基于DCS数据和燃烧图像的垃圾焚烧炉主蒸汽温度预测

Prediction of Main Steam Temperature of Waste Incinerator Based on DCS Data and Combustion Image

针对垃圾焚烧锅炉因燃烧调控滞后而面临的主蒸汽温度波动的问题,该文提出一种基于分散控制系统(distributed control system,DCS)数据与燃烧图像的主汽温未来6min变化趋势的预测建模方法。首先,采集相关的DCS运行数据和燃烧图像并提取图像的谱范数特征;然后,利用互信息和条件互信息算法筛选出与主汽温高相关、变量间低冗余的DCS特征变量;然后,基于互信息算法对DCS特征变量和图像特征进行时延估计,剔除滞后特征变量并对超前特征变量进行时延补偿;最后,将DCS特征变量和图像特征作为输入,建立长短期记忆网络预测模型。结果表明:整体预测均方根误差(root mean square error,RMSE)为1.4722,且前2min的预测RMSE低至0.61;时延补偿和图像的加入可有效降低预测误差,考虑时延补偿的模型预测RMSE降低了22.61%,结合图像输入的模型预测RMSE降低了11.79%。可知,所提模型预测效果良好,可为生产调控提供参考。

Aiming at the problem of main steam temperature fluctuation faced by waste incineration boilers due to the lag of combustion control,a prediction modeling method of main steam temperature in the next 6 minutes based on distributed control system(DCS)data and combustion images is proposed.First,DCS data and combustion images are collected,and spectral norm features of images are extracted.Then,mutual information and conditional mutual information are used to select DCS feature variables highly correlated with main steam temperature and with low redundancy among variables.Then,the time delay of DCS feature variables and image features is estimated based on MI algorithm,the lagged features are eliminated and the delay compensation of advanced features is carried out.Finally,DCS feature variables and image features are used as input,and a prediction model of main steam temperature is established based on long short-term memory.The experiment shows that the root mean square error(RMSE)of the whole prediction is 1.4722,and the RMSE of the first 2 minutes is as low as 0.61.Time delay compensation and the addition of images effectively reduce the prediction error.The model prediction RMSE with time delay compensation is reduced by 22.61%,and the model prediction RMSE with image input is reduced by 11.79%.The model prediction effect is good,which can provide reference for production regulation.