Simulation can be very helpful at the early stage development of a technology in design and optimization prior to the implementation. A number of simulation works on the amine based capture has been carried out. These research activities are typically based on the mass transfer in columns with two simulation methods: the equilibrium stage simulation or the rate based simulation (Browne, 2014).
An equilibrium stage is an ideal condition that the equilibrium of liquid and gas phase is established. Having a series of equilibrium stages makes it possible to simulate the steady state of a separation process. The equilibrium stage modelling is good at non-reactive systems due to its simplicity. However the equilibrium can be hardly achieved in the reactive capture therefore this method is difficult to represent the actual process (Browne, 2014). Several research works based on the equilibrium stage model have been published by Afkhamipour and Mofarahi (2013) and Mores et al. (2011).
The rate based simulation which considers the reaction rates, the electrolytes as well as the mass and heat transfer provides a much greater complexity than the equilibrium stage simulation. The two-film theory of mass transfer is one of the most common analysis methods of the rate based simulation. In this theory the
equilibrium is attained only at the gas-liquid interface and a thin gas film and a thin liquid film distribute on each side of the interface (Kvamsdal et al., 2009, Pacheco and Rochelle, 1998, Al-Baghli et al., 2001). Focusing on the film around the interface of the liquid and gas, this approach divided the phase into four parts based on the position: bulk gas, gas film, liquid film and bulk liquid. This feature makes the more detailed calculations achievable, including the electrolyte, the mass transfer resistances, and the reaction kinetics (Kenig et al., 2001).
Both the equilibrium stage simulation and the rate based simulation are steady state simulations to optimise the operation at the nominal condition where the flow rate and content of the flow gas are constant. The post combustion capture is moving to the industrial implementation, the dynamic study is of increasing importance. However, few research activities reported are focusing on the dynamic simulations at this moment. Lawal et al. (2009) and Kvamsdal et al. (2009) worked on the absorber, Ziaii et al. (2009) studied the regeneration column and Harun et al. (2012) developed a model for the whole system.
2.5
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UMMARYThe coal-fired power generation and the post combustion capture technologies have been reviewed in this chapter. The generation process of the typical coal- fired power plant is introduced in the first part of this chapter. From the basic analysis of thermodynamics it is proved that supercritical plants have a higher generation efficiency comparing with the traditional subcritical boilers. As a
result, the supercritical generation unit will be used as a reference plant for the research on the dynamic response of the post combustion capture.
The second part of this chapter introduces several different post combustion capture technologies available today. Chemical absorption based on aqueous amines, as the major capture method in industry, is focused in this thesis. A detailed study of the capture process has been studied and different modelling approaches are also reviewed in this chapter.
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The goal of this study is to analyse and examine the plant performance while it is integrated with post combustion carbon capture process through plant dynamic simulation. So a mathematical model that is able to represent the power plant generation process is a prerequisite for simulation study. The mathematical description should provide sufficient information of the important component thermodynamics in the process and the model should be derived based on their physical principles. However, sometimes the dynamic behaviour in the actual power generation process is very difficult to be described mathematically. A very significant example is that some of the model parameters differ in a wide range when identified from different level of generation outputs. In the first principle models, this is usually solved by a look up table which hosts various
values of these parameters obtained from past experience and tests. Additionally these parameters of the model may lead to quite different model behaviours. This indicates that it is an unachievable task in the power plant simulationto represent the full range of operation conditions by one single model, especially in the cases which have not been considered in the parameter identification processes. Black- box approaches, including neural network and autoregressive moving average model, are possible options to overcome these difficulties but it is difficult for these approaches based on empirical data to convince industry as they are lack of support from physics and engineering laws.
A lot of work has been reported for the mathematical model of the coal-fired supercritical power plant (Salisbury, 1950, Thomas and Finney, 1979, Usoro et al., 1983, Kola et al., 1989, Shinohara and Koditshek, 1996, Lu, 1999, Lu and Hogg, 2000, Inoue and Amano, 2006, Gu et al., 2009). In this chapter, two simulation approaches for the power plant modelling will be studied: the first principle model and the power plant simulator based on thermodynamics and fluidic networks.