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 - Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) T2 - Chemical Engineering Journal Advances N2 - Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 %), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 %). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity. KW - Jet fuels (C8-C16) KW - Machine learning (ML) KW - Fischer-Tropsch synthesis (FTS) KW - Operational conditions KW - Catalyst preparation Y1 - 2025 U6 - https://doi.org/10.1016/j.ceja.2024.100702 VL - 21 (2025) PB - Elsevier ER - TY - GEN A1 - Mappas, Vasileios A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios A1 - Arellano-Garcia, Harvey T1 - Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept T2 - Chemical engineering research and design N2 - Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP’s bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly’s classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, providing foundational insights towards leveraging QA in Process Systems Engineering. KW - Quantum annealing KW - Bilinear programming KW - Pooling/blending problem KW - Quadratic unconstrained binary optimization (QUBO) Y1 - 2025 U6 - https://doi.org/10.1016/j.cherd.2025.08.031 SN - 1744-3563 VL - 221 SP - 560 EP - 576 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Multiphase systems, particularly trickle bed reactors (TBRs), are critical in various industrial applications and widely employed in catalytic processes such as hydrogenation and oxidation due to their high surface area, low operational and minimal catalyst loss. Despite advancements in modelling techniques, accurately capturing the complex multiphysics and multiscale phenomena remains challenging. Conventional approaches, relying on empirical correlations or Computational Fluid Dynamics (CFD) simulations, often fall short due to high computational demands, limited accuracy, and constraints on the number of catalytic particles that can be effectively simulated [3]. To address these limitations, this contribution presents a new framework tailored for the design and analysis of multiphase systems operating in the low-interaction regimes. This approach is based on the local structure of the packed bed and employs a Lagrangian approach, where flow dynamics within the reactor is represented by various discrete elements. The framework's modular and flexible setup enables the incorporation of multiscale information of both local and global levels, allowing for the additions of new modules or features to enhance modelling fidelity. Y1 - 2025 UR - www.researchgate.net/publication/388846704_Capturing_multiscale_phenomena_in_trickle_bed_reactors_A_flexible_framework_for_flow_and_reaction_analysis ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Kim, Jin-Kuk A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Industrial sectors contribute substantially to global CO2 emissions, emphasizing the need for low-carbon, reliable energy supplies to meet operational demands. Achieving net-zero emissions in industrial processes involves transitioning from fossil fuels to renewable energy sources. However, the intermittent nature of renewables poses challenges to energy reliability and resilience, particularly in utility systems. This contribution addresses industrial decarbonisation and sustainable hydrogen production by developing a comprehensive design and optimization framework for integrating renewable energy systems into industrial operations. This framework incorporates energy storage and grid connections to improve flexibility and stability and is evaluated through two case studies. Both case studies analyse the operational and configurational changes necessary for renewable-powered hydrogen production, estimating the cost of hydrogen or CO2 avoidance cost to analyse economic viability. These insights provide guidelines for sustainable and economically viable energy management in industrial and hydrogen production sectors, supporting broader global energy transition goals. Y1 - 2025 UR - https://www.researchgate.net/publication/388846958_Pathways_to_industrial_decarbonization_Renewable_energy_integration_and_electrified_hydrogen_production ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron T2 - Systems and control transactions N2 - In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. These findings contribute to advancing energy-efficient and scalable hydrogen storage technologies. KW - Hydrogen KW - Hydrogen fuel cells KW - Energy storage KW - Modelling and simulations KW - Optimisation KW - Thermochemical storage KW - Green hydrogen Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.121492 SN - 2818-4734 VL - 4 SP - 631 EP - 636 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Solving complex combinatorial optimization problems using quantum annealing approaches T2 - Systems and control transactions N2 - Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer's architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly's