@misc{PlattfautSuckowKlepeletal., author = {Plattfaut, Julia and Suckow, Matthias and Klepel, Olaf and Erlitz, Marcel and Arellano-Garc{\´i}a, Harvey}, title = {Modellierung und Simulation der templatgest{\"u}tzten Synthese von por{\"o}sen Kohlenstoffger{\"u}sten mittels COMSOL Multiphysics}, series = {Chemie Ingenieur Technik}, volume = {96(2024)}, journal = {Chemie Ingenieur Technik}, number = {3}, issn = {1522-2640}, doi = {10.1002/cite.202300014}, pages = {318 -- 328}, abstract = {Mithilfe einer templatgest{\"u}tzten Synthese wurden por{\"o}se Kohlenstoffger{\"u}ste 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 zus{\"a}tzliche Beschreibung der laminaren Str{\"o}mung verbessert die {\"U}bereinstimmung deutlich.}, language = {de} } @misc{ZhangVassiliadisDorneanuetal., author = {Zhang, Sushen and Vassiliadis, Vassilios S. and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Hierarchical multi-scale parametric optimization of deep neural networks}, series = {Applied Intelligence}, volume = {53}, journal = {Applied Intelligence}, number = {21}, issn = {1573-7497}, doi = {10.1007/s10489-023-04745-8}, pages = {24963 -- 24990}, abstract = {Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system.}, language = {en} } @misc{ArellanoGarciaSafdarShezadetal., author = {Arellano-Garc{\´i}a, Harvey and Safdar, Muddasar and Shezad, Nasir and Dorneanu, Bogdan and Akhtar, Farid}, title = {Synthesis and Characterizations of Ni-doped Perovskite-Type Oxides for Effective CO2 methanation}, series = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, journal = {14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology}, doi = {10.5281/zenodo.10376612}, pages = {2}, abstract = {This work proposes Ni metal supported over rare earth-based emerging perovskite-type oxides as potential catalysts for the CO2 methanation. Presence of oxygen vacancies in perovskite-like materials enable them to exhibit higher catalytic activity. Furthermore, to tune the surface basicity, metal-support interaction and to enhance the activation of CO2, rare earth metals (La, Ce, etc.) are considered best candidates. Moreover, different perovskite-type supports (AxMnxO3, A= La, Ce) based on A-side substitution of rare earth metals were prepared with Ni metal loading of 10 wt.\% via impregnation method.}, language = {en} } @misc{DorneanuNolascoVassiliadisetal., author = {Dorneanu, Bogdan and Nolasco, Eduardo and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {Quantum annealing for global optimization in Chemical Engineering}, series = {Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main}, journal = {Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main}, pages = {15}, abstract = {Classical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binary optimization (QUBO) problems. In this contribution, a novel approach is introduced to transform relevant problems in Chemical Engineering into QUBO at two distinct levels of granularity. Subsequently, these problem systems are embedded within virtual quantum machines employing two different architectures. Additionally, a comparative analysis is performed, wherein the same problem is solved utilizing both classical global optimization methods based on metaheuristics and a hypothetical quantum annealer. The findings indicate that annealing-based solving methods exhibit the most potential, indicating their applicability to the transformed formulation Chemical Engineering problems.}, language = {en} } @misc{MarkowskiArellanoGarciaMeissneretal., author = {Markowski, Jens and Arellano-Garc{\´i}a, Harvey and Meißner, Andr{\´e} and Acker, J{\"o}rg}, title = {Comparative studies on the quality of recovered secondary graphites from the recycling of lithium-ion traction batteries}, series = {Sustainable Minerals}, journal = {Sustainable Minerals}, publisher = {Mining Engineering}, address = {Falmouth (UK)}, abstract = {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.}, language = {en} } @misc{ArellanoGarciaSafdarLewisetal., author = {Arellano-Garc{\´i}a, Harvey and Safdar, Muddasar and Lewis, Allana and Radacsi, Norbert and Fan, Xianfeng and Huang, Yi}, title = {Superhydrophobic ZIF-67 with exceptional hydrostability}, series = {Materials Today Advances}, volume = {Vol. 20}, journal = {Materials Today Advances}, issn = {2590-0498}, doi = {10.1016/j.mtadv.2023.100448}, abstract = {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.