Dynamic modeling and absorption capacity assessment of CO2 capture process

Gáspár, Jozsef; Cormoş, Ana-Maria

doi:10.1016/j.ijggc.2012.01.016

Cited by 67 publications

(13 citation statements)

References 44 publications

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“…Combination of mass transfer by Billet & Schultes with the interfacial area correlation of Tsai et al (2011) gives the minimum error percentage of 38.56% when compared with the actual column height of 1.505m. As reported in Gaspar and Cormos (2012) and Billet and Schultes (1999) mass transfer correlation predicts well for MEA system while other correlations such as of Rocha et al (1996) will give good results for the case of AMP and MDEA systems. Even though some correlations are specifically design for elevated pressure such as Wang et al (2006) and Gualito et al (1997), higher error is observed.…”

Section: Simulation Of Non-equilibrium Rate Based Model For Co 2 Absosupporting

confidence: 52%

Pressure modification index based on hydrodynamics and mass transfer effects for modeling of CO 2 removal from natural gas via absorption at high pressures

Isa

Zabiri

Ramasamy

et al. 2017

International Journal of Greenhouse Gas Control

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Section: Simulation Of Non-equilibrium Rate Based Model For Co 2 Absosupporting

confidence: 52%

Pressure modification index based on hydrodynamics and mass transfer effects for modeling of CO 2 removal from natural gas via absorption at high pressures

Isa

Zabiri

Ramasamy

et al. 2017

International Journal of Greenhouse Gas Control

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“…It was shown by Gaspar and Cormos (2012) that solvents with fast kinetics respond faster than those with slower kinetics. To understand this behaviour, the dynamics of the absorber for -10% step change in the flue gas CO2 content is discussed in detail (Fig.…”

Section: Absorber Simulationmentioning

confidence: 99%

“…To our knowledge, only three studies present dynamic models using other solvents. Gaspar and Cormos (2012) presented an absorber model for MEA, diethanolamine (DEA), 2-amino-methylpropanol (AMP) and methyl-diethanloamine (MDEA) solvents. They demonstrate that kinetics play a key role in the dynamic behaviour of the capture process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic simulation and analysis of a pilot-scale CO2 post-combustion capture unit using piperazine and MEA

Gáspár¹,

Ricardez‐Sandoval

Jørgensen³

et al. 2016

IFAC-PapersOnLine

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Post-combustion capture is a promising technology for developing CO 2 neutral power plants. However, to make it economically and technically feasible, capture plants must follow the fast and large load changes of the power plants without decreasing the overall performance of the plant. Dynamic modeling and simulation is therefore needed to evaluate the performance of this plant under critical operation.In this work, we evaluate the transient response of an absorber and a desorber for step changes of key process parameters, e.g. flue gas flow and composition, lean and rich CO 2 loading, etc. We show the results for the baseline 30 wt% MEA and the low energy piperazine (PZ) solutions. This analysis reveals that the absorber reaches steady-state faster using MEA compared to PZ. This is related to the shift of the mass transfer zone due to changes in temperature. The transient operation in the regeneration unit is somewhat similar while using both solvents: an initial fast decrease of the lean loading is followed by a slow transient period as the system approaches steady-state conditions. We show the presence of inverse response in the stripper column when the rich loading decreases or the feed's temperature reduces using PZ solvent. Thus, we demonstrate that the dynamics of the MEA system cannot be extrapolated to other solvents.

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“…That work highlighted the necessity of building up a dynamic process model of the integrated system (including stripper, lean/rich heat exchanger, mixing tank and main process equipment), to understand the complexities of dynamic operation of the plant. Gaspar and Cormos [31] developed a dynamic process model of the absorber/desorber process and validated with steady-state plant data. Several publications are available, in which the models were validated only with steady-state pilot plant data [11,[32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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 CO 2 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 CO 2 capture ratio at the absorber's inlet and rich solvent flow rate to control the stripper bottom solvent temperature showed the best performance.

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Dynamic modeling and absorption capacity assessment of CO2 capture process

Cited by 67 publications

References 44 publications

Pressure modification index based on hydrodynamics and mass transfer effects for modeling of CO 2 removal from natural gas via absorption at high pressures

Pressure modification index based on hydrodynamics and mass transfer effects for modeling of CO 2 removal from natural gas via absorption at high pressures

Dynamic simulation and analysis of a pilot-scale CO2 post-combustion capture unit using piperazine and MEA

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

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