Implementation of a neural network MPC for heat exchanger network temperature control

Abstract

Heat exchanger network (HEN) control is considered to be a difficult task due to its nonlinear behavior, complexity, presence of disturbances and noise. The optimal operation of HEN implies the implementation of a robust control system able to overcome all these issues. This work presents an advanced technique based on an artificial neural network model predictive control (NNMPC) and compares the controlling performance to three widely used controllers: two PID and a linear MPC. The goal is to control an outlet oil stream temperature of a HEN containing four heat exchangers in counter-current. A lumped parameter model was used to describe the HEN system. The data generated were used to train a neural network in the Matlab environment using the Levenberg-Marquardt algorithm. Closed-loop negative and positive step responses were simulated to investigate set point tracking and disturbance rejection. The ability to handle constraints was evidenced in MPC, as well as faster settling time and minimum overshoot.