Adaptive Neuro-fuzzy Inference System Model Research Articles

Neuro Fuzzy in Predicting the Characteristics of Some Nanomaterials

Unveiling the impressive capabilities of the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study effectively predicts key properties of engineered nanomaterials, opening doors to innovative applications across various industries. We initially investigate the cytotoxic effects of TiO2 and ZnO nanoparticles on immortalized human lung epithelial cells, employing ANFIS to establish correlations between nanoparticle size and behaviour in different media and the resulting cellular membrane damage, quantified by lactate dehydrogenase release. Next, to predict the compressive strength of geopolymers, analysing over previous experimental datasets focused on critical chemical ratios. This model demonstrates its capability to optimize formulations for enhanced mechanical performance in sustainable construction materials. Additionally, we apply ANFIS to evaluate the size of silver nanoparticles in montmorillonite/starch bio nanocomposites, identifying significant factors such as AgNO3 concentration. The ANFIS models achieved high accuracy across all applications, underscoring their utility in predicting material behaviour and optimizing formulations for improved performance and safety. Collectively, these findings illustrate the potential of ANFIS as a robust tool in nanomaterial research and development.

Thermal performance prediction of a V-trough solar water heater with a modified twisted tape using ANFIS, G.L.R., R.T. and SVM models of machine learning

Four distinct neural models were used to evaluate the efficiency of a V-trough solar water heater (VTSWH) equipped with square-cut twisted tape (SCTT) and V-cut twisted tape (VCTT) at two different twist ratios, 3 and 5. The objective of this study was the use of ANFIS (Adaptive Neuro-Fuzzy Inference System), G.L.R. (Generalised linear regression), R.T. (Regression tree), and SVM (Support Vector Machine). A total of 162 data sets were acquired for these models through a variety of trials. Outdoor experiments were done using a twist ratio of Y = 3 and Y = 5, using both SCTT and VCTT. The models included eight distinct variables: ambient temperature, water mass flow rate, water intake temperature, water exit temperature, absorber plate temperature, tube temperature, solar intensity, and twist ratio. The dependent variables in this study are the Nusselt number (Nu), friction factor (FF), and efficiency (η). 130 datasets were chosen for training purposes, while 32 were used for testing. Using the ANFIS, G.L.R., R.T., and SVM techniques, the correlation coefficient (R2) values for Nusselt number were 0.9990, 0.9961, 0.9562, and 0.9280 for friction factor 0.9966, 0.9683, 0.9810, and 0.9560, and for efficiency 0.9997, 0.9976, 0.9845, and 0.9614, respectively. Comparing all models shows that ANFIS is the most effective of the four strategies studied. The ANFIS model outperformed the other models regarding Nu, FF, and η, with RMSE values of 0.0805, 0.0.0004, and 0.4534. According to the above data, the VTSWH thermal performance predicted using the ANFIS approach has the highest accuracy.

Combined ANFIS and numerical methods to reveal the mass transfer mechanism of ultrasound-enhanced extraction of proteins from millet

Millet protein, as a promising plant-based protein substitute source, is an excellent basis for essential amino acids compared to commonly consumed staple grains. Compared with the traditional extraction process, ultrasound has been used to enhance the extraction efficiency of various plant-based proteins. To reveal the mechanism of ultrasound-enhanced extraction of proteins, adaptive neuro-fuzzy inference system (ANFIS) algorithm and numerical simulation based on Fick’s law were applied to illustrate the mass transfer behavior of millet proteins under different ultrasonic conditions including solid–liquid ratios (S/L ratios), pH and acoustic energy density levels (AED). The results showed that AED dominated the changes in effective diffusion coefficient (De), showing a positive correlation relationship (p < 0.05). Specifically, when the AED was 47.07 W/cm2, the De value increased by 95 % compared to that of 23.47 W/cm2. Meanwhile, the ANFIS model successfully predicted protein yields across all investigated parameters, achieving a coefficient of determination (R2) greater than 0.97. This model also elucidated the interactions among four critical factors, among which pH and S/L ratios were the main factors affecting protein yield. Concerning the ultrasonic cavitation bubble dynamics, the bubble collapse efficiency enhanced with an increase in AED, and therefore high AED ultrasound can significantly enhance the cavitation effect. Additionally, the results of the yields and physical properties of millet protein also indicated that in contrast with the traditional extraction methods, the ultrasound impactfully improved extraction yield (by 165 %), and combined with pH condition, it decreased the protein particle size (from 813.55 nm to 299.30 nm) without altering the molecular weight distribution. This study offers a novel perspective on the mechanism underlying ultrasound-enhanced protein extraction, drawing upon principles of ultrasonics and extraction processes. The insights gained can serve as a foundation for the food industry to upscale the extraction process, potentially enhancing efficiency and yield.

Artificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse technology

&lt;p class="Abstract"&gt;As a result of the growing significance and application of technology across a wide range of fields, digital environments such as Metaverse started to take shape over the span of the previous decade. This study aims to discover an area of engineering that could benefit from this new technology by developing an artificial intelligence (AI)—based approach to analyzing and predicting the mechanical properties of carbon fiber reinforced syntactic thermoset composites that are made through additive manufacturing (AM). These composites are intended to be utilized as a tool for metaverse technology in a variety of domains—as the presence of the limitations in the currently experimental methods. The metaverse allows for the generation of simulations through the application of artificial intelligence (AI) and machine learning (ML). Consequently, this paves the way for individuals to investigate various design possibilities and view the virtual manifestation of those possibilities. This is made possible by the use of machine learning algorithms, which allow for the monitoring and evaluation of user performance, as well as the provision of individualized feedback and suggestions for improvement. As a consequence of this, it is feasible that professionals will be able to get education and training that are both more efficient and effective. Consequently, this work aims to introduce an Adaptive Neuro-Fuzzy Inference System (ANFIS)—based model, which is able to effectively anticipate the behavior of mechanical systems in a variety of settings without the need for significant measurements. The validity of the ANFIS model was determined through the utilization of flexure and compression testing. The approach that was used to improve the technical assessment of the manufactured composites—is verified by the model’s near-realistic predictions. Moreover, this method is superb for lowering weight, enhancing mechanical qualities, and minimizing product complexity.&lt;/p&gt;

Maximizing waste cooking oil biodiesel production employing novel Brotia costula derived catalyst through statistical and machine learning optimization techniques

The limitations of homogeneous catalysts associated with biodiesel production such as non-reusability, emulsification, and saponification have led to the increased cost of biodiesel synthesis. To overcome these limitations, the current work aims to investigate the applicability and effectiveness of green catalyst made from Brotia costula shells (BCS) for waste cooking oil biodiesel production. The catalyst was synthesized by calcinating BCS at 800℃−1000℃ for 4 h. Catalyst characterization showed that BC900 (BCS calcined at 900℃) contained 98.15 CaO wt% and exhibited the smallest grain size of 39.23 nm with the highest specific surface area of 14.28 m2/g and highest catalytic activity. Optimization of biodiesel synthesis was carried out using response surface methodology - Box-Behnken design (RSM-BBD), artificial neural network (ANN) coupled with genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) coupled with GA. Maximum experimental biodiesel yield of 97.78±0.59 % at optimum conditions of methanol: WCO molar ratio 9.73: 1, reaction time 2.12 h, catalyst dose wt% 7.59 and reaction temperature 69.3°C was obtained with ANFIS-GA optimization model. The prediction accuracy of the ANFIS model (R2 = 0.988) was better than those of the RSM-BBD (R2 = 0.982) and ANN (R2 = 0.955) models. The synthesized catalyst exhibited high catalytic activity up to four cycles of reusability (yield > 88 %). It is environment-friendly, inexpensive, renewable, and performs on par with catalysts synthesized from other mollusk shells. Using this catalyst for biodiesel production will contribute to the biorefinery's ability to explore sustainable raw materials and may lower overall biodiesel cost.

Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na2SiO3) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies—Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)—the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material’s thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R2 of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R2 of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R2 of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.

