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 BoilersBased on Self⁃Learning Optimization

为了使燃烧模型更准确地反演锅炉中煤粉的燃尽状态,基于遗传算法改进的神经网络模型,结合锅炉尾部烟道CO在线监测系统,构建基于自学习寻优的锅炉燃烧优化模型,建立了CO体积分数与锅炉热效率的关系。根据自学习寻优结果,对燃煤锅炉的出口氧量、配风方式和燃尽风(SOFA风)进行调整。研究发现,将出口氧量由3.0%调整至2.5%和3.5%,锅炉热效率分别提高了0.53%和0.49%;将锅炉的配风方式调整为缩腰配风和正宝塔配风方式,锅炉热效率分别提高了0.57%和0.73%;将锅炉A、B两侧SOFA风风门开度由87.4%调整为86.7%,锅炉热效率提高了0.71%,降低了热损失。

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.