Optimization of Power Plant Design: Stochastic and Adaptive Solution Concepts

Abstract

Optimizing the design of industrial plants requires making optimal structural decisions (concerning the selection and arrangement of various components) as well as optimizing all continuous design variables. This paper presents genetic and stochastic optimization strategies which are based on a representation of the plant by means of a modified decision tree. By taking into account hierarchical dependencies of decisions this representation guarantees that designs generated by mutation operators automatically comply with an important class of constraints. The method is explained and its potential is demonstrated with the example of an important industrial application problem: The design optimization of feed-water heater strings in fossil-fueled power plants. For the treatment of the structural and continuous design variables two strategies have been implemented and tested. The first approach considers structural decisions as the primary problem, which is solved by means of a Metropolis algorithm, and regards the optimization of the continuous variables as a subproblem, which is solved by Sequential Quadratic Programming for each generated plant structure. The second strategy is an evolutionary one-level algorithm which simultaneously optimizes both types of variables. In the design problem which is investigated here, the one-level Evolutionary computation algorithm performs slightly better than the hierarchical method. This result is explained by analyzing the objective function.