Advanced Prediction Models for Scouring Around Bridge Abutments: A Comparative Study of Empirical and AI Techniques

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Analysis and Optimization of Super Duplex Stainless Steel Deposition in Wire Arc Additive Manufacturing Using Machine Learning Techniques

&lt;div&gt;This article presents experimental investigations and machine learning-based analysis on depositions of super duplex stainless steel (SDSS ER2594) material in wire arc additive manufacturing (WAAM) considering the process parameters namely voltage, wire feed rate, torch travel speed, and gas flow rate. Deposition efficiency and surface height values of the accumulated material were measured to build machine learning models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The developed ANN model could predict the deposition efficiency and surface height with mean absolute deviations (MADs) of 8.9% and 16.1%, respectively. The MAD for prediction of the two responses for ANFIS model was found to be 6.1% and 14.9% as compared to the experimental data. Multi-objective optimization was also performed to obtain optimal solutions to achieve desired deposition results. Mechanical properties and microstructures of the deposited materials with optimal processing parameters were found comparable to that of the base materials.&lt;/div&gt;

Predicting chlorophyll-a dynamics in the Ashtamudi estuary using ANN and ANFIS techniques

Excessive nutrients and associated high chlorophyll-a concentration are of growing concern to the public health due to the occurrence of eutrophication in aquatic bodies. Continuous monitoring of chlorophyll-a has limitations, thereby necessitating the development of prediction models. Even though chlorophyll-a prediction models are numerous, they rarely fit into estuarine conditions, which is encountered with dynamic mixing of freshwater and sea water. Thus, the present study identifies the most appropriate driving factors that are relevant for estuarine conditions through chlorophyll-a prediction models developed using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS) approaches in the Ashtamudi estuary, India. Salinity, which is an indirect measure of the estuarine mixing intensity, has been used to replicate the dynamic mixing in the estuary. The results indicate that the model incorporating salinity along with total nitrogen, total phosphorous and turbidity well-predicted chlorophyll-a concentration with a coefficient of determination ranging from 0.73 to 0.99. General Regression Neural Network (GRNN) and ANFIS models outperformed Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models. Of the various ANFIS models, the ones that used Gaussian and Generalized Bell membership functions were most appropriate for the prediction of chlorophyll-a than the models with triangular and trapezoidal membership functions. The study identified that the models that considered salinity were less sensitive and can be used for modelling chlorophyll-a. The chlorophyll-a was observed to have a direct influence of salinity at salinity values greater than 5‰. The study highlighted that estuarine mixing further enhances the primary productivity through increased penetration of light leading to higher chlorophyll-a concentration. Further the study emplasized that the predictive models are important tools fitting well within estuarine environments due to its replicability.

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

ABSTRACT Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and Machine Learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X 1: 62–72% v/v), temperature (X 2: 38–48°C), ultrasonic-exposure time (X 3: 12–24 min), and particle size (X 4: 2–5 mm) were optimized. The maximum yield of phytocompounds was achieved at X 1 = 67%, X 2 = 43°C, X 3 = 18 min, and X 4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y1 = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y2 = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y3 = 66.41%), and %ABTS*sc (y4 = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L.

Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective Genetic Algorithm

Considering uncertainty in the analysis of geotechnical structures is a necessary condition for optimal and robust design. An alternative method for studying the reliability of a mechanically reinforced earth wall in granular soil is used to account for these uncertainties more rigorously. This allows for the inclusion of various uncertainties in a mathematical risk formulation based on random variables. The deterministic model is a benchmark taken from the literature used in a numerical simulation to determine the maximum horizontal displacement of the wall. In this case, the serviceability limit state is considered, allowing the wall's actual behavior to be described. ANOVA was used to identify the most influential parameters on the system's response. As uncorrelated random variables, only the parameters (E, φ and γ) were considered. The mathematical model serving as the limit state function was numerically predictedby three methods, response surface methodology (RSM), artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), and their predictive capacities were then compared. The results showed that the ANN model outperformed the RSM and ANFIS models regarding prediction. ANN models and multi-objective genetic algorithm (MOGA) are used to optimize the Hasofer-Lind reliability index. The analysis is then carried out by taking into account the various types of functions of parameter distributions, which allowed us to better appreciate the effects of the uncertainties and identify the set of parameters with a high incidence.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Prediction of confined compressive strength of concrete column strengthened with FRCM composites

