Developed Adaptive Neuro-fuzzy Inference System Model Research Articles

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%.

Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes

Energy storage devices and systems with better performance, higher reliability, longer life, and wiser management strategies are needed for daily technology advancement. Among these devices, the supercapacitor is the most preferable due to its high-limit capacitance that esteems more than different capacitors. Today, it is considered a significant challenge to design high-performance materials for supercapacitors by exploring the interaction between characteristics and structural features of materials. Because of this, it is essential to predict capacitance when assessing a material’s potential for use in constructing supercapacitor-electrode applications. Machine learning (ML) can significantly speed up computation, capture complex mechanisms to enhance the accuracy of the prediction and make the best choices based on detailed status data. We aimed to develop a new strategy for the assisted design of high-performance supercapacitor materials by applying ML to analyze the relationship between capacitance and structural features of porous carbon materials (PCMs) using hundreds of experimental data in the literature. In the present study, Linear Regression (LR), Regression Tree (RT), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate the supercapacitor’s capacitance. The effectiveness of the ML models was evaluated in terms of the root mean square error (RMSE), mean absolute error (MAE), and the correlation between expected yield and system-provided yield. The developed ANFIS model, with RMSE, MAE, and R values of 22.8, 39.7647, and 0.90004, respectively, compares favourably regarding prediction performance to other models built for this purpose.

Optimization and Prediction of TIG-MIG hybrid Joint Strength using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

In the welding processes, parametric optimization is crucial, and intelligent prediction makes use of data availability to cut the cost of experimental operations. This article proposes adopting the adaptive neuro-fuzzy inference system (ANFIS) model for predicting ultimate tensile strength in TIG-MIG hybrid welding. Experiments are designed and optimized according to Taguchi’s principles. Proposed neural network models are developed using experimental data. Three input process parameters ( MIG voltage, TIG current and gas flow rate) were designed in an L9 orthogonal array at three levels each. The maximum tensile obtained was 868.3 MPa. The signal-to-noise ratio shows that the optimum parameter setting that maximizes the tensile strength corresponds to MIG Voltage (V) = 25, TIG Current (A) =180, and Gas flow rate =19 L/mm. The analysis of variance shows that the gas flow rate had the most influence on the ultimate tensile strength with a 42.35% contribution, followed by the MIG voltage with 31.67%, and TIG current with 18.13% contribution. The developed ANFIS model is 99.9 % accurate at the training (MAPEtraining= 0.1670) and 96.3% accurate at the testing (MAPEtraining = 0.1670) for predicting the ultimate tensile strength. The R2-values of the models at training and testing were closer to unity depicts a good fit between the experimental and predicted values of the response. The lower RMSE values (RMSEtraining=1.8963, RMSEtraining = 4.8194) indicates the lower deviation of the experiment values of ultimate tensile strength from the predicted values. These results imply that ANFIS models can reduce experimental costs and hurdles associated with the trial and error approach to get the appropriate welding parameters. Therefore experimental designs for other plate thicknesses and similar processes could be built and predicted without actual experimentation.

Modelling of Traffic Noise Along Urban Corridor: A Case Study of Amman

Abstract Noise became one of the main environmental indicators of the quality of urban life. The aim of this study was to develop a traffic noise model for an arterial road in Amman, the capital of Jordan, which was being subjected to a persistent increase in traffic and its related issues. The characteristics of the traffic and its relevant noise were analysed to develop a noise prediction model using an adaptive neuro-fuzzy inference system (ANFIS). A geographic information system (GIS) was then implemented to model the current noise along the arterial road. The results of ANFIS model showed that traffic delays and the percentage of heavy vehicles were the main causes of traffic noise generation within the urban area. However, the developed ANFIS model can simulate traffic noise with a relatively low root mean square error. Furthermore, this study will help to improve the perception about traffic noise and its development along arterial roads.

