TY - GEN A1 - Dorneanu, Bogdan A1 - Masham, Elliot A1 - Keykha, Mina A1 - Mechleri, Evgenia A1 - Cole, Rosanna A1 - Arellano-García, Harvey T1 - Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models T2 - Supply Chain Analytics N2 - Traditional food supply chains are often centralised and global in nature, entailing substantial resource consumption. However, in the face of growing demand for sustainability, this strategy faces significant challenges. Adoption of localised supply chains is deemed a more sustainable option, yet its efficacy requires verification. Supply chain analytics methodologies provide invaluable tools to guide decisions regarding inventory management, demand forecasting and distribution optimisation. These solutions not only enhance facilitate operational efficiency, but also pave the way for cost reduction, further aligning with sustainability objectives. This research introduces a novel decision-making approach anchored in mixed integer linear programming (MILP) and neighbourhood flow models defined in cellular automata to compare the environmental benefits and vulnerability to disruption of these two chain configurations. Additionally, a comprehensive cost analysis is integrated to assess the economic feasibility of incorporating layout changes that enhance supply chain sustainability. The proposed framework is applied on an ice cream supply chain across England over a one-year timeframe. The findings indicate the superiority of the localised configuration in terms of economic benefits, leading to savings exceeding £ 1 million, alongside important reductions in environmental impact. However, in terms of resilience, the traditional configuration remains superior in three out of the four examined scenarios. KW - supply chain management KW - flow configuration model KW - ice cream KW - Mixed-integer linear programming Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2949863523000420 U6 - https://doi.org/10.1016/j.sca.2023.100043 VL - Vol. 4 ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Aquino, Gabrielle S. A1 - Silva, Jefferson S. A1 - Paff, Jessica Sophie A1 - Valverde Pontes, Karen A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Mascarenhas, Artur José T1 - Sustainable hydrogen production via biogas reforming over NiO-MxOy - Al2O3 catalysts (M = Na, K, Ca and Mg) T2 - 22 Congreso Brasileiro de Catalise N2 - A sustainable way to generate hydrogen is through dry biogas reforming, which uses methane gas and carbon dioxide to produce hydrogen. This study reveals partial results of the dry reforming of biogas in NiO-MxOy-Al2O3 catalysts (M=Na, K, Ca and Mg). The CO2 conversion varied between 79% and 94%, the CH4 conversion between 58% and 75%, the H2/CO ratio between 0.98 and 1.15 and the H2 yield between 37% and 45%. These values ​​surpass literary references and the industrial catalyst, highlighting the promise of these materials for sustainable hydrogen production. The catalyst with Ca stood out due to its higher surface basicity, exhibiting the best conversion results and yield in H2. Y1 - 2023 UR - https://submissao.cbcat.sbcat.org.br/index.php/2023-cbcat/article/view/409 ER - TY - GEN A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Jafari, Mitra A1 - Shashank Bhat, Sharvendu A1 - Akhtar, Farid A1 - Arellano-García, Harvey T1 - Dry Reforming of Methane for the Syngas Production Catalyzed by Ni-doped Perovskites T2 - 15Th European Congress on Katakysis EUROPACAT2023 N2 - different perovskite-type supports considering ABO3 (such as A= Al, La with B=Ce and A=Mg, Mn with B=Zr) were prepared via the sol-gel method. Ni metal loading of 10 wt.% was deposited on prepared perovskite supports via the impregnation method. The catalysts were characterized using XRD and FTIR techniques. The DRM activity was carried out in a tubular reactor as described in our previous study [5]. The catalytic performance was assessed in the temperature range of 500–700 ◦C, CH4/CO2 = 1/1 and under GHSV of 12,000 h–1. Among the prepared catalysts, Ni-doped perovskite combination (i.e. A=Mg with B=Zr)O3-δ exhibited higher (CH4, CO2) conversion ca. (69, 59) percent and syngas yield of ca. (H2/CO =0.72) at 700 oC. This indicates that the magnesium zirconate perovskite catalyst established strong interfacial metal-support interaction, redox properties and surface basic sites that linked with good performance of the catalyst during DRM process. KW - Dry reforming of methane (DRM) KW - Ni-Perovskites KW - Syngas production KW - Greenhouse gases (GHGs) Y1 - 2023 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Use of Multistage Optimal Control Principles for Novel Design and Implementation of Classical Controllers T2 - AIChE Annual Meeting N2 - Classical controllers, such as Proportional-Integral (PI) and Proportional-Integral Derivative (PID) controllers, are the most long-established and widely used in industry. Various methods for tuning these types of controllers exist (Ziegler et al., 1942; Blondin et al., 2018; Do et al., 2021), and up to this point, there is no fruitful avenue to improve their performance. In this contribution, a new approach for PI and PID controller implementation, based on a Multistage Optimal Control (MSOCP) approach is introduced. Our approach incorporates path and end-point constraints during its controller tuning phase, as well as parameter and disturbance uncertainty. The proposed framework is applied for different case studies and is able to reject any disturbances introduced to the examined systems, with or without uncertainty, satisfies end-point constraints and exhibits quicker response for switching steady states, compared to classical methods. Other aspects of controller design and incorporation within industrial process models, as related to using rigorous optimization methodologies and implementations, will further be highlighted within the context of the PI and PID controllers. Y1 - 2021 UR - https://aiche.confex.com/aiche/2021/meetingapp.cgi/Paper/630692 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Automated Control Loop Selection Via Multistage Optimal Control Formulation and Nonlinear Programming T2 - Chemical Engineering Research and Design N2 - In this work, a novel approach based on the multistage optimal control formulation of the control loop selection problem is introduced. Currently, state-of-the-art approaches for controller loop design have been focused on data that yield only the pairings between input-output variables, and are not able to incorporate path and end-point constraints. Thus, they only produce the optimal loops for control purposes, without the simultaneous consideration of their optimal tuning. This formulation overcomes these drawbacks by producing an automated integrated solution for the task of control loop design, which also obviates the need for any form of combinatorial optimisastion to be used. To illustrate the procedure, as well as the advantages of the proposed scheme, different practical case studies are discussed and the results compared with those obtained with standard controller loop selection methods and their tuning. The results of the proposed approach show improved performance over previous methodologies found in the literature. Furthermore, the framework is extended to the selection of the control loops that must obey path and end-point constraints imposed by the underlying dynamical process. This task is usually difficult for classical methods, which violate them or exhibit underdamped response in some cases. KW - control loop selection KW - controller tuning KW - feasible path approach KW - multistage integer nonlinear optimal control problem (MSINOCP) KW - dynamic constraints Y1 - 2023 U6 - https://doi.org/10.1016/j.cherd.2023.05.041 SN - 1744-3563 VL - 195 SP - 76 EP - 95 ER - TY - GEN A1 - Jafari, Mitra A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Gonzalez-Castaño, Miriam A1 - Arellano-García, Harvey T1 - Green and sustainable fuel from syngas via the Fischer-Tropsch synthesis process: Bifunctional cobalt-based catalysts T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This paper reviews and compares state-of-the-art cobalt-based catalysts and catalytic systems used to produce green and sustainable fuels using FTS. Being focused on comparing the effect of the catalyst formulation and synthesis method, the reactor type and operating parameters, as well as the quality of the obtained fuels, the aim is to identify the research gaps between these relevant research areas concerning production of green and sustainable fuels. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=15565&additions_conferenceschedule_action=detail&additions_conferenceschedule_controller=paperList&pid=44228&hash=be231d3139d7d89da32b1610b7a0d1af3770c06640f246348e3ca8cfa7dd324a ER - TY - GEN A1 - Plattfaut, Julia A1 - Suckow, Matthias A1 - Klepel, Olaf A1 - Erlitz, Marcel A1 - Arellano-García, Harvey T1 - Modellierung und Simulation der templatgestützten Synthese von porösen Kohlenstoffgerüsten mittels COMSOL Multiphysics T2 - Chemie Ingenieur Technik N2 - Mithilfe einer templatgestützten Synthese wurden poröse Kohlenstoffgerüste unter Verwendung von Silicagel als Templat hergestellt. Die chemische Gasphaseninfiltration (CVI) wurde hierbei als Synthese verwendet. Unter Variation verschiedener Reaktionsparameter zur Optimierung der Kohlenstoffabscheidung wurde dieser Prozess mathematisch modelliert and simuliert. Dabei konnten die experimentellen Ergebnisse gut mit den Modellen nachgebildet werden. Die zusätzliche Beschreibung der laminaren Strömung verbessert die Übereinstimmung deutlich. Y1 - 2023 U6 - https://doi.org/10.1002/cite.202300014 SN - 1522-2640 VL - 96(2024) IS - 3 SP - 318 EP - 328 ER - TY - GEN A1 - Zhang, Sushen A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Hierarchical multi-scale parametric optimization of deep neural networks T2 - Applied Intelligence N2 - Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. KW - Deep neural networks KW - Hierarchical multi-scale search KW - Scaling factor KW - Sensitivity analysis KW - Finite difference KW - Automatic differentiation Y1 - 2023 U6 - https://doi.org/10.1007/s10489-023-04745-8 SN - 1573-7497 VL - 53 IS - 21 SP - 24963 EP - 24990 ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Akhtar, Farid T1 - Synthesis and Characterizations of Ni-doped Perovskite-Type Oxides for Effective CO2 methanation T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This work proposes Ni metal supported over rare earth-based emerging perovskite-type oxides as potential catalysts for the CO2 methanation. Presence of oxygen vacancies in perovskite-like materials enable them to exhibit higher catalytic activity. Furthermore, to tune the surface basicity, metal-support interaction and to enhance the activation of CO2, rare earth metals (La, Ce, etc.) are considered best candidates. Moreover, different perovskite-type supports (AxMnxO3, A= La, Ce) based on A-side substitution of rare earth metals were prepared with Ni metal loading of 10 wt.% via impregnation method. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=13659&v=List&do=15&day=all&ses=9628# U6 - https://doi.org/10.5281/zenodo.10376612 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Quantum annealing for global optimization in Chemical Engineering T2 - Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main N2 - Classical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binary optimization (QUBO) problems. In this contribution, a novel approach is introduced to transform relevant problems in Chemical Engineering into QUBO at two distinct levels of granularity. Subsequently, these problem systems are embedded within virtual quantum machines employing two different architectures. Additionally, a comparative analysis is performed, wherein the same problem is solved utilizing both classical global optimization methods based on metaheuristics and a hypothetical quantum annealer. The findings indicate that annealing-based solving methods exhibit the most potential, indicating their applicability to the transformed formulation Chemical Engineering problems. Y1 - 2023 UR - https://dechema.de/PAAT2023_Prg/_/__Progr_PAAT_2023_final.pdf SP - 15 ER - TY - GEN A1 - Markowski, Jens A1 - Arellano-García, Harvey A1 - Meißner, André A1 - Acker, Jörg T1 - Comparative studies on the quality of recovered secondary graphites from the recycling of lithium-ion traction batteries T2 - Sustainable Minerals N2 - Automotive technology is increasingly determined by drives based on electric motors in combination with batteries. The lithium-ion traction battery is a storage medium that combines high electrical efficiency with compact dimensions and relatively low weight. For the recycling of the cathode coatings (esp. Ni, Mn, Co) and peripheral battery components a variety of recycling options already exist. The graphite coating of the anodes has hardly been the focus of research activities to date. State of the art is currently the melting of the complete Copper-anode foils including graphite coating, whereby the graphite contributes only as a carbon carrier to the recycling of the copper. Separation and reuse of the very high-quality graphite on an industrial scale has not yet taken place. At the BTU, a methodology has been developed, with which recovered anode graphites from traction batteries can be comprehensively characterised chemically and mechanically-physically. On this basis, targeted preparation for secondary applications is possible. The secondary graphites achieve a quality that allows them to be reused as second-use anode material and for other applications. KW - Graphitrecycling KW - Li-Ionen-Traction Batteries Y1 - 2023 UR - https://www.ceecthefuture.org/resource-center/comparative-studies-on-the-quality-of-recovered-secondary-graphites-from-the-recycling-of-lithium-ion-traction-batteries PB - Mining Engineering CY - Falmouth (UK) ER - TY - GEN A1 - Arellano-García, Harvey A1 - Safdar, Muddasar A1 - Lewis, Allana A1 - Radacsi, Norbert A1 - Fan, Xianfeng A1 - Huang, Yi T1 - Superhydrophobic ZIF-67 with exceptional hydrostability T2 - Materials Today Advances N2 - In this work, cosolvent-stabilized superhydrophobic, highly hydrostable ZIF-67 was synthesized at room temperature using a facile, one-pot hydrothermal synthesis route, and the effect of cosolvent concentration on ZIF-67 crystal structure properties and hydrostability was studied systematically. The underlying mechanism for the cosolvent-supported hydrostability improvement was also proposed. Furthermore, the influence of hydrotreatment on the resultant ZIF-67s' catalytic performance was studied in the ‘Sabatier reaction’ for CO2 to synthetic natural gas (CH4) conversion. KW - ZIF-67 KW - Superhydrophobicity KW - Hydrostability KW - Cosolvent-stabilization KW - CO2 methanation Y1 - 2023 U6 - https://doi.org/10.1016/j.mtadv.2023.100448 SN - 2590-0498 VL - Vol. 20 ER - TY - GEN A1 - Gonzalez-Castãno, Miriam A1 - Morales, Carlos A1 - Navarro de Miguel, Juan Carlos A1 - Boelte, Jens-H. A1 - Klepel, Olaf A1 - Flege, Jan Ingo A1 - Arellano-García, Harvey T1 - Are Ni/ and Ni5Fe1/biochar catalysts suitable for synthetic natural gas production? A comparison with γ-Al2O3 supported catalysts T2 - Green Energy & Environment N2 - Among challenges implicit in the transition to the post–fossil fuel energetic model, the finite amount of resources available for the technological implementation of CO2 revalorizing processes arises as a central issue. The development of fully renewable catalytic systems with easier metal recovery strategies would promote the viability and sustainability of synthetic natural gas production circular routes. Taking Ni and NiFe catalysts supported over γ-Al2O3 oxide as reference materials, this work evaluates the potentiality of Ni and NiFe supported biochar catalysts for CO2 methanation. The development of competitive biochar catalysts was found dependent on the creation of basic sites on the catalyst surface. Displaying lower Turn Over Frequencies than Ni/Al catalyst, the absence of basic sites achieved over Ni/C catalyst was related to the depleted catalyst performances. For NiFe catalysts, analogous Ni5Fe1 alloys were constituted over both alumina and biochar supports. The highest specific activity of the catalyst series, exhibited by the NiFe/C catalyst, was related to the development of surface basic sites along with weaker NiFe–C interactions, which resulted in increased Ni0:NiO surface populations under reaction conditions. In summary, the present work establishes biochar supports as a competitive material to consider within the future low-carbon energetic panorama. KW - Biochar catalysts KW - Carbon catalysts KW - Ni catalysts KW - NiFe alloy KW - Bimetallic catalysts KW - Synthetic natural gas KW - CO2 methanation Y1 - 2023 U6 - https://doi.org/10.1016/j.gee.2021.05.007 SN - 2468-0257 VL - 8 IS - 3 SP - 744 EP - 756 ER - TY - CHAP A1 - González-Castaño, Miriam A1 - Tarifa, Pilar A1 - Monzon, Antonio A1 - Arellano-García, Harvey T1 - Valorization of unconventional CO2-rich feedstock via Reverse Water Gas Shift reaction T2 - Circular Economy Processes for CO2 Capture and Utilization : Strategies and Case Studies N2 - The implementation of novel CO2 valorization technologies is one of the most promising approaches towards the achievement of sustainable energy models. This chapter highlights the importance of carbon capture and utilization technologies and proposes novel approaches for the valorization of CO2-rich feedstock derived from thermochemical biomass conversion through the production of syngas mixtures via the Reverse Water Gas Shift reaction. After, this classification of the different types of nonconventional gases and biomass-treatment processes, we have also revised the fundamentals of the Reverse Water Gas Shift reaction and the impact of species commonly present in CO2-rich streams on the performance of the catalytic systems are also reviewed. Finally, a catalytic bi-functionalization approach that ensures larger CO productivity from simulated biomass-derived CO2-rich feedstock is demonstrated. KW - Reverse Water Gas Shift KW - Valorization of CO2 KW - Syngas KW - Catalysts Y1 - 2024 SN - 9780323956697 U6 - https://doi.org/10.1016/B978-0-323-95668-0.00001-1 SP - 307 EP - 323 PB - Woodhead Publishing ER - TY - GEN A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Paff, Jessica Sophie A1 - Arellano-García, Harvey T1 - Structural formability of perovskite ABO3 oxide system synthesized via autocombustion technique, ruled by geometric factors T2 - 18th International Congress on Catalysis Y1 - 2024 UR - https://www.researchgate.net/publication/388109875_Structural_formability_of_perovskite_ABO3_oxide_system_synthesized_via_auto-_combustion_technique_ruled_by_geometric_factors ER - TY - GEN A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Machine learning application in kinetic studies: A review T2 - 18th International Congress on