Continuous control of a polymerization system with deep reinforcement learning Y Ma, W Zhu, MG Benton, J Romagnoli Journal of Process Control 75, 40-47, 2019 | 121 | 2019 |
Data mining and clustering in chemical process databases for monitoring and knowledge discovery MC Thomas, W Zhu, JA Romagnoli Journal of Process Control 67, 160-175, 2018 | 83 | 2018 |
Deep learning based soft sensor and its application on a pyrolysis reactor for compositions predictions of gas phase components W Zhu, Y Ma, Y Zhou, M Benton, J Romagnoli Computer Aided Chemical Engineering 44, 2245-2250, 2018 | 53 | 2018 |
Investigation of transfer learning for image classification and impact on training sample size W Zhu, B Braun, LH Chiang, JA Romagnoli Chemometrics and Intelligent Laboratory Systems 211, 104269, 2021 | 51 | 2021 |
A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding W Zhu, ZT Webb, K Mao, J Romagnoli Industrial & Engineering Chemistry Research 58 (22), 9564-9575, 2019 | 44 | 2019 |
Adaptive k-nearest-neighbor method for process monitoring W Zhu, W Sun, J Romagnoli Industrial & Engineering Chemistry Research 57 (7), 2574-2586, 2018 | 36 | 2018 |
A deep learning image-based sensor for real-time crystal size distribution characterization V Manee, W Zhu, JA Romagnoli Industrial & Engineering Chemistry Research 58 (51), 23175-23186, 2019 | 33 | 2019 |
Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system W Zhu, Y Ma, MG Benton, JA Romagnoli, Y Zhan AIChE Journal 65 (2), 582-591, 2019 | 33 | 2019 |
A machine learning approach to optimize shale gas supply chain networks HI Asala, J Chebeir, W Zhu, I Gupta, AD Taleghani, J Romagnoli SPE Annual Technical Conference and Exhibition?, D031S030R005, 2017 | 33 | 2017 |
Operation optimization of a cryogenic NGL recovery unit using deep learning based surrogate modeling W Zhu, J Chebeir, JA Romagnoli Computers & Chemical Engineering 137, 106815, 2020 | 31 | 2020 |
Online optimal feedback control of polymerization reactors: Application to polymerization of acrylamide–water–potassium persulfate (kps) system N Ghadipasha, W Zhu, JA Romagnoli, T McAfee, T Zekoski, WF Reed Industrial & Engineering Chemistry Research 56 (25), 7322-7335, 2017 | 21 | 2017 |
Framework design for weight-average molecular weight control in semi-batch polymerization SD Salas, N Ghadipasha, W Zhu, T Mcafee, T Zekoski, WF Reed, ... Control Engineering Practice 78, 12-23, 2018 | 20 | 2018 |
Generic process visualization using parametric t-SNE W Zhu, Z Webb, X Han, K Mao, W Sun, J Romagnoli IFAC-PapersOnLine 51 (18), 803-808, 2018 | 8 | 2018 |
Benchmark study of reinforcement learning in controlling and optimizing batch processes W Zhu, I Castillo, Z Wang, R Rendall, LH Chiang, P Hayot, JA Romagnoli Journal of Advanced Manufacturing and Processing 4 (2), e10113, 2022 | 5 | 2022 |
Control of a polyol process using reinforcement learning W Zhu, R Rendall, I Castillo, Z Wang, LH Chiang, P Hayot, JA Romagnoli IFAC-PapersOnLine 54 (3), 498-503, 2021 | 5 | 2021 |
General feature extraction for process monitoring using transfer learning approaches W Zhu, J Zhang, J Romagnoli Industrial & Engineering Chemistry Research 61 (15), 5202-5214, 2022 | 4 | 2022 |
Online DEKF for state estimation in semi-batch free-radical polymerization reactors SD Salas, N Ghadipasha, W Zhu, JA Romagnoli, T Mcafee, WF Reed Computer Aided Chemical Engineering 40, 1465-1470, 2017 | 4 | 2017 |
A Deep Learning Approach on Industrial Pyrolysis Reactor Monitoring. W Zhu, Y Zhan, JA Romagnoli CET Journal-Chemical Engineering Transactions 74, 2019 | 1 | 2019 |
Applying Reinforcement Learning to Control Batch Processes W Zhu, Z Wang, I Castillo, R Rendall, L Chiang, JA Romagnoli 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
A Deep Learning Approach on Surrogate Model Optimization of a Cryogenic NGL Recovery Unit Operation W Zhu, J Chebeir, Z Webb, J Romagnoli Computer Aided Chemical Engineering 48, 1285-1290, 2020 | | 2020 |