Zangooei Hossein, Delnavaz Mohammad, Asadollahfardi Gholamreza
Faculty of Engineering, Department of Civil Engineering, Kharazmi University, Tehran, Iran E-mail:
Water Sci Technol. 2016 Sep;74(6):1296-1311. doi: 10.2166/wst.2016.315.
Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R) of 0.96 and root mean square error of 0.0106.
凝聚和絮凝是将胶体颗粒聚集成较大颗粒的两个主要过程，也是初级水处理的两个主要阶段。只有当胶体颗粒在总悬浮固体部分中占显著比例时，才需要凝聚和絮凝过程。我们的目标是在已知混凝剂类型和浓度、pH值以及原水进水浊度等其他参数的情况下，预测凝聚和絮凝过程后水的浊度。我们使用了多层感知器（MLP）、人工神经网络（ANNs）的径向基函数（RBF）以及各种模糊回归分析方法来预测凝聚和絮凝过程后的浊度。中试工厂位于水处理厂，所使用的混凝剂是聚合氯化铝。我们使用了原水的现有数据，包括混凝剂的类型和浓度、pH值以及进水浊度，因为这些数据可从中试工厂获取用于模拟，且数据由德黑兰水务局收集。结果表明，与模糊回归分析相比，人工神经网络在模拟凝聚和絮凝过程以及利用不同实验数据预测浊度去除方面具有更强的能力，并且可能有能力减少耗时且昂贵的烧杯试验次数。在本研究中，与RBF神经网络和模糊回归分析相比，MLP神经网络被证明是最佳网络。MLP神经网络能够以0.96的决定系数（R）和0.0106的均方根误差预测凝聚和絮凝过程后的出水浊度。
