Adaptive Network-based Fuzzy Inference System Model Research Articles

Predictive Modelling and Optimization of the Mechanical Properties of Laser-Coated NB/SiC/Ni Welds Using an ANFIS

In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties were taken as the output of the ANFIS. The microstructure of welds was analysed using scanning electron microscopy with an energy-dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi’s design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.

Torque Distribution Optimization for a Dual-Motor Electric Vehicle Using Adaptive Network-Based Fuzzy Inference System

The development of electric vehicles (EVs) has been considered one of the most efficient ways to reduce the carbon footprint of the transportation system. Among battery EV designs, a dual-motor configuration is introduced as a promising solution to improve dynamic performance and energy efficiency. In this study, a novel energy management strategy framework based on an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed for torque distribution optimization between two different motors. At first, Dynamic Programming (DP) is employed to find global optimization of the torque distribution. After training with the DP-obtained data set, the ANFIS model can execute a torque distribution online. By minimizing the battery energy consumption, the motor torque-speed solution pair is found under representative driving cycles (only three cycles). In addition, the best ANFIS model has been selected using a clustering technique, goodness-of-fit metrics, and sensitivity analysis. This distinguishes the optimization problem in this study from previously published literatures. The simulation results show that over an unknown Urban Dynamometer Driving Schedule (UDDS) cycle, the torque prediction using this ANFIS model achieves 98.3% of the benchmark DP result. As a result, the overall efficiency of the proposed strategy is increased to 73.3%, which is 3.4% higher than that of the rule-based method. Furthermore, the signal hardware-in-the-loop (S-HIL) experiments validate the real-time prediction of the ANFIS-based approach.

Modeling and optimization of oil adsorption capacity on functionalized magnetic nanoparticles using machine learning approach

Using magnetic nanoparticles (MNPs) has emerged as a promising solution to capture oil from emulsified oily wastewater due to their high oil adsorption capacity, low toxicity, and reusability. Various factors affect the oil adsorption process using MNPs; thus, optimization of the process is required to achieve higher oil adsorption capacity. Smart models based on artificial intelligence (AI) are becoming popular as advanced computational tools to assess the non-linear relationships of variables in complex processes. In this study, least squares support vector machines (LSSVM) hybridized with the coupled simulated annealing (CSA) algorithm, adaptive network-based fuzzy inference system (ANFIS), and optimization techniques such as gene expression programming (GEP) are used to predict the oil adsorption capacity as a target variable. Oil concentration, mixing time, and MNP dosage as effective parameters are selected as input variables. After conducting experiments, 149 data points are obtained, 80 % of which is used in the training process and the remaining 20 % for the testing step. The performances of the developed models are evaluated using statistical parameters, including the coefficient of determination (R2), mean percentage error (MPE), and mean absolute percentage error (MAPE). According to the results, ANFIS and LSSVM-CSA models have a better performance than the GEP model in estimating the oil adsorption capacity with higher values of R2 (>0.99) and smaller relative errors (close to zero) for all training, testing, and total datasets. Detailed model evaluation and error analysis indicate that the LSSVM-CSA model predicts slightly better than ANFIS with the highest R2 of 0.9921 and a very small MAPE = 3.7597 % over the total dataset. Although the developed GEP model shows an acceptable prediction with R2 > 0.95, the higher distribution of relative errors of the developed model results in a larger MAPE. Moreover, the GEP model computational time is considerably greater than that of the other models. The relative importance analysis using Pearson’s and Spearman’s correlation coefficients indicates that the oil concentration and MNP dosage are the most influential variables that affect the oil adsorption capacity.

Prediction of milled surface characteristics of carbon fiber-reinforced polyetheretherketone using an optimized machine learning model by gazelle optimizer

Motivated by the nonlinear behavior of composite materials under different machining operations, this work aims at developing an optimized artificial intelligence model to predict milled surface characteristics of carbon fiber-reinforced polyetheretherketone (CFRPEEK). The milling operations were carried out under high-speed dry-cutting conditions. The developed model was employed to predict fractal dimension and surface roughness as functions of cutting speed, cutting width, feed per tooth, and orientation of fibers. The model is composed of an adaptive network-based fuzzy inference system (ANFIS) optimized by a gazelle optimizer (GO). The performance of the model was compared with five other optimized ANFIS models. ANFIS-GO showed outperformance compared with other models to predict milled surface characteristics. The root-mean-square deviation and determination coefficient of predicted data by ANFIS-GO compared with experimental ones are 0.002 and 0.911 for fractal dimension and 0.129 and 0.998 for surface roughness, respectively.

