A novel stochastic optimization method to efficiently synthesize large‐scale nonsharp distillation systems. Issue 9 (25th May 2021)

• Record Type: Journal Article
• Authors: Zhang, Shuo; Luo, Yiqing; Yuan, Xigang
• Is Part Of: AIChE journal. Volume 67:Issue 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-25
• Subjects: heat integration -- nonsharp distillation sequences -- stochastic optimization -- synthesis -- thermal coupling; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17328
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: This study presents a novel stochastic optimization method for the efficient synthesis of large‐scale nonsharp distillation systems, where heat integration and thermal coupling can be involved simultaneously. A new binary tree encoding method was developed to represent distillation sequences with no limits on the number of middle components in nonsharp splits to ensure a complete solution space. Thermally coupled structures were defined by 0–1 binary variables. Evolutionary rules were developed to generate neighboring distillation configurations randomly. Finally, an optimization framework was proposed, where simulated annealing (SA) algorithm was used to optimize distillation configurations; for a certain distillation configuration randomly generated by SA, its continuous variables were optimized using particle swarm optimization algorithm. Four cases—including the synthesis of six‐ and seven‐component nonsharp heat integrated and thermally coupled distillation sequences—were studied to demonstrate that the proposed method was efficient and could obtain optimal and valuable suboptimal solutions with high probabilities.