Resilient moduli of demolition wastes in geothermal pavements: Experimental testing and ANFIS modelling

Construction and demolition (C&D) waste materials have been used in a wide range of civil engineering applications, particularly as unbound pavement materials. A comprehensive understanding of the deformation and thermal properties of C&D materials is necessary for their usage in novel applications related to heat transfer in pavement unbound layers, such as geothermal pavements. This research study focused on developing a correlation between the resilient modulus (MR) and thermal conductivity of C&D materials for geothermal pavement applications. The thermal conductivity of C&D materials, namely recycled concrete aggregate (RCA), crushed brick (CB), reclaimed asphalt pavement (RAP), and waste rock (WR), was evaluated at different moisture contents and dry densities. The MR and permanent deformation responses of C&D materials were characterized at the optimum moisture content (OMC), 85%OMC, and 70%OMC, using the repeated load triaxial (RLT) test. An intelligent model was developed for predicting the MR of C&D materials incorporating thermal conductivity, physical properties, confining stress, and deviator stress as input parameters using adaptive neuro-fuzzy inference system (ANFIS) approach. The developed ANFIS model had excellent performance in predicting the MR of C&D materials, with R2 = 0.99 for both training and testing datasets. The ANFIS model was converted into a mathematical relationship, which can be used by researchers and practitioners for estimating the MR of C&D materials.

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann–Whitney U (M–W) and Anderson–Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM showed that the ANFIS approach outperformed other models for reliability analysis of bearing capacity of pile and ENN is the worst performing model. The value of R2 for all the developed models is close to 1. The best RMSE value is achieved for the training phase of the ANFIS model (0 in training and 2.13 in testing) and the poorest for the ENN (2.03 in training and 31.24 in testing) model. Based on the experimental results of reliability indices, the developed ANFIS model is found to be very close to that computed from the original data.

Performance Evaluation of RSM and ANFIS in Modelling Nano Fluids-based Mixed Insulating Fluids

Transformer liquid dielectric is seen as a critical quality characteristic which must be very strong to withstand the voltage level in an abnormal state. In this research, prediction of dielectric strength, fire point and viscosity through response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) is developed to improve liquid dielectric life in the automated environment. An experimental design with RSM-based box–behnken method was planned and experiments with three distinct input parameters (Wt.% CNT, Wt.% ZrO2 and Stirring temperature) were carried out. The set of data has been used to train and analyse using hybrid and back-propagation optimization methods. Model values of ANFIS were compared with experimental and RSM values. The comparison results showed that the developed ANFIS model is a powerful tool for modelling the nano fluids-based mixed insulating fluids.

COVID-19 Time-Varying Reproduction Number and its Prediction by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Epidemiologists and the COVID-19 Response Acceleration Task Force in Jakarta, Indonesia continue to monitor various parameters of the outbreak in Jakarta and its surroundings. One of the parameters to monitor and predict is the effective reproduction number (Rt ). We propose a machine learning approach called adaptive neuro-fuzzy inference system (ANFIS) to predict the time-varying Rt . We calculated historical data for Rt using the daily confirmed cases, daily death cases, and daily recovered cases by applying a Susceptible-Infected-Recovered-Dead (SIRD) dynamic simulation. We found that using the input data from the last seven previous days of Rt as the training data gave best prediction results based on the evaluation metric of mean absolute percentage error (MAPE). We validated the prediction results with 10 consecutive days of Rt from November 1–10, 2020. The results showed that the developed ANFIS model could predict the reproduction number for COVID-19 in Jakarta, Indonesia with an accuracy of approximately 81.27%. The transmission of COVID-19 in Jakarta, Indonesia has the potential to be controlled because most Rt values were lower than 1, at least during the period analyzed in this study.Funding: None to declare. Declaration of Interest: None to declare.