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed using ANN and ANFIS approaches. These models were evaluated using the coefficient of determination, mean absolute percent error, and root mean square error data. The ANFIS-based prediction model outperformed the ANN model in terms of drying rate performance, but the ANN model outperformed the ANFIS model in terms of moisture content values. Results showed that both methods established can be utilized to estimate zucchini slices.

Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young’s Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson’s ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young’s modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson’s ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.

Application of Adaptive Neuro-Fuzzy Inference Systems with Principal Component Analysis Model for the Forecasting of Carbonation Depth of Reinforced Concrete Structures

The carbonation of reinforced concrete is one of the intrinsic factors that cause a significant decrease in service performance in concrete structures. To decrease the effect of carbonation-induced corrosion during the lifetime of the concrete structure, a prediction of carbonation depth should be made. The carbonation of concrete is affected by many factors, such as the compressive strength of the concrete, service life, carbonation time, carbon dioxide concentration, working stress, temperature, and humidity. On the basis of these seven parameters, combined with the predictive power of the adaptive network-based fuzzy inference system (ANFIS) and principal component analysis (PCA), which can reduce data dimensions before modeling, we introduced a novel approach—the PCA–ANFIS model—that can predict the carbonation of reinforced concrete. Practical engineering examples were adopted to verify the superiority of the suggested PCA–ANFIS model, with 90% of the carbonation depth data used for training and 10% used for testing. The root mean square error (RMSE) values for the ANFIS, ANN, PCA–ANN, and PCA–ANFIS training were 12.23, 6.28, 5.42, and 1.38, respectively. The results showed that the PCA–ANFIS model is accurate and can be used as a fundamental tool for predicting the service life of concrete structures.

ANFIS- and GEP-based model for prediction of scour depth around bridge pier in clear-water scouring and live-bed scouring conditions

Abstract Scour depth prediction is an important aspect of designing a bridge pier structure in a river. Proper modeling of scour depth ensures the sustainability of the structure. An attempt is made to develop a scour depth model for the bridge pier using an adaptive network-based fuzzy inference system (ANFIS) and gene expression programming (GEP). The scour depth is found to be influenced by various independent parameters such as pier diameter, flow depth, approach mean velocity, critical velocity, Froude number, bed sediment, and geometric standard deviation of bed particle size. Gamma tests are performed to identify the best input parameter combinations to predict scour depth. In the present study, two separate models have been developed for clear-water scouring (CWS) and live-bed scouring (LBS). For different ranges of input parameters, the scour depth ratio is computed and error analysis is performed. Results indicate that the ANFIS model (R2CWS = 0.95, MAPECWS = 9.39% and R2LBS = 0.95, MAPELBS = 5.29%) is the most accurate predictive model in both scour conditions as compared to the GEP model and existing models of previous researchers. However, for the low value of pier diameter (b) to flow depth (y) ratio (<0.25), the present ANFIS model apportioned unsatisfactory results for LBS only.

Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications

In this study, two different Artificial neural networks (ANN) and two different adaptive network-based fuzzy inference systems (ANFIS) models were constructed to predict the compressive strength of 7 different cement mortar samples with or without pumice and/or diatomite on different days. Five parameters including day, PC, pumice, diatomite and water were employed as the inputs, and the compressive strength was used as the output variable. The compressive strengths used in the model construction were obtained from laboratory experiments accounting for a total of 168 data. Statistical methods such as R2, RMS and MAPE preferred in the literature were used to compare the four different models. According to the test results obtained from R2, RMS and MAPE, ANN and ANFIS models were able to make very good predictions performance. For this reason, it can be said that these cement mortars' compressive strength can be estimated with a very small error and in a short time with both ANN and ANFIS models.

