Experimental Research on Combustion Optimization of Coal-Fired Boilers Based on Self-Learning Optimization
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.
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