Comparative Study of Machine Learning and System Identification for Process Systems Engineering Dynamics

Abstract

-fold cross-validation and information criteria are small, information criteria emerge as a computationally efficient alternative. Once the "best" model structure is decided, in terms of model performance, we find that ML models with balanced complexity, such as tree ensemble models, consistently achieve superior predictive accuracy and computational efficiency, outperforming both simplistic and overly complex models. These findings provide actionable insights into model selection and performance evaluation for PSE practitioners and demonstrate the potential of incorporating MLOps-inspired workflows into the system identification process.

Topics & Concepts