结合可解释的 CatBoost 算法和改进的 Slime Mould 算法的多目标优化框架,用于解决锅炉燃烧优化问题。

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Shan Gao, Yunpeng Ma
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引用次数: 0

摘要

循环流化床锅炉的燃烧优化问题被认为是一个困难的多目标优化问题,需要同时提高锅炉热效率和降低氮氧化物排放浓度。为了解决上述问题,本文提出了一种新的多目标优化框架,该框架结合了可解释的 CatBoost 模型和改进的粘模算法。首先,结合 TreeSHAP 的可解释 CatBoost 模型被应用于锅炉热效率和氮氧化物排放浓度的建模。同时,根据建立的模型进行数据关联分析。最后,提出了一种改进的粘模算法,并用于优化一台 330 兆瓦循环流化床锅炉的可调运行参数。实验结果表明,所提出的框架能有效提高锅炉热效率并降低氮氧化物排放浓度,其中热效率的平均优化率达到 +0.68%,氮氧化物排放浓度的平均优化率达到 -37.55%,平均优化时间为 6.40 s。因此,所提出的框架是复杂系统建模和优化的有效人工智能方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Objective Optimization Framework That Incorporates Interpretable CatBoost and Modified Slime Mould Algorithm to Resolve Boiler Combustion Optimization Problem.

The combustion optimization problem of the circulation fluidized bed boiler is regarded as a difficult multi-objective optimization problem that requires simultaneously improving the boiler thermal efficiency and reducing the NOx emissions concentration. In order to solve the above-mentioned problem, a new multi-objective optimization framework that incorporates an interpretable CatBoost model and modified slime mould algorithm is proposed. Firstly, the interpretable CatBoost model combined with TreeSHAP is applied to model the boiler thermal efficiency and NOx emissions concentration. Simultaneously, data correlation analysis is conducted based on the established models. Finally, a kind of modified slime mould algorithm is proposed and used to optimize the adjustable operation parameters of one 330 MW circulation fluidized bed boiler. The experimental results show that the proposed framework can effectively improve the boiler thermal efficiency and reduce the NOx emissions concentration, where the average optimization ratio for thermal efficiency reaches +0.68%, the average optimization ratio for NOx emission concentration reaches -37.55%, and the average optimization time is 6.40 s. In addition, the superiority of the proposed method is demonstrated by ten benchmark testing functions and two constrained optimization problems. Therefore, the proposed framework is an effective artificial intelligence approach for the modeling and optimization of complex systems.

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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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