fuzzy inference system

Due to the complexity of operating conditions, predicting corrosion in natural gas pipelines is a challenge, and therefore its use in the gas industry is problematic. So, the present study employs corrosion risk management in control systems and decision prediction with fuzzy inference systems to address the uncertain phenomena of critical factors affecting natural gas pipelines. In the study, 84 factors were derived and 14 critical factors were identified using the Lawshe approach. The Boehm risk management framework was used and the factors were analysed in pairs in a fuzzy inference system. The probability and consequences of each factor were determined according to the API 581 standard and the weight coefficient of each factor was determined using the FUCOM method. In this study, a fuzzy inference system with two fuzzy inference subsystems was developed to comprehensively address both aspects of evaluation and response. First, the system uses the API 581 standard risk matrix to identify the risk level, followed by the risk response strategy determination through the sensitivity risk priority index chart. The results show that at the first level of the system, the highest risk was classified as severe and harsh, while at the second level of the system, the maximum output behavior occurred during the wear-out phase of the pipeline. The proposed fuzzy inference system has the potential to significantly contribute to the effective management of pipeline corrosion risk and the economic productivity of the gas industry.

A facile chemical mixing approach was used to prepare TiO2/ZnO photocatalystswith different mass ratios. The photodegradation activity was tested against paracetamol in an aqueous phase assisted by low UVC-light intensity (9 W). TiO2/ZnO particles mainly exhibited irregular shapes with uniform distributions and high crystallinity degree, the primary oxidation state in the structure is titanium Ti4+ of anatase TiO2 (459.2 and 464.9 eV), and the presence of standard chemical state of Zn2+ (1021.9 and 1044.9 eV). The composite with a 1:5 mass ratio displayed a rapid and outstanding degradation percentage of 95% and a rate of 1.83 × 10-2 min-1. The best photocatalyst can be recycled up to five times towards paracetamol degradation without any regeneration step or severe deactivation. A Fuzzy inference system (FIS) was computed for the first time to investigate the relationship between the TiO2/ZnO ratio, degradation percentage, and rate constant. The optimal concentration of 9 mg/L was obtained, whereby the degradation percentage and rate were sufficiently maintained above 90% and 0.19 mg/L.min, respectively. Using a fuzzy logic controller (FLC) in this work enables future guidance and prediction for developing the best TiO2/ZnO photocatalysts for real-world water remediation processes.Highlights 1.      Facile preparation of TiO2/ZnO composite photocatalyst via simple mixing for degradation of paracetamol under low UVC light intensity (9 W).  2.      TiO2/ZnO particles are mostly exhibited irregular shapes with uniform distributions and high crystallinity degree, the main oxidation state in the structure is titanium Ti4+ of anatase TiO2 (459.2 and 464.9 eV), and the presence of standard chemical state of Zn2+ (1021.9 and 1044.9 eV).  3.      TiO2/ZnO (1:5) displayed a rapid and outstanding degradation percentage of 95% and rate of 1.83 × 10-2 min-1, in accordance with pseudo first-order kinetics. 4.      The optimal concentration of 9 mg/L was computed by the prediction using fuzzy inference system (FIS) for the first time, whereby the degradation percentage and rate were sufficiently maintained above 90% and 0.19 mg/L.min, respectively. 5.      The stability of TiO2/ZnO composite photocatalyst towards paracetamol degradation was retained up to 5 cycles, without undergo any regeneration procedure.

Credit scoring is one of the fundamental concepts in bank industry, used for analysing and evaluating all of the customers requesting for facilities. Because of its importance to banks’ profitability, especially in the developing countries, this study aims to propose a fuzzy inference system (FIS) model for credit scoring of legal clients. This research is applied in terms of purpose and survey in terms of method. In the first step, after reviewing the literature, the evaluation criteria for legal client’s appraisal were identified, and 29 out of them were selected. In the next step, these criteria were analysed by the research experts and using a Delphi method, and 12 more important criteria were selected and organized in 4 categories for FIS modelling. Then, a researcher made questionnaire was developed to constitute rules for Main FIS and its 4 sub-FISs. Performance measure (Sub-FIS1) has four inputs: return of asset, fixed assets to equity, average customer account and customer capital. Leverage measure (Sub-FIS2) has two inputs: debt ratio and equity ratio. Borrowing measure (Sub-FIS3) has four inputs: ratio of deferred amount to current assets, amount of received facilities, borrowing capacity and amount of requested facilities, and finally, credit risk measure (Sub-FIS4) has two inputs; type of guarantee and the credit risk of the previous period. The proposed system designed based on Gaussian membership function and implemented in MATLAB. Finally, model’s validity was tested by extreme condition test. Comparing the results of the proposed FIS and the bank validation system, shows the proposed model can be considered suitable for credit scoring.

