Adaptive Neuro-fuzzy Inference System Research Articles

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.

Comparative analysis of response surface methodology and adaptive neuro-fuzzy inference system for predictive fault detection and optimization in beverage industry

Maintenance is crucial for ensuring equipment reliability and minimizing downtime while managing associated costs. This study investigates a data-driven approach to predicting machine faults using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). RSM was employed to develop a mathematical model to analyze how operational parameters such as pressure, voltage, current, vibration, and temperature affect fault occurrence. Data were collected at three levels for each parameter using a central composite design. The model identified that faults peaked at a pressure of 28.38 N/m2, an operating voltage of 431.77 V, current consumption of 12.54 A, machine vibration of 47.17 Hz, and temperature of 25°C, with a maximum of 25 faults observed. Conversely, the lowest fault detection occurred at a pressure of 29.42 N/m2, an operating voltage of 441.04 V, current consumption of 12.04 A, machine vibration of 49.46 Hz, and temperature of 46.5°C. A strong correlation was found between these parameters and machine faults, with the model achieving high accuracy (R2 = 98.22%) and statistical significance (p-value <0.05), demonstrating its reliability in predicting faults. The study also compared RSM with ANFIS for fault detection and process optimization in the beverage industry. While RSM effectively optimized parameter relationships, ANFIS, with its adaptive learning capabilities, provided superior fault prediction accuracy. This comparative analysis highlighted the strengths of both methods and suggested that integrating them could enhance predictive maintenance strategies. The findings offer valuable insights for industry practitioners, recommending a combined approach to improve fault detection, optimize production processes, and enhance operational efficiency.

Artificial intelligent-based backstepping control of parallel-connected multi-machine wind generation system using a new fifteen-switch rectifier

This academic work offers a new variable-speed wind power generation system (NVS-WPGS) that employs two 5-phase permanent magnet synchronous generators (PMSGs) controlled through a fifteen-switch rectifier (FSR) architecture for a system that’s integrated to the power grid. The two 5-phase PMSGs are linked to the DC bus through an FSR, which, in turn, connects the DC link to the grid via a three-phase inverter. To ameliorate the operational efficiency of the wind system under investigation, the backstepping control (BSC) approach was employed. Building upon this technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was trained and implemented for controlling the wind system. The goal is to boost system performance and control. While BSC is a strong control method for reference signal tracking, its dependence on system parameters is a downside. This dependence typically degrades performance. On the other hand, ANFIS exhibits robustness to variations in system parameters and uncertainties. The efficaciousness of the suggested control methodology was evaluated via experimental investigations conducted using the Matlab/Simulink program. The simulations were performed to verify the system’s capacity to maintain controlled variables in accordance with desired references, even in the presence of fluctuations in wind velocity. Simulations exhibit that the suggested ANFIS-BSC control system accurately tracks controlled intended references. Furthermore, system performance improved. Speed overshoot was decreased by 100%. Additionally, the system’s efficiency was enhanced, reaching a level of 96.5%, beating the BSC approach’s 96%. These findings underscore the remarkable effectiveness of the ANFIS-BSC approach compared to BSC methodologies.

A method for temperature sensor and model selection for machine tool thermal error modelling using ANFIS and ANN

AbstractThermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. These errors are commonly compensated using predictions from temperature-based empirical models. The accuracy and robustness of these predictions are affected by the locations of temperature sensors used to obtain the model’s input data. Methods for sensor selection found in literature are often difficult to replicate and automate because they require tuning of several hyperparameters. This work presents a sensor and model selection approach that uses proper orthogonal decomposition (POD) and QR pivoting to select a subset of sensors that have been preinstalled in the machine tool as possible model inputs. These sensors are then sorted according to their correlation with the thermal error being modelled. The final set of inputs and thermal error model structure is chosen using Bayesian Information Criterion (BIC) to limit model overfitting. The approach was tested by modelling the Y-axis thermal error measured from air-cutting experiments performed under different spindle speeds and feed rates. This enabled determination of the structure and the choice inputs for Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) thermal error models. The accuracy of these models was compared to that of models trained using inputs selected by conventional approaches: the least absolute shrinkage and selection operator (LASSO), fuzzy c-means clustering (FCM), and principal component regression (PCR). The presented approach had better or comparable results to the conventional approaches while using fewer inputs. The presented approach is also well suited for automation compared to conventional approaches that require expert input.