Catalysis N2 - Machine learning (ML) brings new opportunities in the field of heterogenous catalysis and reaction engineering. Here, the advancements brought by ML in the field of kinetic studies are reviewed. Y1 - 2024 UR - https://www.researchgate.net/publication/388109595_Machine_learning_application_in_kinetic_studies_A_review ER - TY - GEN A1 - Shafiee, Parisa A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Novel approaches for preparation of 3D Ni-Al2O3 core with zeolite shell catalysts for dry reforming of methane T2 - 18th International Congress on Catalysis Y1 - 2024 UR - https://www.researchgate.net/publication/388109598_Novel_Approaches_for_Preparation_of_3D_Ni-Al2O3_Core_with_Zeolite_Shell_Catalysts_for_Dry_Reforming_of_Methane ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Silva de Aquino, Gabrielle A1 - Santos da Silva, Jefferson A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Valverde Pontes, Karen A1 - Santos Mascarenhas, Artur José T1 - Estudo do efeito do suporte em catalisadores de Ni preparados pelo método da combustão aplicados na reforma a seco do biogás para produção de hidrogênio sustentável T2 - 63rd Brazilian Chemistry Congress Y1 - 2024 UR - https://www.researchgate.net/publication/388109502_Estudo_do_efeito_do_suporte_em_catalisadores_de_Ni_preparados_pelo_metodo_da_combustao_aplicados_na_reforma_a_seco_do_biogas_para_producao_de_hidrogenio_sustentavel ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Farhadi, Maryam A1 - Arellano-García, Harvey T1 - Conceptual design of a reactive distillation column for the catalytic upgrade of ABE T2 - Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik Y1 - 2024 UR - https://www.researchgate.net/publication/388143625_Conceptual_design_of_a_reactive_distillation_column_for_the_catalytic_upgrade_of_ABE ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - A second-order linesearch procedure within Newton’s method for highly nonlinear steady-state systems simulation T2 - 2024 AIChE Annual Meeting N2 - Linesearch, a crucial component of Newton’s method, ensures global convergence, guaranteeing convergence to a local solution from any starting point while satisfying all simultaneous nonlinear equations (Bellavia and Morini, 2003). Despite Newton’s method being considered established both theoretically and algorithmically, leaving little room for further improvements, this contribution focuses on enhancing the linesearch procedure and revealing significant advancements over existing methods. Specifically, this study aims to incorporate second-order information in a computationally efficient manner to improve the performance of the linesearch procedure, especially for highly nonlinear equation systems. Nonlinearity, particularly near the starting point, can substantially hinder algorithmic efficiency, necessitating frequent step reductions at the expense of function evaluations and major iterations involving Jacobian evaluations and factorizations (Gill and Zhang, 2024). The proposed approach leverages a a higher-order Taylor series expansion around the operating point of a major iteration in Newton’s algorithm, coupled with a custom Jacobian vector product finite difference scheme. This combination requires only one additional Jacobian evaluation to construct a locally accurate fourth-degree polynomial approximating the merit function along the search direction. In addition to the theoretical advancements, this contribution provides computational evidence supporting the claim that for highly nonlinear systems, significant computational savings and enhanced solution procedure stability can be achieved. Utilizing a Python implementation, linear subsets of equations are treated separately to boost the efficiency of function and Jacobian evaluations, aligning with standard practices in professional software development. While Python may not be a high-performance language, its suitability for rapid algorithm prototyping and validation precedes potential transfer to higher-performance languages like C++. Moreover, given Newton’s method central roles in various iterative solution tools, such as its repeated use within a Differential-Algebraic Equations (DAEs) integrators and potentially Partial Differential-Algebraic Equations (PDAEs) solvers, the significance of this work extends even further. Future research endeavors will explore these areas, building upon the foundations laid by this study. Y1 - 2024 UR - https://aiche.confex.com/aiche/2024/meetingapp.cgi/Paper/689676 ER - TY - GEN A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) T2 - Chemical Engineering Journal Advances N2 - Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 %), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 %). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity. KW - Jet fuels (C8-C16) KW - Machine learning (ML) KW - Fischer-Tropsch synthesis (FTS) KW - Operational conditions KW - Catalyst preparation Y1 - 2025 U6 - https://doi.org/10.1016/j.ceja.2024.100702 VL - 21 (2025) PB - Elsevier ER - TY - GEN A1 - Shezad, Nasir A1 - Safdar, Muddasar A1 - Arellano-Garcia, Harvey A1 - Tai, Cheuk-Wai A1 - Chen, Shaojiang A1 - Seo, Dong-Kyun A1 - You, Shujie A1 - Vomiero, Alberto A1 - Akhtar, Farid T1 - Deciphering the role of APTES in tuning the metal support interaction of NiO nanolayers over hierarchical zeolite 13X for CO2 methanation T2 - Carbon Capture Science & Technology N2 - The development of robust nickel catalysts on porous substrates offers great potential for converting carbon dioxide (CO2) into methane, thereby helping to address the global warming and sustainability challenges. This study investigates the dispersion and stability of Ni nanolayers by grafting bifunctional groups over the hierarchical zeolite 13X (h13X) support using (3-aminopropyl)triethoxysilane (APTES). The Ni nanolayers, with a thickness of 1.5-7 nm, were deposited around the edges of h13X and analyzed using STEM imaging. A clear shift in the binding energies was observed by XPS analysis, substantiating the enhanced metalsupport interaction (MSI) between NiO and h13X. The influence of reaction temperature on APTES incorporation into h13X was revealed by H2-TPR and CO2-TPD, with notable variations in the reducibility and surface basicity profiles of the catalysts. The optimized catalyst exhibited CO2 conversion of 61% with CH4 selectivity of 97% under GHSV of 60,000 mlgCat-1h-1 at 400 oC and 1 bar and demonstrated robust stability over a period of 150 h without discernible degradation. The enhanced performance could be attributed to the strengthened MSI and reduced size of Ni nanolayers over h13X. These findings highlight the development of robust heterogeneous catalysts by changing the surface chemistry of support material for various catalytic applications. KW - CO2 methanation KW - Catalyst stability KW - Metal-support interaction KW - APTES functionalization KW - Nickel nanolayers KW - Hierarchical zeolite Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S2772656825000636 U6 - https://doi.org/10.1016/j.ccst.2025.100424 VL - 15 SP - 1 EP - 11 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Mappas, Vasileios A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios A1 - Arellano-Garcia, Harvey T1 - Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept T2 - Chemical engineering research and design N2 - Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP’s bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly’s classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, providing foundational insights towards leveraging QA in Process Systems Engineering. KW - Quantum annealing KW - Bilinear programming KW - Pooling/blending problem KW - Quadratic unconstrained binary optimization (QUBO) Y1 - 2025 U6 - https://doi.org/10.1016/j.cherd.2025.08.031 SN - 1744-3563 VL - 221 SP - 560 EP - 576 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Multiphase systems, particularly trickle bed reactors (TBRs), are critical in various industrial applications and widely employed in catalytic processes such as hydrogenation and oxidation due to their high surface area, low operational and minimal catalyst loss. Despite advancements in modelling techniques, accurately capturing the complex multiphysics and multiscale phenomena remains challenging. Conventional approaches, relying on empirical correlations or Computational Fluid Dynamics (CFD) simulations, often fall short due to high computational demands, limited accuracy, and constraints on the number of catalytic particles that can be effectively simulated [3]. To address these limitations, this contribution presents a new framework tailored for the design and analysis of multiphase systems operating in the low-interaction regimes. This approach is based on the local structure of the packed bed and employs a Lagrangian approach, where flow dynamics within the reactor is represented by various discrete elements. The framework's modular and flexible setup enables the incorporation of multiscale information of both local and global levels, allowing for the additions of new modules or features to enhance modelling fidelity. Y1 - 2025 UR - www.researchgate.net/publication/388846704_Capturing_multiscale_phenomena_in_trickle_bed_reactors_A_flexible_framework_for_flow_and_reaction_analysis ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Kim, Jin-Kuk A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production T2 - Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025 N2 - Industrial