Prediction of terephthalic acid yield in aqueous hydrolysis of polyethylene terephthalate

AbstractAqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to production of high‐quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various factors including PET size, catalyst concentration, and reaction temperature. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material researchers to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict the output results. For the first time, 381 experimental data were gathered to model aqueous hydrolysis of PET. Effective factors on PET hydrolysis were connected to the TPA yield. The logistic regression was applied to rank the effective factors. Two algorithms were proposed, artificial neural network multi‐layer perceptron (ANN‐MLP) and adaptive network‐based fuzzy inference system (ANFIS). The dataset was divided into training, validating, and testing sets to train, validate, and test the models, respectively. The models predicted TPA yield sufficiently where the ANFIS model outperformed. R‐squared (R2) and Root Mean Square Error (RMSE) loss functions were employed to measure the efficiency of the models and evaluate their performance.

Neuro-fuzzy assessment of machined wood fibre–reinforced magnesium oxide composite

ABSTRACT High quality processing key to improving product quality and enterprise benefits. In this work, an adaptive network–based fuzzy inference system (ANFIS) was combined with milling experiments to understand the effects of tool geometry and milling parameters on the surface quality of wood fibre–reinforced magnesium oxide composite (WRMC). Specifically, changes in surface roughness (Ra) and damage of WRMC at different milling conditions were assessed using ANFIS and micro-analysis methods. Development of ANFIS models were confirmed to be reliable for predicting surface roughness. Changes in surface roughness at different milling conditions were determined, and the lowest surface roughness was obtained at the highest rake angle, highest cutting speed, and smallest milling depth. Furthermore, pitting-type damage irregularly distributed on the machined surface is attributed to the pulling out and debonding of wood fibres. Overall, high cutting speed, shallow cutting depth, and high rake angle is recommended for fine machining of WRMC where a smooth surface is desired. This study showcases how neuro-fuzzy models can be combined with conventional micro-analysis to optimize milling parameters for WRMC to minimize surface damage, and paves the way for future studies to optimize cutting tool life and energy consumption.

Hot deformation behavior, microstructure evolution and processing map of Cu–2Be alloy

The hot deformation characteristics of Cu–2Be alloy is studied within the temperature range of 650–950 °C and in strain rate of 0.001–1 s−1. The constitutive analysis and adaptive-network-based fuzzy inference system (ANFIS) were constructed for describing the hot deformation behavior. It is perceived that the developed ANFIS model can be used to accurately predict the hot deformation characteristic of the studied alloy. Corresponding equations for peak stress/strain are achieved and then, processing maps are developed based on the dynamic material model (DMM) theories. The results display that at lower strain, the deformation dissipation (η) increases with increasing temperature and decreasing strain rate; however at higher strain levels, η exhibits a noticeable decline at 900–950 °C and 0.01 s −1, in which momentous grain coarsening tends to happen. At high strain level, the optimal hot deformation domain of studied alloy should be at 850–950 °C and strain rate of 1–10 s−1, in which more uniform and fine grain structure is dominant due to the discontinuous dynamic recrystallization (DDRX). The correlation of recrystallized grains size with Z is determined in terms of power law. Moreover, the unstable flow regions are described in the processing maps using Prasad instability criterion.

Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea

Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model.

Neem-castor seed oil esterification modelling: Comparison of RSM and ANFIS

Even though the desirable fuel properties and enhanced engine characteristics of an IC engine have led to the adoption of hybrid second generational oily feedstock (HSGOF) over a mono-oily feedstock, the high FFA associated with the HSGOF remains a challenge. The Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) were used to model the esterification of neem and seed crude oils (NCSO) with a high free fatty acid content (FFA). The RSM and ANFIS were used to model and optimize input: esterification variables such as NCSO ratio (20–40), catalyst dosage (1.0–2.0 wt%), retention time (1.5–2.5 h), and NCSO/methanol molar ratio (4.5–5.5) to achieve the lowest FFA for the NCSO esterification process and produce composite biodiesel. Optimum acid value (AV) of 2.1 mg KOH/g was attained at NCSO ratio of 20, catalyst dosage of 2 wt%, retention time of 2.0 h, and molar ratio of 4.5. A contrast of the developed models was accomplished by statistical norms such as coefficient of determination (R2), root mean square error (RMSE) with standard Error of Prediction (SEP). The R2 of 0.82522, RMSE of 0.68099, and SEP of 0.21729 for the RSM model (RM) related to the R2 of 0.9982, RMSE of 0.02434, and 0.01992 for the ANFIS model (AM). The AM seems more consistent than the RM in pre-treating and rendering high NCSO for composite biodiesel production. The key fuel properties of the neem-castor oil methyl ester (NCSOME) certified with those of biodiesel international standards.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design, an adaptive network-based fuzzy inference system (ANFIS), and a multi-objective particle swarm optimizer (MOPSO) to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control (CNC) machines. First, rigid tapping parameters and uniform (including 41-level and 19-level) layouts were adopted to collect representative data for modeling. Next, ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data. In tapping center machines, the synchronization errors and cycle times are important considerations, so these two objects were used to build the ANFIS models. Then, a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously. The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the non-systematic method. Additionally, the optimal parameter combination was used to optimize existing CNC tools during the commissioning process. Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping. The position gain and pre-feedback coefficient can reduce the synchronization errors significantly, and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors. The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms; it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield, thereby helping factories achieve carbon reduction.

Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm

The present study investigates the possibility of using random forest (RF) algorithm for prediction of hydraulic conductivity of coarse-sized granular material. Meanwhile, the conventional soft computing methods named artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) are considered to assess the robustness of the established approach. The characterized properties related to the particle size distribution as well as the air void content are utilized to predict the hydraulic conductivity coefficient. In addition, owing to the direct effect of the applied hydraulic head on the measured values of water flow velocity, the hydraulic gradient is also characterized as an influential parameter. The statistical comparison between the prediction capability of the RF and the results of ANN and ANFIS models corroborates the robustness of the developed procedure based on the machine learning algorithm to indirectly predict the hydraulic conductivity of the provided porous media. The results of parametric analysis mainly demonstrate that the air void content among aggregates is by far the most influential factor affecting the permeability of granular media made of coarse-sized aggregate.

Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area

Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed factors affecting wildfire using the Random Forest (RF) model. No single factor played a decisive role in the incidence of wildfires. However, the climatic variables were most critical, dominating the occurrence of wildfires. The probability of wildfire occurrence was simulated and predicted using the Adaptive Network-based Fuzzy Inference System (ANFIS). The particle swarm optimization (PSO) model and genetic algorithm (GA) were used to optimize the ANFIS model. The hybrid ANFIS models performed better than single ANFIS for the training and validation datasets. The hybrid ANFIS models, such as PSO-ANFIS and GA-ANFIS, overcome the over-fitting problem of the single ANFIS model at the learning stage of the wildfire pattern. The high classification accuracy and good model performance suggest that PSO-ANFIS can be used to predict the probability of wildfire occurrence. The probability map illustrates that high-risk areas are mainly distributed in the northeast part of the study area, especially the grassland and forest area of Dornod Province of Mongolia, Buryatia, and Chita state of Russia, and the northeast part of Inner Mongolia, China. The findings can be used as reliable estimates of the relative likelihood of wildfire hazards for wildfire management in the region covered or vicinity.

Enhance modelling predicting for pollution removal in wastewater treatment plants by using an adaptive neuro-fuzzy inference system

Biological and physical treatment in wastewater treatment plants appears to be one of the most important variables in water quality management and planning. This crucial characteristic, on the other hand, is difficult to quantify and takes a long time to obtain precise results. Scientists have sought to devise several solutions to address these issues. Artificial intelligence models are one technique to monitor the pollutant parameters more consistently and economically at treatment plants and regulate these pollution elements during processing. This study proposes using an adaptive network-based fuzzy inference system (ANFIS) model to regulate primary and biological wastewater treatment and used it to model the nonlinear interactions between influent pollutant factors and effluent variables in a wastewater treatment facility. Models for the prediction of removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS) in a wastewater treatment plant were developed using ANFIS. Hydraulic retention time (HRT), temperature (T), and dissolved oxygen (DO) were input variables for BOD, TN, TP, and TSS models, as determined by linear correlation matrices between input and output variables. The findings reveal that the developed system is capable of accurately predicting and controlling outcomes. For BOD, TN, TP, and TSS, ANFIS was able to achieve minimum mean square errors of 0.1673, 0.0266, 0.0318, and 0.0523, respectively. The correlation coefficients for BOD, TN, TP, and TSS are all quite strong. In the wastewater treatment plant, ANFIS' prediction performance was satisfactory and the ANFIS model can be used to predict the efficiency of removing pollutants from wastewater.