入炉垃圾热值不稳定,对焚烧炉的稳定运行有很大影响。采用遗传算法优化BP神经网络的权值和阈值,建立垃圾焚烧炉入炉垃圾热值的预测模型。利用Garson方法和主成分分析法对某垃圾焚烧电厂在线运行数据进行分析后,作为BP神经网络的输入参数,实现入炉垃圾热值的在线测量和预测。研究结果表明,该模型预测平均相对误差为2.64%,检验样本相对误差平均值概率为95%的置信区间为[-1.75,2.59],有较高的准确性和置信度,具有较好的工程应用价值。

The heating values of municipal refuse entering into the incinerator are unstable, which have a great influence on the stable operation of the incineration. By using genetic algorithms to optimize initial weighs and thresholds of BP neural network, a predictive model is established to predict the heating values of municipal refuse. The online operating data are processed based on Garson method and principal component analysis, and then those data are used as input parameters of BP neural network. The measurement and prediction of the heating values of municipal refuse are able to be obtained on-line. The results show that the relative average error for the predicted values is 2.64%. The confidence interval (95%) of the relative average error for the test samples is between -1.75 and 2.59. Therefore, the model has both high accuracy and high confidence level, which is very suitable for engineering applications.