عنوان مقاله [English]
FMEA is one of the experienced and very useful methods for identifying, classifying, analyzing faults, and assessing the risks arising from them. This study aims to identify, evaluate, prioritize, and analyze the risks associated with the equipment of Sahand municipal wastewater disinfection unit using traditional and fuzzy FMEA method.
The present study is a cross-sectional analytical study that was conducted in a period of about 6 months. First, a team of 5 experts was formed and the traditional FMEA worksheet was completed. Then the fuzzy functions were determined by the MATLAB program according to the opinions of experts and the severity, probability of occurrence, and probability of detection and risk priority number (RPN) became fuzzy. According to the fuzzy logic outputs, the risks were prioritized and corrective measures were proposed according to the type of risk.
In this study, a total of 28 faults were identified for chlorination unit equipment. The traditional FMEA results showed that a total of 28 faults (100%) identified for the equipment of the Sahand municipal wastewater chlorination unit is in the low-risk range. While according to the results of fuzzy FMEA, one fault (3.6%) is in the low-risk range, 24 faults (85.7%) are in the medium-risk range and 3 faults (10.7%) are in the high-risk range. The results of the risk assessment of the chlorination unit of Sahand municipal wastewater treatment plant using fuzzy FMEA are more accurate and better compared to traditional FMEA. Using experts’ opinions in risk assessment using fuzzy FMEA leads to more realistic results as well as better and clearer prioritization of corrective actions.
The wastewater treatment plant, as one of the most important urban infrastructures, recycles water and nutrients from wastewater collected from residential, commercial, and industrial units. The occurrence of structural failure in wastewater treatment plants will often have adverse consequences, such as the discharge of untreated effluents containing various chemical and biological contaminants from the treatment plant and their entry into the environment. This event could lead to a more serious crisis in society, such as the spread of infectious diseases. Therefore, identifying failures, estimating the likelihood of adverse incidents, and the severity of the effects in wastewater treatment plants are very important to develop and implement risk management programs. There are several methods for identifying hazards and assessing risks, which can be referred to as PHA (Preliminary Risk Analysis), FMEA (Failure Mode and Effect Analysis), JSA (Job Safety Analysis), and FTA (Fault Tree Analysis).
FMEA is one of the most important methods in systems safety engineering, which has been developed based on reliability engineering. Using this method, various types of faults can be identified and corrective actions can be suggested to improve the reliability and safety of systems, processes, and products. However, the application of traditional FMEA in practice is limited. Therefore, to overcome the limitations of the traditional FMEA method in evaluating and prioritizing failure modes, this study was conducted to evaluate the risk of the chlorination unit equipment of Sahand city municipal wastewater treatment plant with a fuzzy FMEA approach.
The present study is a cross-sectional analytical study that was conducted to prioritize the risk of chlorination unit equipment in Sahand municipal wastewater treatment plant using traditional and fuzzy FMEA method. First, a 5-member team consisted of the treatment plant manager, laboratory and process expert, operating engineer, a repairman, and a system operator. The data of this study (list of equipment and parts and potential state of faults in each part) were gathered in the field through interviews with operating engineers, engineers familiar with the system, repairmen, and traditional FMEA worksheets were completed. Then these data were used to form a fuzzy FMEA and a fuzzy inference system model. MATLAB software was used to design the fuzzy inference system. In order to implement fuzzy FMEA, the fuzzy toolbox of MATLAB software was used. In this regard, first, language variables and fuzzy membership functions were defined with the opinion of experts and members of the FMEA team. In the next step, the if-then fuzzy rule database was defined and formed. Then the field inference engine was used for de-fuzzification.
In this study, the risks related to health, safety, and environment were assessed using the traditional FMEA method. After reviewing the documents as well as collecting data by different methods, 28 risks of health, safety, and environment (HSE) were identified and recorded. After transferring the identified risk data to the FMEA-specific worksheet, the team members completed the columns related to the fault mode, the cause of the fault, the effects of the fault, as well as the existing controls. Using the opinions of experts and members of the FMEA team, for each of the identified risks, a numerical value appropriate to them is selected from table 1 and entered in the FMEA worksheet, and then multiplied by these 3 indicators, the risk priority number calculated for each fault. An example of a completed worksheet of the traditional FMEA method is also provided in table 2. to create fuzzy sets of indicators of severity, probability of occurrence, and detection and risk priority number, all four of the above indicators based on linguistic variables and experts opinion (15 people) were divided into three parts: low, medium and high (table 2).
According to the traditional FMEA, a total of 28 identified faults were in the low-risk range, while according to the fuzzy FMEA, out of identified 28 faults, 1 fault was in the low-risk level range, 24 faults were in the medium-risk range and 3 faults were in the high-risk level range (Figure 3). According to the traditional FMEA results, the highest and lowest risk levels were related to the failure of the chlorine heating capsule and the leakage of the overhead crane bolts, as well as the failure of the solenoid valve of the shower section with RPN 96 and RPN 12 respectively. Also, according to the fuzzy FMEA results, the highest and lowest risk levels were related to loose pipe connection screw, chlorine gas pipe fracture and chlorination sensor failure, and solenoid valve failure of chlorination unit showers with RPN 875 and RPN 124 respectively (Table 3).
The results of the risk assessment of the chlorination unit of Sahand municipal wastewater treatment plant using fuzzy FMEA is more accurate and better compared to traditional FMEA. Using experts’ opinions in risk assessment using fuzzy FMEA leads to more realistic results as well as better and clearer prioritization of corrective actions. Overall, the results of this study showed that the fuzzy FMEA method has the ability to overcome the limitations of traditional FMEA and better prioritize the identified risks.