Process synthesis for the separation of coal-to-ethanol products

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 that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes. © 2024 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd