4.7 Article

An extended stage-wise superstructure for heat exchanger network synthesis with intermediate placement of multiple utilities

Journal

ENERGY
Volume 248, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123372

Keywords

Process integration; Heat exchanger networks synthesis; Simultaneous optimization; Optimal utility placement; Genetic algorithm

Funding

  1. National Natural Science Foundation of China [21736004, 21676101]

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The synthesis of heat exchanger networks is an important method to improve energy utilization efficiency and techno-economic performance. In this study, a new structure is proposed, which places multiple utilities at intermediate stages instead of at the ends of streams. The solution approach based on Genetic Algorithm is used to solve the complex mathematical model, and the validity of the method is verified through analysis of four examples.
The heat exchanger network (HEN) synthesis methodologies based on mathematical programming are recognized to be the significant methods to improve energy utilization efficiency and achieve a prominent techno-economic performance. As impacted by the combinatorial and nonconvex nature in the mathematical model, the problem of HEN synthesis may be easily trapped into suboptimal solutions. In this study, an extended stage-wise superstructure with intermediate placement of multiple utilities was presented. The heaters and coolers can be put in different stream split branches at intermediate stages, as opposed to the traditional allocation at stream ends. Such an extended superstructure yielded a more complex mathematical model compared to the stage-wise superstructure. To solve the model, the solution approach based on Genetic Algorithm (GA) was adopted. The local optimizing strategy, the adaptive scheme, the elite strategy and the structure identification and structure control strategy were introduced to enhance the efficiency and improve the search ability of the GA. Finally, the analysis of four examples verifies the validity of the presented meta-heuristics GA with optimization strategies, demonstrating that the proposed method can solve the complex model with acceptable computational effort and obtain better solutions with lower total annual costs than those reported in previous works. (c) 2022 Published by Elsevier Ltd.

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