RESUMO
Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.
RESUMO
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-1 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.
