This paper presents a method using an artificial neural network for creating an optimal model of energy consumption in wastewater treatment plant (WWTP) using activated sludge process. The advantage of this method is the use data usually measured in most of WWTP to optimize the energy consumption of the biological process. This method consists of selecting the data that represent the best energy consumption using different performance criteria then use this data to train a deep neural network. The procedure of selection is divided into two parts, in the first selection we selected the data that respect the environmental standards, and in the second part we selected the data with optimal energy consumption using different pollution indicators, and this data was used to train a deep neural network, finally the best model was used to estimate the energy savings on the data not selected. The model showed good results with a coefficient of determination that varies between 90% and 92% in training period and 74%–82% in testing period, the application of the best model on the data not selected showed a gain in energy for the most of the data.

本文提出了一种使用人工神经网络的方法，用于使用活性污泥工艺创建废水处理厂（WWTP）的能耗优化模型。该方法的优点是使用通常在大部分污水处理厂中测量的数据来优化生物过程的能耗。该方法包括使用不同的性能标准选择代表最佳能耗的数据，然后使用该数据训练深度神经网络。选择过程分为两部分，第一部分选择符合环境标准的数据，第二部分选择使用不同污染指标的具有最佳能耗的数据，然后使用这些数据来训练深度神经网络 最后，使用最佳模型估算未选择数据的节能量。该模型显示出良好的结果，其确定系数在训练期间介于90％和92％之间，在测试期间介于74％–82％之间，对未选择的数据应用最佳模型显示出最大的能量消耗的数据。