Development of an Algorithm for Evaluation of a Water Treatment Plant Performance

Water Treatment Plants (WTP) have a key role in water supply with adequate quantity and quality in desired pressure condition. Sustainable performance of these systems is very important and quantifies the readiness of them especially in critical conditions. System readiness is one of the main issues that water users, stakeholders and decision makers of water resources consider in order to decrease the probable damages. In this paper, a methodology for assessment of a WTP performance has been proposed. This methodology quantifies the readiness of the system in different stages of plant's operation. For this purpose, three system operation indices including reliability, resiliency and vulnerability are considered. Due to the differences between the indices and especial characteristics of them, a hybrid index of them can be a good indicator of the system performance. In this study, the mentioned indices have been hybridized considering the importance of water shortages in system readiness evaluation utilizing Artificial Neural Networks (ANNs). In order to evaluate a WTP performance, the proposed hybrid index is developed. It is categorized into different levels for assessing the system readiness in critical conditions. The proposed methodology has been applied for a WTP in Tehran metropolitan area in Iran. The Results show that this algorithm can be effectively used for evaluation of the WTP performance.