TY - JOUR

T1 - Machine learning methods for precise calculation of temperature drop during a throttling process

AU - Farzaneh-Gord, M.

AU - Rahbari, H. R.

AU - Mohseni-Gharyehsafa, B.

AU - Toikka, A.

AU - Zvereva, I.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - It is vital for the designers of the throttling facilities to predict natural gas temperature drop along a throttling valve exactly. Generally, direct prediction of the temperature drop is not possible even by employing equations of states. In this work, artificial neural network method, specifically multilayer perceptron, is utilized to predict the physical properties of natural gas. Then, the method is employed for direct calculation of the temperature drop along a throttling process. To train, validate and test the network, a large database of natural gas fields of Iran plus some experimental data (30,000 random datasets) are gathered from the literature. In addition, according to complexity of the multilayer perceptron model, a group method of data handling approach is used to simplify the major trained network. For the first time, an equation is developed for calculating natural gas temperature drop as a function of molecular weight as well as pressure drop. The results show that the multilayer perceptron and group method of data handling methods have the error R2 = 0.998 and R2 = 0.997, respectively. In addition, the results indicate that both developed machine learning methods present a high accuracy in the calculations over a wide range of gas mixtures and input properties ranges.

AB - It is vital for the designers of the throttling facilities to predict natural gas temperature drop along a throttling valve exactly. Generally, direct prediction of the temperature drop is not possible even by employing equations of states. In this work, artificial neural network method, specifically multilayer perceptron, is utilized to predict the physical properties of natural gas. Then, the method is employed for direct calculation of the temperature drop along a throttling process. To train, validate and test the network, a large database of natural gas fields of Iran plus some experimental data (30,000 random datasets) are gathered from the literature. In addition, according to complexity of the multilayer perceptron model, a group method of data handling approach is used to simplify the major trained network. For the first time, an equation is developed for calculating natural gas temperature drop as a function of molecular weight as well as pressure drop. The results show that the multilayer perceptron and group method of data handling methods have the error R2 = 0.998 and R2 = 0.997, respectively. In addition, the results indicate that both developed machine learning methods present a high accuracy in the calculations over a wide range of gas mixtures and input properties ranges.

KW - Artificial neural network

KW - Group method of data handling

KW - Multilayer perceptron

KW - Natural gas compositions effects

KW - Natural gas temperature drop

KW - Throttling process

UR - http://www.scopus.com/inward/record.url?scp=85076030773&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/machine-learning-methods-precise-calculation-temperature-drop-during-throttling-process

U2 - 10.1007/s10973-019-09029-3

DO - 10.1007/s10973-019-09029-3

M3 - Article

AN - SCOPUS:85076030773

VL - 140

SP - 2765

EP - 2778

JO - Journal of Thermal Analysis and Calorimetry

JF - Journal of Thermal Analysis and Calorimetry

SN - 1388-6150

IS - 6

ER -