AbstractNowadays, retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a growing need of the construction industry instead of demolishing aged structures. The application of fabric‐reinforced cementitious matrix (FRCM) on the existing concrete structures is one of the sustainable solutions to retrofit the concrete structures. This study used machine learning (ML) models such as linear regression (LR), support vector machines (SVM), and adaptive neuro‐fuzzy inference systems (ANFIS) to estimate the compressive strength (CS) of columns wrapped with FRCM. The experimental dataset of 301 column specimens was collected including input parameters such as cross‐sectional properties, mechanical properties of concrete and steel, and characteristics of FRCM material. Apart from ML models, seven analytical models were also used to compare the accuracy and precision of ML models. The results illustrate that the ANFIS model outperformed other ML models and established itself as a dependable and precise model. The R‐value of the ANFIS model was 0.9816, whereas R‐values of 0.9269 and 0.9572 were achieved by LR and SVM models, respectively. In addition, the MAPE value acquired by the ANFIS model was 1.52% which was lower than those of the LR model by 73.24%, and the SVM model by 60.60%, respectively. As the precision of the ANFIS model was higher as compared with SVM and LR models, so, the developed ANFIS‐based mathematical model can be easily used to predict the CS of FRCM‐strengthened concrete columns. The developed model is accurate, economical, and fast; and can be utilized by FRCM applicators and structural designers.

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90% accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.

Integration of experimental and intelligent modeling for optimizing iron electrocoagulation-flocculation recovery of aquafarm effluent

The present study integrates experimental and intelligent models in the modeling and optimization of the iron electrode-based electrocoagulation-flocculation (EC-FLC) process for the treatment of aquafarm effluent (AFE). The modeling was done using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), while the optimization was performed with Particle Swarm Optimization (PSO). The physicochemical properties of AFE were determined before and post-treatment to assess the effectiveness of the process. Central composite design (CCD) embedded in RSM was employed for experimental design. ANFIS model utilized the grid partition and ANN employed a multi-layer perceptron-based feed-forward architecture. The statistical analysis revealed superior prediction accuracy in the decreasing order ANFIS (R2: 0.9968, AAD: 0.0009, RMSE: 0.2373), ANN (R²: 0.9896, AAD: 0.0023, RMSE: 0.4388), and RSM (R²: 0.9655, AAD: 0.0067, RMSE: 0.7764), demonstrating the superiority of ANFIS model. Models’ reliability were validated by the strong correlation between the actual/predicted values of turbidity removal. The experimentally validated optimization solutions for turbidity were RSM numerical (96.71 %), RSM-PSO (97.46 %), ANN-PSO (98.83 %), and ANFIS-PSO (99.01 %). Post-treatment analysis revealed significant reductions in turbidity, nutrient content, and organic matter. These findings demonstrated the effectiveness of the intelligent tools in the modeling/optimization of EC-FLC recovery of AFE.

Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae

Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.

An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique

AbstractSand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.

Optimizing the energy values of solid biofuel through acidic pre-treatment: An evolutionary-based neuro-fuzzy modelling and feature importance analysis