sectors contribute substantially to global CO2 emissions, emphasizing the need for low-carbon, reliable energy supplies to meet operational demands. Achieving net-zero emissions in industrial processes involves transitioning from fossil fuels to renewable energy sources. However, the intermittent nature of renewables poses challenges to energy reliability and resilience, particularly in utility systems. This contribution addresses industrial decarbonisation and sustainable hydrogen production by developing a comprehensive design and optimization framework for integrating renewable energy systems into industrial operations. This framework incorporates energy storage and grid connections to improve flexibility and stability and is evaluated through two case studies. Both case studies analyse the operational and configurational changes necessary for renewable-powered hydrogen production, estimating the cost of hydrogen or CO2 avoidance cost to analyse economic viability. These insights provide guidelines for sustainable and economically viable energy management in industrial and hydrogen production sectors, supporting broader global energy transition goals. Y1 - 2025 UR - https://www.researchgate.net/publication/388846958_Pathways_to_industrial_decarbonization_Renewable_energy_integration_and_electrified_hydrogen_production ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Safdar, Muddasar A1 - Helle, Sven A1 - Fischer, Thomas A1 - Arellano-García, Harvey T1 - Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst T2 - Chemie - Ingenieur - Technik : CIT N2 - Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. This study investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts under polychromatic UV–VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites (NiMn2O4, LaMnO3, NdMnO3, and their nickel-doped variants) were synthesized via a facile co-precipitation technique and characterized using X-ray diffraction (XRD), UV–VIS diffuse reflectance spectroscopy (UV–VIS-DRS), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), N2-physisorption, and microwave plasma atomic emission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradation efficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C–C bond cleavage. KW - Lanthanoids KW - Perovskites KW - PFAS KW - PFOA KW - Photocatalysis Y1 - 2025 U6 - https://doi.org/10.1002/cite.70027 SN - 1522-2640 SP - 1 EP - 11 PB - Wiley-VCH GmbH CY - Weinheim ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron T2 - Systems and control transactions N2 - In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. These findings contribute to advancing energy-efficient and scalable hydrogen storage technologies. KW - Hydrogen KW - Hydrogen fuel cells KW - Energy storage KW - Modelling and simulations KW - Optimisation KW - Thermochemical storage KW - Green hydrogen Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.121492 SN - 2818-4734 VL - 4 SP - 631 EP - 636 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Solving complex combinatorial optimization problems using quantum annealing approaches T2 - Systems and control transactions N2 - Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer's architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly's pooling and blending problem is examined while demonstrating the potential of the proposed approach. The results indicate that the succinct formulation exhibited higher success rate during the embedding procedure for the different examined architectures, and the quantum annealing solver exhibited the best performance among the various solvers investigated. This highlights the potential of the approach for solving this type of problems with the rapid development and improvement of quantum hardware and expanding it to more complex chemical engineering optimization systems. KW - Optimization KW - Scheduling KW - Algorithms KW - Quantum Computing KW - Quantum Annealing Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.188358 SN - 2818-4734 VL - 4 SP - 1561 EP - 1566 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Shafiee, Parisa A1 - Jafari, Mitra A1 - Schowarte, Julia A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis T2 - Systems and control transactions N2 - Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization. KW - Catalysis KW - Machine learning KW - Modelling KW - Optimization KW - Alternative fuels KW - Environment KW - FischerTropsch synthesis KW - Photocatalysis Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.135551 SN - 2818-4734 VL - 4 SP - 1866 EP - 1871 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Park, Haryn A1 - Lee, Joowha A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Kim, Jin-Kuk T1 - Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems T2 - Systems and control transactions N2 - Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined with renewable-based electrification. The proposed optimization model evaluates the economic trade-offs of integrating carbon capture, renewable energy, and energy storage. By applying this approach, systematic design guidelines are developed for the transition of a conventional steady-state utility system toward renewable energy integration, ensuring economically viable and sustainable energy management in process industries. KW - Renewable energy KW - CO2 capture KW - Industrial utility operation KW - Cost optimization KW - Process integration Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.107403 SN - 2818-4734 VL - 4 SP - 1175 EP - 1180 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-Garcia, Harvey T1 - An efficient and unified modeling framework for trickle bed reactors : a modular approach T2 - Chemie - Ingenieur - Technik : CIT N2 - Trickle bed reactors (TBRs) involve complex and multiscale dynamics that challenge their design, modeling, and optimization. Current approaches often suffer from high computational cost and limited scalability, restricting their applicability in large-scale cases. This work introduces a modular, computationally efficient framework to address these issues by systematically capturing key transport and reaction phenomena. Furthermore, it provides a critical review of existing modeling strategies for TBRs, outlining their strengths and limitations and highlighting opportunities for enhancement through modularization. By offering a structured and scalable approach, the proposed framework improves predictive capabilities and supports the development of optimized and adaptable reactor designs. KW - Catalytic multiphase reactors KW - Modeling KW - Trickle bed reactors Y1 - 2025 U6 - https://doi.org/10.1002/cite.70035 SN - 1522-2640 VL - 97 IS - 11-12 SP - 1110 EP - 1126 PB - Wiley CY - Weinheim ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Santos da Silva, Jefferson A1 - De Aquino, Gabrielle A1 - Dos Santos, Mauricio A1 - Cruz, Fernanda A1 - Fiuza-Junior, Raildo A. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Pontes, Karen A1 - Mascarenhas, Artur T1 - Influência do Suporte em Catalisadores de Ni Obtidos Pelo Método da Combustão na Reforma a Seco do Biogás para Produção de Hidrogênio Sustentável T2 - 23º CBCAT : Congresso Brasileiro de Catalise T2 - 23rd Brazilian Congress of Catalysis N2 - Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biogás. As caracterizações revelaram variações na dispersão metálica, área metálica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior área metálica e melhor dispersão de Ni, favorecendo altas conversões de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e estável por 8 horas de reação. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resistência ao coque. O NiO-CeO₂, embora ativo no início, desativou com o tempo devido à deposição de coque (6,4%). A análise pós-reação mostrou baixa formação de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resistência dos catalisadores na reforma a seco do biogás. Palavras-chave: Hidrogênio sustentável; Reforma a seco do biogás; Catalisadores de NiO; efeito do suporte ABSTRACT-This study evaluates NiO-based catalysts supported on MgO, ZrO₂, Al₂O₃, La₂O₃, and CeO₂ for the dry reforming of biogas. Characterization of the samples revealed differences in metal dispersion, metallic area, and surface morphology. NiO-Al₂O₃ and NiO-CeO₂ show higher metallic areas and better Ni dispersion, leading to higher CH₄ and CO₂ conversions and good H₂ yield. NiO-Al₂O₃ is the most efficient and stable catalyst over 8 hours of reaction. NiO-La₂O₃ shows a gradual increase in activity and good coke resistance. Conversely, NiO-CeO₂, despite high initial activity, deactivates over time due to coke deposition (6.4%). Post-reaction analysis confirmed low coke formation for most catalysts. The results indicate that the choice of support directly affects catalyst activity, stability, and resistance. KW - Hidrogêniosustentável KW - Reforma a secodo biogás KW - Catalisadores de NiO KW - Efeito dosuporte KW - Sustainable hydrogen KW - Biogas dry reforming KW - NiO Catalysts KW - Supportrole Y1 - 2025 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203 UR - https://submissao.cbcat.sbcat.org/index.php/23CBCAT/article/view/203/303 VL - 1 IS - 1 SP - 1 EP - 6 ER - TY - GEN A1 - Mbuya, Christel Olivier Lenge A1 - Pawar, Kunal A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Okoye Chine, Chike George A1 - Tarifa, Pilar A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems T2 - Journal of CO2 utilization N2 - The dry reforming of methane (DRM) is a promising