Toluleke E. Akinola
Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process
Akinola, Toluleke E.; Oko, Eni; Wu, Xiao; Ma, Keming; Wang, Meihong
Dr Eni Oko E.Oko@hull.ac.uk
Lecturer in Chemical Engineering
The flexible operation capability of solvent-based post-combustion capture (PCC) process is vital to efficiently meet the load variation requirement in the integrated upstream power plant. This can be achieved through the deployment of an appropriate control strategy. In this paper, a nonlinear model predictive control (NMPC) system was developed and analysed for the solvent-based PCC process. The PCC process was represented as a nonlinear autoregressive with exogenous (NARX) inputs model, which was identified through the forward regression with orthogonal least squares (FROLS) algorithm. The FROLS algorithm allows the selection of an accurate model structure that best describes the dynamics of the process. The simulation results showed that the NMPC gave better performance compared with linear MPC (LMPC) with an improvement of 55.3% and 17.86% for CO2 capture level control under the scenarios considered. NMPC also gave a superior performance for reboiler temperature control with the lowest ISE values. The results from this work will support the development and implementation of NMPC strategy on the PCC process with reduced computational time and burden.
Akinola, T. E., Oko, E., Wu, X., Ma, K., & Wang, M. (2020). Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process. Energy, 213, https://doi.org/10.1016/j.energy.2020.118840
|Journal Article Type||Article|
|Acceptance Date||Sep 12, 2020|
|Online Publication Date||Sep 17, 2020|
|Publication Date||Dec 15, 2020|
|Deposit Date||Sep 23, 2020|
|Publicly Available Date||Sep 18, 2021|
|Peer Reviewed||Peer Reviewed|
|Keywords||Post-combustion carbon capture; Chemical absorption; Nonlinear system identification; Nonlinear MPCFROLS-ERR; Flexible operation|
This file is under embargo until Sep 18, 2021 due to copyright reasons.
Contact E.Oko@hull.ac.uk to request a copy for personal use.
You might also like
Modelling of a post-combustion CO₂ capture process using neural networks