Predictive modeling using Copula Particle Filter and Adaptive Network-Based Fuzzy Inference

This paper introduces a novel prediction algorithm, CPF-ANFIS, designed to overcome the challenges posed by high-dimensional input data in Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS's performance deteriorates with increasing input dimensionality due to the distortion of its membership functions. To address this limitation, CPF-ANFIS leverages a two-stage approach: A Copula Particle Filter (CPF) for robust state estimation and ANFIS for nonlinear mapping. By incorporating copulas, CPF effectively addresses the impoverishment and degeneracy problems commonly encountered in traditional particle filters. This enhanced robustness allows for more accurate state estimation, which in turn improves the overall performance of the CPF-ANFIS algorithm. By decoupling state estimation from nonlinear modeling, CPF-ANFIS effectively mitigates the curse of dimensionality. The proposed method is evaluated on real-world applications, such as hybrid PV-wind systems and SLAM. Experimental results demonstrate that CPF-ANFIS consistently outperforms ANFIS and the Copula Particle Filter individually, as well as previously proposed methods such as ANFIS-PF, highlighting its effectiveness in achieving accurate predictions under challenging conditions. The results show that the CPF-ANFIS algorithm increases prediction accuracy by at least 5% compared to using each algorithm separately.

Air Quality Prediction and Control Systems Using Machine Learning and Adaptive Neuro-Fuzzy Inference System

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.

Performance evaluation of machine learning algorithms in predicting machining responses of superalloys

This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN)—to predict machining responses during the milling of Inconel 690, a superalloy known for its exceptional mechanical properties and oxidation resistance. Machining Inconel 690 presents significant challenges due to its toughness and work-hardening tendencies, which can lead to rapid tool wear and poor surface finish. Traditional optimization methods often rely on empirical models and trial-and-error approaches, which are time-consuming and costly. In contrast, machine learning techniques can effectively model complex, nonlinear relationships between machining parameters and performance outcomes, such as surface roughness, cutting force, and cutting temperature. This study employs statistical metrics, including Root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), to determine the predictive performance of the models. The results show that the GEP model achieved an R2 ranging from 0.944 572 to 0.992 999, with an RMSE between 0.015 527% and 0.694 523% and a MAPE ranging from 1.452 397% to 4.947 892%. ANFIS and ANN also demonstrated strong predictive capabilities, although GEP outperformed them. The importance of this study lies in its demonstration of advanced AI techniques as effective tools for optimizing machining processes, ultimately contributing to improved efficiency and quality in manufacturing superalloys.

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.

Enhanced direct torque control based on intelligent approach for doubly-fed induction machine fed by three-level inverter

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) based on 24 sectors direct torque command (DTC) for a doubly-fed induction machine (DFIM) by using a 3-level neutral point clamped inverter. The DTC approach is used in this paper with 24 sectors based on the ANFIS regulator to minimize the torque fluctuations, flux fluctuations, and stator stream THD (Total Harmonic Distortion) of the DFIM drive. The composed technique is accomplished by replacing the hysteresis controllers of the flux and torque with the ANFIS controller. On the other hand, the results of the designed approach based on ANFIS controls were obtained compared to the traditional approach that uses usual controls, as MATLAB was used to realize these approaches in different working conditions. In all tests, the designed method shows improved performance in minimizing torque and flux undulations while reducing the THD of the stator stream. These results highlight the extent of efficiency, high competence, and effectiveness in improving machine features compared to the conventional approach, where the designed approach minimized the THD of current by ratios of approximately 77.50%, 48.34%, 75%, and 81.43% in all tests. Also, the torque undulation value was reduced by 30%, 39.24%, 31.94%, and 59.31% in all tests compared to the DTC. The designed approach minimized the speed overshoot compared to the DTC by percentages estimated at 98.83%, 97.11%, 96.75%, and 95.56% in all tests. These percentages demonstrate the high efficiency and effectiveness of the 24 sectors DTC-ANFIS compared to the conventional approach in improving the features of the control system, making it a promising solution in all electrical fields in the future.

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.

The effect of Plantago major seed mucilage fortified with cell-free supernatant (CFS) of probiotic Lactococcus lactis NJ414 on extending shelf life and ensuring the safety of lamb meat slices stored at 4 °C

The present study was conducted to create a bioactive edible coating using Plantago major mucilage (PMM) and the cell-free supernatant (CFS) of probiotic Lactococcus lactis NJ414 to extend the shelf life and ensure the safety of lamb meat slices stored at 4 °C. The PMM+2%CFS coated slices showed a significant decrease in total viable count (8.14–5.48 log CFU/g), and psychrotrophic bacteria count (5.24–3.54 log CFU/g). Additionally, the pH value decreased from 5.93 to 5.71, the thiobarbituric acid (TBA) reduced by 65%, and the peroxide value (PV) decreased by 60%. Moreover, PMM+2%CFS coated slices had higher hardness (68.87 N vs. 62.10 N), moisture content (67.57% vs. 64.35%), and overall acceptance (6.40 vs. 3.00) compared to the control. Adaptive neuro-fuzzy inference systems (ANFIS) and gaussian process regression (GPR) models were used to predict the population of microbial pathogens, physicochemical and sensory characteristics of coated lamb products based on three inputs including PMM (0% and 2%), CFS (0%, 1% and 2%), time (1, 3, 6 and 9 days). The result showed that artificial intelligence-based models, especially ANFIS (R2 > 0.99) are able to estimate the population of microbial pathogens, physiochemical and sensory characteristics.

