Reliability analysis of a wastewater treatment plant using fault tree analysis and Monte Carlo simulation.

The reliability of a wastewater treatment plant is a critical issue when the effluent is reused or discharged to water resources. Main factors affecting the performance of the wastewater treatment plant are the variation of the influent, inherent variability in the treatment processes, deficiencies in design, mechanical equipment, and operational failures. Thus, meeting the established reuse/discharge criteria requires assessment of plant reliability. Among many techniques developed in system reliability analysis, fault tree analysis (FTA) is one of the popular and efficient methods. FTA is a top down, deductive failure analysis in which an undesired state of a system is analyzed. In this study, the problem of reliability was studied on Tehran West Town wastewater treatment plant. This plant is a conventional activated sludge process, and the effluent is reused in landscape irrigation. The fault tree diagram was established with the violation of allowable effluent BOD as the top event in the diagram, and the deficiencies of the system were identified based on the developed model. Some basic events are operator's mistake, physical damage, and design problems. The analytical method is minimal cut sets (based on numerical probability) and Monte Carlo simulation. Basic event probabilities were calculated according to available data and experts' opinions. The results showed that human factors, especially human error had a great effect on top event occurrence. The mechanical, climate, and sewer system factors were in subsequent tier. Literature shows applying FTA has been seldom used in the past wastewater treatment plant (WWTP) risk analysis studies. Thus, the developed FTA model in this study considerably improves the insight into causal failure analysis of a WWTP. It provides an efficient tool for WWTP operators and decision makers to achieve the standard limits in wastewater reuse and discharge to the environment.