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Combustion optimization of a boiler based on the chaos am Levy flight vortex search algorithm

机译:基于混沌征费飞行涡搜索算法的锅炉燃烧优化

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The boiler combustion optimization has attracted growing research interest of more and more researchers in thermal energy engineering field. To achieve combustion optimization objective, the combustion characteristics model of the boiler needs to be built, and the efficient optimization algorithm also needs to be found. Therefore, in this study, a logic self-map chaos and Lévy flight Vortex Search (I-VS) algorithm is proposed. Then, comparative study between the I-VS algorithm and some other state-of-the-art optimization algorithms is performed. Next, the combustion efficiency model is built based on fast learning network (FLN). The I-VS algorithm is used to optimize FLN, and then an I-VS-FLN model is built for predicting NOxemissions of a boiler. Experimental results show that the I-VS-FLN model has better generalization ability than five other models. Finally, the I-VS algorithm is used to tune the operating parameters of the boiler based on the FLN model and the I-VS-FLN model to achieve the combustion optimization objective.
机译:锅炉燃烧的优化吸引了热能工程领域越来越多的研究者的兴趣。为了达到燃烧优化的目的,需要建立锅炉的燃烧特性模型,并找到高效的优化算法。因此,本研究提出了一种逻辑自映射混沌和Lévy飞行涡旋搜索(I-VS)算法。然后,对I-VS算法与其他一些最先进的优化算法进行了比较研究。接下来,基于快速学习网络(FLN)建立燃烧效率模型。使用I-VS算法优化FLN,然后建立I-VS-FLN模型来预测锅炉的NOx排放。实验结果表明,I-VS-FLN模型具有比其他五个模型更好的泛化能力。最后,基于FLN模型和I-VS-FLN模型,使用I-VS算法对锅炉的运行参数进行调整,以达到燃烧优化的目的。

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