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ebrahimi, seyed hossain, afshari, Ahmad. (1399). An Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression. , 31(3), 259-275. doi: 10.22059/jsciences.2020.297045.1007495
seyed hossain ebrahimi; Ahmad afshari. "An Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression". , 31, 3, 1399, 259-275. doi: 10.22059/jsciences.2020.297045.1007495
ebrahimi, seyed hossain, afshari, Ahmad. (1399). 'An Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression', , 31(3), pp. 259-275. doi: 10.22059/jsciences.2020.297045.1007495
ebrahimi, seyed hossain, afshari, Ahmad. An Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression. , 1399; 31(3): 259-275. doi: 10.22059/jsciences.2020.297045.1007495

An Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression

Journal of Sciences, Islamic Republic of Iran
مقاله 6، دوره 31، شماره 3، آذر 2020، صفحه 259-275    اصل مقاله (2.06 M)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2020.297045.1007495
نویسندگان
seyed hossain ebrahimi* 1؛ Ahmad afshari2
1industry engineering ,shomal university,amol,iran
2industrial engineering,MehrAlborz university,tehran,iran
چکیده
Nowadays, centrifugal compressors are commonly used in the oil and gas industry, particularly in the energy transmission facilities just like a gas pipeline stations. Therefore, these machines with different operational circumstances and thermodynamic characteristics are to be exploited according to the operational necessities. Generally, the most important operational parameters of a gas pipeline booster station includes the compressor's input and output pressures, input and output temperatures and also the flow rate passing from the compressors. Different values of those parameters related to every point of operational conditions will exactly affect on the compressor poly-tropic efficiency and their driver fuel consumption. Although, calculating of the poly tropic efficiency and fuel consumption using the existing thermodynamic relations, would need to apply rather awkward equations for each operating point. In this research, a feed forward perceptron artificial neural network is presented to predict the output operational conditions. The network would be trained at least in two scenarios applying by practical data in the neuro solution software version.5 using the Levenberg-Marquadt algorithm and the optimum model is experimentally selected according to R2, MSE and NMSE.
کلیدواژه‌ها
Centrifugal compressor؛ Artificial neural network؛ Ridge regression؛ Performance prediction؛ Pipeline gas booster station
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