pooling and blending problem is examined while demonstrating the potential of the proposed approach. The results indicate that the succinct formulation exhibited higher success rate during the embedding procedure for the different examined architectures, and the quantum annealing solver exhibited the best performance among the various solvers investigated. This highlights the potential of the approach for solving this type of problems with the rapid development and improvement of quantum hardware and expanding it to more complex chemical engineering optimization systems. KW - Optimization KW - Scheduling KW - Algorithms KW - Quantum Computing KW - Quantum Annealing Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.188358 SN - 2818-4734 VL - 4 SP - 1561 EP - 1566 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Shafiee, Parisa A1 - Jafari, Mitra A1 - Schowarte, Julia A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis T2 - Systems and control transactions N2 - Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization. KW - Catalysis KW - Machine learning KW - Modelling KW - Optimization KW - Alternative fuels KW - Environment KW - FischerTropsch synthesis KW - Photocatalysis Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.135551 SN - 2818-4734 VL - 4 SP - 1866 EP - 1871 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Kim, Jin-Kuk T1 - Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems T2 - Systems and control transactions N2 - Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined with renewable-based electrification. The proposed optimization model evaluates the economic trade-offs of integrating carbon capture, renewable energy, and energy storage. By applying this approach, systematic design guidelines are developed for the transition of a conventional steady-state utility system toward renewable energy integration, ensuring economically viable and sustainable energy management in process industries. KW - Renewable energy KW - CO2 capture KW - Industrial utility operation KW - Cost optimization KW - Process integration Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.107403 SN - 2818-4734 VL - 4 SP - 1175 EP - 1180 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arelano-Garcia, Harvey T1 - Machine learning–enhanced Fischer–Tropsch synthesis : optimizing catalysts and process conditions for efficient fuel production T2 - Chemie - Ingenieur - Technik : CIT N2 - 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. KW - Fischer–Tropsch synthesis KW - Liquid fuel KW - Machine learning KW - Zeolite Y1 - 2025 U6 - https://doi.org/10.1002/cite.70030 SN - 1522-2640 VL - 97 IS - 11-12 SP - 1085 EP - 1093 PB - Wiley CY - Weinheim ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-Garcia, Harvey T1 - An efficient and unified modeling framework for trickle bed reactors : a modular approach T2 - Chemie - Ingenieur - Technik : CIT N2 - Trickle bed reactors (TBRs) involve complex and multiscale dynamics that challenge their design, modeling, and optimization. Current approaches often suffer from high computational cost and limited scalability, restricting their applicability in large-scale cases. This work introduces a modular, computationally efficient framework to address these issues by systematically capturing key transport and reaction phenomena. Furthermore, it provides a critical review of existing modeling strategies for TBRs, outlining their strengths and limitations and highlighting opportunities for enhancement through modularization. By offering a structured and scalable approach, the proposed framework improves predictive capabilities and supports the development of optimized and adaptable reactor designs. KW - Catalytic multiphase reactors KW - Modeling KW - Trickle bed reactors Y1 - 2025 U6 - https://doi.org/10.1002/cite.70035 SN - 1522-2640 VL - 97 IS - 11-12 SP - 1110 EP - 1126 PB - Wiley CY - Weinheim ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Santos da Silva, Jefferson A1 - De Aquino, Gabrielle A1 - Dos Santos, Mauricio A1 - Cruz, Fernanda A1 - Fiuza-Junior, Raildo A. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Pontes, Karen A1 - Mascarenhas, Artur T1 - Influência do Suporte em Catalisadores de Ni Obtidos Pelo Método da Combustão na Reforma a Seco do Biogás para Produção de Hidrogênio Sustentável T2 - 23º CBCAT : Congresso Brasileiro de Catalise T2 - 23rd Brazilian Congress of Catalysis N2 - Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biogás. As caracterizações revelaram variações na dispersão metálica, área metálica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior área metálica e melhor dispersão de Ni, favorecendo altas conversões de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e estável por 8 horas de reação. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resistência ao coque. O NiO-CeO₂, embora ativo no início, desativou com o tempo devido à deposição de coque (6,4%). A análise pós-reação mostrou baixa formação de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resistência dos catalisadores na reforma a seco do biogás. Palavras-chave: Hidrogênio sustentável; Reforma a seco do biogás; Catalisadores de NiO; efeito do suporte ABSTRACT-This study evaluates NiO-based catalysts supported on MgO, ZrO₂, Al₂O₃, La₂O₃, and CeO₂ for the dry reforming of biogas. Characterization of the samples revealed differences in metal dispersion, metallic area, and surface morphology. NiO-Al₂O₃ and NiO-CeO₂ show higher metallic areas and better Ni dispersion, leading to higher CH₄ and CO₂ conversions and good H₂ yield. NiO-Al₂O₃ is the most efficient and stable catalyst over 8 hours of reaction. NiO-La₂O₃ shows a gradual increase in activity and good coke resistance. Conversely, NiO-CeO₂, despite high initial activity, deactivates over time due to coke deposition (6.4%). Post-reaction analysis confirmed low coke formation for most catalysts. The results indicate that the choice of support directly affects catalyst activity, stability, and resistance. KW - Hidrogêniosustentável KW - Reforma a secodo biogás KW - Catalisadores de NiO KW - Efeito dosuporte KW - Sustainable hydrogen KW - Biogas dry reforming KW - NiO Catalysts KW - Supportrole Y1 - 2025 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203/303 VL - 1 IS - 1 SP - 1 EP - 6 ER - TY - GEN A1 - Mbuya, Christel Olivier Lenge A1 - Pawar, Kunal A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Okoye Chine, Chike George A1 - Tarifa, Pilar A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems T2 - Journal of CO2 utilization N2 - The dry reforming of methane (DRM) is a promising route for converting greenhouse gases such as methane (CH4) and carbon dioxide (CO2) into valuable syngas, hydrogen (H2) and carbon monoxide (CO). However, traditional nickel (Ni)-based catalysts suffer from rapid deactivation due to carbon deposition and sintering, especially when supported on low thermal conductivity materials. In this work, a novel post-synthesis microwave irradiation (MIR) treatment is introduced to systematically optimize the performance of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts for DRM. Unlike previous studies that have used MIR during reaction or with different supports, this approach tunes the metal – support interactions and textural properties of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts by varying the MIR exposure time after catalyst synthesis. MIR post-treatment (10–25 s) increased the CH4 conversion to 65 % and the CO2 conversions to 62 % for Ni–β–SiC catalysts and improved the H₂/CO ratio to 0.80, with stable performance over 20 h. For Ni–Ti–Cβ–SiC, MIR (10–20 s) maintained CH4 conversion up to 60 % and CO2 conversion to 58 % over 20 h, while the untreated catalyst, though initially higher, deactivated rapidly. Excessive MIR (30 s) reduced performance for both catalyst types, underscoring the need for optimal exposure time. These findings demonstrate post-synthesis MIR provides a tuneable approach for enhancing both the activity and durability of Ni/SiC – based DRM catalysts through controlled modification of metal – support interactions. This work offers new insights for the design of robust catalysts aimed at greenhouse gas utilization and sustainable syngas production, with activity and stability enhancements linked to controlled changes in metal – support interactions. KW - Carbon dioxide KW - Dry reforming KW - Methane KW - Microwave irradiation KW - Ni Silicon carbide catalysts Y1 - 2025 U6 - https://doi.org/10.1016/j.jcou.2025.103270 SN - 2212-9839 VL - 102 SP - 1 EP - 9 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Lee, Joohwa A1 - Park, Haryn A1 - Dorneanu, Bogdan A1 - Kim, Jin-Kuk A1 - Arellano-Garcia, Harvey T1 - Decarbonized hydrogen production : integrating renewable energy into electrified SMR process with CO₂ capture T2 - Systems and control transactions N2 - Electrified steam methane reforming has emerged as a promising technology for electrifying the hydrogen production process industries. Unlike conventional fossil fuel-based steam methane reforming, the electrified steam methane reforming process relies exclusively on electrical heating, eliminating the need for fossil fuel combustion. Beyond that, however, significant amounts of electricity required for the electrified process should be imported from the renewable energy-based system rather than fossil fuel-based grid electricity to have an environmental advantage over the conventional process. This study suggests a framework for integrating renewable energy systems into the electrified process for decarbonized hydrogen production. Considering the variability of renewable energy, wind and solar power are supplemented by battery storage, to facilitate a stable electricity supply to the electrified hydrogen production process. A Mixed-Integer Linear Programming (MILP) model is developed to optimally size and operate both the renewable system and potential grid imports. Case studies under various carbon tax