Sparse Identification of Chemical Processes: A Feasibility Study

Abstract

In the ongoing search for efficient techniques to identify and validate dynamic models in various fields of science and engineering, the sparse identification of nonlinear dynamics (SINDy) algorithm has emerged as a promising tool to carry out this task. In this study, SINDy is applied to identify two chemical processes: pH neutralization and temperature control in a bioreactor. To accomplish this, firstly, the dynamic behavior of each system was modeled, and data were collected for both processes. Then, the studied algorithm was employed to identify the dynamic models of each process. Then, the identified models by SINDy were validated by comparing them with first-principles models. Several tests were conducted to assess the capability of SINDy-based models in predicting the dynamic behavior of the pH and temperature processes. The results revealed a satisfactory agreement between the models obtained by SINDy and the first-principles models. Hence, we show the SINDy algorithm could be reliable tool for identifying dynamic models for chemical processes.