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Poomiwat Phadungbut. Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2020.
Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon
Name: Atcharapun Phirom
Organization :
Mahidol University. Faculty of Engineering
Name: Makkawan Burana-amnuay
Organization :
Mahidol University. Faculty of Engineering
Name: Nikom Klomkliang
Organization :
Suranaree University of Technology. Institute of Engineering
Name: Poomiwat Phadungbut
Organization :
Mahidol University. Faculty of Engineering
Email :
poomiwat.pha@mahidol.ac.th
LCSH:
Natural gas
LCSH:
Carbon, Activated
Abstract:
In this work, feedforward artificial neural network (ANN) with backpropagation training algorithm is developed to forecast the natural gas storage capacity in activated carbon with idealized slit-pore models under supercritical condition. In order to create a dataset, Grand Canonical Monte Carlo (GCMC) simulation is first employed to generate the absolute adsorption capacity of natural gas in carbon slit pores with different pore sizes, temperatures and pressures. Secondly, multilayer neural network composed of an input layer with three neurons, hidden layers, and an output layer with a single neuron is designed to fit the model on the GCMC training dataset and to investigate the dependences of number of neurons, number of hidden layers, activation function and backpropagation training algorithm on statistical parameters. To evaluate the trained model, 20 percent of the total dataset are selected based on commensurate and incommensurate packing of methane with the size of the slit pore model. The Bayesian-Regularization training algorithm and hyperbolic tangent sigmoid activation function were found to be the best with acceptable statistical values and time consuming. It is implied that our ANN model could be further applied in predicting the natural gas storage capacity of actual structures of activated carbons and in designing the adsorbed natural gas tank
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2020
Modified:
2021-05-11
Issued:
2021-05-11
บทความ/Article
application/pdf
BibliograpyCitation :
In Thai Institute of Chemical Engineering and Applied Chemistry. The 29th Thai Institute of Chemical Engineering and Applied Chemistry Conference (TiChE 2020) (pp.482-493). Bangkok : Chulalongkorn University
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.034274 วินาที
Atcharapun Phirom
| Title | Contributor | Type |
|---|---|---|
| Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Atcharapun Phirom;Makkawan Burana-amnuay;Nikom Klomkliang;Poomiwat Phadungbut | บทความ/Article |
Makkawan Burana-amnuay
| Title | Contributor | Type |
|---|---|---|
| Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Atcharapun Phirom;Makkawan Burana-amnuay;Nikom Klomkliang;Poomiwat Phadungbut | บทความ/Article |
Nikom Klomkliang
| Title | Contributor | Type |
|---|---|---|
| Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Atcharapun Phirom;Makkawan Burana-amnuay;Nikom Klomkliang;Poomiwat Phadungbut | บทความ/Article | |
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Poomiwat Phadungbut
| Title | Contributor | Type |
|---|---|---|
| Artificial neural network approach for modeling natural gas storage capacity in slit-shaped activated carbon
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Atcharapun Phirom;Makkawan Burana-amnuay;Nikom Klomkliang;Poomiwat Phadungbut | บทความ/Article | |
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