Global Self-Optimizing Control With Data-Driven Optimal Selection of Controlled Variables With Application to Chiller Plant

Abstract

In this paper, we propose a global self-optimizing control (SOC) approach, where nonlinear dynamic model is obtained from historical data of plant operation via the framework of sparse identification of nonlinear dynamics (SINDy) combined with regularized regression. With the nonlinear static input–output map obtained by forcing steady-state operation, the globally optimal solutions of controlled variables (CVs) can be found by tracking the necessary conditions of optimality (NCO) in an analytical fashion. After validation with a numerical example, the proposed method is evaluated using a modelica-based dynamic model of a chilled-water plant. The economic objective for chiller plant operation is to minimize the total power of compressor, condenser water pump, and cooling tower fan, while the cooling tower fan speed and condenser water mass flow rate are used as manipulated inputs. The operating data are generated based on realistic ambient and load conditions and a best-practice rule-based strategy for chiller operation. The control structure with the SOC method yields a total power consumption close to the global optimum and substantially smaller than that of a best-practice rule-based chiller plant control strategy. The proposed method promises a global SOC solution using dynamic operation data, for cost-effective and adaptive control structure optimization.