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

Performance assessment of a solar powered hydrogen production system and its ANFIS model

Apart from many limitations, the usage of hydrogen in different day-to-day applications have been increasing drastically in recent years. However, numerous techniques available to produce hydrogen, electrolysis of water is one of the simplest and cost-effective hydrogen production techniques. In this method, water is split into hydrogen and oxygen by using external electric current. In this research, a novel hydrogen production system incorporated with Photovoltaic – Thermal (PVT) solar collector is developed. The influence of different parameters like solar collector tilt angle, thermal collector design and type of heat transfer fluid on the performance of PVT system and hydrogen production system are also discussed. Finally, thermal efficiency, electrical efficiency, and hydrogen production rate have been predicted by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique. Based on this study results, it can be inferred that the solar collector tilt angle plays a significant role to improve the performance of the electrical and thermal performance of PVT solar system and Hydrogen yield rate. On the other side, the spiral-shaped thermal collector with water exhibited better end result than the other hydrogen production systems. The predicted results ANFIS techniques represent an excellent agreement with the experimental results. In consequence, it is suggested that the developed ANFIS model can be adopted for further studies to predict the performance of the hydrogen production system.

Integration of Traffic Management and an Artificial Intelligence to Evaluate Urban Air Quality

Emissions from motor vehicles are the primary source of air pollution, especially in congested urban centres. However, through effective traffic management, it has been found that the level of pollution can be significantly reduced, facilitating the mobility of urban arterials. This study aims to quantify the extent of traffic emissions and to identify the influence of traffic management to improve air quality and reducing traffic emissions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to estimate the extent of traffic emissions (NO2 and PM10) at certain intersections. Then, a traffic management simulation software was also used to simulate traffic and to build a traffic improvement scenario at these intersections. This was followed by measuring the improvement in air quality due to traffic management modification, analysed using the developed ANFIS model. The results showed that reducing the delay at certain intersections may reduce NO2 and PM10 significantly. The proposed hybrid model increased the forecasting accuracy and improved the perception between the relationship between traffic characteristics and pollutant emissions. Additionally, it facilitates the work of city planners and helps decision making regarding urban air quality.

Machine learning approach to handle data‐driven model for simulation and forecasting of the cone crusher output in the stone crushing plant

AbstractGrinding and crushing of stones and other particles are associated with various significant applications. Different sectors have continuously evolved in this area. In the crushing industry, plants function under strict conditions, many of which involve grinding materials. Therefore, various factors are responsible for how the crushers perform. This research investigated the ability of the adaptive neuro fuzzy inference system (ANFIS) to simulate the effects of throw, eccentric speed, closed side setting, and the size of the particle on crusher output. The developed simulation model was adjusted and authenticated alongside the experimental data of the investigated parameters. The model's performance was computed by the use of several prediction criteria skills. The results of the study indicated that the developed ANFIS model could simulate the Cone crusher output and give a dependable forecast of the cumulative weight fraction. The researchers resolved that the model fostered was a suitable instrument for the onsite cone crusher assessment.

ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM

Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE 30-bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.

Making decision toward overseas construction projects

PurposeThe purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS).Design/methodology/approachThis study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.FindingsThe result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.Research limitations/implicationsThe use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.Originality/valueThis study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

Comparison of RSM with ANFIS in predicting tensile strength of dissimilar friction stir welded AA2024 -AA5083 aluminium alloys

Friction stir welding (FSW) is a solid-state joining technique which has been employed in aerospace, marine, and automotive industries for joining aluminum, copper, titanium and other alloys. The FSW process parameters such as welding speed, tool rotational speed, pin profile, and axial force have a main role in determining the joint quality. A comparative study was achieved between the response surface method (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) to improve the mechanical properties of dissimilar friction stir welded AA2024-AA5083 aluminium alloys in terms of the ultimate tensile strength (UTS). The effects of the welding parameters on the UTS were investigated using four-factor, three-level ANFIS model. The statistical results of the ANFIS model were compared with those of RSM. The results reveal that the developed ANFIS model is more powerful than the RSM model.