The increasing focus on sustainable energy solutions and eco-friendly fuel alternatives has sparked interest in exploring methods to enhance the energy content of solid biofuels through pre-treatment processes. This study investigates the impact of acidic (H2SO4) pretreatment on the energy content of solid biofuel derived from watermelon peel waste using machine learning methods. The biofuel properties, and calorific value (CV) were determined experimentally for both H2SO4-treated and untreated biofuel samples. Subsequently, an Adaptive neuro-fuzzy inference system (ANFIS) model with Genetic Algorithm (GA) and Grid Partitioning (GP) clustering techniques was developed to predict heating value of the biofuel, with performance evaluation based on root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), Mean Absolute Error (MAE) and R2-values. The decision tree regressor was employed to evaluate the feature importance, predictive power and impact of each input variable on CV prediction based on Gini importance metrics. To establish its superior performance, the hybrid ANFIS-GA-GP model was compared with ordinary-ANFIS and ANN model using same metrics. Results show that acid-pretreatment increased the CV of the solid biofuel by 3.53 MJ/kg (31.83 % improvement) and reduced ash content by 1.77 %. FTIR analysis revealed surface modifications, and a shift in the C-O vibrational stretch, while SEM micrographs displayed denser surfaces and reduced porosity as indicated by a lesser fiber diameter in treated samples. The GP-clustered ANFIS-GA model with a triangular membership function (tri-MF) exhibited superior performance and higher accuracy (RMSE of 0.1309, MAPE of 9.343, MAD of 0.1036, MAE of 0.1110, and R2-value of 0.9773 at model training). Furthermore, a tree decision regressor identified Fixed carbon as the most significant predictor for CV with a Gini importance of 0.988375. This study demonstrates the substantial enhancement in the energy content of biofuel using acidic pre-treatment and insights into a cutting-edge approach of improving the combustion properties of solid biofuel with machine learning models. This significantly contributes to the field of sustainable bioenergy research.

Understanding the relationship between electric dipole polarizability and photonuclear (-2) moment in odd-A actinide nuclei

The nuclear electric dipole (E1) polarizability (α E1) is mainly dominated by the dynamics of the giant dipole resonance (GDR). α E1 is proportional to the (-2) moment of the total photo nuclear cross-section (σ −2). This research investigates the relationship between α E1 and σ −2, along with the effects of the Pygmy Dipole Resonance (PDR) and GDR in odd-mass actinide nuclei. For the first time, α E1 and σ −2 values have been calculated using the Translational and Galilean Invariant Quasiparticle Phonon Nuclear Model (TGI-QPNM) approach for odd-A actinide nuclei. According to TGI-QPNM results, E1 dipole transitions in the GDR region significantly contribute to σ −2 due to the energy weighting factor. Below the neutron separation threshold, the PDR in neutron-rich nuclei shows a contribution of about 5% to σ −2 values. In this region, E1 polarizability can reach values of 20%–25%. The α E1 values indicate the presence of PDR in these nuclei. Additionally, the Adaptive Neuro-Fuzzy Inference System (ANFIS), a new machine learning method, has been performed to analyze the relationship between α E1 and σ −2. The ANFIS results have been compared with those from the TGI-QPNM and experimental data. The TGI-QPNM model achieves an R2 of 0.85–0.95, while the ANFIS model achieves an R2 of 0.99. Moreover, the study suggests that the ANFIS model, consistent with TGI-QPNM results, could be an effective tool for estimating σ −2 in odd-A actinide nuclei.

New and Highly Accurate Static Young's Modulus Model Using Machine Learning Techniques.

Static Young's modulus (E s) is a critical property required in numerous petroleum calculations. Various models to forecast E s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using trend, group error, and statistical error analyses. To test the efficacy of the proposed models, the optimally performing model was identified and used to detect the rock types along with the previously published models. Results indicated that ANFIS is the optimum model and can predict E s with an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602. The ANFIS method has some benefits over other machine learning approaches insofar as its superiority in reaching a quicker decision about the mapped relationship between the inputs and outputs because it combines artificial neural networks and fuzzy logic in one tool. The ANFIS can perform a highly nonlinear mapping and displays a better learning ability. The proposed ANFIS model demonstrates its ability to capture accurate physical relationships between input rock properties and E s through trend analysis, which shows that increasing the RHOB increases the E s. Contrarily, increasing the DTc and DTs reduces the E s. Furthermore, the ANFIS model can accurately detect the rock types based on its E s determinations. This research demonstrates the importance of accurately predicting E s for the proper identification of rock types. Thus, this study offers potential advancements in geological assessments of hydrocarbon reservoirs and improvements in many petroleum engineering applications.