基于GA-BP-Garson模型的市政污泥干燥过程含水率预测

Prediction of moisture content in municipal sludge drying process based on GA-BP-Garson model

市政污泥干燥过程中内部水分检测困难,为准确预测市政污泥热风干燥过程中内部水分的变化规律,将干燥时间、干燥温度、泥层厚度、流量压差作为输入变量,含水率作为输出变量,采用BP神经网络以及GA-BP神经网络分别建立市政污泥热风干燥过程的水分预测模型;对GA-BP神经网络进行敏感性分析,研究了4个输入变量对预测结果的影响。结果表明,BP和GA-BP 2种水分预测模型测试集的决定系数(R^(2))分别为0.99955和0.99964,均方根误差(RMSE)分别为0.51317和0.45523,即GA-BP预测模型的预测效果更佳,能更准确地预测市政污泥干燥过程中含水率的动态变化。敏感性分析表明,干燥时间对GA-BP含水率预测模型的影响最为显著。研究结果可为污泥干燥工艺和过程的优化提供理论依据,为污泥资源化利用提供参考。

During the municipal sludge drying process,the detection of internal moisture poses challenges.To accurately predict the variation pattern of internal moisture during the hot air drying process of municipal sludge,Back Propagation(BP)neural network and GA-BP(Genetic Algorithm Back Propagation)neural network algorithms were utilized to establish moisture prediction models for the municipal sludge hot air drying process,with drying time,drying temperature,sludge layer thickness,and flow differential pressure as input variables,and moisture content as the output variable.Moreover,a sensitivity analysis was conducted on the GA-BP neural network to investigate the impact of the four input variables on the prediction outcomes.The results indicated that the coefficients of determination(R^(2))for the BP and GA-BP moisture prediction models on the test set were 0.99955 and 0.99964,with root mean square errors(RMSE)of 0.51317 and 0.45523,respectively,demonstrating that the GA-BP algorithm-based prediction model achieved better performance,accurately predicting the dynamic changes of moisture during the municipal sludge drying process.The sensitivity analysis revealed that drying time had the most significant impact on the GA-BP moisture prediction model.These findings could provide a theoretical basis for optimising sludge drying processes and procedures and offer a reference for the resource utilization of sludge.