Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control

This paper presents a controller design study for the supercritical coal fired power plant (CFPP) integrated with solvent-based post-combustion CO2 capture (PCC) system. The focus of the study is on the steam drawn-off from turbine to the re-boiler, which is the key interaction between the CFPP and PCC plants. The simulation study of a 660 MW supercritical CFPP-PCC unit model has shown that the impact of re-boiler steam change on the power generation of CFPP is more than 100 times faster than that on the PCC operation. Considering this finding, a collaborative predictive control strategy is proposed for the CFPP-PCC system where the re-boiler steam flowrate is manipulated for the CFPP load ramping and then gradually set to the required value for CO2 capture. The PCC is thereby exploited as an energy storage device, which can quickly store/release extra energy for the CFPP in addition to the primary function of carbon emission reduction. The simulation results show that the proposed collaborative predictive controller can effectively improve the load ramping performance of CFPP without much performance degradation on the PCC operation

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

New Agegraphic Dark Energy in f(R)f(R) Gravity

In this paper we study cosmological application of new agegraphic dark energy density in the $f(R)$ gravity framework. We employ the new agegraphic model of dark energy to obtain the equation of state for the new agegraphic energy density in spatially flat universe. Our calculation show, taking $n<0$, it is possible to have $w_{\rm \Lambda}$ crossing -1. This implies that one can generate phantom-like equation of state from a new agegraphic dark energy model in flat universe in the modified gravity cosmology framework. Also we develop a reconstruction scheme for the modified gravity with $f(R)$ action.Comment: 8 pages, no figur

Interacting new agegraphic Phantom model of dark energy in non-flat universe

In this paper we consider the new agegraphic model of interacting dark energy in non-flat universe. We show that the interacting agegraphic dark energy can be described by a phantom scalar field. Then we show this phantomic description of the agegraphic dark energy and reconstruct the potential of the phantom scalar field.Comment: 8 pages, no figur

Equations of State in the Brans-Dicke cosmology

We investigate the Brans-Dicke (BD) theory with the potential as cosmological model to explain the present accelerating universe. In this work, we consider the BD field as a perfect fluid with the energy density and pressure in the Jordan frame. Introducing the power-law potential and the interaction with the cold dark matter, we obtain the phantom divide which is confirmed by the native and effective equation of state. Also we can describe the metric $f(R)$ gravity with an appropriate potential, which shows a future crossing of phantom divide in viable $f(R)$ gravity models when employing the native and effective equations of state.Comment: 23 pages, 7 figure

A New Type of Dark Energy Model

In this paper, we propose a general form of the equation of state (EoS) which is the function of the fractional dark energy density $\Omega_{d}$. At least, five related models, the cosmological constant model, the holographic dark energy model, the agegraphic dark energy model, the modified holographic dark energy model and the Ricci scalar holographic dark energy model are included in this form. Furthermore, if we consider proper interactions, the interactive variants of those models can be included as well. The phase-space analysis shows that the scaling solutions may exist both in the non-interacting and interacting cases. And the stability analysis of the system could give out the attractor solution which could alleviate the coincidence problem.Comment: Minor modifications, references adde