Modeling and optimization of CO2 capture using 4-diethylamino-2-butanol (DEAB) solution

Modeling and optimization of CO2 capture using 4-diethylamino-2-butanol (DEAB) solution Article history: A multi-layer perceptron neural network (MLPNN) model with Levenberg–Marquardt learning algo- Received 1 September 2015 rithm were applied to model CO capture by a novel amine solution called 4-diethylamino-2-butanol Received in revised form 11 February 2016 (DEAB). The MLPNN model predicted the CO concentration and temperature profiles along the height Accepted 12 February 2016 of the packed column as the model output. Inlet feed conditions of the absorber column (flue gas and Available online 27 February 2016 amine) were selected as the inputs of the MLPNN model. Experimental data about random and structured packed columns were extracted from the literature and used to train the MLPNN model. In addition, a Keywords: systematic procedure, i.e. Taguchi method, was applied to obtain the significant sequence of process Packed column parameters affecting CO removal efficiency and to optimize the variables in the absorber column. Five MLPNN model levels of five variables, including lean amine temperature, amine concentration, CO loading of amine, Taguchi method gas temperature, and amine flow rate, were used for the optimization of the absorber column. The DEAB CO removal efficiency average absolute relative deviations (AARD) between the predicted results and the experimental data suggested that our MLPNN http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Greenhouse Gas Control Elsevier

Modeling and optimization of CO2 capture using 4-diethylamino-2-butanol (DEAB) solution

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
1750-5836
eISSN
1878-0148
D.O.I.
10.1016/j.ijggc.2016.02.019
Publisher site
See Article on Publisher Site

Abstract

Article history: A multi-layer perceptron neural network (MLPNN) model with Levenberg–Marquardt learning algo- Received 1 September 2015 rithm were applied to model CO capture by a novel amine solution called 4-diethylamino-2-butanol Received in revised form 11 February 2016 (DEAB). The MLPNN model predicted the CO concentration and temperature profiles along the height Accepted 12 February 2016 of the packed column as the model output. Inlet feed conditions of the absorber column (flue gas and Available online 27 February 2016 amine) were selected as the inputs of the MLPNN model. Experimental data about random and structured packed columns were extracted from the literature and used to train the MLPNN model. In addition, a Keywords: systematic procedure, i.e. Taguchi method, was applied to obtain the significant sequence of process Packed column parameters affecting CO removal efficiency and to optimize the variables in the absorber column. Five MLPNN model levels of five variables, including lean amine temperature, amine concentration, CO loading of amine, Taguchi method gas temperature, and amine flow rate, were used for the optimization of the absorber column. The DEAB CO removal efficiency average absolute relative deviations (AARD) between the predicted results and the experimental data suggested that our MLPNN

Journal

International Journal of Greenhouse Gas ControlElsevier

Published: Jun 1, 2016

References

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