The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach

Abstract The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10−4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

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Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.576.91 ◽

2012 ◽

Vol 576 ◽

pp. 91-94 ◽

Cited By ~ 8

Author(s):

Erry Yulian Triblas Adesta ◽

Muataz H.F. Al Hazza ◽

M.Y. Suprianto ◽

Muhammad Riza

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

End Milling ◽

Back Propagation ◽

Cutting Temperature ◽

Neural Network Modeling ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

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Modeling of Phenol Degradation in Spouted Bed Contactor Using Artificial Neural Network (ANN)

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1138 ◽

2008 ◽

Vol 3 (2) ◽

Cited By ~ 2

Author(s):

Madhukar A. Dabhade ◽

M. B. Saidutta ◽

D. V. R. Murthy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Square Error ◽

Model Prediction ◽

Phenol Degradation ◽

Mean Square ◽

Ann Model ◽

Spouted Bed ◽

The Mean ◽

Artificial Neural Network Ann

Presence of phenol and phenolic compounds in various wastewaters and its harmful effects has led to the use of different treatment methods. Work on biological methods shows the use of different microorganisms and different bioreactors so as to improve the removal efficiency economically. The present work deals with the use of N. hydrocarbonoxydans (NCIM 2386), an actinomycetes, for the degradation of phenol. N. hydrocarbonoxydans was immobilized on GAC and used in a spouted bed contactor for effective contact of microorganisms and the substrate. The contactor performance was studied by varying flow rates, influent concentrations and the solids loading in the contactor. The effect of these variables on phenol degradation was investigated and modeling study was carried out using the artificial neural network (ANN). A feed forward neural network with back propagation was used for the model development. The experiments were planned as per the face centered cube design (FCCD) and used for training of the model, whereas data from four other experimental runs were used for testing and validation of the model. The network was optimized for the number of neurons based on the mean square error. The ANN model with three layers with three input neurons, eight neurons in hidden layers and one output neuron was found to predict effectively the effluent concentration for the given operating conditions in the spouted bed contactor. The mean square error was found to be 9.318e-12 for this ANN model. Also the experimental data was used to develop second order nonlinear empirical model obtained using multiple regression (MR) and the results compared with ANN using correlation coefficient (R2), average absolute error (AAE) and root mean square error (RMSE). Results show that R2, AAE and RMSE values of MR model were 0.9363, 2.085 % and 2.338 % respectively, while in case of ANN model these values were 0.9995, 0.59 % and 1.263 % respectively. This shows that ANN model prediction is better than multiple regression model prediction.

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Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

Earth Sciences Research Journal ◽

10.15446/esrj.v19n1.38712 ◽

2015 ◽

Vol 19 (1) ◽

pp. 85-93 ◽

Cited By ~ 56

Author(s):

Ehsan Momeni ◽

Ramli Nazir ◽

Danial Jahed Armaghani ◽

Harnedi Maizir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Back Propagation ◽

Load Test ◽

Static Load Test ◽

Concrete Piles ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model. ResumenLa Capacidad Axial de Soporte (ABC, en inglés) de un pilote de construcción se determina usualmente a través de una Prueba de Carga Estática (SLT, inglés). Sin embargo, estas pruebas son costosas y demandan tiempo. La evaluación de las Dinámicas de Alto Esfuerzo de Pilotes (HSDPT, inglés), que la provee el programa de Análisis de Excavación (PDA, inglés), es una forma de aproximación más reciente para preveer la Capacidad Axial de Soporte. En comparación con la Prueba de Cargas Estática, la evaluación PDA es rápida y económica. La implementación de Redes Neuronales Arficiales (ANN, en inglés) que permita resolver problemas geotécnicos ha ganado atención recientemente debido a su posibilidad de hallar relaciones no lineales entre los diferentes parámetros. En este estudio se propone un modelo predictivo ANN para estimar la Capacidad Axial de Soporte de pilotes y su distribución. Para fines de una red de construcción se realizaron 36 pruebas PDA en pilotes de diferentes proyectos. Los resultados de los Análisis de Excavación, las características geométricas de los pilotes, al igual que los datos de investigación del suelo se utilizaron para probar los modelos ANN. Los resultados indican la viabilidad del modelo ANN en predecir la resistencia de los pilotes. Los coeficientes de correlación, R², que alcanzaron 0.941, 09.36 y 0.951 para la evaluación de los datos, revelan que la capacidad del pilotaje en el último rodamiento, en el cojinete del eje y en la punta que se predijeron con el modelo ANN concuerda con las establecidas a través del HSDPT. A través del análisis de respuesta se determinó que la longitud y el área de los pilotes son factores dominantes en el modelo predictivo propuesto.

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ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

Journal of Environmental Engineering and Landscape Management ◽

10.3846/jeelm.2021.15553 ◽

2021 ◽

Vol 29 (3) ◽

pp. 368-380

Author(s):

Cristina Ghinea ◽

Petronela Cozma ◽

Maria Gavrilescu

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Root Mean Square Error ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Square Error ◽

Coefficient Of Determination ◽

Mean Square ◽

Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

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Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200046 ◽

2020 ◽

Vol 24 (3) ◽

pp. 243-251

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Transfer Function ◽

Mean Square Error ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coefficient Of Determination ◽

Hydraulic Structures ◽

Mean Square ◽

Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

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Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

Food Research ◽

10.26656/fr.2017.5(s1).022 ◽

2021 ◽

Vol 5 (S1) ◽

pp. 144-151

Author(s):

S.E. Adebayo ◽

N. Hashim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optical Properties ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Square ◽

Classification Models ◽

Pear Fruit ◽

Laser Imaging

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

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Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

ASEAN Journal on Science and Technology for Development ◽

10.29037/ajstd.95 ◽

2017 ◽

Vol 23 (1&2) ◽

pp. 89 ◽

Cited By ~ 1

Author(s):

WaiChi Wong ◽

HingWah Lee ◽

Ishak A. Azid ◽

K.N. Seetharamu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Feed Forward Back Propagation ◽

The Neural Network ◽

Creep Analysis ◽

Simulation Results ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Microcantilever Beam

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress deve loped with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compar ed to FE prediction.

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Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

UIC Research Journal ◽

10.17158/320 ◽

2014 ◽

Vol 18 (2) ◽

Author(s):

Eric John G. Emberda ◽

Den Ryan L. Dumas ◽

Timothy Pierce M. Rentillo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Ann Model ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Forecast Data ◽

The Mean ◽

Artificial Neural ◽

Artificial Neural Network Ann

This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.

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Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent

International Journal of Molecular Sciences ◽

10.3390/ijms20174206 ◽

2019 ◽

Vol 20 (17) ◽

pp. 4206 ◽

Cited By ~ 1

Author(s):

Seef Saadi Fiyadh ◽

Mohamed Khalid AlOmar ◽

Wan Zurina Binti Jaafar ◽

Mohammed Abdulhakim AlSaadi ◽

Sabah Saadi Fayaed ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Root Mean Square Error ◽

Adsorption Capacity ◽

Mean Square Error ◽

Root Mean Square ◽

Deep Eutectic Solvent ◽

Mean Square ◽

Mercury Ions ◽

Narx Model

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10−3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10−3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10−3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

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