Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS)
Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS)
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DOI：
10.1007/s40201-021-00710-0
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发表时间：
2021-08-09
影响因子：
3.4
通讯作者：
Mohammad, Khazaei
中科院分区：
文献类型：
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作者：
Narges, Shakeri;Ghorban, Asgari;Mohammad, Khazaei
混凝和絮凝是水处理厂的主要工艺和单元操作。水处理过程中最具挑战性的操作之一是混凝剂剂量的确定。方法常用瓶法测定凝血剂剂量。考虑到这种传统方法耗时长，存在人为误差，受原水水质波动影响大。采用基于减法聚类（SUB）的人工模糊神经网络（ANFIS）来确定水处理厂混凝剂的最佳投加量。结果采用SUB方法，在提高模型责任和智能模型识别能力的同时，有利于调节规则的数量和相互联系。输入pH值、原水浊度、碱度、温度、电导率等数据。结论ANFIS模型对明矾和聚氯化铝（PAC）混凝剂剂量的相关系数分别为0.85和0.84，RMSE分别为1.32和1.83，表明ANFIS是确定水厂最佳混凝剂剂量的有效方法。
Purpose Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose. Method The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants. Results Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data. Conclusions The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant.