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 -