Department of Environmental Engineering, Engineering and Architectural Faculty, Selcuk University, Campus, Selcuklu, Konya, Turkey.
Bioprocess Biosyst Eng. 2010 Nov;33(9):1051-8. doi: 10.1007/s00449-010-0430-x. Epub 2010 May 6.
Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.
人工神经网络（ANNs）是一种人工智能方法，为复杂过程提供了有效的预测模型。开发了三个独立的基于反向传播算法的 ANN 模型，用于预测安卡拉中心污水处理厂的出水化学需氧量（COD）、悬浮固体（SS）和曝气池混合液悬浮固体（MLSS）浓度。通过对模型进行多次训练和测试，确定了 ANN 模型的适当结构。ANN 模型产生了令人满意的预测结果。COD 的均方根误差、平均绝对误差和平均绝对百分比误差分别为 3.23、2.41mg/L 和 5.03%；SS 分别为 1.59、1.21mg/L 和 17.10%；MLSS 分别为 52.51、44.91mg/L 和 3.77%，表明所开发的模型可以有效地使用。总体结果还证实，ANN 建模方法可能在污水处理厂的模拟、精确性能预测和过程控制方面具有很大的实施潜力。