scenarios, using historical weather data from a region in Germany, are conducted, followed by a techno-economic assessment to estimate the Cost of Hydrogen (COH). The results show that higher carbon taxes and reduced capital costs for wind, solar, and storage technologies significantly increase the share of renewable-based electricity. These findings highlight the importance of more stringent carbon taxation and improvements in the technology readiness level (TRL) of renewable energy are critical for accelerating large-scale, clean hydrogen production and industrial decarbonization. KW - Hydrogen KW - Renewable energy KW - Electrification Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.152295 SN - 2818-4734 VL - 4 SP - 613 EP - 618 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Arellano-Garcia, Harvey T1 - A novel approach to gradient evaluation and efficient deep learning : a hybrid method T2 - Systems and control transactions N2 - Deep learning faces significant challenges in efficiently training large-scale models. These issues are closely linked, as efficient training often depends on precise and computationally feasible gradient calculations. This work introduces innovative methodologies to improve deep learning network (DLN) training in complex systems. A novel approach to DLN training is proposed by adapting the block coordinate descent (BCD) method, which optimizes individual layers sequentially. This is combined with traditional batch-based training to create a hybrid method that harnesses the strengths of both techniques. Additionally, the study explores Iterated Control Random Search (ICRS) for initializing parameters and applies quasi-Newton methods like L-BFGS with restricted iterations to enhance optimization. By tackling DLN training efficiency, this contribution offers a comprehensive framework to address key challenges in modern machine learning. The proposed methods improve scalability and effectiveness, especially for handling complex real-world problems. Examples from Process Systems Engineering illustrate how these advancements can directly enhance the training of large-scale systems. KW - Artificial intelligence KW - Machine learning KW - Numerical methods Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.120349 SN - 2818-4734 VL - 4 SP - 1872 EP - 1877 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A comprehensive modelling approach to enhance performance and scalability of iron-oxide based hydrogen storage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Hydrogen storage remains a major bottleneck in realizing a competitive hydrogen economy due to the energy intensity and economic limitations of existing solutions [1]. This contribution addresses these barriers through a model-driven optimization framework for a solid-state thermochemical hydrogen storage (TCS) based on reversible redox cycling of iron oxide/iron. While iron-based TCS offers inherent safety and scalability advantages, key limitations persist, including high reduction temperatures (requiring significant energy input), suboptimal energy storage density, and sluggish redox kinetics [2]. Kinetic parameters for the reduction and oxidation reactions were systematically derived through isothermal thermogravimetric analysis (TGA) coupled with kinetic model regression. A first-principles dynamic model of a fixed-bed reactor was developed, integrating mass, energy, and momentum balances, and validated against experimental data from a lab-scale apparatus. The experimental system featured precision gas flow control, an electric furnace reactor, rapid air-cooled condensation, molecular sieve dehydration, and online effluent analysis via flow meters and gas chromatography. Dynamic simulations were carried out to investigate the reactor's transient behaviour under variations in critical parameters, including H2/H2O partial pressures, reaction temperatures, and gas flow rates. These studies revealed trade-offs between energy efficiency (favoured by lower temperatures), and reaction rates (enhanced at higher temperatures), while identifying key bottlenecks in redox cycling. The model further demonstrated how optimizing feed composition and flow dynamics mitigates kinetic degradation during charge/discharge cycles. REFERENCES [1] Elberry A.M. et al. KW - Hydrogen KW - Green hydrogen KW - Storage KW - Thermochemical storage KW - Process modelling KW - Optimisation Y1 - 2025 UR - https://www.researchgate.net/publication/399753698_A_comprehensive_modelling_approach_to_enhance_performance_and_scalability_of_iron-oxide_based_hydrogen_storage_systems SP - 1 EP - 5 CY - Frankfurt am Main ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Adjoint methods for fast sensitivity analysis in nonlinear multistage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Parametric sensitivity analysis is critical for optimization, control, and decision-making in engineering systems, enabling precise understanding of system responses to changes in parameters [1]. In