Optimal operation strategies for batch distillation by using a fast adaptive simulated annealing algorithm

Batch distillation processes are widely used in the chemical industry. In this work, the optimal operation strategies for such processes are studied by using a fast adaptive simulated annealing (FASA) algorithm. Simulated annealing algorithm is stochastic in nature, and it converges towards a global optimum. However, its computational load is usually much too heavy. In this study, a FASA algorithm was presented with fast and adaptive moves in the searched neighborhood range to decrease the computation load. According to the characteristics of batch distillation process, a FASA-based parallelized optimization computation approach was proposed and then it was applied to a model of a batch distillation plant. The optimal operation strategies with respect to minimal production time and maximal profit were studied. The results show the effectiveness of the method.