Augmented Model Based Double Iterative Loop Techniques for Integrated System Optimisation and Parameter Estimation of Large Scale Industrial Processes

Abstract

Two novel hierarchical structures are presented which extend the applicability of previous model based double iterative loop techniques to non-convex problems. The models incorporate integrated system optimization and parameter estimation which utilize process measurements to achieve real process optimality inspite of model reality differences. The double iterative loop structures of the proposed algorithms use the real process measurements within the outer loops while the inner loops involve model based computation only. By this means the algorithm use available information from the real process efficiently and a significant reduction in set-point alterations to real subprocesses is achieved. In order to cater for process complexity the inner loops are organized in the form of two level hierarchical structures. The paper presents the convergence conditions of the augmented techniques and simulation examples are provided to illustrate and compare the methods.