the case of large-scale multistage systems, characterized by interconnected components, high-dimensional parameter spaces, and nonlinear constraints, traditional gradient evaluation methods often face significant challenges in scalability, computational efficiency, and accuracy [2]. This contribution introduces a novel framework for evaluating parametric gradients tailored specifically for generally constrained multistage systems, which leverages adjoint-based techniques [3] to compute exact gradients efficiently, addressing the inherent complexity of these systems. This reduces the number of simulations required by direct numerical differentiation or finite difference methods. The framework accommodates continuous real-value parameters and is designed to handle high-dimensional spaces typical of multistage systems. The proposed methodology is validated through case studies involving large-scale modular systems with nonlinear constraints. Results demonstrate substantial improvements in computational efficiency and gradient accuracy compared to conventional techniques. These advancements enable optimization algorithms to converge more quickly and reliably while navigating complex solution spaces effectively. By facilitating accurate sensitivity analysis, the framework enhances the exploration of design alternatives and increases the likelihood of identifying globally optimal solutions. KW - Parametric sensitivities KW - Adjoint equations KW - Multistage systems KW - Gradient evaluation KW - Nonlinear optimization KW - Process systems engineering Y1 - 2025 UR - https://www.researchgate.net/publication/399753602_Adjoint_methods_for_fast_sensitivity_analysis_in_nonlinear_multistage_systems SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Mbuya, Christel-Olivier Lenge A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Structured FeMnK catalysts for aviation fuel production via Fischer-Tropsch synthesis : a channel geometry study T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Fischer–Tropsch synthesis (FTS) is a promising route for sustainable aviation fuel production but is often limited by heat and mass transfer constraints in fixed-bed reactors. This study investigates the role of structured catalyst geometry in intensifying FTS performance using FeMnK catalysts selectively tailored for aviation-range hydrocarbons. The catalyst was synthesized via organic combustion and further modified by microwave irradiation, with structural properties characterized by X-ray diffraction. Aluminum honeycomb monoliths with square, circular, and triangular channel geometries were designed and fabricated by 3D printing, followed by catalyst deposition through dip-coating. High-pressure FTS experiments conducted at 30 bar and 300 °C demonstrate that channel geometry significantly influences conversion and selectivity by affecting flow dynamics and reactant mixing. The results highlight the potential of geometry-optimized structured catalysts to enhance FTS efficiency and support advanced reactor designs for sustainable aviation fuel production. Y1 - 2025 UR - https://www.researchgate.net/publication/391697232_Structured_FeMnK_catalysts_for_aviation_fuel_production_via_Fischer-Tropsch_synthesis_A_channel_geometry_study SP - 1 EP - 3 ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Combinatorial optimization problems : a quantum-based approach T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Classical computing faces challenges in global optimization (GO) when addressing non-convex problems, where the number of solutions grows exponentially with problem size 1. Quantum computing emerges as a potential solution. Gate-based quantum computing offers a broader range of applicability due to its ability to implement general quantum algorithms, in contrast to quantum annealing which is more specialized 2,3. This contribution explores the application of gate-based quantum computing to solving combinatorial optimization (CO) problems, specifically focusing on overcoming the limitations of classical computing. The proposed approach utilizes gate-based quantum computers, using IBM's hardware and software as an example, and the quantum approximate optimization algorithm (QAOA) for solving the reformulated problem using the Qiskit toolbox. The methodology includes translating the quadratic unconstrainted optimization problem (QUBO) into an Ising Hamiltonian, constructing the QAOA ansatz circuit, and optimizing its parameters. Furthermore, real quantum device architectures are employed to solve the optimized QUBO formulations. To demonstrate the capabilities of the approach, Haverly's pooling-blending problem is selected as a case study. Through the application of different discretization strategies, resolution levels, and circuit architectures, a comparative analysis of solver performance is conducted. The resulting QUBO formulations, efficiently embedded and solved on real quantum devices, underscore the potential of gate-based quantum computing as a promising solution approach for complex CO challenges. Y1 - 2025 UR - https://www.researchgate.net/publication/399789138_Combinatorial_Optimization_Problems_A_quantum-based_approach N1 - Sammelmail an Dorneanu. 