Plengsangsri, TakornTakornPlengsangsriJeffrey Daniel Ward2023-10-302023-10-302023-01-019798350339055https://scholars.lib.ntu.edu.tw/handle/123456789/636658Polybutylene succinate (PBS) is a biodegradable plastic that has received attention due to its strength and versatility in a variety of applications. In this research, a neural network model-based predictive control strategy (NNMPC) and multiple neural network model based predictive control (Multi-NNMPC) using Python are developed for temperature control of the polybutylene succinate polymerization process during the esterification and polycondensation steps. The polymerization process is highly nonlinear. Therefore, a conventional model predictive controller requires a long time to perform the optimization at each time step. A neural network model can learn the process dynamics and efficiently predict the optimal value of the manipulated variable 5 to 20 times faster using the SLSQP optimization method from the SciPy library. NNMPC and Multi-NNMPC were compared to split range PID and first-principles model based MPC control using the IAE performance criteria for several scenarios.artificial neural network | machine learning | model predictive control | poly butylene succinate | split range controlconference paper10.1109/ICCSSE59359.2023.102453472-s2.0-85173905953https://api.elsevier.com/content/abstract/scopus_id/85173905953