Demonstration of non-linear model predictive control for optimal flexible operation of a CO2 capture plant

Due to the penetration of renewable intermittent energy, there is a need for coal and natural gas power plants to operate flexibly with variable load. This has resulted in an increasing interest in flexible and operational issues in the capture plant as well. In the present paper a nonlinear model predictive control (NMPC) system was tested at the Tiller pilot plant in Norway. The most important part of the NMPC software is the dynamic model representing the absorber/desorber plant. A previous first principle (mechanistic) dynamic model of the plant using MEA was modified for a solvent of AMP and piperazine, and then successfully verified by step response tests. The NMPC, which was set up to minimize the deviation from the capture rate setpoint and minimize the specific reboiler duty was then tested in a closed loop with large changes in flue gas flow and CO2 composition. Even for gas rate variations of more than 300% (110–340 m3/h) and CO2 concentration changes of 30%, the dynamic response was satisfactory. A test with frequently occurring constraints on the reboiler duty revealed a need for an extension to include direct control of the lean loading. Test of setpoint changes in total CO2 recovery showed that the control system managed to rapidly change from one capture rate to another with a time constant of typically 10 min. This might be used in a second layer of optimization, a dynamic real-time optimizer, that minimizes the capture costs during a longer horizon considering varying energy prices.publishedVersio

Investigation of control strategies for adsorption-based CO2 capture from a thermal power plant under variable load operation

This work considers the closed-loop behavior of a moving bed temperature swing adsorption process designed to capture CO2 from a coal-fired power plant. Four decentralized control strategies were studied based on step changes and ramps of flue gas feed flow rate and controller setpoint changes. A proportional-integral (PI) control configuration, where CO2 purity was controlled by hot fluid velocity to the desorption section and CO2 recovery was controlled by the sorbent flow rate, demonstrated the overall best performance. The 99% settling time for higher-level control variables varied from 0 to 13 min for most control configurations and the settling time for CO2 purity was generally longer than for CO2 recovery. The simulations show that using ratio controllers lead to larger offsets but can give around 10 times faster purity response compared to PI-control. All investigated control combinations were able to keep the controlled variables relatively close to the setpoints and the largest relative steady state setpoint offset was 2%.publishedVersio

Non-linear system Identification and control of Solvent-Based Post-Combustion CO2 Capture Process

Solvent-based post-combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control system design. Typically, first principles models are used, however, they usually require comprehensive knowledge and in-depth understanding of the process. In addition, the high computational time required and high complexity of the first principles models makes it unsuitable for control system design implementation. This thesis is aimed at the development of a reliable dynamic model via system identification technique as well as a suitable process control strategy for the solvent-based post-combustion CO2 capture process. The nonlinear autoregressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two multiple-input single-output (MISO) sub-systems. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process. The model obtained was adopted for various process control system design of the solvent-based PCC process (conventional PI, MPC, and NMPC). For the conventional PI controller design, multivariable control analysis was carried out to determine a suitable control structure. Control performance evaluation of the control schemes reveals that the NMPC scheme was suitable to control the solvent-based PCC process at flexible operations. Findings obtained from the thesis underlines the advancement in dynamic modelling and control implementation of solvent-based PCC process

MANAGEMENT DECISION SUPPORT SYSTEM OF SOLVENT-BASED POST-COMBUSTION CARBON CAPTURE

A management decision-support framework for a coal-fired power plant with solvent based post combustion CO2 capture (PCC) (integrated plant) is proposed and developed in this thesis. A brief introduction pertaining to the solvent-based PCC technology, thesis motivations and objectives are given in Chapter 1. Chapter 2 comprises a comprehensive literature review of solvent-based PCC plant from the bottom level (PCC instrumentation level) until the top level (managerial decision of PCC system). Chapter 3 describes the development of solvent-based PCC dynamic model via empirical methods. Open-loop dynamic analyses are presented to provide a deeper understanding of the dynamic behaviour of key variables in solvent-based PCC plant. Chapter 4 presents the design of the control architecture for solvent-based PCC plant. Two control algorithms developed, which utilise conventional proportional, integral and derivative (PID) controller and advanced model predictive control (MPC). Chapter 5 proposes a conceptual framework for optimal operation of the integrated plant. The MPC scheme is chosen as the control algorithm while mixed integer non-linear programming (MINLP) using genetic algorithm (GA) function is employed in the optimization algorithm. Both algorithms are integrated to produce a hybrid MPC-MINLP algorithm. Capability and applicability of the algorithm is evaluated based on 24 hours and annual operation of integrated plant. Chapter 6 extends the scope of Chapter 5 by evaluating the relevance of solvent-based PCC technology in the operation of black coal-fired power plant in Australia. This chapter considers a prevailing climate policy established in Australia namely Emission Reduction Fund (ERF). Finally, the concluding remarks and future extensions of this research are presented in Chapter 7

