Software

Note: you can find some open-source codes developed in our papers. Please kindly cite them if you find the codes useful. Thank you.

 

1. Machine-learning-based model predictive control | Link: https://github.com/GuoQWu/RNN-based-MPC

  • Wu, Z., A. Tran, D. Rincon and P. D. Christofides, “Machine Learning-Based Predictive Control of Nonlinear Processes. Part I: Theory,” AIChE J., 65, e16729, 2019.
  • Wu, Z., A. Tran, D. Rincon and P. D. Christofides, “Machine Learning-Based Predictive Control of Nonlinear Processes. Part II: Computational Implementation,” AIChE J., 65, e16734, 2019.

2. Physics-informed machine learning for modeling chemical processes | Link: https://github.com/Keerthana-Vellayappan/Demonstration-of-Physics-Informed-Machine-Learning-Model

  • Zheng, Y., C. Hu, X. Wang, and Z. Wu, “Physics-Informed Recurrent Neural Network Modeling for Predictive Control of Nonlinear Processes“, J. Proc. Contr., 128, 103005, 2023.

3. Transfer learning for modeling chemical processes | Link: https://github.com/MingXiaop/Transfer-Learning-for-nonlinear-chemical-process

  • Xiao, M., C. Hu, and Z. Wu, “Modeling and Predictive Control of Nonlinear Processes Using Transfer Learning Method“, AIChE J., 69, e18076, 2023.

4. Lipschitz-constrained neural network for modeling nonlinear systems | Link: https://github.com/killingbear999/lipschitz-constrained-neural-networks

  • Tan, W., and Z. Wu. “Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks“, Comp. & Chem. Eng., 180, 108466, 2024.

5. Explicit model predictive control for machine-learning-based MPC | Link: https://github.com/Wenlong-Codes/ExplicitML-MPC

  • Wang, W., Y. Wang, Y. Tian, and Z. Wu. “Explicit Machine Learning-Based Model Predictive Control of Nonlinear Processes via Multi-Parametric Programming“, Comp. & Chem. Eng., 186, 108689, 2024.

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