scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

Voltage Control Based on a Back-Propagation Artificial Neural Network Algorithm

2020 ◽  
Author(s):  
Jazmin Ramirez-Hernandez ◽  
Oswaldo-Ulises Juarez-Sandoval ◽  
Leobardo Hernandez-Gonzalez ◽  
Abigail Hernandez-Ramirez ◽  
Raul-Sebastian Olivares-Dominguez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Voltage Control ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Algorithm

Performance Prediction of Mechanical Draft Wet Cooling Tower Using Artificial Neural Network

2014 ◽  
Vol 1070-1072 ◽  
pp. 1994-1997
Author(s):  
Zhe Tian Xu ◽  
Jia Chen Mao ◽  
Yi Qun Pan ◽  
Zhi Zhong Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cooling Tower ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Prediction Approach

This paper proposed a prediction approach for the performance of the mechanical draft wet cooling tower based on artificial neural network (ANN). The inlet water temperature, the ambient wet bulb temperature and the ratio of water to air mass flow rate in the cooling tower were selected as the input parameters of a four-layer back propagation neural network (BPNN) to predict the temperature of the water at the tower outlet. After the test of the available data set, the BPNN results in a correlation coefficient of 0.9 between the predicted and experimental values. Thus the prediction performance is good and such prediction approach proves to be feasible and effective.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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