Levenberg-Marquardt Backpropagation Algorithm Research Articles

Demand forecasting for delivery platforms by using neural network

This paper deals with the tricky issue of forecasting the number of daily orders received by a delivery company that operates through the internet. The research tries to address the problem through the Multilayer Perceptron Neural Network (MLP). The more important step of the methodology is the identification and characterization of the features to adopt as inputs for the MLP in the cited case. The number of visits (NVs) to the company website, months, days of the week and the public holidays are the four features used to predict the number of received orders (NOs). The Levenberg Marquardt back-propagation algorithm was used to train the model. The proposed methodology was applied by a delivery company, which operates in Italy, to forecast the daily demand. The results showed a good accuracy of the MLP in predicting the NOs, with a Root Mean Squared Error of the 20% from the actual NOs.

Artificial neural network modelling of green synthesis of silver nanoparticles by honey

Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.

Application of ANN for evaluating WMA Marshall parameters

Marshall Parameters (MPs) of warm mix asphalts (WMA) are predicted using an artificial neural network (ANN) taking the experimental data to find a nonlinear correlation among the input and output variables. Medium setting bitumen emulsion pre-coated aggregates and filler were used for WMA samples at varying mixing temperature, using bitumen in a two-stage process. Type and amount of filler, mixing temperature, and optimum bitumen to emulsion ratio is determined based upon the design MPs. ANN model with one input layer and two hidden layers with five neurons each, and one output layer with a single neuron, is developed for predicting WMA design parameters. Coarse aggregate%, fine aggregate%, filler%, bitumen to emulsion ratio, and bitumen content, are used as input for predicting WMA MPs 'stability,' 'flow,' and 'air voids' as outputs using Levenberg-Marquardt backpropagation algorithm. Output correlation coefficients of 0.98, 0.96, and 0.97 are obtained for stability, flow, and air voids respectively.

Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines.

Acid Mine Drainage (AMD) is the formation and movement of highly acid water rich in heavy metals. Prediction of heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals in the AMD (Zn, Fe, Mn, Si and Ni) from South African mines using Neural Network (NN) techniques. The Backpropagation (BP) neural network model has three layers with the input layer (pH, SO42- and total dissolved solids (TDS)) and output layer (Cu, Fe, Mn and Zn). After BP training, the NN techniques were able to predict heavy metals in AMD with a tangent sigmoid transfer function (tansig) at hidden layer with five neurons and linear transfer function (purelin) at output layer. The Levenberg-Marquardt back-propagation (trainlm) algorithm was found as the best of 10 BP algorithms with mean-squared error (MSE) value of 0.00041 and coefficient of determination (R) for all (training, validation and test) value of 0.99984. The results indicate that NN can be considered as an easy and cost-effective technique to predict heavy metals in the AMD.

Wastewater treatment by adsorption process on mineral actived carbon: modeling and prediction using an intelligent artificial approach

Currently there are several wastewater treatments processes, and several adsorbent materials consist of separating and purifying the various industrial effluents. In this work an artificial neural network (ANN) was developed to describe the dynamic adsorption of sodium decanesulfonate using actived carbon obtained by the calcination of mineral biomass under different conditions. Three inputs (time, mass of adsorbent and fixed bed height) were used in the input layer, three neurons in the hidden layer and one in the output layer for the reduced concentration. The Levenberg Marquardt back-propagation algorithm was applied. The tangent sigmoid and linear transfer functions are used for the hidden layer and the output layer respectively. The results showed a correlation coefficient R2 = 0.9965 with root mean squared error RMSE = 0.0276. An interpolation and an extrapolation stage are made to test the accuracy of the network. The results showed a high correlation coefficient R2 = 0.9969 and 0.984 respectively for the interpolation and the extrapolation. These results show the robustness and the high capacity of ANN to describe the dynamic adsorption of sodium decanesulfonate onto actived carbon.

