| Deep hybrid modeling of chemical process: Application to hydraulic fracturing MSF Bangi, JSI Kwon Computers & Chemical Engineering 134, 106696, 2020 | 124 | 2020 |
| Operable adaptive sparse identification of systems: Application to chemical processes B Bhadriraju, MSF Bangi, A Narasingam, JSI Kwon AIChE Journal 66 (11), e16980, 2020 | 75 | 2020 |
| Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters P Shah, MZ Sheriff, MSF Bangi, C Kravaris, JSI Kwon, C Botre, J Hirota Chemical Engineering Journal 441, 135643, 2022 | 67 | 2022 |
| Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae MSF Bangi, K Kao, JSI Kwon Chemical Engineering Research and Design 179, 415-423, 2022 | 58 | 2022 |
| Deep reinforcement learning control of hydraulic fracturing MSF Bangi, JSI Kwon Computers & Chemical Engineering 154, 107489, 2021 | 34 | 2021 |
| Deep hybrid model‐based predictive control with guarantees on domain of applicability MSF Bangi, JSI Kwon AIChE Journal 69 (5), e18012, 2023 | 29 | 2023 |
| Enlarging the domain of attraction of the local dynamic mode decomposition with control technique: Application to hydraulic fracturing MSF Bangi, A Narasingam, P Siddhamshetty, JSI Kwon Industrial & Engineering Chemistry Research 58 (14), 5588-5601, 2019 | 26 | 2019 |
| Dynamic modeling and offset-free predictive control of LNG tank YP Jo, MSF Bangi, SH Son, JSI Kwon, SW Hwang Fuel 285, 119074, 2021 | 20 | 2021 |
| Multi‐rate observer design and optimal control to maximize productivity of an industry‐scale fermentation process P Shah, MZ Sheriff, MSF Bangi, C Kravaris, JSI Kwon, C Botre, J Hirota AIChE Journal 69 (2), e17946, 2023 | 18 | 2023 |
| Deep neural network-based hybrid modeling and experimental validation for a full-scale bio-fermentation process: Identification of time-varying dependencies among parameters P Shah, Z Sheriff, MSF Bangi, J Kwon, C Kravaris, C Botre, J Hirota Available at SSRN 3978852, 2022 | 3 | 2022 |
| Universal hybrid modeling of batch kinetics of aerobic carotenoid production using Saccharomyces Cerevisiae MSF Bangi, JSI Kwon 2022 American Control Conference (ACC), 3716-3721, 2022 | 2 | 2022 |
| Modeling, order-reduction, and controller design of hydraulic fracturing P Shah, MSF Bangi, JS Kwon Energy Systems and Processes: Recent Advances in Design and Control, 12-1, 0 | 2 | |
| Developing a hybrid model of a biochemical fermentation process C Kravaris, J Kwon, P Shah, MZ Sheriff, MSF Bangi, C Botre, J Hirota US Patent App. 18/037,296, 2024 | | 2024 |
| Control Lyapunov-Barrier function-based predictive control using a deep hybrid model with guarantees on domain of applicability MSF Bangi, JSI Kwon 2023 American Control Conference (ACC), 1819-1824, 2023 | | 2023 |
| ORDER-REDUCTION, AND CONTROLLER DESIGN OF HYDRAULIC FRACTURING P Shah, MSF Bangi, JSI Kwon Energy Systems and Processes, 1, 2023 | | 2023 |
| Hybrid Modeling Using Universal Differential Equations for Lab-Scale Batch Production of β-Carotene Using Saccharomyces Cerevisiae MSF Bangi, K Kao, J Kwon 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Closed-Loop Optimal Control Operation of an Industry-Scale Bioreactor and Experimental Validation P Shah, MZ Sheriff, MSF Bangi, J Kwon, C Kravaris, C Botre, J Hirota 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Hybrid Model-Based MPC with Guarantees on Stability and Applicability Domain: Application to Chemical, Biochemical, and Hydraulic Fracturing Systems MSF Bangi, J Kwon 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Meta-Modeling-Based Sensitivity Analysis of Hybrid Models SV Lakhwani, P Shah, MSF Bangi, J Kwon 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Hybrid Modeling Approaches Integrating Physics-Based Models with Machine Learning for Predictive Control of Biological and Chemical Processes MSF Bangi Texas A&M University, 2022 | | 2022 |
Joseph Sang-Il KwonKenneth R. Hall Associate Professor, Artie McFerrin Department of Chemical Engineering, TAMU在 tamu.edu 的电子邮件经过验证
