The coal-to-ethanol process

as the clean coal utilization

faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol. Referring to at least eight columns

the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used. This work adopts a superstructure-based framework

which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis. High-performance computing significantly reduces time consumption

and the adaptive strategy substantially lowers the complexity of the superstructure model. Moreover

parallel computing

elite search

population redistribution

and retention strategies for irrelevant parameters are used to improve the optimization efficiency further. The optimization terminates after 3000 generations

providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence. As a result

the 59-dimension superstructure-based optimization was solved efficiently

via

a differential evolution algorithm

and a high-quality solution with a 28.34% lower total annual cost than the benchmark was obtained. Meanwhile

the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one. It indicates that the superstructure-based optimization tha

t combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.