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RESEARCH PAPER · Industrial & Engineering Chemistry Research · 2022

Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models

Junwei Luo, Vito Canuso, Joonbaek Jang, Zhe Wu, Carlos G. Morales‐Guio, Panagiotis D. Christofides

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42 direct citing works

Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical ModelsOriginalMachine Learning and Data Science in Chemical Engineering30Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor25Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform11Machine learning-based run-to-run control of a spatial thermal atomic layer etching reactor18On generalization error of neural network models and its application to predictive control of nonlinear processes21Data-driven digitalization of an armfield UOP3CC distillation unit1Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor21Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems9Computational applications using data driven modeling in process Systems: A review72Model predictive control of nonlinear processes using neural ordinary differential equation models22Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design33Key engineering design aspects of photo-assisted electrochemical reactors for water treatment1Physics-Informed Neural Networks for Process Systems: Handling Plant-Model Mismatch32Microenvironment Regulation, Promoting CO2 Conversion to Mono- and Multicarbon Products over Cu-Based Catalysts9A tutorial review of machine learning-based model predictive control methods47Prediction and Explainable Analysis of Molecular Weight Distribution of Polystyrene Based on Machine Learning and SHAP5Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review9Inverse design of the membrane reactors enabled by an inverse-forward physics-informed learning framework6Electrolyzers in focus: advances in CO2 electrolyzer designs11Control Lyapunov barrier function-based predictive control of nonlinear systems using physics-informed recurrent neural networks2A multimodal foundation model of multi-physics for chemical reactor design2Data‐Driven Models and Accurate Transfer Methods for Screw Extrusion Process of Medical Catheters Considering Viscous Fluid Flow Behavior130 years of AI for electrocatalysis: Where we are and what’s next?12Integrating large language models and machine learning for sustainable electrochemical upgrading of biomass-derived 5-hydroxymethylfurfural to 2,5-bis(hydroxymethyl)furan0Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0

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  1. Machine Learning and Data Science in Chemical Engineering

    Industrial & Engineering Chemistry Research · 2022-06-22

    Hanyu Gao, Li‐Tao Zhu, Zheng‐Hong Luo, Marco André Fraga, I‐Ming Hsing

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  2. Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

    Process Safety and Environmental Protection · 2022-07-01

    Berkay Çıtmacı, Junwei Luo, Joonbaek Jang, Vito Canuso, Derek Richard, Yi Ming Ren, Carlos G. Morales‐Guio, Panagiotis D. Christofides

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  3. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

    Industrial & Engineering Chemistry Research · 2022-07-07

    Li‐Tao Zhu, Xizhong Chen, Bo Ouyang, Wei‐Cheng Yan, He Lei, Zhe Chen, Zheng‐Hong Luo

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  4. Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

    Digital Chemical Engineering · 2022-08-24

    Berkay Çıtmacı, Junwei Luo, Joonbaek Jang, Prakashan Korambath, Carlos G. Morales‐Guio, James F. Davis, Panagiotis D. Christofides

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  5. Machine learning-based run-to-run control of a spatial thermal atomic layer etching reactor

    Computers & Chemical Engineering · 2022-10-28

    Matthew Tom, Sungil Yun, Henrik Wang, Feiyang Ou, Gerassimos Orkoulas, Panagiotis D. Christofides

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  6. On generalization error of neural network models and its application to predictive control of nonlinear processes

    Process Safety and Environmental Protection · 2022-12-05

    Mohammed S. Alhajeri, Aisha Alnajdi, Fahim Abdullah, Panagiotis D. Christofides

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  7. Data-driven digitalization of an armfield UOP3CC distillation unit

    Digital Chemical Engineering · 2022-12-17

    Willy Araujo, Marcílio Máximo, Jailson Nicácio, Heleno Bispo

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  8. Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

    Process Safety and Environmental Protection · 2023-02-07

    Berkay Çıtmacı, Junwei Luo, Joonbaek Jang, Carlos G. Morales‐Guio, Panagiotis D. Christofides

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  9. Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems

    iScience · 2023-05-29

    Derek Richard, Joonbaek Jang, Berkay Çıtmacı, Junwei Luo, Vito Canuso, Prakashan Korambath, Olivia Morales-Leslie, James F. Davis, Haresh Malkani, Panagiotis D. Christofides, Carlos G. Morales‐Guio

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  10. Computational applications using data driven modeling in process Systems: A review

    Digital Chemical Engineering · 2023-07-13

    Sumit Kr. Bishnu, Sabla Y. Alnouri, Dhabia M. Al‐Mohannadi

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  11. Model predictive control of nonlinear processes using neural ordinary differential equation models