}, language = {en} } @misc{GonzalezCastanoMoralesNavarrodeMigueletal., author = {Gonzalez-Cast{\~a}no, Miriam and Morales, Carlos and Navarro de Miguel, Juan Carlos and Boelte, Jens-H. and Klepel, Olaf and Flege, Jan Ingo and Arellano-Garc{\´i}a, Harvey}, title = {Are Ni/ and Ni5Fe1/biochar catalysts suitable for synthetic natural gas production? A comparison with γ-Al2O3 supported catalysts}, series = {Green Energy \& Environment}, volume = {8}, journal = {Green Energy \& Environment}, number = {3}, issn = {2468-0257}, doi = {10.1016/j.gee.2021.05.007}, pages = {744 -- 756}, abstract = {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.}, language = {en} } @incollection{GonzalezCastanoTarifaMonzonetal., author = {Gonz{\´a}lez-Casta{\~n}o, Miriam and Tarifa, Pilar and Monzon, Antonio and Arellano-Garc{\´i}a, Harvey}, title = {Valorization of unconventional CO2-rich feedstock via Reverse Water Gas Shift reaction}, series = {Circular Economy Processes for CO2 Capture and Utilization : Strategies and Case Studies}, booktitle = {Circular Economy Processes for CO2 Capture and Utilization : Strategies and Case Studies}, publisher = {Woodhead Publishing}, isbn = {9780323956697}, doi = {10.1016/B978-0-323-95668-0.00001-1}, pages = {307 -- 323}, abstract = {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.}, language = {en} } @misc{SafdarDorneanuPaffetal., author = {Safdar, Muddasar and Dorneanu, Bogdan and Paff, Jessica Sophie and Arellano-Garc{\´i}a, Harvey}, title = {Structural formability of perovskite ABO3 oxide system synthesized via autocombustion technique, ruled by geometric factors}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, language = {en} } @misc{JafariDorneanuArellanoGarcia, author = {Jafari, Mitra and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Machine learning application in kinetic studies: A review}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, abstract = {Machine learning (ML) brings new opportunities in the field of heterogenous catalysis and reaction engineering. Here, the advancements brought by ML in the field of kinetic studies are reviewed.}, language = {en} } @misc{ShafieeMbuyaDorneanuetal., author = {Shafiee, Parisa and Mbuya, Christel-Olivier Lenge and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Novel approaches for preparation of 3D Ni-Al2O3 core with zeolite shell catalysts for dry reforming of methane}, series = {18th International Congress on Catalysis}, journal = {18th International Congress on Catalysis}, pages = {2}, language = {en} } @misc{CunhaCordeiroSilvadeAquinoSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Silva de Aquino, Gabrielle and Santos da Silva, Jefferson and Safdar, Muddasar and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Valverde Pontes, Karen and Santos Mascarenhas, Artur Jos{\´e}}, title = {Estudo do efeito do suporte em catalisadores de Ni preparados pelo m{\´e}todo da combust{\~a}o aplicados na reforma a seco do biog{\´a}s para produ{\c{c}}{\~a}o de hidrog{\^e}nio sustent{\´a}vel}, series = {63rd Brazilian Chemistry Congress}, journal = {63rd Brazilian Chemistry Congress}, pages = {11}, language = {pt} } @misc{DorneanuFarhadiArellanoGarcia, author = {Dorneanu, Bogdan and Farhadi, Maryam and Arellano-Garc{\´i}a, Harvey}, title = {Conceptual design of a reactive distillation column for the catalytic upgrade of ABE}, series = {Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik}, journal = {Jahrestreffen der DECHEMA-Fachgruppe Fluidverfahresntechnik}, pages = {2}, language = {en} } @misc{DorneanuMappasVassiliadisetal., author = {Dorneanu, Bogdan and Mappas, Vassileios and Vassiliadis, Vassilios S. and Arellano-Garc{\´i}a, Harvey}, title = {A second-order linesearch procedure within Newton's method for highly nonlinear steady-state systems simulation}, series = {2024 AIChE Annual Meeting}, journal = {2024 AIChE Annual Meeting}, abstract = {Linesearch, a crucial component of Newton's method, ensures global convergence, guaranteeing convergence to a local solution from any starting point while satisfying all simultaneous nonlinear equations (Bellavia and Morini, 2003). Despite Newton's method being considered established both theoretically and algorithmically, leaving little room for further improvements, this