基于机器学习的预测模型对火电燃料燃烧效果的优化研究
Research on optimization of thermal power fuel combustion effect based on machine learning prediction model
王利兵1
作者信息
- 1. 国能长源荆门发电有限公司,湖北 荆门 448000
- 折叠
摘要
文章旨在提出一种基于机器学习的预测模型,用于优化火电燃料燃烧效果.使用了深度神经网络(DNN)和支持向量机(SVM)等机器学习算法,结合燃烧理论和工程实践,建立了燃烧效果预测模型.通过对燃料特性、燃烧参数及环境因素的综合分析,提出了一种有效的优化策略,可提高火电厂燃料燃烧效率和减少排放.实验结果表明,所提出的模型具有良好的预测精度和鲁棒性,为火电行业提供了一种新的优化手段.
Abstract
This paper aims to propose a prediction model based on machine learning to optimize the combustion effect of thermal power fuel.Using deep neural network(DNN)and support vector machine(SVM)and other machine learning algorithms,combined with combustion theory and engineering practice,a combustion effect prediction model is established.Through the comprehensive analysis of fuel characteristics,combustion parameters and environmental factors,an effective optimization strategy is proposed,which can improve the fuel combustion efficiency and reduce emissions in thermal power plants.The experimental results show that the proposed model has good prediction accuracy and robustness,and provides a new optimization method for the thermal power industry.
关键词
机器学习/火电燃料/燃烧效果/预测模型/优化策略Key words
machine learning/thermal power fuel/combustion effect/prediction model/optimization strategy引用本文复制引用
出版年
2024
NETL
NSTL
万方数据