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics systems, where a large number of transportation and storage units appear. Therefore, the aim of this paper is to develop an ANFIS (Adaptive neuro-fuzzy inference system) model in a warehouse system with two servers for defining QS optimization parameters. The research was conducted in a company for the manufacturing of brown paper located in the territory of Bosnia and Herzegovina, which represents a significant share of the total export production of the country. In this paper, the optimization criterion is the time spent in the system, which is important both from the aspect of all customers of the system, and from that of the owner of the company. The time criterion directly affects the efficiency of the system, but also the overall costs that this system causes. The developed ANFIS model was compared with a mathematical model through a sensitivity analysis. The mathematical model showed outstanding results, which justifies its development and application.

An adaptive neuro–fuzzy inference model for prediction of coal char conversion in CO2 gasification

ABSTRACTToday, in industry, the gasification of coal as an important energy source is an interesting perspective. In this study, the application of adaptive neuro–fuzzy inference system (ANFIS) technique for estimation of char conversion in CO2 gasification is investigated. The main variables affecting char conversion are particle size, reaction time, and reaction temperature, which are chosen as input variables of the proposed model. Experimental data which are gathered from the literature are applied for training, testing, and validation of developed ANFIS model. The results reveal the exact estimation of char conversions with the corresponding experimental values with the regression coefficients (R2) greater than 0.99.

Modeling of a re-heat two-stage adsorption chiller by AI approach

A distinct advantage of adsorption chillers is their ability to be driven by heat of near ambient temperature. However the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. It is the result of a poor heat transfer coefficient between the bed and the immersed heating surfaces of a built-in heat exchanger system. The aim of this work is to study the effect of thermal conductance values as well as other design parameters on the performance of a re-heat two-stage adsorption chiller. One of the main energy efficiency factors in cooling production, i.e. cooling capacity (CC) for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence (AI) approach for the optimization study of the adsorption cooler. The Adaptive Neuro – Fuzzy Inference System (ANFIS) was employed in the work. The developed ANFIS model can be applied for optimizations purposes and may constitute a submodel or a separate module in engineering calculations, capable to predict the CC of the re-heat two-stage adsorption chiller.

ANFIS-based PCA to predict the longitudinal dispersion coefficient in rivers

Study on the river water quality is the main part of environmental engineering. Longitudinal dispersion coefficient (DL) is one of the main important parameters in the river water quality studies. The DL is proportional to the flow velocity, channel width, depth of flow, and shear velocity. In this study the most effective parameters on DL were derived using principal component analysis (PCA). The results of PCA indicate that the width of the river, flow depth, and flow velocity are the most important parameters on the DL. In this paper with aim of the PCA results, the adaptive neuro fuzzy inference system (ANFIS) model was developed to predict the DL. The performance of developed ANFIS model based on PCA with error indices (R2 = 0.95 and RSME = 0.055) is suitable to predict the DL. During the development of ANFIS model, five neurons to the width channel, four neurons to the flow depth, four neurons to the velocity and two neurons to the shear velocity were assigned. The hyperbolic tangent sigmoid (tansig) function was considered as membership function for the neurons.

ANFIS modeling of surface roughness in abrasive waterjet machining of carbon fiber reinforced plastics

This study discusses the development of an Adaptive neuro–fuzzy inference system (ANFIS) model for determining the surface roughness (Ra) during machining of multidirectional woven fabric Carbon fiber reinforced plastics (CFRP) using Abrasive waterjet machining (AWM). Three variable input parameters—Jet pressure (JP), Traverse speed (TS), and Standoff distance (SOD)—were selected to assess the roughness of the CFRP along the traverse direction of the cut surface. The experimental results show that a lower JP deteriorated the finish by creating surface rupture. On the other hand, a poor surface finish was observed in the case of machining at higher TS and SOD. Further, the developed ANFIS model was used to validate the results and it was found that the predicted values were in good agreement with a 95 % confidence level. It was also evident that the ANFIS technique is helpful for better prediction of the experimental data with minimum error. Finally, the cut surface morphology was analyzed using a 3D non-contact surface profilometer and the results are discussed.