The Iranian plateau is located in the high seismicity belt. Earthquake can inflict severe loss of life and property, especially when they occur in densely populated areas. Therefore, seismic hazard evaluation is very essential to prevent the harmful effects. The region of the study is located in the northwest of Iran, between 43°-50° E longitude and 35.5°-40.5° N latitude. This city which is located in the center of East Azerbaijan province, has been ruined by terrible earthquakes, which is due to the presence of active faults in the region. Seismic hazard assessment similar to other seismology researches is very complicated due to the effect of different parameters in an earthquake occurring with uncertainty. The amount of uncertainty should be considered in a rational way. The fuzzy method is a suitable method that is used as a decision-making method for solving problems and modeling uncertainties and ambiguities. We used a fuzzy inference system, as the practice is based on uncertainty estimation of seismic hazard for Tabriz region. Peak ground Acceleration value is estimated for fuzzy Logic System in deterministic method 0.55g which is obtained from a seismic source with a Mmax=8.0 at a distance of  36.98 km of Tabriz city.The contour map of the peak ground acceleration throughout Tabriz city can help in urban planning.

Always the decision of the government to the private sector is faced with the challenges and high level of importance, because these decisions should be taken at the meeting of standards and functional requirements to overcome investors' interests. These decisions include education, and due to the governance nature of education and the governments' strategies in this area, the private sectors who act in it, have been affected by the consequences of different decisions. \In this paper, our motivation is to propose a system to decide on how to develop applied academic educations at the Applied ScienceEducation Centers (ASEC's) which are supervised by the University of Applied Science and Technology (UAST) in Iran. The method used is theFuzzy Inference System (FIS) to reach this goal. The model performed in this study consists of a two-part FIS for two purposes straight ahead.The first concerns the quantitative development of higher applied education, including the number of Course Request (CR) for each ASEC with 4 inputs and 242 rules. The latter, is related to the qualitative development of higher applied education, including the assessment of the capability and competence of each ASEC for specific CR with the course and student admission background, specialty, committee's and council’s opinion, and frequency of course in city or province with 9 inputs, 350 rules, and an output. Each section has its inputs and rules that are determined and used by experts in this domain. \Our results show that using this method, provides some cost and time savings. This model analyzes in less than 2 minutes, while in practice it could be gain within 1650 person /hours work in different committees that all of them have their costs. Also, results are very close to reality with the advantage that there is no way to apply personal preferences, which largely meets the needs of research.

The purpose of this study was to develop a model for the estimation of rock mass classification of Sarvak limestone in the Bakhtiari dam site, south-west (SW) Iran. Q system had been used as the starting point for the rock mass classification. This method was modified for sedimentary rock mass which is known as Qsrm. Because Qsrm considers a wide range of rock mass properties, it has become a tool for rock mass classification that more correlates with geophysical parameters. This study tried to revise and empower the correlation between P-wave velocity (Vp) with Q and Qsrm in Sarvak limestone. By using data sets of Bakhtiari Dam Site (BDS) in SW Iran and multivariate regression and the Fuzzy Inference System (FIS), models were rendered for prediction of Q and Qsrm. About 700 sets of data were used for modeling and Vp was considered as the input parameter. The regression equations showed the relationship between Vp with Q and Qsrm,under conditions of quadratic relations, obtained coefficients of determination (R2) of 0.49 and 0.66, respectively. The correlation coefficient was calculated as 0.82 for the Qsrm obtained from FIS models. Also, Variance Accounted For (VAF) and Root Means Square Error (RMSE) indexes were also used for evaluation of prediction accuracy of models. Results showed that Vp has better performance in prediction of Qsrm than Q and theFIS model showed the best prediction results. Because these models have accuracy, they could be used in similar conditions.

In the present work, the influences of temperature, solvent concentration and ultrasonic irradiation time were numerically analyzed on viscosity reduction of residue fuel oil (RFO). Ultrasonic irradiation was applied at power of 280 W and low frequency of 24 kHz. The main feature of this research is prediction and optimization of the kinematic viscosity data. The measured results of eighty-four samples, including 336 data points, were developed by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The ANN predictions were also compared with the ANFIS approach by means of various descriptive statistical indicators, including absolute average deviation (AAD), average relative deviation (ARD) and coefficient of correlation (R2). The AAD and R2 of the developed ANN model for kinematic viscosity prediction of overall set were 0.0107 and 0.99384, respectively. On the other hand, for ANFIS approach, the AAD of 0.02112 and R2 of 0.99279 were attained. Although accuracy and precision of the ANN model were more than the ANFIS approach, it has been illustrated that the proposed ANN and ANFIS models have a superior performance with acceptable errors on the RFO kinematic viscosity estimation. Findings of this research clearly revealed that the neural network and neuro-fuzzy approaches could be successfully employed for prediction and optimization of kinematic viscosity of RFO and high viscosity materials in oil processes.