route for converting greenhouse gases such as methane (CH4) and carbon dioxide (CO2) into valuable syngas, hydrogen (H2) and carbon monoxide (CO). However, traditional nickel (Ni)-based catalysts suffer from rapid deactivation due to carbon deposition and sintering, especially when supported on low thermal conductivity materials. In this work, a novel post-synthesis microwave irradiation (MIR) treatment is introduced to systematically optimize the performance of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts for DRM. Unlike previous studies that have used MIR during reaction or with different supports, this approach tunes the metal – support interactions and textural properties of Ni – β – SiC and Ni – Ti – Cβ – SiC catalysts by varying the MIR exposure time after catalyst synthesis. MIR post-treatment (10–25 s) increased the CH4 conversion to 65 % and the CO2 conversions to 62 % for Ni–β–SiC catalysts and improved the H₂/CO ratio to 0.80, with stable performance over 20 h. For Ni–Ti–Cβ–SiC, MIR (10–20 s) maintained CH4 conversion up to 60 % and CO2 conversion to 58 % over 20 h, while the untreated catalyst, though initially higher, deactivated rapidly. Excessive MIR (30 s) reduced performance for both catalyst types, underscoring the need for optimal exposure time. These findings demonstrate post-synthesis MIR provides a tuneable approach for enhancing both the activity and durability of Ni/SiC – based DRM catalysts through controlled modification of metal – support interactions. This work offers new insights for the design of robust catalysts aimed at greenhouse gas utilization and sustainable syngas production, with activity and stability enhancements linked to controlled changes in metal – support interactions. KW - Carbon dioxide KW - Dry reforming KW - Methane KW - Microwave irradiation KW - Ni Silicon carbide catalysts Y1 - 2025 U6 - https://doi.org/10.1016/j.jcou.2025.103270 SN - 2212-9839 VL - 102 SP - 1 EP - 9 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Santos da Silva, Jefferson A1 - Silva de Aquino, Gabrielle A1 - Vaz dos Santos Rios, João Gabriel A1 - Brandão dos Santos, Maurício A1 - Teixeira Cruz, Fernanda A1 - Alves Fiuza-Junio, Raildo A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Valverde Pontes, Karen A1 - Santos Mascarenhas, Artur José T1 - Effect of support on Ni catalysts prepared by the combustion method applied in the dry reforming of biogas for production of sustainable hydrogen T2 - International journal of hydrogen energy N2 - This work investigated Ni catalysts on different supports (MgO, ZrO2, NiAl2O4, CeO2 and La2O3) prepared by the combustion method aiming for sustainable hydrogen production via simulated biogas dry reforming. The Ni/NiAl2O4 catalyst stood out among the materials due to its high Ni dispersion, low crystallite size and strong metal-support interaction, being stable for 8 h of reaction with high H2 yield and low coke deposition. The Ni/CeO2 catalyst showed good catalytic activity, but with high coke deposition (11.7 %). The Ni/La2O3 catalyst showed an increase over the reaction time, due to the dynamic reconstruction of the surface. The Ni/MgO and Ni/ZrO2 catalysts did not present satisfactory performance when compared to the other catalysts, due to the low Ni dispersion and high crystallite size. The Ni/NiAl2O4 catalyst is very promising, due to the high production of H2, low coke deposition, thermal stability, but new studies on durability and economic viability are necessary. KW - Sustainable hydrogen production KW - Biogas dry reforming KW - Ni-supported catalysts KW - Support effect KW - Combustion synthesis method Y1 - 2026 U6 - https://doi.org/10.1016/j.ijhydene.2025.153150 SN - 1879-3487 VL - 204 SP - 1 EP - 25 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Lee, Joohwa A1 - Park, Haryn A1 - Dorneanu, Bogdan A1 - Kim, Jin-Kuk A1 - Arellano-Garcia, Harvey T1 - Decarbonized hydrogen production : integrating renewable energy into electrified SMR process with CO₂ capture T2 - Systems and control transactions N2 - Electrified steam methane reforming has emerged as a promising technology for electrifying the hydrogen production process industries. Unlike conventional fossil fuel-based steam methane reforming, the electrified steam methane reforming process relies exclusively on electrical heating, eliminating the need for fossil fuel combustion. Beyond that, however, significant amounts of electricity required for the electrified process should be imported from the renewable energy-based system rather than fossil fuel-based grid electricity to have an environmental advantage over the conventional process. This study suggests a framework for integrating renewable energy systems into the electrified process for decarbonized hydrogen production. Considering the variability of renewable energy, wind and solar power are supplemented by battery storage, to facilitate a stable electricity supply to the electrified hydrogen production process. A Mixed-Integer Linear Programming (MILP) model is developed to optimally size and operate both the renewable system and potential grid imports. Case studies under various carbon tax scenarios, using historical weather data from a region in Germany, are conducted, followed by a techno-economic assessment to estimate the Cost of Hydrogen (COH). The results show that higher carbon taxes and reduced capital costs for wind, solar, and storage technologies significantly increase the share of renewable-based electricity. These findings highlight the importance of more stringent carbon taxation and improvements in the technology readiness level (TRL) of renewable energy are critical for accelerating large-scale, clean hydrogen production and industrial decarbonization. KW - Hydrogen KW - Renewable energy KW - Electrification Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.152295 SN - 2818-4734 VL - 4 SP - 613 EP - 618 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Arellano-Garcia, Harvey T1 - A novel approach to gradient evaluation and efficient deep learning : a hybrid method T2 - Systems and control transactions N2 - Deep learning faces significant challenges in efficiently training large-scale models. These issues are closely linked, as efficient training often depends on precise and computationally feasible gradient calculations. This work introduces innovative methodologies to improve deep learning network (DLN) training in complex systems. A novel approach to DLN training is proposed by adapting the block coordinate descent (BCD) method, which optimizes individual layers sequentially. This is combined with traditional batch-based training to create a hybrid method that harnesses the strengths of both techniques. Additionally, the study explores Iterated Control Random Search (ICRS) for initializing parameters and applies quasi-Newton methods like L-BFGS with restricted iterations to enhance optimization. By tackling DLN training efficiency, this contribution offers a comprehensive framework to address key challenges in modern machine learning. The proposed methods improve scalability and effectiveness, especially for handling complex real-world problems. Examples from Process Systems Engineering illustrate how these advancements can directly enhance the training of large-scale systems. KW - Artificial intelligence KW - Machine learning KW - Numerical methods Y1 - 2025 SN - 978-1-7779403-3-1 U6 - https://doi.org/10.69997/sct.120349 SN - 2818-4734 VL - 4 SP - 1872 EP - 1877 PB - PSE Press CY - Notre Dame, IN ER - TY - GEN A1 - Shezad, Nasir A1 - Samikannu, Ajaikumar A1 - Safdar, Muddasar A1 - Arellano-Garcia, Harvey A1 - Mikkola, Jyri-Pekka A1 - Seo, Dong-Kyun A1 - Akhtar, Farid T1 - Nickel supported over hierarchical zeolite 13X catalysts for enhanced conversion of carbon dioxide into methane T2 - International journal of energy research N2 - Catalytic conversion of carbon dioxide (CO2) into useful chemicals such as methane (CH4) is a promising carbon utilization method that effectively mitigates CO2 and partially meets energy needs. The characteristics of commonly used nickel (Ni) supported meso/microporous catalysts for CO2 methanation can be tailored by tuning the structural properties of the support and adding promoters. This work investigated the Ni supported over hierarchical zeolite 13X (h13X) and incorporated with different promoters (Mg, Ca, Ce, and La) developed using the wet-impregnation method. The catalysts were thoroughly characterized using SEM, EDS, XRD, H2-TPR, CO2-TPD, thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), and N2 sorption and desorption techniques and evaluated for CO2 methanation. The impact of promoters on the characteristics of the catalysts was observed with improved surface basicity in CO2-TPD and metal-support interaction in H2-TPR analysis. Among the promoted catalysts, the NiLa/h13X catalyst exhibited the highest catalytic activity with a maximum conversion of 76% and CH4 selectivity of 98.5% at 400°C and 20 bar at GHSV of 60,000 mL gcat−1 h−1, respectively. Regarding stability, the Mg-promoted catalyst exhibited better stability during 24 h of reaction than other catalysts, demonstrating better resilience against deactivation. The enhanced performance of the NiLa/h13X catalyst could be credited to the increased surface basicity, high surface area, and dispersion. This study highlights the potential of hierarchical porous zeolites for CO2 methanation and other heterogeneous reactions. KW - Carbon dioxide KW - Catalyst KW - Hierarchical zeolite KW - Methane KW - Nickel KW - Promoters Y1 - 2025 U6 - https://doi.org/10.1155/er/4728304 SN - 1099-114X VL - 2025 SP - 1 EP - 14 PB - Wiley CY - Hoboken, NJ ER - TY - GEN A1 - Safdar, Muddasar A1 - Sher, Farooq A1 - Arellano-Garcia, Harvey T1 - Perovskite materials for catalytic CO₂ valorisation : structural characteristics, synthesis and lattice substitutions for gas-phase reactions T2 - Journal of environmental chemical engineering N2 - Perovskites are emerging materials that are being extensively investigated for converting greenhouse gases (GHGs) through thermochemical processes due to their versatile properties. Given their distinct physical and chemical characteristics and their unique structure (ABO3, general formula), they are desirable candidates for designing state-of-the-art catalytic systems. For instance, they can be prepared with modified oxygen vacancies, enhanced redox potential, and tailored nanoparticle formulations for use in various catalytic gas-phase CO2 conversion processes, thereby facilitating the formation of valuable, renewable raw materials such as fuels and chemicals. This comprehensive review explains the perovskite structures, including their crystallographic properties, standard synthesis methods, recent advancements in A, B, and X-site substitutions, and their effectiveness in upgrading CO2 to produce valuable commodities via different synthetic routes in gas-phase reactions via methanation, reverse water gas shift reaction (rWGS), and dry reforming of methane (DRM). To achieve a sustainable clean energy supply, application-oriented, efficient, and advanced catalytic systems that support the necessary reaction conditions and serve as the most active and selective catalysts are reported in each synthetic gas-phase production section. This study highlights current advancements and optimised research efforts to design potential catalytic materials that meet future requirements for developing efficient decarbonised energy systems. The proposed synthesis methods are the most effective techniques for conserving time and energy. They can also yield favourable morphology and allow manipulation of nanoparticle size, both of which are essential for designing innovative catalysts. To address concerns about CO2 emissions harming the environment, this study focuses on adaptable, sustainable gas-phase reaction methods with diverse industrial applications. The primary emphasis is on effective, robust perovskite-based catalysts that enable the efficient conversion of CO2 into value-added chemicals and fuels, thereby supporting low-carbon energy and chemical technologies. This review delineates explicit correlations among synthesis, structure, properties, performance, and stability by relating perovskite lattice design, defect chemistry, and compositional flexibility to catalytic activity, selectivity, and durability in heterogeneous catalytic reactions. KW - Perovskites KW - Advanced materials KW - Clean energy KW - CO2 utilization KW - Substitutional sites KW - Catalytic applications Y1 - 2026 U6 - https://doi.org/10.1016/j.jece.2026.121473 SN - 2213-3437 VL - 14 IS - 2 SP - 1 EP - 35 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Safdar, Muddasar A1 - Helle, Sven A1 - Fischer, Thomas A1 - Arellano-Garcia, Harvey T1 - Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst T2 - Chemie Ingenieur Technik N2 - Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. Thisstudy investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts underpolychromatic UV–VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites(NiMn2 O 4 , LaMnO 3 , NdMnO 3 , and their nickel-doped variants) were synthesized via a facile co-precipitation techniqueand characterized using X-ray diffraction (XRD), UV–VIS diffuse reflectance spectroscopy (UV–VIS-DRS), scanning elec-tron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), N2 -physisorption, and microwave plasma atomicemission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradationefficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C–C bond cleavage. KW - Lanthanoids KW - Perovskites KW - PFAS KW - PFOA KW - Photocatalysis Y1 - 2026 U6 - https://doi.org/10.1002/cite.70027 SN - 1522-2640 VL - 98 IS - 1-2 SP - 7 EP - 17 PB - Wiley-VCH CY - Weinheim ER - TY - GEN A1 - Yentumi, Richard A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A comprehensive modelling approach to enhance performance and scalability of iron-oxide based hydrogen storage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Hydrogen storage remains a major bottleneck in realizing a competitive hydrogen economy due to the energy intensity and economic limitations of existing solutions [1]. This contribution addresses these barriers through a model-driven optimization framework for a solid-state thermochemical hydrogen storage (TCS) based on reversible redox cycling of iron oxide/iron. While iron-based TCS offers inherent safety and scalability advantages, key limitations persist, including high reduction temperatures (requiring significant energy input), suboptimal energy storage density, and sluggish redox kinetics [2]. Kinetic parameters for the reduction and oxidation reactions were systematically derived through isothermal thermogravimetric analysis (TGA) coupled with kinetic model regression. A first-principles dynamic model of a fixed-bed reactor was developed, integrating mass, energy, and momentum balances, and validated against experimental data from a lab-scale apparatus. The experimental system featured precision gas flow control, an electric furnace reactor, rapid air-cooled condensation, molecular sieve dehydration, and online effluent analysis via flow meters and gas chromatography. Dynamic simulations were carried out to investigate the reactor's transient behaviour under variations in critical parameters, including H2/H2O partial pressures, reaction temperatures, and gas flow rates. These studies revealed trade-offs between energy efficiency (favoured by lower temperatures), and reaction rates (enhanced at higher temperatures), while identifying key bottlenecks in redox cycling. The model further demonstrated how optimizing feed composition and flow dynamics mitigates kinetic degradation during charge/discharge cycles. REFERENCES [1] Elberry A.M. et al. KW - Hydrogen KW - Green hydrogen KW - Storage KW - Thermochemical storage KW - Process modelling KW - Optimisation Y1 - 2025 UR - https://www.researchgate.net/publication/399753698_A_comprehensive_modelling_approach_to_enhance_performance_and_scalability_of_iron-oxide_based_hydrogen_storage_systems SP - 1 EP - 5 CY - Frankfurt am Main ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Mappas, Vasileios K. A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Adjoint methods for fast sensitivity analysis in nonlinear multistage systems T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Parametric sensitivity analysis is critical for optimization, control, and decision-making in engineering systems, enabling precise understanding of system responses to changes in parameters [1]. In the case of large-scale multistage systems, characterized by interconnected components, high-dimensional parameter spaces, and nonlinear constraints, traditional gradient evaluation methods often face significant challenges in scalability, computational efficiency, and accuracy [2]. This contribution introduces a novel framework for evaluating parametric gradients tailored specifically for generally constrained multistage systems, which leverages adjoint-based techniques [3] to compute exact gradients efficiently, addressing the inherent complexity of these systems. This reduces the number of simulations required by direct numerical differentiation or finite difference methods. The framework accommodates continuous real-value parameters and is designed to handle high-dimensional spaces typical of multistage systems. The proposed methodology is validated through case studies involving large-scale modular systems with nonlinear constraints. Results demonstrate substantial improvements in computational efficiency and gradient accuracy compared to conventional techniques. These advancements enable optimization algorithms to converge more quickly and reliably while navigating complex solution spaces effectively. By facilitating accurate sensitivity analysis, the framework enhances the exploration of design alternatives and increases the likelihood of identifying globally optimal solutions. KW - Parametric sensitivities KW - Adjoint equations KW - Multistage systems KW - Gradient evaluation KW - Nonlinear optimization KW - Process systems engineering Y1 - 2025 UR - https://www.researchgate.net/publication/399753602_Adjoint_methods_for_fast_sensitivity_analysis_in_nonlinear_multistage_systems SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Mbuya, Christel-Olivier Lenge A1 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Structured FeMnK catalysts for aviation fuel production via Fischer-Tropsch synthesis : a channel geometry study T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Fischer–Tropsch synthesis (FTS) is a promising route for sustainable aviation fuel production but is often limited by heat and mass transfer constraints in fixed-bed reactors. This study investigates the role of structured catalyst geometry in intensifying FTS performance using FeMnK catalysts selectively tailored for aviation-range hydrocarbons. The catalyst was synthesized