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

Akhil Ahmed¹, Ehecatl Antonio Del Rio-Chanona¹, Mehmet Mercangöz¹

¹Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2BX, U.K.

Industrial & Engineering Chemistry Research

|March 3, 2025

Summary

This summary is machine-generated.

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This study benchmarks traditional and machine learning (ML) models for dynamical systems modeling in process systems engineering (PSE). ML models with balanced complexity, like tree ensembles, offer superior predictive accuracy and efficiency.

Area of Science:

Process Systems Engineering (PSE)
Data-Driven Modeling
Machine Learning (ML)

Background:

Traditional system identification methods face challenges in complex dynamical systems.
Modern machine learning (ML) offers potential for improved data-driven modeling in PSE.
Integrating ML into PSE requires robust frameworks and evaluation strategies.

Purpose of the Study:

To benchmark traditional system identification versus ML models for PSE applications.
To evaluate the efficacy of MLOps-inspired tools for system identification tasks.
To provide insights into optimal model selection and performance evaluation in PSE.

Main Methods:

Utilized AutoSID, an automated framework inspired by Machine Learning Operations (MLOps).

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Published on: June 1, 2022

Compared 12 diverse model architectures (system identification, ML, deep learning) across 11 PSE case studies.

Employed 4 model search/hyperparameter optimization algorithms and 3 model selection criteria.

Main Results:

Model selection is critical for effective system identification.
Bayesian optimization with Tree-structured Parzen Estimators (TPE) is effective for balanced model selection.
K-fold cross-validation is a robust performance evaluation metric; information criteria are efficient for large datasets.
ML models with balanced complexity, such as tree ensembles, demonstrate superior predictive accuracy and computational efficiency.

Conclusions:

MLOps-inspired workflows can enhance system identification in PSE.
Balanced complexity ML models are recommended for superior performance in PSE.
Findings offer actionable insights for PSE practitioners in model selection and evaluation.