Enhanced frequency control of a hybrid microgrid using RANFIS for partially shaded photovoltaic systems under uncertainties

Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.

Prediction of polarization curves of PEMFC membrane electrode assembly using artificial intelligence technics

Proton exchange membrane fuel cells (PEMFCs) are used commercially in automobiles, buses, uninterruptible power supplies, and combined heat power systems, holding a significant place in the fuel cell market. Fuel cell performance is characterized by polarization curves in design and manufacturing processes. This study predicts a PEMFC’s polarization curves using comparative artificial intelligence (AI) models trained and tested under different operational conditions. The AI model inputs are cell temperature, humidity, anode-cathode flow, and membrane resistance. The outputs are cell voltage and current density. The model outputs are compared with experimental values for 50°C, 100% humidity using MATLAB software. The average Root Mean Square Error (RMSE) for the ANFIS prediction is 0.056112, while for the ANN prediction it is 0.011919. These results indicate that the Artificial Neural Network (ANN) method outperforms the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the behavior of the PEMFC’s Membrane Electrode Assembly (MEA). The models showed promising results with high accuracy.

Developing some models to predict the uniaxial compressive strength of various sedimentary rocks (Case studies: Large dam site and mine in Southeast China)

Direct determination of uniaxial compressive strength (UCS) is time-consuming, expensive, and challenging. Therefore, this study aims to indirectly predict UCS using statistical and machine learning methods. First, laboratory measurements were conducted to determine carbonate percentage, water absorption percentage, moisture content, porosity percentage, P-wave velocity, and UCS of sandstone, dolomite, dolomitic limestone, marl limestone, and limestone samples. Subsequently, various models were established to predict UCS using multivariate linear regression (MVLR), random forest regression (RFR), Gaussian process regression (GPR) based on squared exponential kernel function, and adaptive neuro-fuzzy inference system (ANFIS). The influence of Jurassic limestone texture on physical and mechanical properties in stable and unstable slopes in the Three Gorges Dam site and reservoir (TGDSR) showed that the percentage of mudstone texture in unstable slopes is higher than in stable slopes. The UCS, density, compressional wave velocity, and percentage of carbonate in unstable slopes is less than stable slopes. The results indicated that the average measured UCS is 60.60 MPa. Moreover, the average predicted UCS based on the three intelligent methods was 60.55 MPa. The percentage difference between the measured and predicted UCS was −0.09 %, demonstrating that the average error of the three employed methods is less than one percent, highlighting the high accuracy of these methods in estimating rock UCS. Notably, based on error level, Taylor diagram, Wilmot agreement index (WAI), A10 and A20 indices, Nash-Sutcliffe efficiency (NSE), and PM (performance metric) criteria the RFR exhibited superior precision (R2=99 %, and RMSE= 0.06 MPa) compared to the other used methods. The Kruskal-Wallis test (KWT) results showed that there is no significant difference between the measured and predicted values.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

AIFIS controlled BLDC Motor Drive for Agricultural Application with Solar System

In India, it is of utmost significance to ensure that the agricultural fields receive supplies of water. Agricultural applications are the focus of this paper, which depicts the implementation of an advanced BLDC motor connected drive that is controlled by an adaptive neuro fuzzy inference system (ANFIS). In this work, the implementation of the bidirectional power flow among the load and the renewable energy source (RES) is carried out. In order to power the water pump, renewable energy sources (RES) are generated through the use of solar photovoltaic (PV) cells. Additionally, a BLDC motor load is connected to the water pump through a single phase bi-directional converter and a voltage source inverter (VSI) inside the design. The PV system is able to get the maximum amount of power while simultaneously lowering the main grid and enabling consumers to run their motors and loads at their maximum efficiency throughout the entire day. This is made possible by the integration of fusing into the solar generating framework. Using the suggested controller, the PV system is able to reduce conversion costs and total harmonic content of the grid, all while maintaining the power factor, good quality in power, and stability of the system.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Resonant frequency, phase and power estimation in a broadband substrate integrated waveguide two- channel T- type power divider using adaptive network-based fuzzy inference system

Resonant frequency, phase and power estimation in a broadband substrate integrated waveguide two- channel T- type power divider using adaptive network-based fuzzy inference system

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.