Next Article in Journal
Multi-Modal Ptychography: Recent Developments and Applications
Next Article in Special Issue
Acetic Acid as an Indirect Sink of CO2 for the Synthesis of Polyhydroxyalkanoates (PHA): Comparison with PHA Production Processes Directly Using CO2 as Feedstock
Previous Article in Journal
Thermal Characterisation of Micro Flat Aluminium Heat Pipe Arrays by Varying Working Fluid and Inclination Angle
Previous Article in Special Issue
Role of Amine Type in CO2 Separation Performance within Amine Functionalized Silica/Organosilica Membranes: A Review
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1053; https://doi.org/10.3390/app8071053

Nonlinearity Analysis and Multi-Model Modeling of an MEA-Based Post-Combustion CO2 Capture Process for Advanced Control Design

1
Key laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China
2
Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA
*
Author to whom correspondence should be addressed.
Received: 2 May 2018 / Revised: 4 June 2018 / Accepted: 11 June 2018 / Published: 28 June 2018
(This article belongs to the Special Issue Carbon Capture Utilization and Sequestration (CCUS))
Full-Text   |   PDF [4580 KB, uploaded 28 June 2018]   |  

Abstract

The monoethanolamine (MEA)-based post-combustion CO2 capture plant must operate flexibly under the variation of the power plant load and the desired CO2 capture rate. However, in the presence of process nonlinearity, conventional linear control strategy cannot achieve the best performance under a wide operation range. Considering this problem, this paper systematically studies the multi-model modeling of the MEA-based CO2 capture process for the purpose of (1) implementing well-developed linear control techniques to the design of an advanced controller and (2) achieving a wide-range flexible operation of the CO2 capture process. The local linear models of the CO2 capture process are firstly established at given operating points using the method of subspace identification. Then the nonlinearity distribution at different loads of an upstream power plant and different CO2 capture rates is investigated via the gap metric. Finally, based on the nonlinearity investigation results, the suitable linear models are selected and combined together to form the multi-model system. The proposed model is validated using the measurement data, which is generated from a post-combustion CO2 capture model developed in the go-carbon capture and storage (gCCS) simulation platform. As the proposed multi-linear model has a simple mathematical expression and high prediction accuracy, it can be directly employed as the control model of a practical advanced control strategy to achieve a wide operating range control of the CO2 capture process. View Full-Text
Keywords: monoethanolamine-based post-combustion CO2 capture; multi-model modeling; nonlinearity investigation; subspace identification; control design monoethanolamine-based post-combustion CO2 capture; multi-model modeling; nonlinearity investigation; subspace identification; control design
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liang, X.; Li, Y.; Wu, X.; Shen, J.; Lee, K.Y. Nonlinearity Analysis and Multi-Model Modeling of an MEA-Based Post-Combustion CO2 Capture Process for Advanced Control Design. Appl. Sci. 2018, 8, 1053.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top