Surrogate modeling-based multi-objective optimization for the integrated distillation processes

Although multi-objective optimization of integrated distillation processes can substantially improve process design, the nonlinearity and complexity of the process results in high computational expense for optimization. Here, an approach incorporating surrogate modeling into multi-objective optimization is proposed, in which surrogate models for function evaluation are constructed by using the RBF neural network. Central composite design was adopted as a sampling strategy and surrogate models were individually constructed for different optimization objectives to improve prediction accuracy. Multi-objective bat algorithm was set as an optimizer to obtain the Pareto front. This surrogate modeling-based multi-objective optimization approach was applied to the design of dividing wall column and side-reactor column configuration, and the satisfied design options realizing the trade-offs between capital and operating costs were successfully obtained thereafter.