Abstract: This paper describes an application of fuzzy-logic and evolutionary computation to the optimization of the start-up phase of a combined cycle power plant. We modelled process experts’ knowledge with fuzzy sets over the process variables in order to get the needed cost function for the Genetic Algorithm (GA) we used to obtain the optimal regulations. Due to the obvious impossibility to test the resulting inputs on the real plant we used a complex software simulator to evaluate the performance of the solutions. In order to reduce the computational load of the whole procedure we implemented for the genetic algorithm a novel fitness approximation technique, cutting by 98% the number of fitness evaluations, i.e. software simulator runs with respect to a Genetic Algorithm without fitness approximation. Moreover, solutions found by our methods remarkably improved the solutions given by the plant operators.
Abstract: This paper proposes a hybrid approach based on soft computing techniques in order to estimate monthly and daily ambient temperature. Indeed, we combine the back-propagation (BP) algorithm and the simple Genetic Algorithm (GA) in order to effectively train artificial neural networks (ANN) in such a way that the BP algorithm initialises a few individuals of the GA’s population. Experiments concerned monthly temperature estimation of unknown places and daily temperature estimation for thermal load computation. Results have shown remarkable improvements in accuracy compared to traditional methods.
