  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.
