内蒙古电力技术2024，Vol.42Issue(3)：94-100,7.DOI:10.19929/j.cnki.nmgdljs.2024.0046
基于自学习寻优对燃煤锅炉燃烧优化的试验研究
Experimental Research on Combustion Optimization of Coal-Fired Boilers Based on Self-Learning Optimization
摘要
Abstract
In order to enhance the accuracy of the combustion model in reversing the burnout of pulverized coal in the boiler,the author constructs a boiler combustion optimization model based on self-learning optimization.The model is achieved by combining the improved neural network model of genetic algorithm with the CO online monitoring system.Besides,a relationship between CO volume fraction and boiler thermal efficiency is established.The outlet oxygen,air distribution methods,and burnout air(SOFA air)are adjusted based on self-learning optimization results.It is found that adjusting the export oxygen content from 3.0%to 2.5%and 3.5%increases the boiler thermal efficiency by 0.53%and 0.49%,respectively.Adjusting the air distribution method of the boiler to waist reduction and positive tower air distribution resulted in an increase in the boiler′s thermal efficiency by 0.57%and 0.73%,respectively.The opening of the SOFA air distribution doors on both of A an B sides is adjusted from 87.4%to 86.7%,resulting in a 0.71%increase in boiler thermal efficiency,which reduces heat loss.关键词
神经网络模型/遗传算法/CO在线监测/燃煤锅炉/燃烧效率/自学习寻优Key words
neural network model/genetic algorithm/CO on-line monitoring/coal-fired boilers/combustion efficiency/self-learning optimization分类
动力与电气工程引用本文复制引用
彭昭雄,周健,刘兵兵,龙飞,冯欣,杨祖旺..基于自学习寻优对燃煤锅炉燃烧优化的试验研究[J].内蒙古电力技术,2024,42(3):94-100,7.基金项目
四川广安发电有限责任公司科技项目"超低排放背景下锅炉燃烧控制与脱硝深度优化综合提效研究" （）
