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.

中文翻译：

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一种高效合成大规模非尖锐蒸馏系统的新型随机优化方法

本研究提出了一种新的随机优化方法，用于有效合成大规模非尖锐蒸馏系统，其中可以同时涉及热集成和热耦合。开发了一种新的二叉树编码方法来表示蒸馏序列，在非锐化分裂中没有中间分量的数量限制，以确保完整的解空间。热耦合结构由 0-1 个二元变量定义。进化规则被开发来随机生成相邻的蒸馏配置。最后，提出了一个优化框架，其中使用模拟退火（SA）算法来优化蒸馏配置；对于SA随机生成的某个蒸馏配置，其连续变量使用粒子群优化算法进行优化。