27.01.2026 TR SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Jafari, Mitra A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME T2 - 58. Jahrestreffen Deutscher Katalytiker N2 - Dimethyl ether (DME, methoxymethane) is a clean-burning fuel and a promising alternative to conventional fossil fuels, especially in transportation and power generation. Its production from methanol through dehydration offers a viable pathway toward energy sustainability, not only because of its environmental benefits but also due to the high purity of the resulting products and the efficient conversion rate of methanol [1]. However, optimizing this process requires understanding the intricate dependencies among reaction parameters, including temperature, pressure, catalyst type, and feedstock composition [2]. Machine learning offers transformative potential in this context by identifying complex, non-linear interactions among variables and providing predictive insights that can improve reaction efficiency, yield, and product quality. Through predictive modeling, machine learning can significantly reduce the need for experimental trial-and-error by identifying optimal reaction conditions quickly, thereby decreasing costs, enhancing scalability, and supporting continuous, real-time process optimization [3, 4]. In this study, first a dataset is generated including different descriptors like catalyst formulation, pretreatment, characteristics, activation, and reaction conditions. This dataset is then preprocessed by encoding, imputation, and normalization to make it ready for modelling, followed by data analysis to identify patterns and dependencies. Different models, including Gradient Boosting Regressor, XGBoost, LightGBM, and neural networks, are applied to predict methanol conversion and DME yield based on input variables. The models were evaluated through cross-validation, achieving highaccuracy and underscoring the potential of data-driven optimization in enhancing DME production. These steps are illustrated in Figure 1. Finally, the prediction accuracy of each model is investigated, and the best algorithm is selected. The effect of different descriptors on the respond have also been assessed to find out the most effective parameters on the catalyst performance. The best model is then used to predict DME yield and optimize the catalyst and reaction parameters. Y1 - 2025 UR - https://www.researchgate.net/publication/390271407_Towards_Digitalization_of_Catalysis_Design_and_Reaction_Engineering_Data-Driven_Insights_into_Methanol_to_DME SP - 1 EP - 2 ER - TY - GEN A1 - Hamdan, Mustapha A1 - Hamdan, Malak A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Modular high-temperature thermal energy storage for industrial decarbonisation using a particle-based heat battery T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Industrial heat contributes over 9 Gt of annual CO₂ emissions, with high-grade requirements (>1000°C) posing exceptional decarbonization challenges [1]. This contribution present the two-loop (2LP) Heat Battery, a particle-based thermal energy storage system delivering dispatchable zero-carbon heat and electricity at temperatures up to 1600°C. The modular design employs a dual-loop recirculating bed of advanced ceramic particles, achieving 98% round-trip efficiency through controlled particle metering and high surface area heat transfer. Unlike bulk thermal storage systems that exhibit thermocline-induced temperature decay [2], the 2LP architecture maintains steady-state outlet temperatures during 24-hour discharge cycles. Key innovations include a volumetric energy density of 1280 kWh/m 3 (surpassing molten salts, lithium-ion batteries, and refractory brick systems) and thermal output density exceeding 1MWth/m 3. The technology reduces levelised cost of storage from €20/kWh (conventional molten salt) to below €3/kWh while supporting ultra-efficient supercritical CO2 Brayton cycles (thermal-to-electric efficiency >50%). System performance exceeds EU SET Plan targets, achieving 98% electro-thermal round-trip efficiency and 90% combined heat and power efficiency. This scalable solution addresses critical gaps in industrial electrification, enabling grid congestion mitigation and providing a cost-effective pathway to decarbonize hard-to-abate sectors like steel and cement production. The 2LP Heat Battery demonstrates technical and economic viability to support EU Net Zero objectives through high-temperature electrification. Y1 - 2025 UR - https://www.researchgate.net/publication/399753836_Modular_High-Temperature_Thermal_Energy_Storage_for_Industrial_Decarbonisation_Using_a_Particle-Based_Heat_Battery SP - 1 EP - 3 CY - Frankfurt am Main ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Modeling multiphase reactors with