基于智能优化算法的电站锅炉燃烧优化
发布时间：2018-08-23 20:14
【摘要】：燃煤电站锅炉不仅消耗大量的煤炭资源,也排放出了大量的大气污染物。在提高锅炉效率降低煤耗和控制污染物排放来缓解高耗能和重污染的突出问题上,基于智能算法的锅炉燃烧优化方法相比传统方法有其独特优势,可以为电站锅炉高效率低污染运行提供有效的指导。通过分析300MW锅炉和1000MW锅炉的运行数据,利用支持向量回归机建立了NOx排放SVM模型和锅炉热效率SVM模型,比较了基于RBF核函数和Sigmoid核函数的模型性能；分别采用自适应遗传算法(GA)和果蝇优化算法(FOA)对模型参数进行优化,并比较了各自模型的泛化能力和预测精度性能,结果表明FOA优化的SVM模型更具有优势。以建立好的锅炉NOx排放和锅炉热效率的SVM模型为基础,结合自适应遗传算法,在不考虑对效率影响的情况下,单一对NOx排放进行优化时,NOx排放水平在优化后能够得到大幅降低,但是与此同时也会造成锅炉热效率的下降；在不考虑对NOx排放影响的情况下,单一对锅炉热效率进行优化时,锅炉热效率在优化后能有较大的增幅,但是同时会导致NOx排放量有所上升。文中以1000MW机组锅炉为对象,在建立NOx生成量和锅炉热效率SVM模型的基础上提出了一种运用改进的多目标遗传算法(改进型NSGA-Ⅱ)进行多目标燃烧优化的方法,同时考虑锅炉热效率和NOx生成这两个目标,对锅炉运行参数进行优化,得出了由一系列可行解组成的Pareto最优解集,其中有很多可行解同时满足锅炉效率的提高和NOx生成的降低这两个目标,为机组运行人员提供参考,达到高效率且低NOx生成的锅炉燃烧优化目的。
[Abstract]:The coal-fired utility boiler not only consumes a lot of coal resources, but also emits a lot of air pollutants. In order to improve boiler efficiency, reduce coal consumption and control pollutant emissions to alleviate the problem of high energy consumption and heavy pollution, the combustion optimization method based on intelligent algorithm has its unique advantages compared with traditional methods. It can provide effective guidance for high efficiency and low pollution operation of utility boiler. By analyzing the operation data of 300MW boiler and 1000MW boiler, the SVM model of NOx emission and the SVM model of boiler thermal efficiency are established by using support vector regression machine. The performance of the model based on RBF kernel function and Sigmoid kernel function is compared. Adaptive genetic algorithm (GA) and Drosophila Optimization algorithm (FOA) are used to optimize the model parameters, and the generalization ability and prediction accuracy of each model are compared. The results show that the SVM model optimized by FOA has more advantages. Based on the established SVM model of boiler NOx emission and boiler thermal efficiency, combined with adaptive genetic algorithm, without considering the effect on efficiency, the level of NOx emission can be greatly reduced when single optimization of NOx emissions is carried out. But at the same time, the boiler thermal efficiency will decrease. Without considering the influence of NOx emission, the boiler thermal efficiency can increase greatly when the boiler thermal efficiency is optimized. But it will also lead to an increase in NOx emissions. Taking the boiler of 1000MW unit as an object, based on the establishment of SVM model of NOx production and boiler heat efficiency, a method of multi-objective combustion optimization using improved multi-objective genetic algorithm (NSGA- 鈪，
本文编号：2199802
[Abstract]:The coal-fired utility boiler not only consumes a lot of coal resources, but also emits a lot of air pollutants. In order to improve boiler efficiency, reduce coal consumption and control pollutant emissions to alleviate the problem of high energy consumption and heavy pollution, the combustion optimization method based on intelligent algorithm has its unique advantages compared with traditional methods. It can provide effective guidance for high efficiency and low pollution operation of utility boiler. By analyzing the operation data of 300MW boiler and 1000MW boiler, the SVM model of NOx emission and the SVM model of boiler thermal efficiency are established by using support vector regression machine. The performance of the model based on RBF kernel function and Sigmoid kernel function is compared. Adaptive genetic algorithm (GA) and Drosophila Optimization algorithm (FOA) are used to optimize the model parameters, and the generalization ability and prediction accuracy of each model are compared. The results show that the SVM model optimized by FOA has more advantages. Based on the established SVM model of boiler NOx emission and boiler thermal efficiency, combined with adaptive genetic algorithm, without considering the effect on efficiency, the level of NOx emission can be greatly reduced when single optimization of NOx emissions is carried out. But at the same time, the boiler thermal efficiency will decrease. Without considering the influence of NOx emission, the boiler thermal efficiency can increase greatly when the boiler thermal efficiency is optimized. But it will also lead to an increase in NOx emissions. Taking the boiler of 1000MW unit as an object, based on the establishment of SVM model of NOx production and boiler heat efficiency, a method of multi-objective combustion optimization using improved multi-objective genetic algorithm (NSGA- 鈪，
本文编号：2199802
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