roles of Co/Fe-supported properties were elucidated. This ML framework provides a powerful tool for the rational design of FTS catalysts and operating windows to maximize jet fuel productivity Y1 - 2024 UR - https://www.cetjournal.it/cet/24/114/098.pdf U6 - https://doi.org/10.3303/CET24114098 SN - 2283-9216 VL - 114 SP - 583 EP - 588 ER -