    Computers & Chemical Engineering · 2023-08-01

    Junwei Luo, Fahim Abdullah, Panagiotis D. Christofides

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  12. Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

    Advanced Science · 2024-03-13

    Joel Y. Y. Loh, Andrew Wang, Abhinav Mohan, Athanasios A. Tountas, Abdelaziz Gouda, Alexandra Tavasoli, Geoffrey Alan Ozin

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  13. Key engineering design aspects of photo-assisted electrochemical reactors for water treatment

    Chem Catalysis · 2024-07-01

    Ashley Hesterberg Butzlaff, Mohamed Ateia

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  14. Physics-Informed Neural Networks for Process Systems: Handling Plant-Model Mismatch

    Industrial & Engineering Chemistry Research · 2024-07-23

    Farshad Moayedi, Aswin Chandrasekar, Sarah Rasmussen, Samardeep Sarna, Brandon Corbett, Prashant Mhaskar

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  15. Microenvironment Regulation, Promoting CO2 Conversion to Mono- and Multicarbon Products over Cu-Based Catalysts

    Industrial & Engineering Chemistry Research · 2024-11-16

    Ying‐Ya Liu, Zhichao Sun, Chong Peng, Anjie Wang

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  16. A tutorial review of machine learning-based model predictive control methods

    Reviews in Chemical Engineering · 2024-12-09

    Zhe Wu, Panagiotis D. Christofides, Wanlu Wu, Yujia Wang, Fahim Abdullah, Aisha Alnajdi, Yash A. Kadakia

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  17. Prediction and Explainable Analysis of Molecular Weight Distribution of Polystyrene Based on Machine Learning and SHAP

    Macromolecular Reaction Engineering · 2025-03-25

    Shanbao Lai, Zhitao Li, Jiajun Wang

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  18. Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review

    Digital Chemical Engineering · 2025-04-25

    Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Çıtmacı, Carlos G. Morales‐Guio, Panagiotis D. Christofides

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  19. Inverse design of the membrane reactors enabled by an inverse-forward physics-informed learning framework

    Chemical Engineering Science · 2025-05-23

    Hong Huang, Yimeng Li, Runrun Song, Jingzheng Ren, Haoshui Yu, Xiantai Zhou, Chang He

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  20. Electrolyzers in focus: advances in CO2 electrolyzer designs

    Trends in Chemistry · 2025-07-15

    Konstantina G. Mason, Jenna M. Ynzunza, Jesús M. Velázquez

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  21. Control Lyapunov barrier function-based predictive control of nonlinear systems using physics-informed recurrent neural networks

    Chemical Engineering Science · 2025-09-28

    Mohammed S. Alhajeri, Fahim Abdullah, Panagiotis D. Christofides

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  22. A multimodal foundation model of multi-physics for chemical reactor design

    Chemical Engineering Science · 2025-11-20

    Hong Huang, Ben Zhang, Jingzheng Ren, Qing‐Lin Chen, Bingjian Zhang, Xiantai Zhou, Chang He, Haoshui Yu

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  23. Data‐Driven Models and Accurate Transfer Methods for Screw Extrusion Process of Medical Catheters Considering Viscous Fluid Flow Behavior

    Journal of Applied Polymer Science · 2025-11-21

    Mingyang Su, Xiaobo Song, Zongjing Mu, Xing Qi Huang, Nanyang Zhao, Zhongbin Xu, Bin Xue

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  24. 30 years of AI for electrocatalysis: Where we are and what’s next?

    eScience · 2025-12-18

    Haotian Chen, Enno Kätelhön, Yuanyuan Lu, Jun Cheng, Zhong‐Qun Tian, Richard G. Compton

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  25. Integrating large language models and machine learning for sustainable electrochemical upgrading of biomass-derived 5-hydroxymethylfurfural to 2,5-bis(hydroxymethyl)furan

    Process Safety and Environmental Protection · 2026-01-23

    Cheng Zeng, Zhichao Tan, Youcai Zhao, Tao Zhou

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  26. Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells

    Computers & Chemical Engineering · 2026-04-18

    Theofilos Xenitopoulos, Αθανάσιος Ι. Παπαδόπουλος, Panos Seferlis

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  27. Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

    Industrial & Engineering Chemistry Research · 2026-09-30

    Michael Bâldea, Linda J. Broadbelt, Marianthi Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas Alan Kwan, Fèlix Llovell, Andrew James Medford, Ilias Mitrai, Joel A. Paulson, Junyi Qiao, Matthew P. Rivera, Kirti Chandra Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Ví­ctor M. Zavala, Huacheng Zhang, Dan Zhao

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