Department of Civil and Environmental Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Chemistry, Faculty of Sciences, University of Neyshabur, Neyshabur, Iran.
Environ Monit Assess. 2019 Feb 8;191(3):141. doi: 10.1007/s10661-019-7277-7.
Preoxidation is an important unit process which can partially remove organic and microbial contaminations. Due to the high concentrations of organic matter entering the water treatment plant, originating from surface water resources, preoxidation by using chlorinated compounds may increase the possibility of trihalomethane (THM) formation. Therefore, in order to reduce the concentration of THMs, different alternatives such as injection of potassium permanganate are utilized. The present study attempts to investigate the efficiency of the microbial removal from raw water entering the water treatment plant No. 1 in Mashhad, Iran, through various doses of potassium permanganate. Then, an examination of the predictive models is done in order to indicate the residual Escherichia coli and total coliform resulted from injecting the potassium permanganate. Finally, the coefficients of the proposed models were optimized using the genetic algorithm. The results of the study show that 0.5 mg L of potassium permanganate would remove 50% of total coliform as well as 80% of Escherichia coli in the studied water treatment plant. Also, assessing the performance of different models in predicting the residual microbial concentration after injection of potassium permanganate suggests the Gaussian model as the one resulting the highest conformity. Moreover, it can be concluded that employing smart models leads to an optimization of the injected potassium permanganate at the levels of 27% and 73.5%, for minimum and maximum states during different seasons of a year, respectively.
预氧化是一个重要的单元操作，可以部分去除有机物和微生物污染物。由于进入水处理厂的有机物浓度很高，来源于地表水，因此使用氯化化合物进行预氧化可能会增加三卤甲烷（THM）形成的可能性。因此，为了降低 THM 的浓度，采用了不同的替代方法，如注入高锰酸钾。本研究试图通过各种剂量的高锰酸钾来研究从伊朗马什哈德第一座水处理厂进入的原水的微生物去除效率。然后，对预测模型进行了检查，以指示注入高锰酸钾后残留的大肠杆菌和总大肠菌群。最后，使用遗传算法对所提出的模型的系数进行了优化。研究结果表明，0.5 mg/L 的高锰酸钾可去除 50%的总大肠菌群和 80%的大肠杆菌。此外，评估不同模型在预测注入高锰酸钾后残留微生物浓度方面的性能表明，高斯模型的一致性最高。此外，可以得出结论，采用智能模型可以分别在一年中不同季节的最小和最大状态下，将注入的高锰酸钾优化 27%和 73.5%。
