Induction motors (IMs), as essential components in industrial operations, are subject to various operational abnormalities, such as voltage unbalance, harmonic distortions, under/over voltage supply, and ambient temperature variations. These factors necessitate the de-rating of torque to ensure motor reliability, efficiency, and safe operation within rated power loss limits. Traditional methods for estimating de-rated torque often involve complex and time-intensive calculations, creating challenges in real-time applications. To address these limitations, this manuscript introduces the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a robust predictive tool for de-rated torque estimation under abnormal conditions. This study defines and quantifies main de-rating factors (Dfs), including voltage unbalance, harmonic distortions, and temperature rise, employing MATLAB/Simulink simulations for performance analysis. The proposed ANFIS controller integrates neural networks and fuzzy logic, enabling efficient evaluation of de-rated torque by dynamically adjusting to real-time operating conditions. Validation of the ANFIS predictions against Simulink outcomes highlights its reliability and accuracy, with minimal deviations observed. Results reveal the significant impact of DFs on induction motor (IM) performance. Voltage unbalance and harmonic distortions emerged as primary contributors to reduced torque output, while temperature rise exacerbates power losses and thermal stress on IM components. By mitigating the need for extensive calculations, ANFIS simplifies the process of assessing torque de-rating and ensures rapid, precise predictions. ANFIS controller is trained offline to assess the de-rated torque of the IM under different operating conditions. The results from this training have been validated against Simulink outcomes, confirming the reliability and accuracy of the ANFIS technique. This research advances the understanding of IM performance under non-ideal conditions, offering a practical framework for de-rating torque evaluation and management. The integration of ANFIS as a control mechanism not only optimizes motor efficiency but also extends operational longevity, underscoring its potential for real-world industrial applications.

• Keywords
• ANFIS, Harmonic distorted supply, High ambient temperature, Induction motors (IMs), Unbalanced supply, Under/over voltage supply,
• Publication type
• Journal Article MeSH

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.

• Keywords
• Adaptive neuro-fuzzy inference system, Air quality, Artificial intelligence, Machine learning,
• Publication type
• Journal Article MeSH

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50-70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

• Keywords
• Data-driven, Flood, Machine learning, Risk assessment, Sefidrud river,
• Publication type
• Journal Article MeSH

Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.

• Publication type
• Journal Article MeSH

The effects of surface pretreatments on the cerium-based conversion coating applied on an AA5083 aluminum alloy were investigated using a combination of scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), polarization testing, and electrochemical impedance spectroscopy. Two steps of pretreatments containing acidic or alkaline solutions were applied to the surface to study the effects of surface pretreatments. Among the pretreated samples, the sample prepared by the pretreatment of the alkaline solution then acid washing presented higher corrosion protection (~3 orders of magnitude higher than the sample without pretreatment). This pretreatment provided a more active surface for the deposition of the cerium layer and provided a more suitable substrate for film formation, and made a more uniform film. The surface morphology of samples confirmed that the best surface coverage was presented by alkaline solution then acid washing pretreatment. The presence of cerium in the (EDS) analysis demonstrated that pretreatment with the alkaline solution then acid washing resulted in a higher deposition of the cerium layer on the aluminum surface. After selecting the best surface pretreatment, various deposition times of cerium baths were investigated. The best deposition time was achieved at 10 min, and after this critical time, a cracked film formed on the surface that could not be protective. The corrosion resistance of cerium-based conversion coatings obtained by electrochemical tests were used for training three computational techniques (artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine regression (SVMR)) based on Pretreatment-1 (acidic or alkaline cleaning: pH (1)), Pretreatment-2 (acidic or alkaline cleaning: pH (2)), and deposition time in the cerium bath as an input. Various statistical criteria showed that the ANFIS model (R2 = 0.99, MSE = 48.83, and MAE = 3.49) could forecast the corrosion behavior of a cerium-based conversion coating more accurately than other models. Finally, due to the robust performance of ANFIS in modeling, the effect of each parameter was studied.

• Keywords
• anfis, artificial neural network, cerium conversion coatings, modeling, surface pretreatment,
• Publication type
• Journal Article MeSH

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

• Keywords
• ANFIS, Fe-Al-O, Oxide Precipitation-Hardened (OPH) alloys, artificial neural network (ANN), particle swarm optimization, tensile test, toughness,
• Publication type
• Journal Article MeSH

This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + Al2O3 + TiN coated carbide tool insert. Three machining parameters with four levels considered in this research are feed, revolutions per minute (RPM), and depth of cut (ap). The influences of those three factors on material removal rate (MRR), surface roughness (Ra), and cutting force (Fc) were of specific interest in this research. The results showed that turning control variables has a substantial influence on the process responses. Furthermore, the paper demonstrates an adaptive neuro fuzzy inference system (ANFIS) model to predict the process response at various parametric combinations. It was observed that the ANFIS model used for prediction was accurate in predicting the process response at varying parametric combinations. The proposed model presents correlation coefficients of 0.99, 0.98, and 0.964 for MRR, Ra, and Fc, respectively.

• Keywords
• ANFIS, RPM, alloy steel, feed, turning,
• Publication type
• Journal Article MeSH