Data-driven LQG benchmaking for economic performance assessment of advanced process control systems

Abstract

In this paper, a data-driven subspace approach for economic performance assessment of the advanced process control (APC) systems is presented. The method introduces LQG tradeoff curve to estimate potential of reduction in variance, which is directly obtained from subspace matrices using closed loop data. To exploit feasible economic performance of the APC systems, the proposed approach considers the uncertainties induced by process variability and evaluates the economic performance through solving stochastic optimization problem. Results of the performance evaluation provide a guideline for the control system tuning to realize the potential improvement in profitability of process. The application of the proposed method is illustrated by its benefits evaluation on a simulated example.