complex particle geometries T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Trickle bed reactors (TBRs) are the backbone of the catalytic multiphase reactors in industrial processes, owning to their simple design, flexible controllability and large surface area. One of the key aspects for designing TBRs is the flow simulation inside the reactor and the hydrodynamics phenomena that take place during its operation. Literature offers various methods for simulating the behaviour and the performance of TBRs based on empirical methods or Computational Fluid Dynamics (CFD) simulations leading to inaccurate results, high computational burden, case studies with small catalytic beds or considering only spherical particles particles 1. To overcome these drawbacks, a modular and flexible toolbox for the modelling and study of TBRs is proposed which is adapted to the local structure of the catalytic bed 2. To improve the contact point calculation and extend to more complex geometries (i.e., cylinders, Raschig rings, trilobes), an approach based on liquid element tracking (LET) is applied, where the particle's surface is discretised over a finite number of triangles. Therefore, a pointwise sequence of the fluid over individual partial surfaces, based on the applied forces, is implemented for the liquid flow path estimation. The benefits of this procedure lie in its effectiveness, modular interconnection, and robust capability to represent a diverse range of phenomena for simulating flow patterns during TBRs operation in the low-interaction regime. Furthermore, the required computational time is significantly reduced compared to CFD simulations and a large number of particles can be introduced in the examined case study. The new particle representation is successfully implemented and the results are in good agreement with the static holdup prediction and radial flow distribution based on the previous contact point model, based solely on geometric calculations of the distance between the spheres and the liquid-solid interactions. References [1] Fathiganjehlou, A., et al. (2024). Multi-scale pore network modeling of a reactive packed bed. Y1 - 2025 UR - https://www.researchgate.net/publication/399788961_Multiphase_catalytic_reactors_a_modular_approach SP - 1 EP - 4 CY - Frankfurt am Main ER - TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Advancing catalyst design with machine learning : insights from FTS and DME production T2 - Annual Meeting on Reaction Engineering 2025 N2 - This contribution presents a ML-based framework to optimize heterogeneous catalysts, with a primary focus on the targeted application for FTS and DME production. However, the framework is designed to be generic, with applicability to a broader range of heterogeneous catalytic processes. The study analyzes a variety of catalyst parameters, including composition, pretreatment, and operating conditions, and employs advanced ML techniques such as regression models, ensemble learning, and neural networks to model the relationships between these factors and reaction outcomes. Hyperparameter optimization and performance evaluation using metrics like R², RMSE, MSE, and AIC further improve the accuracy and robustness of the models. In addition, the study provides a comparative analysis of applying this ML framework to both FTS and DME processes, highlighting the similarities and differences in optimizing catalyst performance and operating conditions. The study aims to identify the most influential parameters that drive catalyst performance and to assess the predictive power of each model. By enhancing understanding of how catalyst properties and operating conditions influence the efficiency of FTS and DME production, this work provides valuable insights into more efficient and sustainable catalytic process design. Y1 - 2025 UR - https://www.researchgate.net/publication/392137173_Advancing_catalyst_design_with_Machine_Learning_Insights_from_FTS_and_DME_production SP - 1 EP - 6 ER - TY - GEN A1 - Jafari, Mitra A1 - Abadi, Amirreza A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Fischer-Tropsch synthesis and hydrocracking process integration : a study on mesoporosity modification and acidity optimization T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Machine learning (ML) is employed to identify the key parameters influencing CO conversion and C5+ selectivity. Insights from both the literature review and ML analysis highlight pore volume and acidity as the most critical factors. Therefore, this study focuses on developing cobalt/beta zeolite catalysts with tailored mesoporosity and acidity to enhance catalytic performance. Specifically, the goal is to optimize the acidity of the zeolite by determining the ideal concentration of NH4+ during the ion-exchange step. Y1 - 2025 UR - https://www.researchgate.net/publication/391564315_Fischer-Tropsch_Synthesis_and_Hydrocracking_Process_Integration_A_Study_on_Mesoporosity_Modification_and_Acidity_Optimization SP - 1 EP - 3 ER -