Dynamic Process Model Validation and Control of the Amine Plant at CO2 Technology Centre Mongstad

Rubén Montañés,Nina Flø,Lars Nord

doi:10.3390/en10101527

Abstract

This paper presents a set of steady-state and transient data for dynamic process model validation of the chemical absorption process with monoethanolamine (MEA) for post-combustion CO2 capture of exhaust gas from a natural gas-fired power plant. The data selection includes a wide range of steady-state operating conditions and transient tests. A dynamic process model developed in the open physical modeling language Modelica is validated. The model is utilized to evaluate the open-loop transient performance at different loads of the plant, showing that pilot plant main process variables respond more slowly at lower operating loads of the plant, to step changes in main process inputs and disturbances. The performance of four decentralized control structures is evaluated, for fast load change transient events. Manipulation of reboiler duty to control CO2 capture ratio at the absorber’s inlet and rich solvent flow rate to control the stripper bottom solvent temperature showed the best performance.

Highlights

Carbon capture and storage (CCS) is a group of technologies that can significantly contribute to the reduction of anthropogenic CO2 emissions from thermal power generation and other carbon-intensive industries [1]
The results shown are for main process variables during pilot plant operation, namely CO2 lean (Ll ) and rich (Lr ) loadings, product CO2 flow rate (Fprod ), specific reboiler duty (SRD) and stripper bottom temperature Tstr
The absolute percentage errors (AP) and the mean absolute percentage errors (MAP) are calculated as in Equations (10) and (11), where xm is the value of the process variable predicted by the process model simulation, x p is the value of the process variable measured at the pilot plant at the given steady-state operation case, and n is the number of steady-state cases studied

Summary

Introduction

Carbon capture and storage (CCS) is a group of technologies that can significantly contribute to the reduction of anthropogenic CO2 emissions from thermal power generation and other carbon-intensive industries [1]. In future energy systems with a high penetration of renewable energy sources, the variability in demand and generation will introduce a change in the operating patterns of thermal power generation plants, which will have to change operating conditions [4,5,6]; there will be a higher frequency of significant transient events including load changes, and start-up and shut-down events [7,8] In this regard, Boot-Handford et al.’s carbon capture and storage update 2014 concludes that the financial case for CCS requires that it operates in a flexible manner and that load-following ability is extremely important to the long-term economics [9]

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