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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。
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.
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基本信息:
DOI：10.19929/j.cnki.nmgdljs.2024.0046
中图分类号:
引用信息:
[1]彭昭雄1,周健1,刘兵兵1等.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(03):94-100.DOI:10.19929/j.cnki.nmgdljs.2024.0046.
基金信息:
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”