MODELLING THE PERFORMANCE OF A CAMEL MILK STORAGE STRUCTURE WITH EVAPORATIVE COOLING USING ARTIFICIAL NEURAL NETWORK

Storage and marketing of camel milk in arid lands of Kenya is hampered by lack of cold storage facilities. This problem can be alleviated by using storage structures incorporating evaporative cooling whose performance depends on climatic conditions. The objective of this work was to develop an artificial neural network (ANN) to predict cooled milk temperature and cooling efficiency of a locally fabricated cooler. Data were obtained from the cooler which was tested under various experimental conditions. Using some of the experimental data for training, a three-layer feed-forward ANN model based on back propagation Levenberg-Marquardt algorithm was developed using the Neural Network Toolbox for MATLAB®. The optimal model had a 4-4-2 structure with sigmoid transfer function in both layers. The inputs of the model were ambient dry bulb temperature, wet bulb temperature, wind speed and temperature of drip water, whereas the outputs were cooled milk temperature and cooling efficiency. The experimental data set (n=165) was randomly divided into training (75%) and testing (25%) sub-sets. The performance of the ANN predictions was evaluated by comparing the predicted and experimental results. The predictions agreed well with experimental values with mean squared error of 10.2, mean relative error of 4.02% and correlation coefficients in the range of 0.86-0.93. This study reveals that, as an alternative to conventional modelling techniques, the ANN approach can be used successfully for predicting the performance of locally fabricated camel milk storage structures incorporating evaporative cooling in arid pastoral areas of Kenya. The model can be used as a design tool to estimate the sizing and performance of future coolers, as it allows the prediction of the performance of hypothetical coolers designed without a need for time demanding experimentation. This can aid in up-scaling the technology.

Feedforward Neural Network Controller for Quadrotor in the Presence of Payload and Wind Disturbances

Recently, Artificial Neural Networks (ANN) have been used in broad research fields such as control system engineering. Inspired by the human brain system, this algorithm uses the learning process to improve system performance using the training dataset. The ANN algorithm is based on a weighted connection between the neurons to deal with the controller design. These connection parameters represent one of the crucial elements necessary to apply the learning process efficiently. However, finding the connection parameters becomes problematic when the controlled system is complex, nonlinear, and subjected to external disturbances. This work uses the most available ANN architecture called the Feedforward Neural Network to stabilize the quadrotor system in reference trajectory tracking when subjected to model payload and Dryden turbulent wind. A Deterministic Algorithm (DA) trains the FNN weight and bias without initialization with another training method to improve the control performance. Besides, three simulation tests are performed to control the quadrotor system. The first simulation uses classical PID to collect the necessary training data for the learning process. Secondly, the DA algorithm trains the FNN parameters to design the controllers necessary to stabilize the quadrotor in-flight tests. The third simulation uses the most common training method: the Levenberg-Marquardt Backpropagation (LMB) algorithm to tune the FNN parameters. The final simulation tests these control methods' efficiency when the quadrotor system is subjected to the model payload and the Dryden turbulent wind. A comparison test between the proposed DA-based feedforward neural network, the classical PID, and the LMB-based feedforward neural network is performed in the nominal case (without disturbance) and in the case of adding disturbance. The test result is obtained using Matlab software and demonstrates much accuracy and efficacity of the proposed DA-based FNN controller compared to the other methods. This result considers the proposed control approach an additional innovative method-based neural network to stabilize the quadrotor's Unmanned Aerial Vehicles (UAV).

Utilization of a double-cross-linked amino-functionalized three-dimensional graphene networks as a monolithic adsorbent for methyl orange removal: Equilibrium, kinetics, thermodynamics and artificial neural network modeling

