随机模型预测UASB反应器对奶牛养殖废水处理效果

摘要

为实现UASB反应器运行人工智能控制,采用三层BP神经网络(Back Propagation Artificial Neural Network,BP-ANN)预测升流厌氧反应器处理奶牛养殖废水COD去除效果.运用BP神经网络构建出水与进水COD浓度、水力停留时间、pH、温度、碱度、挥发性有机酸、有机负载率和悬浮固体之间非线性模型,比较不同算法.Levenberg-Marquardt算法为BP神经网络最佳算法,最佳结构为8-8-1,模拟训练效果较好.BP神经网络预测值与真实值接近,一致性较高,模型拟合程度较好.利用线性-非线性模型评价不同输入参数对废水COD去除率影响,比较BP-ANN与线性-非线性模型预测效果,为奶牛养殖废水处理智能化管理提供技术支持.%Wastewater effluents from intensive dairy farm are characterized by high volumes and extremely variable composition.The discharge of dairy farm wastewater into the environment damages the quality of the receiving water and might be toxic to food chain organism and aquatic life.Therefore upflow Anaerobic Sludge Blanket (UASB) reactors are widely used to treat this kind of wastewater.A three-layer Artificial Neural Network model for the prediction of Chemical Oxygen Demand Removal Efficiency (CODRE) of UASB reactors treating real dairy wastewater was presented in this paper.To validate the proposed method,an experimental study was carried out in lab-scale UASB reactors to investigate the efficiency of the total COD reduction.The reactors were operated for 365 at the mesophilic conditions and 162 sets of data could be chosen to build the model.CODRE of UASB reactors being output parameter of the conducted anaerobic treatment was estimated by eight input parameters such as HRT,pH,operating temperatureand so on,according to Backpropagation(BP) training combined with principal component analysis (PCA).In the Artificial Neural Network(ANN) study,theLevenberg-Marquardt Algorithm (LMA) was found as the best of five BP (Back Propagation) algorithms,and the best structure was 8-8-1.ANN model predicted CODRE values based on experimental data and all the predictions were proven to be satisfactory.So BP-ANN model in this paper could be used to prediction the CODRE values in the future real factories.In addition to determination of the optimal ANN structure,a linear-nonlinear study was also employed to investigate the effects of input variables on CODRE values in this study.Both ANN outputs and linear-nonlinear study results were compared and advantages and further developments were evaluated in this paper.