TY - GEN A1 - Jafari, Mitra A1 - Shafiee, Parisa A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis T2 - Annual Meeting of Process Engineering and Materials Technology 2024 Y1 - 2024 UR - https://www.researchgate.net/publication/388143384_Towards_efficient_material_design_Use_of_Machine_Learning_to_predict_chemical_reactions_and_retrosynthesis ER - TY - GEN A1 - Jafari, Mitra A1 - Mbuya, Christel-Olivier Lenge A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey T1 - Sustainable aviation fuel production through Fischer-Tropsch synthesis and hydrocracking integration using Co bifunctional catalysts: Support effects T2 - 18th International Congress on Catalysis N2 - Considering the increasing demand for clean and sustainable aviation fuel, in this study, cobalt bifunctional catalysts are used to convert syngas from biomass to aviation fuel. Y1 - 2024 UR - https://www.researchgate.net/publication/388109868_Sustainable_aviation_fuel_production_through_Fischer-Tropsch_synthesis_and_hydrocracking_integration_using_Co_bifunctional_catalysts_Support_effects ER - TY - CHAP A1 - Jafar Khan, Maria A1 - Safdar, Muddasar A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - Methods of indirect conversion of CO2 to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - The promptly increasing CO2 concentration in the atmosphere causes a major climate change, requiring effective way of its mitigation. The indirect conversion of CO2 to methanol via syngas is a promising strategy to control greenhouse gas emissions and produce valuable feedstock's and chemicals. This chapter focuses on different indirect CO2 conversion methods to methanol, multistep processes that involve capturing of carbon dioxide, intermediate formation syngas, type of catalyst used, and then hydrogenation to methanol. Indirect conversion of CO2 involves two steps, the production of syngas which is known as a mixture of carbon monoxide and hydrogen followed by methanol integration and catalyst-based hydrogenation of CO2. The economic feasibility, the effectiveness of different methods, development, and optimization of catalysts along with reaction conditions are thoroughly discussed in this chapter. The chapter concluded with the direction of suitable methods to convert carbon dioxide into methanol along with the future research development in the methodology to reduce greenhouse emissions and advance the production of sustainable chemicals. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00155-5 VL - 2024 PB - Elsevier ER - TY - CHAP A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - CO2 sources and features for direct CO2 conversion to methanol T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - In recent years, global concern over climate change caused by the accumulation of atmospheric CO2 has intensified. While various technologies for capturing CO2 have been proposed, utilizing captured CO2 from power plants is gaining popularity due to the concerns about the safety and effectiveness of underground and ocean storage methods. This article explores several techniques for utilizing CO2 from exhaust gases emitted by power plants. It provides a comprehensive review of current and emerging technologies worldwide that aim to harness CO2 for beneficial purposes. The conversion of CO2 into chemicals and energy products represents a promising approach to not only mitigate CO2 emissions but also enhance economic value. However, since CO2 lacks hydrogen, which is essential for many chemical processes, the development of clean, sustainable, and cost-effective hydrogen sources is crucial. This chapter delves into the literature surrounding the production of biofuels derived from microalgae cultivated using captured CO2, the conversion of CO2 combined with hydrogen into various chemicals, specially methanol and the exploration of sustainable hydrogen sources. These efforts collectively underscore the potential of CO2 utilization as a pivotal strategy in the battle against climate change and for fostering sustainable industrial practices. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00127-0 VL - 2024 ER - TY - CHAP A1 - Jafari, Mitra A1 - Arellano-Garcia, Harvey T1 - Shift from syngas to CO2 for methanol production T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - This chapter delves into diverse methodologies for converting carbon dioxide (CO2) into methanol, employing homogeneous and heterogeneous catalysts through hydrogenation, photochemical, electrochemical, and photo-electrochemical techniques. Given the significant contribution of CO2 to global warming, utilizing it for fuel and chemical production stands as a sustainable approach to environmental conservation. However, due to high stability and low reactivity of CO2, the development of appropriate methods and catalysts is crucial for breaking its bonds to yield valuable chemicals like methanol. Also, in this chapter various methods and their mechanisms for CO2 conversion to methanol are described. Finally, new types of catalyst and their characteristics for CO2 hydrogenation to methanol are introduced and discussed in detail. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-443-15740-0.00126-9 VL - 2024 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 - 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 - 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 - Khosravani, Hadiseh A1 - Taghadom, Kambiz A1 - Jafari, Mitra T1 - Geothermal energy in Asia T2 - Encyclopedia of Renewable Energy, Sustainability and the Environment N2 - Geothermal energy has been utilized worldwide for thousands of years as a sustainable and eco-friendly energy resource. The utilization of this form of energy can vary based on the existing resources and technologies attainable, whereby it may serve various objectives and assume diverse modes of application. Moving forward, a case study approach will be employed to examine the potential of geothermal energy in Asia. This investigative method will be utilized to explore the viability of this energy source in multiple nations throughout the Asian region. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/B978-0-323-93940-9.00207-3 VL - 2024 IS - 2 SP - 321 EP - 330 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 - Jafari, Mitra A1 - Dorneanu, Bogdan A1 - Arelano-Garcia, Harvey T1 - Machine learning–enhanced Fischer–Tropsch synthesis : optimizing catalysts and process conditions for efficient fuel production T2 - Chemie - Ingenieur - Technik : CIT N2 - Fischer–Tropsch synthesis (FTS) offers a promising route for producing clean, renewable fuels. Yet, designing efficient catalysts and determining optimal process conditions remain major hurdles. Machine learning (ML) provides powerful means to address these challenges. Despite their potential, metal/zeolite catalysts are scarcely studied in ML-driven FTS research. This work applies an ML-based framework to model and optimize metal/zeolite catalysts for liquid fuel synthesis via FTS. Supervised learning methods reveal key structure–performance correlations, whereas multi-objective optimization identifies ideal catalyst and process parameters. The top solution is benchmarked against nearest experimental data. Results show CatBoost as the best-performing model, with Pt–Co/Beta treated with NaOH and NH4+ emerging as the optimal catalyst. KW - Fischer–Tropsch synthesis KW - Liquid fuel KW - Machine learning KW - Zeolite Y1 - 2025 U6 - https://doi.org/10.1002/cite.70030 SN - 1522-2640 VL - 97 IS - 11-12 SP - 1085 EP - 1093 PB - Wiley CY - Weinheim ER - TY - GEN A1 - 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 - 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 - 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 - 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 -