contribution focuses on enhancing the linesearch procedure and revealing significant advancements over existing methods. Specifically, this study aims to incorporate second-order information in a computationally efficient manner to improve the performance of the linesearch procedure, especially for highly nonlinear equation systems. Nonlinearity, particularly near the starting point, can substantially hinder algorithmic efficiency, necessitating frequent step reductions at the expense of function evaluations and major iterations involving Jacobian evaluations and factorizations (Gill and Zhang, 2024). The proposed approach leverages a a higher-order Taylor series expansion around the operating point of a major iteration in Newton's algorithm, coupled with a custom Jacobian vector product finite difference scheme. This combination requires only one additional Jacobian evaluation to construct a locally accurate fourth-degree polynomial approximating the merit function along the search direction. In addition to the theoretical advancements, this contribution provides computational evidence supporting the claim that for highly nonlinear systems, significant computational savings and enhanced solution procedure stability can be achieved. Utilizing a Python implementation, linear subsets of equations are treated separately to boost the efficiency of function and Jacobian evaluations, aligning with standard practices in professional software development. While Python may not be a high-performance language, its suitability for rapid algorithm prototyping and validation precedes potential transfer to higher-performance languages like C++. Moreover, given Newton's method central roles in various iterative solution tools, such as its repeated use within a Differential-Algebraic Equations (DAEs) integrators and potentially Partial Differential-Algebraic Equations (PDAEs) solvers, the significance of this work extends even further. Future research endeavors will explore these areas, building upon the foundations laid by this study.}, language = {en} } @misc{ShafieeDorneanuArellanoGarcia, author = {Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16)}, series = {Chemical Engineering Journal Advances}, volume = {21 (2025)}, journal = {Chemical Engineering Journal Advances}, publisher = {Elsevier}, doi = {10.1016/j.ceja.2024.100702}, pages = {25}, abstract = {Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 \%), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 \%). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity.}, language = {en} } @misc{ShezadSafdarArellanoGarciaetal., author = {Shezad, Nasir and Safdar, Muddasar and Arellano-Garcia, Harvey and Tai, Cheuk-Wai and Chen, Shaojiang and Seo, Dong-Kyun and You, Shujie and Vomiero, Alberto and Akhtar, Farid}, title = {Deciphering the role of APTES in tuning the metal support interaction of NiO nanolayers over hierarchical zeolite 13X for CO2 methanation}, series = {Carbon Capture Science \& Technology}, volume = {15}, journal = {Carbon Capture Science \& Technology}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.ccst.2025.100424}, pages = {1 -- 11}, abstract = {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.}, language = {en} } @misc{MappasDorneanuNolascoetal., author = {Mappas, Vasileios and Dorneanu, Bogdan and Nolasco, Eduardo and Vassiliadis, Vassilios and Arellano-Garcia, Harvey}, title = {Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept}, series = {Chemical engineering research and design}, volume = {221}, journal = {Chemical engineering research and design}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1744-3563}, doi = {10.1016/j.cherd.2025.08.031}, pages = {560 -- 576}, abstract = {Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP's bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly's classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, providing foundational insights towards leveraging QA in Process Systems Engineering.