Double loop iterative strategies for hierarchical control of industrial processes

Abstract Two novel hierarchical structures are presented which determine the optimum steady-state operation of interconnected industrial processes despite deficiencies in the mathematical model. The techniques are of an iterative type and involve the successive solution of system optimization and model parameter estimation problems, utilizing information feedback from the real process. The computational task of each iterative strategy is divided into two nested iterative loops. The inner loop involves model based computations only, while the outer loop requires measurements from the real process. It is shown that this model based double iterative loop strategy has an important practical advantage in that it reduces the required number of set point changes to real subprocesses in order to achieve optimality. The paper investigates the optimality and convergence conditions of the techniques and gives a simulation example to illustrate the methods.