PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent
In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these c...
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doaj-8208f311ec054bb6969ef047cc7db6dd2020-11-25T01:29:33ZengUniversity of TehranJournal of Chemical and Petroleum Engineering2423-673X2423-67212019-12-0153219120110.22059/JCHPE.2019.269113.1256PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC AdsorbentSohrab Fathi0Abbas Rezaei1Majid Mohadesi2Mona Nazari3Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, IranDepartment of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, IranDepartment of Electrical Engineering, Kermanshah University of Technology, Kermanshah, IranDepartment of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, IranIn this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (SA), temperature (T), and pressure (P) were related to the output parameter which is propylene or propane adsorption. A thorough comparison between the experimental, artificial neural network and particle swarm optimization-adaptive neuro-fuzzy inference system models was carried out to prove its efficiency in accurate prediction and computation time. The obtained results show that both investigated methods have good agreements in comparison with the experimental data, but the proposed artificial neural network structure is more precise than our proposed PSO-ANFIS structure. Mean absolute error (MAE) for ANN and ANFIS models were 0.111 and 0.421, respectively.https://jchpe.ut.ac.ir/article_72487.htmladsorptionanncu-btcpropylene/propanepso-anfis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sohrab Fathi Abbas Rezaei Majid Mohadesi Mona Nazari |
spellingShingle |
Sohrab Fathi Abbas Rezaei Majid Mohadesi Mona Nazari PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent Journal of Chemical and Petroleum Engineering adsorption ann cu-btc propylene/propane pso-anfis |
author_facet |
Sohrab Fathi Abbas Rezaei Majid Mohadesi Mona Nazari |
author_sort |
Sohrab Fathi |
title |
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent |
title_short |
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent |
title_full |
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent |
title_fullStr |
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent |
title_full_unstemmed |
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent |
title_sort |
pso-anfis and ann modeling of propane/propylene separation using cu-btc adsorbent |
publisher |
University of Tehran |
series |
Journal of Chemical and Petroleum Engineering |
issn |
2423-673X 2423-6721 |
publishDate |
2019-12-01 |
description |
In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (SA), temperature (T), and pressure (P) were related to the output parameter which is propylene or propane adsorption. A thorough comparison between the experimental, artificial neural network and particle swarm optimization-adaptive neuro-fuzzy inference system models was carried out to prove its efficiency in accurate prediction and computation time. The obtained results show that both investigated methods have good agreements in comparison with the experimental data, but the proposed artificial neural network structure is more precise than our proposed PSO-ANFIS structure. Mean absolute error (MAE) for ANN and ANFIS models were 0.111 and 0.421, respectively. |
topic |
adsorption ann cu-btc propylene/propane pso-anfis |
url |
https://jchpe.ut.ac.ir/article_72487.html |
work_keys_str_mv |
AT sohrabfathi psoanfisandannmodelingofpropanepropyleneseparationusingcubtcadsorbent AT abbasrezaei psoanfisandannmodelingofpropanepropyleneseparationusingcubtcadsorbent AT majidmohadesi psoanfisandannmodelingofpropanepropyleneseparationusingcubtcadsorbent AT monanazari psoanfisandannmodelingofpropanepropyleneseparationusingcubtcadsorbent |
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1725096407537287168 |