Spatiotemporal integrated control for ballast water heat treatment via the kernel learning and model predictive path integral

Abstract

In this article, a spatiotemporal integrated control scheme for ballast water heat treatment is proposed that utilizes an improved nonlinear predictive control algorithm relying on a kernel-learning-based model to lower the concentration of microorganisms by manipulating the temperature of heated water indirectly. Firstly, multiple heat exchangers treating process is simplified into a plug flow reactor model with the properties of distributed parameter systems (DPSs). Based on the simplified model, the kernel-learning-based model is derived by using kernel principal component analysis (KPCA) and kernel extreme learning machine (KELM) for modeling the spatiotemporal temperature data. Further, the hyperparameters of the KELM involved therein are determined by a numerical optimization approach. The superiority of this design is to accurately explore the nonlinear dynamics and uncertainties of the actual system. Associated with the modeling method, the nonlinear predictive control strategy is designed to control and maintain the heating temperature. The remarkable trait is that a model predictive path integral (MPPI) is introduced to avoid the problem of “sinking into the local optimal solution”, which often emerges searching for the optimal control sequence. Finally, the stability analysis and numerical experiments support the effectiveness of the proposed scheme.