Augmented model-based double iterative loop techniques for hierarchical control of complex industrial processes

Abstract

Two novel hierarchical structures are presented which extend the applicability of previous model-based double iterative loop techniques using input-output information feedback to non-convex problems. The techniques incorporate integrated system optimization and parameter estimation, which utilize process measurements to achieve real process optimality in spite of model reality differences. The double iterative loop structures of the proposed algorithms use the real process measurements within the output loops while the inner loops involve model-based computations only. By this means the algorithms 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. An applicability of the techniques to non-convex problems is achieved by a suitable convexification of an original process performance index. It is a very important property of the convexification that it preserves two-level decomposibility of the problem. Sufficient conditions for convergence of the augmented techniques are derived. Simulation examples are provided to illustrate the methods.

Modifier Adaptation for Real-Time Optimization—Methods and Applications

“…Hence, one might wonder whether the exploitation of structures in the spirit of distributed NLP algorithms will yield benefits to RTO and MA schemes. The early works on distributed ISOPE methods [7,100,101] point into such a direction. Furthermore, a recent paper by Wenzel et al [102] argues for distributed plant optimization for the sake of confidentiality.…”