Herein, it is aimed to develop a high-performance monolithic adsorbent to be utilized in methyl orange (MO) adsorption. Therefore, amino-functionalized three-dimensional graphene networks (3D-GNf) fulfilling the requirements of reusability and high capacity have been fabricated via hydrothermal self-assembly approach followed by a double-crosslinking strategy. The potential utilization of 3D-GNf as an adsorbent for removal MO has been assessed using both batch-adsorption studies and an artificial neural network (ANN) approach. Graphene oxide sheets have been amino-functionalized and cross-linked, by ethylenediamine (EDA) during hydrothermal treatment, following the glutaraldehyde has used as a double-crosslinking agent to facilitate the crosslinking of architecture. The successful fabrication of 3D-GNf has been confirmed by field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR), Raman and X-ray photoelectron spectroscopy (XPS). Moreover, N2 adsorption/desorption isotherms have revealed the high specific surface area (1015 m2 g−1) with high pore volume (1.054 cm3 g−1) and hierarchical porous structure of 3D-GNf. The effect of initial concentration, contact time, and temperature on adsorption capacity have been thoroughly studied, and the kinetics, isotherms, and thermodynamics of MO adsorption have been modelled. The MO adsorption has been well defined by the pseudo-second-order kinetic model and Langmuir isotherm model with a monolayer adsorption capacity of 270.27 mg g−1 at 25 °C. The thermodynamic findings have revealed MO adsorption has occurred spontaneously with an endothermic process. The Levenberg-Marquardt backpropagation algorithm has been implemented to train the ANN model, which has used the activation functions of tansig and purelin functions at the hidden and output layers, respectively. An optimum ANN model with high-performance metrics (coefficient of determination, R2 = 0.9995; mean squared error, MSE = 0.0008) composed of three hidden layers with 5 neurons in each layer was constructed to forecast MO adsorption. The findings have shown that experimental results are consistent with ANN-based data, implying that the suggested ANN model may be used to forecast cationic dye adsorption.

Measurement, modelling and optimization of the average temperature at the tool work interface for turning of AISI 1040 steel using ANN-GA methodology

Due to the mechanical properties and machinability of AISI 1040 steel, it has a broad range of industrial applicability. The present work investigates the dependence of the average temperature generated at the tool work interface on machining parameters such as cutting speed, feed rate and depth of cut. The machining parameters were varied at three equally spaced levels. Experiments were carried out following combinations in Taguchi’s L27 orthogonal array. A simple and cost-effective tool work thermocouple was devised and calibrated to obtain the tool work interface temperature. A 3-5-1 feed forward artificial neural network (ANN) model trained using the Levenberg-Marquardt (LM) back propagation algorithm resulted in efficient modelling of the complex relationship between the average cutting temperature and machining parameters (R-value of 0.99). The trained network was optimized using genetic algorithm (GA) to predict optimal turning parameters. This methodology has been termed as ANN-GA method. A significant reduction in the average cutting temperature could be realized due to optimization using ANN-GA method. Analysis of variance revealed highest contribution from the cutting speed and its square term in controlling the cutting temperature at tool work interface.

A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN

A new formulation to estimate the elastic modulus of concrete containing recycled coarse aggregate is proposed in this work using artificial neural networks (ANN) and nonlinear regression. Up to six predictors variables were used to training 243 ANN. The models were generated based on results obtained from experimental campaigns. Feedforward neural network and Levenberg–Marquardt back propagation algorithm were used for training the ANN. The best ANN was found with the architecture 6-4-2-1 (input -1st hidden layer -2nd hidden layer -output), attaining a root-mean-square error of 2.4 GPa associated with a coefficient of determination of 0.91. Once the ANN model was established, 46,656 concrete samples were created. These were employed to formulate the model using nonlinear regression. The developed model showed a highly efficient performance to predict the elastic modulus. Lastly, considering the parametric study conducted, the results pointed out that the approach can be applied to predict the concrete elastic modulus and can indicate better mix proportions for concretes containing natural and/or recycled coarse aggregates, enabling its use as a simulation tool in the development of engineering projects focused on durability and sustainability.

Fault detection in rotor system by discrete wavelet neural network algorithm

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

Artificial neural network-based standalone tunable RF sensor system

An artificial neural network (ANN) based tunable automated standalone RF sensor system is proposed to realize an improved sensing module involving a quite accurate solution of the non-linear inverse characterization problem. The presented tunable sensor system is quite novel as it alleviates the need for any active tuning circuitry. Moreover, the proposed unified design topology facilitates a relatively higher tuning range (1900MHz) than that of the earlier reported (580MHz) capacitor-based tunable complementary split-ring resonator (CSRR). The higher tuning range of structures resulted from the improved design configuration comprising a modified CSRR design coupled with a modified microstrip line. The obtained dielectric sensitivity is ∼8.8%. The numerically generated S-parameters of various dielectric samples are used here as a training dataset for the ANN, which is trained using the Levenberg-Marquardt backpropagation algorithm in combination with the Bayesian regularization. Finally, several standard test samples at different unloaded tuned frequencies are measured to record the corresponding resonant frequency and magnitude of the S-parameter in order to process them using the proposed ANN-based sensor system. It is found that the developed ANN-based sensor system provides a reasonably accurate value of the extracted complex permittivity over the frequency range under consideration, which basically removes the need for designing multiple resonant structures unlikely to the conventional resonant sensors.

