Ashraf, Waqar Muhammad;
Dua, Vivek;
(2023)
Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality.
Digital Chemical Engineering
, 8
, Article 100115. 10.1016/j.dche.2023.100115.
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Abstract
The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal.
| Type: | Article |
|---|---|
| Title: | Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.dche.2023.100115 |
| Publisher version: | https://doi.org/10.1016/j.dche.2023.100115 |
| Language: | English |
| Additional information: | © 2023 The Author(s). Published by Elsevier Ltd on behalf of Institution of Chemical Engineers (IChemE). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Carbon capture using MEA, Machine learning, Operation optimization, Carbon neutrality |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174756 |
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