fuzzy inference system

Pipelines are widely used to transport large volumes of oil and gas over long distances. Risk assessment can help identify risk factors and create an appropriate action plan and strategy to reduce or eliminate them. The main goal of this research is to provide a method for assessing the risk of pipelines based on the Fuzzy Inference System (FIS), creating a systematic format that is expected to be a more effective, accurate, and reliable model for controlling risks related to oil and gas pipelines.

In this article, fuzzy logic is used to model uncertainty and present a model for assessing pipeline risk. The Muhlbauer method, one of the most common risk assessment methods for oil and gas pipelines, has been employed to determine critical factors affecting the lines. This method has been implemented using the Mamdani algorithm and based on expert knowledge in the fuzzy logic toolbox of MATLAB software. To validate the results of the proposed model, data from the interphase pipelines of the fifth refinery of the South Pars Gas Field have been used as a study sample.

The findings from the implementation of the model created in South Pars Phases 9-10 pipelines (on shore) show that the studied pipelines are divided into three parts (A, B, and C) based on indicators such as population density and equipment deployment. Part C of the pipeline has the highest risk, with third-party damage and design being the most important factors affecting it. Part B has the lowest level of risk and results in the fewest consequences for human accidents. It was also observed that corrosion is essential in increasing leakage and risk in all three pipeline parts.

To verify the developed model, the inter-phase shore pipe of phase 9-10 refinery in the South Pars Gas Field was considered as a case study. The findings indicate that the proposed method provides more accurate and reliable results than traditional methods. Factors such as improper operation, dispersion, receptors, leakage volume, and product risk, which are other factors affecting pipeline risk, were not considered in traditional methods. Therefore, the risk level of oil and gas pipelines can be calculated using this model as a comprehensive and intelligent tool.

In todaychr('39')s world, for many reasons, accidents occur in many organizations. One of the goals of these events is to hit countrieschr('39') industrial bases. To minimize the risk of such threats, it is necessary to design and implement risk identification and risk assessment programs. The purpose of this study was to evaluate the risks associated with conventional and fuzzy FMEA-aware threats and compare the results of these two methods. M

Methods and Materials:

In this study fuzzy FMEA and FMEA methods were used to identify and evaluate hazards. TOPSIS method was used to prioritize the two techniques. Data were analyzed using MATLAB software.

The results showed that in the fuzzy FMEA method, despite the high time and cost of implementation and higher educational needs, due to the high level of accuracy of the results, it is possible to more accurately estimate the risk levels and thus to prioritize the units more efficiently. Therefore, the output of this study introduced the fuzzy FMEA approach as the superior approach.

It was found that using a fuzzy approach can lead to a reduction of uncertainty. This increases the efficiency and thus the efficiency of the organizations

The most common way of transporting hazardous materials in many countries, including Iran, is road transport. This increases the risk of exposure to these materials. Hence, the assessment of the consequences of exposure can help to manage the transportation of these materials. Therefore, the purpose of this study was to assess the health consequences of accidents in road transport of chemicals.

In this study, a fuzzy inference system was designed and implemented. The system has three input parameters: "The concentration of released chemicals", "The degree of vulnerability of the exposed community" and "The toxicity of the released material". The output of this system expresses the degree of exposure severity coefficient. This coefficient demonstrates the severity of the damage to individuals in the exposure of chemicals.

Due to the release of chlorine gas in the target area, the radii affected were divided into two low risk areas (up to 187 m) and safe (more than 187 m). The most severe exposures in the sensitive group of society (children, elderly people and individuals with underlying problems in group 4 with a coefficient of 0.92) and adolescents and middle aged in group 3 (with severity coefficient of 0.776).​

Our results showed that group 4 was considered as the most sensitive exposure group in determining the safe distance. Also showed that the use of fuzzy approach to simulating the health consequences of chemical transport can be used.

Liver disease is one of the most common chronic liver problems and cirrhosis. Liver problems include a wide range of diseases and disorders that damage the liver tissue or its function. Early diagnosis and treatment of this disease can reduce the severity of the disease and mortality rate. In this descriptive-analytic study, database was consisted of 583 independent records, including 11 features in the UCI machine learning database and through using fuzzy logic that its parameters are determined by Genetic Algorithm (GA), a method for the diagnosis of liver disease is proposed. For this purpose, first, the features of the dataset were ranked using the entropy feature and then, the dataset data were optimized using GA. Ultimately, liver disease was diagnosed using the genfis2 and genfis3 Fuzzy Inference System (FIS). The results show that the accuracy of detection of liver disease using the genfis2 FIS with 8 features is 91.66% and using the genfis3 FIS with 6 features, it is equal to 89.87%. Moreover, the rates of error for genfis2 and genfis3 were 0.034 and 0.047 respectively. Liver disease is one of the most common diseases in population. Early diagnosis of disease while reducing costs can increase the chance of treatment success. According to the obtained results, the proposed model can identify people with liver disease with a fairly high degree of accuracy.

Traditional methods for discrimination iron deficiency anemia (IDA) and β-thalassemia trait (BTT), which using CBC indices, are not accurate enough and complementary tests such as Hb electrophoresis are time consuming and expensive. In this study, we introduced the methods with higher accuracy.
In this study, 510 CBC samples were collected from several screening centers in north of Iran. The number of samples associated with IDA, BTT, and normal subjects were 167, 132, and 211, respectively. The collected samples were used to establish the methods, adaptive neuro fuzzy inference system (ANFIS) and multi-layer perceptron (MLP), through the use of 10-Fold cross validation. In each step of cross validation mathematical methods such as MI, E&FI, S&BI, S&LI, G&KI, EI and SI were investigated by the test samples.
Several indices, such as sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and Youden’s index (YI), have been obtained for the all mentioned methods in each step of Cross Validation. T test showed that the ANFIS and the MLP had not difference (p This study indicates that using artificial intelligence as medical diagnostic tools can help the physicians in discrimination between similar diseases and also it increases accuracy in difficult cases.

With a growing interest in organizational factors in occupational safety, many researches have been carried out in relation to a study of the relationship between organizational factors and safety performance in different industries in recent years. According to this, the purpose of the research is to introduce a model for safety performance assessment according to a fuzzy inference system. The study is an applied study with respect to purpose and it is a descriptive-modeling study in terms of methodology. In this paper, reviewing research literature, organizational factors affecting the safety performance of industries has been extracted. Subsequently, in order to assess the safety performance in accordance with organizational factors, an inference system was developed according to research literature and experts ‘views. Having been developed, the model was implemented in an auto spare part corporation of East Azerbaijan Province. The findings of the research indicate that the developed model is capable of assessing safety performance in industries, based on which the score of safety performance of industries can be calculated. The application of the model in a cooperation as an experiment site exhibits that the score of safety performance is equal to 0.607; the value is desirable with the membership degree 0.528 and desirable with the membership degree 0.472. The system of interest in the present study is an instrument to score and can be applied when we want to know the level of company’s and organization’s interest in safety performance. Given the fact that the size of determining factors in safety performance score can be expressed by verbal variables, and on the other hand the variables have internal interactions with one another in that they exert different effect on the score of safety performance at times, the fuzzy inference system is an appropriate instrument for scoring.