Applying artificial intelligent and empirical techniques for prediction of total organic carbon content from wireline data: A case study from Pabdeh Formation in Mansuri oilfield, SW Iran

Total organic carbon (TOC) content is one of the most crucial parameters needed for petroleum source rock evaluation. In this study, artificial neural network (ANN) and empirically derived ΔlogR techniques were used to estimate organic richness of the Paleocene-Oligocene Pabdeh source rock in the Mansuri oilfield (SW Iran) using the wireline data. The ANN model trained by the Levenberg-Marquardt back propagation algorithm was employed for prediction of TOC content from the responses of the sonic, neutron, density and spectral gamma-ray well logs. The linear regression analysis between TOC values measured by Rock-Eval analysis and the corresponding ones predicted by ΔlogR and ANN methods was performed to validate the performance of each method. The ANN model is capable of predicting TOC contents with higher coefficient of determination (R2) value (0.7830) compared to ΔlogR technique (0.5091). Correspondingly, the mean squared error (MSE) value for the constructed neural network (0.1647) is lower than ΔlogR method (0.2410). Overall, it can be concluded that the proposed ANN model provides more accurate qualitative and quantitative predictions of organic richness, and can be reliably applied for high-resolution estimation of TOC contained in the Pabdeh source rock throughout the Mansuri oilfield.

Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells

Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high-nonlinear, multi-variable, and strongly coupled features usually lead to thorny obstacles that hinder conventional methods to identify them with a high speed, high accuracy, and reliable stability. Hence, a Levenberg-Marquardt backpropagation (LMBP) algorithm-based parameter identification technique is proposed in this study, which is applied to efficiently train designed artificial neural networks (ANNs) to implement the identification task. Furthermore, two typical models, for example, electrochemical model (ECM) and steady-state model (SSM), are taken into account to validate the identification performance of the LMBP algorithm under different operation conditions. Simulation results based on MATLAB demonstrate that the LMBP algorithm can extremely improve the accuracy, speed, and stability for estimating these unknown parameters via a comprehensive comparison with four mainstream meta-heuristic algorithms, that is, artificial ecosystem-based optimization (AEO), equilibrium optimizer (EO), grey wolf optimization (GWO), and moth-flame optimization (MFO).

Biosynthesis of Silver Nanoparticles by Using Invasive Caulerpa cylindracea Sonder

Caulerpa cylindracea is an invasive seaweed in the Mediterranean Sea. In this study, we propose an alternative method to utilise the biomass of C. cylindracea to synthesize silver nanoparticles (AgNPs). UV-VIS spectroscopy, FT-IR and X-ray diffraction were used in characterization steps. The results show that optimum conditions such as time, initial concentration of AgNO3 and temperature were found to be 240 min, 0.1 M AgNO3 and 85˚C, respectively. The measured particle size of the synthesized AgNPs were 22 nm. Since AgNPs were encapped by the secondary metabolites of C. cylindracea, the synthesized AgNPs showed stable solubility in aqueous conditions. The experimental data related to synthesis of AgNPs were modelled by using artificial neural network (ANN). Agitation time and rate, temperature, pH, concentration of AgNO3 and extract were chosen as input and absorbance values were chosen as output. ANN modelling study exhibited that Bayesian regularization backpropagation, scaled conjugate gradient backpropagation and Levenberg-Marquardt backpropagation algorithms better modelled the experimental data compared to studied 11 backpropagation algorithms. In conclusion, biomass of invasive seaweeds can be used to synthesize AgNPs for commercial use and this will bring a new perspective to utilisation of the biomass of invasive seaweeds. A possible economical value may create a stress on the spread of these alien species in the Mediterranean Sea.

