Controlling Paracetamol Unseeded Batch Crystallization with NMPC and Inverse Model

In this work, two model-based controllers were developed, one based on a nonlinear model-based controller (NMPC) using a population balance model (PBM) and another using a machine learning approach based on a neural network inverse model-based controller (NNIMC). The performance of the two model-bas...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2024, Vol.58 (14), p.31-36
Main Authors: Lima, Fernando Arrais R.D., de Moraes, Marcellus G.F., Grover, Martha A., Barreto, Amaro G., Secchi, Argimiro R., de Souza, Maurício B.
Format: Article
Language:English
Subjects:
Batch process control
Machine learning
Model predictive control
Neural networks
NNIMC
Noise
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Summary:In this work, two model-based controllers were developed, one based on a nonlinear model-based controller (NMPC) using a population balance model (PBM) and another using a machine learning approach based on a neural network inverse model-based controller (NNIMC). The performance of the two model-based controllers was compared for different scenarios to obtain optimal temperature policies for controlling the mass yield and crystals’ size for the unseeded batch cooling crystallization of paracetamol. The results show that both strategies are effective for crystallization control, presenting comparable results for the controlled variables in different scenarios. The controllers were also tested by applying random noise in the state variables. In these cases, the NNIMC presented advantages in having a lower computational cost for optimum control action calculations and less control effort regarding the manipulated variable’s variation to reach values for the control variables at the end of the batch close to the NMPC and the setpoints.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2024.08.309