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

Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation

Solvent-based post-combustion CO2 capture (PCC) appears to be the most effective choice to overcome the CO2 emission issue of fossil fuel fired power plants. To make the PCC better suited for power plants, growing interest has been directed to the flexible operation of PCC in the past ten years. The flexible operation requires the PCC system to adapt to the strong flue gas flow rate change and to adjust the carbon capture level rapidly in wide operating range. In-depth study of the dynamic characteristics of the PCC process and developing a suitable control approach are the keys to meet this challenge. This paper provides a critical review for the dynamic research of the solvent–based PCC process including first-principle modelling, data-driven system/process identification and the control design studies, with their main features being listed and discussed. The existent studies have been classified according to the approaches used and their advantages and limitations have been summarized. Potential future research opportunities for the flexible operation of solvent-based PCC are also given in this review

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

Fluidized bed plants for heat and power production in future energy systems

Fluidized bed (FB) plants are used for heat and power production in several energy systems around the world, with particular importance in systems using large shares of renewable solid fuel, e.g., biomass. These FB plants are traditionally operated for base-load electricity production or for heat production, and thus characterized by relatively small and slow load changes. In parallel, as the transition towards energy systems with net-zero emissions increases the share of variable renewable energy (VRE) sources, the need for implementing variation management strategies at various timescales arises – giving heat and power plants the possibility to adapt their operations to accommodate the inherent variability of VRE sources. Following this, FB technology is envisioned for a wide range of novel applications expected to play significant roles in the decarbonization of energy systems, such as thermochemical energy storage and carbon capture and storage. In this context, research efforts are needed to investigate the technical and economic features of FB plants in energy systems with high levels of VRE.The aim of this thesis is to elucidate the capabilities of FB plants for heat and power production in net-zero emissions energy systems. For this purpose, two main pathways are explored: i) transient operation as fuel-fed plants, and ii) the potential conversion into decarbonized plants, i.e., into VRE-fed layouts providing dispatchable outputs.For fuel-fed FB plants, a dynamic model of biomass-fired FB plants has been developed, considering the two types of FB boilers (BFB and CFB) and including validation against steady-state and transient operational data collected from two commercial plants. As a novelty of this work the model describes both the gas (in-furnace) and water-steam sides such that the interactions between the two can be assessed. The results of the simulations show that i) the characteristic times for the gas side are shorter in BFB furnaces than in CFBs, albeit these times are for both furnace types not longer than those for the water-steam side; ii) the computed timescales for the dynamics of FB plants fall well within those required for offering complementing services to the grid; and iii) the use of control and operational strategies for the water-steam side can confer capabilities superior to fuel-feeding control in terms of avoiding undesirable unburnt emissions and providing temporary overload operation. The retrofit of fuel-fed FB plants into poly-generation facilities cogenerating a combustible biogenic gas is also assessed, revealing that partial combustion of this gas can be used to provide faster inherent dynamics than the original configuration.For VRE-fed FB layouts, techno-economic process modeling has been carried out for large-scale deployment of solar- and electricity-charging processes based on three different chemical systems: i) carbonation/calcination (calcium); ii) thermally reduced redox (cobalt oxides); and iii) chemically reduced redox (iron oxides). One attractive aspect of these layouts is the possibility to build part of them by retrofitting current fuel-fed FB plants. While the technical assessment for solar applications indicates that cobalt-based layouts offer the highest levels of efficiency and dispatchability, calcium-based processes present better economics owing to the use of inexpensive calcium material. The results also show that electricity-charged layouts such as iron looping can play an important role in the system providing variation management strategies to the grid while avoiding costly H2 storage. Further, the economic performances of VRE-fed FB layouts are benefitted by the generation of additional services and products (e.g., carbon capture and on-demand production of H2), and by scenarios with high volatility of the electricity prices

Investment decision of monoethanolamine based post combustion CO2 capture plant via application of control strategies

The abatement of anthropogenic CO2 gas and extensive demand for electricity has motivated cleaner power production from fossil fuels. Monoethanolamine (MEA) based post combustion CO2 capture plant (PCC) is a promising and mature technology to realize large scale cuts in carbon emissions at national and global levels. A carbon capture plant features non-linearity and multifaceted process interactions, therefore presents operational challenges requiring robust control strategies to ensure optimal but flexible operation of the plant as it responds to variable power plant output. This paper investigates two control strategies (viz, conventional feedback (PID) control and model predictive control (MPC)) with the control objective being formulated as economic functions around CO2 emissions (US$/t-CO2) and operational cost (US$/d). This presents a management capability to the power plant operator unlike the commonly used operational (technical only) objective of maximising CO2 capture (CO2%) at a given setpoint in conjunction with plant net energy performance (EPn). This economics-based formulation in the control strategy together with a demonstrated stability analysis fits well into plant-wide control implementation of MEA based PCC plants and supports cleaner production of electricity while helping such operation economically viable. It can be seen that embedment of MPC into PCC plant features attractive economic value (positive investment decision) based on the two above criteria. Whereas, CO2 emission cost and operational cost exhibit 30% and 60% of cost saving compared with the deployment of PID controller