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A comparative study of model approximation methods applied to economic MPC
Author(s) -
Huang Zhiyinan,
Liu Qinyao,
Liu Jinfeng,
Huang Biao
Publication year - 2022
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.24398
Subject(s) - benchmark (surveying) , computational complexity theory , computer science , process (computing) , artificial neural network , identification (biology) , decomposition , mathematical optimization , reduction (mathematics) , model predictive control , subspace topology , control (management) , artificial intelligence , mathematics , algorithm , ecology , botany , geometry , geodesy , biology , geography , operating system
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for next‐generation smart manufacturing. It has the potential to not only improve economic performance but also significantly increase computational complexity. 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. 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.