基于数据挖掘技术的污泥厌氧消化模拟研究

Simulation Research of Sewage Sludge Anaerobic Digestion Based on Data Mining Technology

为了更好地模拟实际工程中污泥厌氧消化系统的产气效果,以北京某大型污泥厌氧消化工程为例,以大量的工程数据为基础,分别采用多元线性回归模型、神经网络模型、分类回归模型和邻近算法模型等数据挖掘技术,对系统的产沼气效率进行了模拟预测,其中邻近算法模型具有最好的拟合效果.对邻近算法模型进行进一步研究分析,通过交叉验证法近一步优化了模型k值的选取,从测试结果可以看出随着k值增加,训练集的拟合度先下降后趋于平稳,测试集的拟合度则相反.最终确定当k值取5时,模型预测值与实际值的相关度达0.862,优于系统默认参数下的拟合效果.试验证明:数据挖掘技术可以很好地应用于污泥厌氧消化工程的模拟计算,对于数学模拟在污水处理领域的应用具有一定指导意义.

This research was based on a large sludge anaerobic digestion project in Beijing,using a large number of engineering data. The multiple linear regression model, the neural network model, the classification and regression model and k nearest neighbor model to was adopted fit the system biogas production to simulate the biogas production of sluge anaerobic digestion system in practical engineering. Results show that the kNN model has the best fitting effect. For further kNN model analysis, cross validation error statistics selection method was used to determin the best k value. From the test results,it can be seen that with the increase of k value,the fitting degree of the training set first decreases and then tends to be stable, and the fitting degree of the test set was the opposite. Finally,when the k value was 5 ,the correlation between the model predictive value and the actual value was 0. 862,which is better than the fitting effect of the system 爷 s default parameters. T he research shows that the data mining technology can be applied to the simulation of sludge anaerobic digestion very well, and has certain guiding significance for the application of mathematical simulation in the field of wastewater treatment.