via organic combustion and further modified by microwave irradiation, with structural properties characterized by X-ray diffraction. Aluminum honeycomb monoliths with square, circular, and triangular channel geometries were designed and fabricated by 3D printing, followed by catalyst deposition through dip-coating. High-pressure FTS experiments conducted at 30 bar and 300 °C demonstrate that channel geometry significantly influences conversion and selectivity by affecting flow dynamics and reactant mixing. The results highlight the potential of geometry-optimized structured catalysts to enhance FTS efficiency and support advanced reactor designs for sustainable aviation fuel production. Y1 - 2025 UR - https://www.researchgate.net/publication/391697232_Structured_FeMnK_catalysts_for_aviation_fuel_production_via_Fischer-Tropsch_synthesis_A_channel_geometry_study SP - 1 EP - 3 ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Combinatorial optimization problems : a quantum-based approach T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Classical computing faces challenges in global optimization (GO) when addressing non-convex problems, where the number of solutions grows exponentially with problem size 1. Quantum computing emerges as a potential solution. Gate-based quantum computing offers a broader range of applicability due to its ability to implement general quantum algorithms, in contrast to quantum annealing which is more specialized 2,3. This contribution explores the application of gate-based quantum computing to solving combinatorial optimization (CO) problems, specifically focusing on overcoming the limitations of classical computing. The proposed approach utilizes gate-based quantum computers, using IBM's hardware and software as an example, and the quantum approximate optimization algorithm (QAOA) for solving the reformulated problem using the Qiskit toolbox. The methodology includes translating the quadratic unconstrainted optimization problem (QUBO) into an Ising Hamiltonian, constructing the QAOA ansatz circuit, and optimizing its parameters. Furthermore, real quantum device architectures are employed to solve the optimized QUBO formulations. To demonstrate the capabilities of the approach, Haverly's pooling-blending problem is selected as a case study. Through the application of different discretization strategies, resolution levels, and circuit architectures, a comparative analysis of solver performance is conducted. The resulting QUBO formulations, efficiently embedded and solved on real quantum devices, underscore the potential of gate-based quantum computing as a promising solution approach for complex CO challenges. Y1 - 2025 UR - https://www.researchgate.net/publication/399789138_Combinatorial_Optimization_Problems_A_quantum-based_approach N1 - Sammelmail an Dorneanu. 27.01.2026 TR SP - 1 EP - 7 CY - Frankfurt am Main ER - TY - GEN A1 - Safdar, Muddasar A1 - Schowarte, Julia A1 - Arellano-Garcia, Harvey T1 - Sustainable production of synthetic natural gas : CO2 methanation on 3D-printed structured Ni-based perovskite catalysts T2 - Annual Meeting on Reaction Engineering 2025 N2 - This study explores the thermo-catalytic conversion of captured CO₂ with renewable H₂ to produce synthetic natural gas (SNG) using Ni-based perovskite-type oxides (Ni-PTOs) as cost-effective and thermally stable catalysts in Power-to-Gas (PtG) applications. Structured monoliths improved the catalytic performance under reaction conditions. Use of 3D-printed geometries enhanced thermal/mechanical stability and process efficiency. Structured designs outperformed conventional powder-based configurations, indicating promise for scalable and sustainable SNG production. KW - CO₂ methanation KW - Power-to-Gas KW - Ni-PTOs catalysts KW - 3D-printed structures Y1 - 2025 UR - https://www.researchgate.net/publication/392195919_Sustainable_production_of_synthetic_natural_gas_CO2_methanation_on_3D-printed_structured_Ni-based_perovskite_catalysts SP - 1 EP - 4 CY - Würzburg ER - TY - GEN A1 - Jafari, Mitra A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME T2 - 58. Jahrestreffen Deutscher Katalytiker N2 - Dimethyl ether (DME, methoxymethane) is a clean-burning fuel and a promising alternative to conventional fossil fuels, especially in transportation and power generation. Its production from methanol through dehydration offers a viable pathway toward energy sustainability, not only because of its environmental benefits but also due to the high purity of the resulting products and the efficient conversion rate of methanol [1]. However, optimizing this process requires understanding the intricate dependencies among reaction parameters, including temperature, pressure, catalyst type, and feedstock composition [2]. Machine learning offers transformative potential in this context by identifying complex, non-linear interactions among variables and providing predictive insights that can improve reaction efficiency, yield, and product quality. Through predictive modeling, machine learning can significantly reduce the need for experimental trial-and-error by identifying optimal reaction conditions quickly, thereby decreasing costs, enhancing scalability, and supporting continuous, real-time process optimization [3, 4]. In this study, first a dataset is generated including different descriptors like catalyst formulation, pretreatment, characteristics, activation, and reaction conditions. This dataset is then preprocessed by encoding, imputation, and normalization to make it ready for modelling, followed by data analysis to identify patterns and dependencies. Different models, including Gradient Boosting Regressor, XGBoost, LightGBM, and neural networks, are applied to predict methanol conversion and DME yield based on input variables. The models were evaluated through cross-validation, achieving highaccuracy and underscoring the potential of data-driven optimization in enhancing DME production. These steps are illustrated in Figure 1. Finally, the prediction accuracy of each model is investigated, and the best algorithm is selected. The effect of different descriptors on the respond have also been assessed to find out the most effective parameters on the catalyst performance. The best model is then used to predict DME yield and optimize the catalyst and reaction parameters. Y1 - 2025 UR - https://www.researchgate.net/publication/390271407_Towards_Digitalization_of_Catalysis_Design_and_Reaction_Engineering_Data-Driven_Insights_into_Methanol_to_DME SP - 1 EP - 2 ER - TY - GEN A1 - Hamdan, Mustapha A1 - Hamdan, Malak A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Modular high-temperature thermal energy storage for industrial decarbonisation using a particle-based heat battery T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Industrial heat contributes over 9 Gt of annual CO₂ emissions, with high-grade requirements (>1000°C) posing exceptional decarbonization challenges [1]. This contribution present the two-loop (2LP) Heat Battery, a particle-based thermal energy storage system delivering dispatchable zero-carbon heat and electricity at temperatures up to 1600°C. The modular design employs a dual-loop recirculating bed of advanced ceramic particles, achieving 98% round-trip efficiency through controlled particle metering and high surface area heat transfer. Unlike bulk thermal storage systems that exhibit thermocline-induced temperature decay [2], the 2LP architecture maintains steady-state outlet temperatures during 24-hour discharge cycles. Key innovations include a volumetric energy density of 1280 kWh/m 3 (surpassing molten salts, lithium-ion batteries, and refractory brick systems) and thermal output density exceeding 1MWth/m 3. The technology reduces levelised cost of storage from €20/kWh (conventional molten salt) to below €3/kWh while supporting ultra-efficient supercritical CO2 Brayton cycles (thermal-to-electric efficiency >50%). System performance exceeds EU SET Plan targets, achieving 98% electro-thermal round-trip efficiency and 90% combined heat and power efficiency. This scalable solution addresses critical gaps in industrial electrification, enabling grid congestion mitigation and providing a cost-effective pathway to decarbonize hard-to-abate sectors like steel and cement production. The 2LP Heat Battery demonstrates technical and economic viability to support EU Net Zero objectives through high-temperature electrification. Y1 - 2025 UR - https://www.researchgate.net/publication/399753836_Modular_High-Temperature_Thermal_Energy_Storage_for_Industrial_Decarbonisation_Using_a_Particle-Based_Heat_Battery SP - 1 EP - 3 CY - Frankfurt am Main ER - TY - GEN A1 - Mappas, Vasileios K. A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Arellano-Garcia, Harvey T1 - Modeling multiphase reactors with complex particle geometries T2 - PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology N2 - Trickle bed reactors (TBRs) are the backbone of the catalytic multiphase reactors in industrial processes, owning to their simple design, flexible controllability and large surface area. One of the key aspects for designing TBRs is the flow simulation inside the reactor and the hydrodynamics phenomena that take place during its operation. Literature offers various methods for simulating the behaviour and the performance of TBRs based on empirical methods or Computational Fluid Dynamics (CFD) simulations leading to inaccurate results, high computational burden, case studies