surface tension prediction of hydrocarbon mixtures using artificial neural network
Authors
abstract
in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the artificial neural network was more accurate as the result showed that while standard deviation of ard for artificial neural network was 3.63001, the standard deviation of ard for brock and bird, pitzer, zuo-stenby and sastri-rao models were 23.77569, 18.44848, 13.00388 and 9.63137 respectively.
similar resources
Surface Tension Prediction of Hydrocarbon Mixtures Using Artificial Neural Network
In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...
full textThe Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks
In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocar...
full textthe prediction of surface tension of ternary mixtures at different temperatures using artificial neural networks
in this work, artificial neural network (ann) has been employed to propose a practical model forpredicting the surface tension of multi-component mixtures. in order to develop a reliable modelbased on the ann, a comprehensive experimental data set including 15 ternary liquid mixtures atdifferent temperatures was employed. these systems consist of 777 data points generally containinghydrocarbon ...
full textscour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
Prediction of ultimate strength of shale using artificial neural network
A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimat...
full textPrediction of Cardiovascular Diseases Using an Optimized Artificial Neural Network
Introduction: It is of utmost importance to predict cardiovascular diseases correctly. Therefore, it is necessary to utilize those models with a minimum error rate and maximum reliability. This study aimed to combine an artificial neural network with the genetic algorithm to assess patients with myocardial infarction and congestive heart failure. Materials & Methods: This study utilized a m...
full textMy Resources
Save resource for easier access later
Journal title:
journal of oil, gas and petrochemical technologyPublisher: persian gulf university
ISSN 2383-2770
volume 2
issue Number 1 2014
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023