Narges Shakeri, Ghorban Asgari, Hassan Khotanlou, Mohammad Khazaei
Department of Environmental Health Engineering, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
Social Determinants of Health Research Center (SDHRC), Hamadan University of Medical Sciences, Hamadan, Iran.
J Environ Health Sci Eng. 2021 Aug 9;19(2):1543-1553. doi: 10.1007/s40201-021-00710-0. eCollection 2021 Dec.
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.
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.
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.
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.
混凝和絮凝是水处理厂中的重要工艺和单元操作。水处理过程中最具挑战性的操作之一是确定混凝剂剂量。
通常采用烧杯试验法来确定混凝剂剂量。鉴于这种传统方法耗时、存在人为误差且受原水水质波动影响较大。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）来确定水处理厂混凝剂的最佳剂量。
采用SUB方法有助于减少规则数量和连接数量，同时增强模型的可靠性和智能模型识别能力。收集进水原水的pH值、浊度、碱度、温度和电导率作为输入数据。
对于明矾和聚合氯化铝（PAC）混凝剂剂量，ANFIS建模结果的相关系数分别为0.85和0.84，均方根误差分别为1.32和1.83，表明ANFIS是确定水处理厂最佳混凝剂量的有效方法。