with small catalytic beds or considering only spherical particles particles 1. To overcome these drawbacks, a modular and flexible toolbox for the modelling and study of TBRs is proposed which is adapted to the local structure of the catalytic bed 2. To improve the contact point calculation and extend to more complex geometries (i.e., cylinders, Raschig rings, trilobes), an approach based on liquid element tracking (LET) is applied, where the particle's surface is discretised over a finite number of triangles. Therefore, a pointwise sequence of the fluid over individual partial surfaces, based on the applied forces, is implemented for the liquid flow path estimation. The benefits of this procedure lie in its effectiveness, modular interconnection, and robust capability to represent a diverse range of phenomena for simulating flow patterns during TBRs operation in the low-interaction regime. Furthermore, the required computational time is significantly reduced compared to CFD simulations and a large number of particles can be introduced in the examined case study. The new particle representation is successfully implemented and the results are in good agreement with the static holdup prediction and radial flow distribution based on the previous contact point model, based solely on geometric calculations of the distance between the spheres and the liquid-solid interactions. References [1] Fathiganjehlou, A., et al. (2024). Multi-scale pore network modeling of a reactive packed bed. Y1 - 2025 UR - https://www.researchgate.net/publication/399788961_Multiphase_catalytic_reactors_a_modular_approach SP - 1 EP - 4 CY - Frankfurt am Main ER - TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Santos da Silva, Jefferson A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - Advancing catalyst design with machine learning : insights from FTS and DME production T2 - Annual Meeting on Reaction Engineering 2025 N2 - This contribution presents a ML-based framework to optimize heterogeneous catalysts, with a primary focus on the targeted application for FTS and DME production. However, the framework is designed to be generic, with applicability to a broader range of heterogeneous catalytic processes. The study analyzes a variety of catalyst parameters, including composition, pretreatment, and operating conditions, and employs advanced ML techniques such as regression models, ensemble learning, and neural networks to model the relationships between these factors and reaction outcomes. Hyperparameter optimization and performance evaluation using metrics like R², RMSE, MSE, and AIC further improve the accuracy and robustness of the models. In addition, the study provides a comparative analysis of applying this ML framework to both FTS and DME processes, highlighting the similarities and differences in optimizing catalyst performance and operating conditions. The study aims to identify the most influential parameters that drive catalyst performance and to assess the predictive power of each model. By enhancing understanding of how catalyst properties and operating conditions influence the efficiency of FTS and DME production, this work provides valuable insights into more efficient and sustainable catalytic process design. Y1 - 2025 UR - https://www.researchgate.net/publication/392137173_Advancing_catalyst_design_with_Machine_Learning_Insights_from_FTS_and_DME_production SP - 1 EP - 6 ER - TY - GEN A1 - Jafari, Mitra A1 - Abadi, Amirreza A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Fischer-Tropsch synthesis and hydrocracking process integration : a study on mesoporosity modification and acidity optimization T2 - 100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference N2 - Machine learning (ML) is employed to identify the key parameters influencing CO conversion and C5+ selectivity. Insights from both the literature review and ML analysis highlight pore volume and acidity as the most critical factors. Therefore, this study focuses on developing cobalt/beta zeolite catalysts with tailored mesoporosity and acidity to enhance catalytic performance. Specifically, the goal is to optimize the acidity of the zeolite by determining the ideal concentration of NH4+ during the ion-exchange step. Y1 - 2025 UR - https://www.researchgate.net/publication/391564315_Fischer-Tropsch_Synthesis_and_Hydrocracking_Process_Integration_A_Study_on_Mesoporosity_Modification_and_Acidity_Optimization SP - 1 EP - 3 ER - TY - GEN A1 - Riedel, Ramona A1 - Schowarte, Julia A1 - Semisch, Laura A1 - Gonzalez Castano, Miriam A1 - Ivanova, Svetlana A1 - Martienssen, Marion A1 - Arellano-Garcia, Harvey T1 - Improving the photocatalytic degradation of EDTMP : effect of doped NPs (Na, Y, and K) into the lattice of modified Au/TiO2 nano-catalysts T2 - Chemical engineering journal N2 - This study presents the photocatalytic degradation of the aminophosphonate ethylenediaminetetra(methylenephosphonic acid) (EDTMP) with a range of different doped nanoparticles (NP). The photocatalysts were based on TiO2 benchmark P25 and gold (Au) doped either with sodium (Na), potassium (K) or yttrium (Y). The synthesized photocatalysts were characterized via TEM, XRF, XRD, UV-DRS (band gap estimation) and N2-physisorption. Photocatalytic pre-screening at pH values of 3, 7 and 10 indicated highest o-PO4 release of EDTMP at pH 7 and 10 for NP either doped with K or Y. The results of LC/MS analysis showed that the NPs doped with 5 % Y (Au2/Y5/P25) resulted in the fastest degradation of EDTMP. The target compound was completely degraded within 60 min, 4 times faster than photochemical treatment of unadulterated EDTMP. Importantly, also the transformation products were accelerated by the photocatalytic treatment with Au2/P25 either doped with 5 % Y or 10 % K. The results of scavenger experiments indicated that the enhanced photocatalytic degradation of EDTMP is primarily attributable to the presence of hydroxyl radicals in the bulk and to a lesser extent to •O2− and electron-holes (h+) at the surface of the catalysts. The study demonstrates that the catalytic efficiency of TiO2 nanocomposites is significantly influenced by the choice of dopants, which affect particle size, band gap, and photocatalytic activity. Yttrium at low concentrations (i.e., 5 wt% Y) doping emerged as particularly effective, enhancing both the visible light absorption and h+ separation, leading to superior photocatalytic performance in the degradation of EDTMP. The Au content also plays a crucial role in enhancing the photocatalytic efficiency. However, the combination of Au and Na doping was found to be less effective for this photocatalysis in aqueous media, potentially due to larger particle sizes and insufficient dopant contents. In conclusion, the findings emphasise the necessity of optimising both the selection of dopants and the design of catalysts in order to enhance photocatalytic applications. KW - EDTMP KW - Nanoparticles KW - Phosphonate KW - TiO2 KW - Yttrium Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S1385894725009088?via%3Dihub U6 - https://doi.org/10.1016/j.cej.2025.160109 VL - 506 ER - TY - GEN A1 - Schowarte, Julia A1 - Riedel, Ramona A1 - Helle, Sven A1 - Martienssen, Marion A1 - Arellano-Garcia, Harvey T1 - Synergistic enhancement of PFOA and 6:2-FTAB photodegradation using Au/Y-doped TiO₂ nanocatalysts T2 - Chemical engineering journal advances N2 - Efficient degradation of perfluoroalkyl substances (PFAS) requires photocatalysts capable of promoting strong C-F bond cleavage and selective interfacial charge transfer. In this proof-of-concept-study, a dual-doped TiO2 nanophotocatalyst (Au2/Y5/P25) was synthesized by combining gold (Au) nanoparticles and yttrium (Y) dopants to enhance charge separation and reactive oxygen species (ROS) generation. Structural characterization supported Au deposition on the TiO2 surface and Y incorporation into the lattice, accompanied by a slight band-gap narrowing. Under UV irradiation in aqueous solution (unbuffered pH 5.8, room temperature) the nanophotocatalyst exhibited distinct degradation pathways for 1000 µg L−1 of two representative PFAS, perfluorooctanoic acid (PFOA) and Capstone B (6:2 FTAB), reflecting environmentally prevalent groups. PFOA underwent 99 % degradation within 100 min via a stepwise CF2-cleavage mechanism, generating a sequence of perfluorocarboxylic acids down to perfluorobutanoic acid (PFBA), consistent with enhanced electron-hole separation. In contrast, Capstone B showed rapid, single-step S-N bond cleavage to 6:2 perfluorooctanesulfonic acid (6:2 PFOS), primarily driven by hole- and •OH-mediated oxidation under oxygen-rich conditions. This process achieved 96 % degradation within 20 min but did not proceed to further defluorination, indicating oxidative limitations. Dissolved oxygen analysis revealed efficient electron utilization and sustained oxidative turnover without excessive oxygen depletion. The findings demonstrate that Au/Y co-doping promotes selective PFAS activation, enabling rapid precursor oxidation while exposing the kinetic limits of secondary C-F bond cleavage. These discoveries offer new insights into the design of plasmonic-rare-earth-modified TiO2 photocatalysts for efficient PFAS degradation through interface-driven oxidation pathways. KW - 6:2-FTAB KW - Nanoparticles KW - PFAS KW - PFOA KW - Photocatalysis KW - TiO₂ Y1 - 2026 U6 - https://doi.org/10.1016/j.ceja.2026.101121 SN - 2666-8211 VL - 26 SP - 1 EP - 12 PB - Elsevier BV CY - Amsterdam ER -