@misc{JafariShafieeDorneanuetal., author = {Jafari, Mitra and Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey}, title = {Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis}, series = {Annual Meeting of Process Engineering and Materials Technology 2024}, journal = {Annual Meeting of Process Engineering and Materials Technology 2024}, language = {en} } @misc{ShafieeDorneanuArellanoGarcia, author = {Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Towards Machine Learning-driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis}, series = {Chemical Engineering Transactions}, volume = {114}, journal = {Chemical Engineering Transactions}, issn = {2283-9216}, doi = {10.3303/CET24114098}, pages = {583 -- 588}, abstract = {Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing the catalyst design and operating conditions to maximize the desired C8-C16 jet fuel range is a challenging task. This study introduces the application of a machine learning (ML) framework to guide the design of Co/Fe-supported FTS catalysts and operating conditions for enhanced fuel selectivity. A comprehensive dataset was constructed with 21 input features spanning catalyst structure, preparation method, activation procedure, and FTS operating parameters. The random forest ML algorithm was evaluated for predicting CO conversion and C8-C16 selectivity using this dataset. Feature engineering identified the most significant descriptors influencing performance. A principal component analysis reduced the dataset dimensionality prior to ML modelling. The random forest algorithm achieved high prediction accuracy for the conversion of CO (R2 = 0.92) and C8-C16 selectivity (R2 = 0.90). In addition to confirming the known effects of operating conditions, key roles of Co/Fe-supported properties were elucidated. This ML framework provides a powerful tool for the rational design of FTS catalysts and operating windows to maximize jet fuel productivity}, language = {en} } @incollection{ShafieeArellanoGarcia, author = {Shafiee, Parisa and Arellano-Garcia, Harvey}, title = {Photocatalysts in CO2 direct conversion to methanol}, series = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, volume = {2024}, booktitle = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, doi = {https://doi.org/10.1016/B978-0-443-15740-0.00121-X}, abstract = {The escalating global industrialization has led to fossil fuel scarcity and environmental deterioration, with CO2 levels rising significantly. To combat climate change, researchers are focusing on renewable energy and carbon capture technologies. This chapter reviews recent progress on photocatalytic conversion of CO2 to methanol, a promising approach for greenhouse gas reduction and sustainable energy production. Methanol, a versatile chemical feedstock and potential renewable fuel, can be synthesized from CO2 using solar energy and semiconductor photocatalysts. This chapter covers the fundamentals, mechanisms, materials development, photocatalysts design strategies, and preparation processes for this technology. Despite challenges in achieving high efficiency, CO2 photocatalytic reduction to methanol offers an attractive green alternative to traditional fossil-based methanol production. This comprehensive overview consolidates the current research landscape, providing insights to guide future advancements towards scalable and economically viable CO2 photocatalytic methanol synthesis.}, language = {en} } @incollection{ShafieeArellanoGarcia, author = {Shafiee, Parisa and Arellano-Garcia, Harvey}, title = {Heterogeneous and Homogeneous Catalysts in CO2 Direct Conversion to Methanol}, series = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, volume = {2024}, booktitle = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, publisher = {Elsevier}, doi = {https://doi.org/10.1016/B978-0-443-15740-0.00119-1}, abstract = {Catalytic conversion of CO2 into valuable products offers a promising solution to mitigate climate change by closing the carbon cycle. However, activating the thermodynamically stable and kinetically inert CO2 molecule remains a significant scientific challenge. This chapter focuses on homogeneous and heterogeneous catalysts for the direct conversion of CO2 to methanol, a valuable chemical feedstock and potential fuel. It introduces the importance of this process for reducing carbon emissions and outlines the chapter's objectives. The fundamentals of heterogeneous catalysis and catalyst design principles for methanol synthesis from CO2 are discussed. Various types of heterogeneous catalysts are examined, along with the mechanisms involved in CO2 activation and hydrogenation to methanol. Strategies to enhance catalyst selectivity, product distribution, and performance are explored, as well as challenges and future research directions. This comprehensive chapter serves as a guide to understanding the pivotal role of heterogeneous catalysts in the direct catalytic conversion of CO2 to methanol.}, language = {en} } @incollection{ShafieeArellanoGarcia, author = {Shafiee, Parisa and Arellano-Garcia, Harvey}, title = {Electrocatalysts in CO2 direct conversion to methanol}, series = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, volume = {2024}, booktitle = {Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}, doi = {https://doi.org/10.1016/B978-0-443-15740-0.00120-8}, abstract = {By now, atmospheric CO2 levels necessitate new ways of reducing them more than ever with increasing global warming and climate change. Converting CO2 into valuable products like methanol fuel presents a solution by reducing atmospheric CO2 while creating economic opportunities. This chapter reviews the state-of-the-art in electrocatalytic CO2-to-methanol conversion technologies, covering basic electrocatalysis principles, electrocatalyst materials, design strategies, performance optimization, mechanistic pathways, catalyst compositions, technological hurdles, and potential solutions. It also examines environmental impacts, economic aspects, and scaling up possibilities, aiming to provide a comprehensive technological, economic and environmental overview of this sustainable energy solution for climate change mitigation, highlighting the critical role of innovation in addressing global challenges for a more sustainable future.}, 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{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{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{MbuyaPawarJafarietal., author = {Mbuya, Christel Olivier Lenge and Pawar, Kunal and Jafari, Mitra and Shafiee, Parisa and Okoye Chine, Chike George and Tarifa, Pilar and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems}, series = {Journal of CO2 utilization}, volume = {102}, journal = {Journal of CO2 utilization}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2212-9839}, doi = {10.1016/j.jcou.2025.103270}, pages = {1 -- 9}, abstract = {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.}, language = {en} } @misc{JafariShafieeSantosdaSilvaetal., author = {Jafari, Mitra and Shafiee, Parisa and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Advancing catalyst design with machine learning : insights from FTS and DME production}, series = {Annual Meeting on Reaction Engineering 2025}, journal = {Annual Meeting on Reaction Engineering 2025}, pages = {1 -- 6}, abstract = {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.}, language = {en} }