Scheduling System using Genetic Algorithm for Batch Processes considering Heat Recovery

Although energy conservation is in demand worldwide, energy efficiency has not been given much consideration in batch processes where high-value-added products are produced in. However, even in batch processes, energy saving needs to be taken into consideration. In the chemical plants, direct heat recovery between hot and cold streams is one of the major technologies to achieve energy conservation. This technology has been introduced mainly in continuous processes such as petrochemical production. And it is widely used in current continuous plants. However, it is difficult to implement in batch processes because there must be both hot and cold streams at the same time for direct heat recovery. Therefore, I have developed a scheduling system to consider heat recovery in batch processes. In this system, the schedules are generated using genetic algorithm. In this study, the performances of the schedules are compared from the perspective of the "selection" and/or "crossover" methods of the genetic algorithm.