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Application of artificial neural network to control the coagulant dosing in water treatment plant

机译：人工神经网络在水厂控制混凝剂投加中的应用

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Goagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the Feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pH(in)) and conductivity (Con(in)) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R-2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment. [References: 11]
机译：Goagulant的投加量是水处理厂的主要运营成本之一，对于大多数工厂，此过程的常规控制通常由广口瓶试验确定。但是，这种方法只能提供周期性的信息，很难应用于自动控制。本文提出了应用人工神经网络（ANN）自动控制水处理厂中混凝剂投加量的可行性。使用五个在线监测变量来构建浊度（NTUin），原水中的pH（pH（in））和电导率（Con（in）），沉淀池的出水浊度（NTUout）和明矾剂量（Dos）凝结剂量预测模型。采用回归模型，时间序列模型和人工神经网络模型三种方法预测明矾用量。根据这项研究的结果，回归模型对凝血剂量的预测较差。时间序列模型和人工神经网络模型都可以对剂量进行精确的预测。具有提前凝结剂剂量的ANN模型对明矾剂量的最佳预测是R-2为0.97（RMS = 0.016），在ANN模型中还发现非常低的平均预测误差为0.75 mg / L明矾。因此，在水处理中应用神经网络模型控制混凝剂的投加量是可行的。 [参考：11]

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