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Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Liao, Peizhi; Li, Yiguo; Wu, Xiao; Wang, Meihong; Oko, Eni

Authors

Peizhi Liao

Yiguo Li

Xiao Wu

Meihong Wang

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Dr Eni Oko E.Oko@hull.ac.uk
Lecturer in Chemical Engineering



Abstract

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances.

Citation

Liao, P., Li, Y., Wu, X., Wang, M., & Oko, E. (2020). Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control. International journal of greenhouse gas control, 95, https://doi.org/10.1016/j.ijggc.2020.102985

Journal Article Type Article
Acceptance Date Feb 8, 2020
Online Publication Date Feb 17, 2020
Publication Date 2020-04
Deposit Date Feb 19, 2020
Publicly Available Date Nov 30, -0001
Journal International Journal of Greenhouse Gas Control
Print ISSN 1750-5836
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 95
Article Number 102985
DOI https://doi.org/10.1016/j.ijggc.2020.102985
Keywords General Energy; Industrial and Manufacturing Engineering; Pollution; Management, Monitoring, Policy and Law; Post-combustion carbon capture; Coal-fired power plant; Dynamic modelling; Dynamic simulation; Neural network inverse control
Public URL https://hull-repository.worktribe.com/output/3439144
Additional Information This article is maintained by: Elsevier; Article Title: Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control; Journal Title: International Journal of Greenhouse Gas Control; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ijggc.2020.102985; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.