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.

Experimental investigations and developing multilayer neural network models for prediction of CO2 solubility in aqueous MDEA/PZ and MEA/MDEA/PZ blends

AbstractIn this research, a new set of experimental data for CO2 solubility in aqueous blended amine solvents were investigated experimentally over the CO2 partial pressure range from 8 to 100 kPa at 40 °C and were compared with the benchmark aqueous 30 wt.% MEA solution. This work developed two multilayer neural network models named models A and B, for predicting the CO2 solubility in various aqueous blended amine solvents including 36 wt.% MDEA + 17 wt.% PZ, 24 wt.% MDEA + 26 wt.% PZ, and 6 wt.% MEA + 25 wt.% MDEA + 17 wt.% PZ. Models A and B were developed by using Levenberg–Marquardt back propagation algorithm with 427 and 301 of reliable experimental data sets gathered from the published data, respectively. The results indicate that the high accuracy prediction of the CO2 solubility in Methyldiethanolamine/Piperazine (MDEA/PZ) blends could be obtained by the network developed by Tan‐sigmoid transfer function with two hidden layers consist of eight and four neurons, while the network developed by Tan‐sigmoid transfer function with three hidden layers consist of 20, 10, and five neurons provided the highest accuracy for predicting the CO2 solubility in MEA/MDEA/PZ blends comparing to other model structures. The comparison results show that the neural network modeling provided more closer predictions to the experimental results than the simulator and other thermodynamic models when predicting the CO2 equilibrium solubility in blended amine solvents. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60°C), nanoparticles (CuO, Al2O3, TiO2, and mesoporous SiO2), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm. An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 × 10−5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

Early Recognition of Rainfall to Plan and Manage the Cultivation: A Forecasting Model Using Artificial Neural Networks for Dompe Divisional Secretariat in Gampaha District, Sri Lanka

As the agricultural productivity is climate sensitive, forecasting climatic factors is important to maximize the harvest and to planning and management of the cultivation. Even though all the climatic factors have influence on cultivation, rainfall is one of the major influential factor to Sri Lankan agriculture. Rainfall forecasting is vital in agriculture and it is a challenging task due to the uncertainty of natural phenomena. Artificial neural network (ANN) approach is being applied for forecasting real time rainfall using climatic factors affecting to rainfall. The aim of this study was to identify a neural network model which is capable of forecasting rainfall of Dompe Divisional Secretariat in Gampaha District with a high accuracy. Feed forward neural network model (FFNN) was applied with Levenberg-Marquardt back propagation algorithm and the network parameters were adjusted to minimize the forecasting error. The suggested FFNN model consisted of 12 input variables with two hidden layers with 13 and 10 hidden neurons in first and second layers, respectively. Log sigmoid transfer function used in hidden layer 1 and 2 while pure linear transfer function was used in the output layer. The model forecasts rainfall with mean squared error of 0.0895 and normalized mean squared error of 0.1975. The coefficient of determination (R²) of the testing set was 0.8. These results demonstrated the suitability of other climatic factors: temperature, wind speed, air pressure, humidity, percentage of clouds and their lags in forecasting rainfall using ANN technique in forecasting rainfall with high accuracy.

Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics

In this study, Levenberg Marquardt back propagation algorithm was used to train the Artificial Neural Network (ANN) and to predict the adsorptive removal of cationic dye Basic Violet 03 (BV03) by biochar derived from biowaste of groundnut hull. The experimental conditions such as solution pH, biochar dose, initial dye concentration, contact time and temperature were used as input variables and BV03 percentage removal as target. The hidden and the output layer of the network was trained by tangent sigmoid and liner transfer functions. The feasibility of the adsorption process is evaluated by the kinetic studies and it exhibited that pseudo-second order kinetic models fit well with experimental data. The adsorbent stability and adsorption mechanism has been discoursed by the thermodynamic characteristics and sorption free energy. The predicted target values were compared with the experiment resulted in a better correlation coefficient of 0.9920. Thus, the results attained from this ANN model was found to be effective in predicting the percentage removal of BV03 dye at any given operating condition.