Two-tier search space optimisation technique for tuning of explicit plant-model mismatch in model predictive controller for industrial cement kiln process

Abstract

Optimal control of cement kiln is demanding to ensure cement quality and minimal energy usage in cement industries. Plant-model mismatch (PMM) in the prediction model predominantly determines the Model Predictive Controller (MPC) performance. The proposed work aims to determine the optimal PMM parameters that can improve the MPC performance under various scenarios of cement kiln operations. Many parameters in a MIMO transfer function model of cement kiln make it a higher-dimensional problem. Gain and time-constant of the individual First Order Plus Time Delay Model models are considered as tunable PMM parameters. A novel two-tier optimisation algorithm has been proposed to optimise the search space and reduce PMM tuning complexity. Tier-1 uses Ant Colony Optimisation (ACO) to identify the PMM parameters using combinatorial optimisation, and Tier-2 employs a Genetic algorithm (GA) to tune the identified PMM parameters. Five control scenarios encountered during cement kiln operations, including tracking and rejection of Pulse and Gaussian disturbances, have been considered in this study. Experimental results illustrate a reduction of 32.5% of PMM parameters with the use of Tier-1. GA-tuned PMM parameters improve MPC’s transient behaviour at a reduced energy loss across all the control scenarios.