In a water treatment plant, the decision to carry out a jar test, for determining the required coagulant dosage, is made based on notable changes in treated water qualities such as treated water turbidity and color, which is essentially a reactive response to changes in water qualities. In addition, until a change that the operator deems as ‘significant’ occurs, the plant tends to use the same dosage determined previously using the jar test for an elongated period of time. In this study, artificial neural network (ANN) models were developed to proactively decide what coagulant dosages to use based on changes in raw water parameters. Use of ANNs also prevents the regular use of costly chemicals used for jar tests and enables responding to sudden changes in water qualities. The general regression neural network (GRNN) and extreme learning machine neural networks require minimal computational effort for model development as they involve minimal model parameters and their training algorithms are not iterative. The current study determines the more convenient and efficient model of the two for aiding the coagulation process. The GRNN and ELM-RBF models predicted test data with R values of 0.9737 and 0.9783, respectively. It was noted that the GRNN was prone to overfitting and the ELM-RBF model demonstrated higher generalization ability than the GRNN. Therefore, it was concluded that the ELM-RBF model was the more suitable model for the prediction of the coagulant dosage.

在水处理厂中，决定是否进行罐装试验以确定所需的混凝剂剂量，是根据处理后水质的显着变化（例如处理后水的浊度和颜色）做出的，该变化本质上是对水质变化的反应品质。此外，直到操作人员认为发生“重大”变化之前，工厂往往会使用先前使用广口瓶测试确定的相同剂量延长时间。在这项研究中，开发了人工神经网络（ANN）模型，以根据原水参数的变化主动决定要使用的凝结剂剂量。使用人工神经网络还可以防止经常使用昂贵的罐子测试化学药品，并能够应对水质突然变化的情况。通用回归神经网络（GRNN）和极限学习机神经网络需要最少的计算工作来进行模型开发，因为它们涉及的模型参数最少，并且其训练算法不是迭代的。当前的研究确定了两种更方便​​有效的模型来辅助凝血过程。GRNN和ELM-RBF模型预测的测试数据的R值分别为0.9737和0.9783。注意到GRNN容易过度拟合，并且ELM-RBF模型显示出比GRNN高的泛化能力。因此，可以得出结论，ELM-RBF模型是更适合预测凝血剂量的模型。