Co-Teaching Approach to Machine Learning-based Predictive Control of Nonlinear Processes

Abstract

Abstract Machine learning modeling of chemical processes using noisy data is practically a challenging task due to the occurrence of overfitting during learning. In this work, we propose a co-teaching learning algorithm that develops Long short-term memory (LSTM) networks to capture the ground truth (i.e., underlying process dynamics) from noisy data. We consider an industrial chemical reactor example and use Aspen Plus Dynamics to generate process operational data that is corrupted by sensor noise generated by industrial noisy measurements. An LSTM model is developed using the co-teaching method with additional noise-free data generated from simulations of the reactor first-principles model. Through open-loop and closed-loop simulations, we demonstrate that compared to the LSTM model developed from the standard training process, the co-teaching LSTM model is more accurate in predicting process dynamics, and therefore, achieves better closed-loop performance under model predictive control.