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Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS)
Journal of Environmental Health Science and Engineering ( IF 3.4 ) Pub Date : 2021-08-09 , DOI: 10.1007/s40201-021-00710-0
Shakeri Narges 1 , Asgari Ghorban 2 , Khotanlou Hassan 3 , Khazaei Mohammad 1
Affiliation  

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.



中文翻译:

基于人工神经网络模糊推理系统(ANFIS)的开发模型预测水处理厂混凝剂的最佳用量

目的

混凝和絮凝是水处理厂的主要过程和单元操作。水处理过程中最具挑战性的操作之一是确定混凝剂剂量。

方法

罐试验法通常用于确定促凝剂的剂量。考虑到这种传统方法费时,与人为错误相关,并且受原水质量波动的影响很大。在本研究中,应用基于减法聚类(SUB)方法的人工模糊神经网络(ANFIS)以确定水处理厂中混凝剂的最佳剂量。

结果

采用SUB方法除了增强模型责任和智能模型识别外,还倾向于减少规则数量和互连。收集原水进水的 pH 值、浊度、碱度、温度和电导率作为输入数据。

结论

ANFIS 对明矾和聚合氯化铝 (PAC) 混凝剂剂量的相关系数分别为 0.85 和 0.84 和 RMSE 分别为 1.32 和 1.83 的建模结果表明,ANFIS 是确定水处理中最佳混凝剂量的有效方法植物。

更新日期:2021-08-10
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