Abstract
This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a dynamic simulation, a neural network, and an interactive multi-objective programming technique. In this study, a new intelligent optimization method using the neural network and a genetic algorithm to realize a satisficing trade-off method, which is an interactive multi-objective programming technique, has been developed and introduced into this system. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) an optimal and flexible start-up schedule can be determined with a reasonable computing time and calculation accuracy through human-computer interactions. The system is applied to a simulation for a combined cycle power plant, and to optimize from among multiple objectives, based on varying daily requirements. The application results show that optimal and flexible start-up schedules can be obtained within a reasonable computing time and with acceptable calculation accuracy.
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