scholarly journals Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

2021 ◽  
Author(s):  
Ahmed Benyekhlef ◽  
Brahim Mohammedi ◽  
Djamel Hassani ◽  
Salah Hanini
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Exchanger ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inlet Temperature ◽  
Fouling Resistance ◽  
Data Points ◽  
Artificial Neural

Abstract In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

scholarly journals An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

2021 ◽  
Vol 53 (1) ◽  
pp. 37-53
Author(s):  
Milica Vidak-Vasic ◽  
Lato Pezo ◽  
Vivek Gupta ◽  
Sandeep Chaudhary ◽  
Zagorka Radojevic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Ash ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clay Bricks ◽  
Coal Ashes ◽  
Artificial Neural

This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

scholarly journals The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Şükrü Özşahin ◽  
Hilal Singer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Densified Wood ◽  
Percentage Error ◽  
Angle Of Incidence ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

scholarly journals Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

2016 ◽  
Vol 4 (1) ◽  
pp. 60-68 ◽  
Author(s):  
A.K. Gupta ◽  
P. Kumar ◽  
R.K. Sahoo ◽  
A.K. Sahu ◽  
S.K. Sarangi
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Simulated Annealing ◽  
Heat Exchanger ◽  
Performance Measurement ◽  
Compact Heat Exchanger ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA) utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA) results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS) has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN) model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN. Highlights Performance of compact plate fin heat exchanger has been measured. Predicted data given by ANN has been verified by simulation data and the experimental data. Depicted the applications of optimization methods upon fin heat exchanger.

scholarly journals Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models

2018 ◽  
Vol 9 (2) ◽  
pp. 2
Author(s):  
J.E. Etu ◽  
O. J. Oyedepo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Linear Regression Model ◽  
High Density ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Trip Generation ◽  
Home Based ◽  
Artificial Neural ◽  
Household Travel

Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in formulating trip generation forecasts in Nigeria, rather the practice has consisted more on use of regression techniques. Therefore, in this study, the accuracy of Radial Basis Function Neural Network (RBFNN) and Multiple Linear Regression model (MLR) in formulating home-based trips generation forecasts was assessed. Datasets for the study were acquired from a household travel survey in the high density zones of Akure, Nigeria and were analysed using SPSS 22 statistical software. Results of data analysis showed that the RBFNN model with higher Coefficient of Determination (R2) value of 0.913 and lower Mean Absolute Percentage Error (MAPE) of 0.421 performed better than the MLR with lower R2 value of 0.552 and higher MAPE of 0.810 in predicting the number of home-based trips generated in the study area. The study demonstrated the higher accuracy of the RBFNN in producing trip generation forecasts in the study area and is consequently recommended for researchers in executing such forecasts.

scholarly journals Modeling of overall heat transfer coefficient of a concentric double pipe heat exchanger with limited experimental data by using curve fitting and ANN combination

2019 ◽  
Vol 23 (6 Part A) ◽  
pp. 3579-3590 ◽  
Author(s):  
Necati Kocyigit ◽  
Huseyin Bulgurcu
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Heat Exchanger ◽  
Transfer Coefficient ◽  
Curve Fitting ◽  
Overall Heat Transfer Coefficient ◽  
Artificial Neural

The modeling accuracy of artificial neural networks (ANN) was evaluated by using limited heat exchanger data acquired experimentally. The artificial neural networks were used for predicting the overall heat transfer coefficient of a concentric double pipe heat exchanger where oil flowed inside the inner tube while the water flowed in the outer tube. In the cases of parallel and counter flows, the experimental data were collected by testing heat exchanger in wide range of operating conditions. Curve fitting and artificial neural network combination was used for the estimation of the overall heat transfer coefficient to compensate the experimental errors in the data. The curve fitting was used to detect the trend and generate data points between the experimentally collected points. The artificial neural network was trained better from the generated data set. The feed forward type artificial neural network was trained by using the Levenberg-Marquardt algorithm. Two backpropagation network type artificial neural network algorithms were also used, and their performance were compared with the estimation of the Levenberg-Marquardt algorithm. The average estimation error between the predictions and the experimental data were in the range of 1.31e?4 to 4.35e?2%. The study confirmed that curve fitting and artificial neural network combination could be used effectively to estimate the overall heat transfer coefficient of heat exchanger.

scholarly journals Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks

2021 ◽  
Vol 67 (9) ◽  
pp. 411-420
Author(s):  
Dragan Milčić ◽  
Amir Alsammarraie ◽  
Miloš Madić ◽  
Vladislav Krstić ◽  
Miodrag Milčić
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Friction Coefficient ◽  
Journal Bearing ◽  
Hydrodynamic Lubrication ◽  
Percentage Error ◽  
Radial Load ◽  
Neural Network Models ◽  
Artificial Neural

This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed.

scholarly journals A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network

2021 ◽  
Author(s):  
Ahmed Benyekhlef ◽  
Brahim Mohammedi ◽  
Salah Hanini ◽  
Mouloud Boumahdi ◽  
Ahmed Rezrazi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Exchanger ◽  
Fouling Resistance ◽  
Artificial Neural

scholarly journals A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price

2018 ◽  
Vol 3 (6) ◽  
pp. 10 ◽  
Author(s):  
Azme Bin Khamis ◽  
Phang Hou Yee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Artificial Neural

The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed.  Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

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