A penalty-free hybrid algorithm framework based on feasible stream matching principle for large-scale heat exchanger networks synthesis

•A penalty-free hybrid stochastic-deterministic algorithm framework is proposed for large-scale HENS.•The proposed framework significantly reduces the complexity of HENS and effectively takes advantages of the strengths of stochastic and deterministic methods.•A novel HEZV is proposed to expand the...

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Bibliographic Details
Published in:	Chemical engineering science Vol. 298; p. 120419
Main Authors:	Yang, Lu, Ren, Jingzheng, Eden, Mario, Chang, Chenglin, Shen, Weifeng
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 05.10.2024
Subjects:	Feasible stream matching principle Heat exchanger networks synthesis Hybrid stochastic-deterministic algorithm Penalty terms Reduced-size MINLP model Feasible stream matching principle Heat exchanger networks synthesis Hybrid stochastic-deterministic algorithm Reduced-size MINLP model Penalty terms
ISSN:	0009-2509
Online Access:	Get full text
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Summary:	•A penalty-free hybrid stochastic-deterministic algorithm framework is proposed for large-scale HENS.•The proposed framework significantly reduces the complexity of HENS and effectively takes advantages of the strengths of stochastic and deterministic methods.•A novel HEZV is proposed to expand the potential of the framework for producing HEN populations.•A feasible stream matching principle is developed to prevent the generations of infeasible HENs without the need for penalty terms. In this work, a penalty-free hybrid stochastic-deterministic algorithm framework is proposed for large-scale heat exchanger networks (HENs) synthesis (HENS), formulated as a computationally-hard mixed-integer nonlinear programming (MINLP) problem. In the outer level, an improved genetic algorithm (GA) is developed to optimize process stream matches represented by integer variables whose values are generated by a unique heat exchanger vector. Unlike previous researches, the improved GA does not rely on any penalty terms, because we propose a feasible stream matching principle to exclude all infeasible process stream matches and only feasible matches are considered in optimization process. In the inner level, a reduced-size MINLP model is solved using deterministic methods to minimize total annualized costs (TACs), which are then used to evaluate the fitness of candidate HENs. Through this way, the proposed framework combines deterministic and stochastic methods to enhance optimization efficiency and global search capability. Illustrative tests on six benchmark cases demonstrate that the framework can efficiently achieve lower-cost solutions compared to deterministic, stochastic, or hybrid methods. The results show a decrease in TAC for all six cases and a reduction in solution time ranging from 11.1% to 97.2%. Importantly, the proposed framework can be extended to solve MINLP problems in other process networks.
ISSN:	0009-2509
DOI:	10.1016/j.ces.2024.120419