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, abstract = {Multiphase systems, particularly trickle bed reactors (TBRs), are critical in various industrial applications and widely employed in catalytic processes such as hydrogenation and oxidation due to their high surface area, low operational and minimal catalyst loss. Despite advancements in modelling techniques, accurately capturing the complex multiphysics and multiscale phenomena remains challenging. Conventional approaches, relying on empirical correlations or Computational Fluid Dynamics (CFD) simulations, often fall short due to high computational demands, limited accuracy, and constraints on the number of catalytic particles that can be effectively simulated [3]. To address these limitations, this contribution presents a new framework tailored for the design and analysis of multiphase systems operating in the low-interaction regimes. This approach is based on the local structure of the packed bed and employs a Lagrangian approach, where flow dynamics within the reactor is represented by various discrete elements. The framework's modular and flexible setup enables the incorporation of multiscale information of both local and global levels, allowing for the additions of new modules or features to enhance modelling fidelity.}, language = {en} } @misc{ParkLeeKimetal., author = {Park, Haryn and Lee, Joowha and Kim, Jin-Kuk and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, pages = {1}, abstract = {Industrial sectors contribute substantially to global CO2 emissions, emphasizing the need for low-carbon, reliable energy supplies to meet operational demands. Achieving net-zero emissions in industrial processes involves transitioning from fossil fuels to renewable energy sources. However, the intermittent nature of renewables poses challenges to energy reliability and resilience, particularly in utility systems. This contribution addresses industrial decarbonisation and sustainable hydrogen production by developing a comprehensive design and optimization framework for integrating renewable energy systems into industrial operations. This framework incorporates energy storage and grid connections to improve flexibility and stability and is evaluated through two case studies. Both case studies analyse the operational and configurational changes necessary for renewable-powered hydrogen production, estimating the cost of hydrogen or CO2 avoidance cost to analyse economic viability. These insights provide guidelines for sustainable and economically viable energy management in industrial and hydrogen production sectors, supporting broader global energy transition goals.}, language = {en} } @misc{SchowarteRiedelSafdaretal., author = {Schowarte, Julia and Riedel, Ramona and Safdar, Muddasar and Helle, Sven and Fischer, Thomas and Arellano-Garc{\´i}a, Harvey}, title = {Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst}, series = {Chemie - Ingenieur - Technik : CIT}, journal = {Chemie - Ingenieur - Technik : CIT}, publisher = {Wiley-VCH GmbH}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70027}, pages = {1 -- 11}, abstract = {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.}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.121492}, pages = {631 -- 636}, abstract = {In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. These findings contribute to advancing energy-efficient and scalable hydrogen storage technologies.}, language = {en} } @misc{MappasDorneanuArellanoGarcia, author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Solving complex combinatorial optimization problems using quantum annealing approaches}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.188358}, pages = {1561 -- 1566}, abstract = {Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer's architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly's pooling and blending problem is examined while demonstrating the potential of the proposed approach. The results indicate that the succinct formulation exhibited higher success rate during the embedding procedure for the different examined architectures, and the quantum annealing solver exhibited the best performance among the various solvers investigated. This highlights the potential of the approach for solving this type of problems with the rapid development and improvement of quantum hardware and expanding it to more complex chemical engineering optimization systems.}, language = {en} } @misc{ShafieeJafariSchowarteetal., author = {Shafiee, Parisa and Jafari, Mitra and Schowarte, Julia and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.135551}, pages = {1866 -- 1871}, abstract = {Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization.}, language = {en} } @misc{ParkLeeDorneanuetal., author = {Park, Haryn and Lee, Joowha and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Kim, Jin-Kuk}, title = {Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.107403}, pages = {1175 -- 1180}, abstract = {Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined with renewable-based electrification. The proposed optimization model evaluates the economic trade-offs of integrating carbon capture, renewable energy, and energy storage. By applying this approach, systematic design guidelines are developed for the transition of a conventional steady-state utility system toward renewable energy integration, ensuring economically viable and sustainable energy management in process industries.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Schnitzlein, Klaus and Arellano-Garcia, Harvey}, title = {An efficient and unified modeling framework for trickle bed reactors : a modular approach}, series = {Chemie - Ingenieur - Technik : CIT}, volume = {97}, journal = {Chemie - Ingenieur - Technik : CIT}, number = {11-12}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70035}, pages = {1110 -- 1126}, abstract = {Trickle bed reactors (TBRs) involve complex and multiscale dynamics that challenge their design, modeling, and optimization. Current approaches often suffer from high computational cost and limited scalability, restricting their applicability in large-scale cases. This work introduces a modular, computationally efficient framework to address these issues by systematically capturing key transport and reaction phenomena. Furthermore, it provides a critical review of existing modeling strategies for TBRs, outlining their strengths and limitations and highlighting opportunities for enhancement through modularization. By offering a structured and scalable approach, the proposed framework improves predictive capabilities and supports the development of optimized and adaptable reactor designs.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and De Aquino, Gabrielle and Dos Santos, Mauricio and Cruz, Fernanda and Fiuza-Junior, Raildo A. and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Pontes, Karen and Mascarenhas, Artur}, title = {Influ{\^e}ncia do Suporte em Catalisadores de Ni Obtidos Pelo M{\´e}todo da Combust{\~a}o na Reforma a Seco do Biog{\´a}s para Produ{\c{c}}{\~a}o de Hidrog{\^e}nio Sustent{\´a}vel}, series = {23º CBCAT : Congresso Brasileiro de Catalise}, volume = {1}, journal = {23º CBCAT : Congresso Brasileiro de Catalise}, number = {1}, pages = {1 -- 6}, abstract = {Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biog{\´a}s. As caracteriza{\c{c}}{\~o}es revelaram varia{\c{c}}{\~o}es na dispers{\~a}o met{\´a}lica, {\´a}rea met{\´a}lica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior {\´a}rea met{\´a}lica e melhor dispers{\~a}o de Ni, favorecendo altas convers{\~o}es de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e est{\´a}vel por 8 horas de rea{\c{c}}{\~a}o. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resist{\^e}ncia ao coque. O NiO-CeO₂, embora ativo no in{\´i}cio, desativou com o tempo devido {\`a} deposi{\c{c}}{\~a}o de coque (6,4\%). A an{\´a}lise p{\´o}s-rea{\c{c}}{\~a}o mostrou baixa forma{\c{c}}{\~a}o de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resist{\^e}ncia dos catalisadores na reforma a seco do biog{\´a}s. Palavras-chave: Hidrog{\^e}nio sustent{\´a}vel; Reforma a seco do biog{\´a}s; Catalisadores de NiO; efeito do suporte ABSTRACT-This study evaluates NiO-based catalysts supported on MgO, ZrO₂, Al₂O₃, La₂O₃, and CeO₂ for the dry reforming of biogas. Characterization of the samples revealed differences in metal dispersion, metallic area, and surface morphology. NiO-Al₂O₃ and NiO-CeO₂ show higher metallic areas and better Ni dispersion, leading to higher CH₄ and CO₂ conversions and good H₂ yield. NiO-Al₂O₃ is the most efficient and stable catalyst over 8 hours of reaction. NiO-La₂O₃ shows a gradual increase in activity and good coke resistance. Conversely, NiO-CeO₂, despite high initial activity, deactivates over time due to coke deposition (6.4\%). Post-reaction analysis confirmed low coke formation for most catalysts. The results indicate that the choice of support directly affects catalyst activity, stability, and resistance.}, language = {pt} }