A comparative study of model approximation methods applied to economic MPC

TL;DR

This paper compares three model approximation methods—model reduction, system identification, and neural networks—for economic model predictive control, analyzing their computational efficiency and economic performance on two different processes.

Contribution

It provides a comprehensive comparison of three representative approximation methods applied to EMPC, highlighting their strengths and limitations through simulation on benchmark processes.

Findings

Model reduction offers computational efficiency but may reduce accuracy.

System identification provides explicit models with balanced performance.

Neural networks capture complex dynamics but can be computationally intensive.

Abstract

Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing. It can lead to improving economic performance but at the same time increases the computational complexity significantly. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely an alkylation process and a wastewater treatment plant (WWTP). The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control oriented model approximation proposed at the end.