Design and operations optimization of membrane separation for flexible carbon capture from natural gas combined cycle systems

We explore the concept of flexible carbon capture using membrane separation. Flexible carbon capture has been studied in recent years as a measure to decarbonize fossil-fired power generation in response to electricity market conditions, including varying electricity prices and/or fluctuating electricity supply requirements. The importance of flexible operation of carbon capture systems is highlighted by the increasing penetration of intermittent energy generation from renewable sources such as wind and solar PV. To accommodate the integration of renewable energy into the grid, fossil-fired power plants would need to generate varying electricity load, producing flue gas with varying characteristics and at varying volumes. Carbon capture systems in a high-renewables grid will be required to operate flexibly to respond to these changes. Flexible carbon capture has been studied for amine absorption,1-2 a leading CCS technology. This work explores the possibility of flexible carbon capture using membrane separation, a promising alternative to amine absorption. In particular, membranes are considered to be very responsive to system changes and require very short start-up times.3 In addition, membrane separation has advantages such as having a smaller footprint, being more environmentally benign (no corrosive chemicals involved in the separation process), and potentially incurring lower separation energy. In this work, we perform optimization to determine the optimal process design and time-varying operations of a polymeric membrane system separating CO2 from a natural gas combined cycle (NGCC) with wind energy integration. The total net present value (NPV) of the gas turbines and membrane system is maximized. Both design and operations of the capture plant are optimized. Design decision variables include membrane process configuration, membrane size, CO2/N2 selectivity and CO2 permeance (two key membrane properties relevant to separation performance), and compressor and vacuum pump sizes. These parameters are determined before a capture unit is built. After the plant is built, operational decision variables include gas flowrates, the pressure ratio across the membrane and permeate-side (low-pressure side) pressure. These are parameters that can be adjusted, within limits, given electricity market conditions for a given time period. Time-varying electricity output from gas turbines as well as the associated flue gas flowrate and composition will be determined by HyPPO, an in-house software developed at Stanford University for modeling and optimization of flexible and renewable-integrated power systems. HyPPO models beneficial operating strategies for a set of statistically representative days. HyPPO results will be used with membrane separation models for various process configurations as modeled in MATLAB. 1. Cohen, S. M.; Rochelle, G. T.; Webber, M. E., Optimizing post-combustion CO2 capture in response to volatile electricity prices. International Journal of Greenhouse Gas Control 2012, 8, 180–195. 2. Mac Dowell, N.; Shah, N., The multi-period optimisation of an amine-based CO2 capture process integrated with a super-critical coal-fired power station for flexible operation. Computers & Chemical Engineering 2015, 74, 169–183. 3. Brunetti, A.; Scura, F.; Barbieri, G.; Drioli, E., Membrane technologies for CO2 separation. Journal of Membrane Science 2010, 359 (1), 115–125

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances

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

Carbon capture from natural gas combined cycle power plants: Solvent performance comparison at an industrial scale

Natural gas is an important source of energy. This article addresses the problem of integrating an existing natural gas combined cycle (NGCC) power plant with a carbon capture process using various solvents. The power plant and capture process have mutual interactions in terms of the flue gas flow rate and composition vs. the extracted steam required for solvent regeneration. Therefore, evaluating solvent performance at a single (nominal) operating point is not indicative and solvent performance should be considered subject to the overall process operability and over a wide range of operating conditions. In the present research, a novel optimization framework was developed in which design and operation of the capture process are optimized simultaneously and their interactions with the upstream power plant are fully captured. The developed framework was applied for solvent comparison which demonstrated that GCCmax, a newly developed solvent, features superior performances compared to the monoethanolamine baseline solvent

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

Flexible operation of CSIRO's post-combustion CO2 capture pilot plant at the AGL Loy Yang power station

Flexible operation has the potential to significantly improve the economic viability of post-combustion CO2 capture (PCC). However, the impact of disturbances from flexible operation of the PCC process is unclear. The purpose of this study was to investigate the effects of flexible operation in a PCC pilot plant by implementing step-changes for improved dynamic data reliability. The flexible operation campaign was conducted at the CSIRO PCC pilot plant at AGL Loy Yang using monoethanolamine (MEA) absorbent. The pilot plant was operated under a broad range of transient conditions (changing flue gas flow, liquid absorbent flow and steam pressure) to capture the dynamics of a PCC process during flexible operation. The study demonstrated that the dynamics of flue gas flow rate was faster than absorbent flow rate. The greatest CO2 removal% was achieved at the lowest flue gas flow rate or at the highest absorbent flow rate; however, the latter provided improved energy efficiency. The steam pressure parameter could adjust the temperature of all columns simultaneously which can be used to compensate for effects from ambient conditions or heat losses. These results verify the technical feasibility of flexible PCC operation and provide a suitable dataset for dynamic model validation

Carbon capture from pulverized coal power plant (PCPP): Solvent performance comparison at an industrial scale

Coal is the most abundant fossil fuel on the planet. However, power generation from coal results in large amounts of greenhouse gas emissions. Solvent-based carbon capture is a relatively mature technology which can potentially mitigate these emissions. Although, much research has been done on this topic, single-point performance analysis of capture plant and ignoring operational characteristics of the upstream power plant may result in unrealistic performance assessments. This paper introduces a new methodology to assess the performance of CO2 capture solvents. The problem is posed as retrofitting an existing pulverized coal power plant with post-combustion carbon capture using two solvents: CDRMax, a recently developed amine-promoted buffer salt (APBS) solvent by Carbon Clean Solutions Limited (CCSL) and the monoethanolamine (MEA) baseline solvent. The features of interest include model development and validation using pilot plant data, as well as integrated design and control of the capture process. The emphasis is on design and operation of the capture plant, when integrated with the upstream coal-fired power plant, subject to variations in the electricity load. The results suggest that optimal design and operation of capture plant can significantly mitigate the energetic penalties associated with carbon capture form the flue gas, while providing effective measures for comparing solvent performances under various scenarios

Flexible operation of coal fired power plant integrated with post combustion CO2 capture using model predictive control

The growing demand for CO2 capture from coal-fired power plant (CFPP) has increased the need to improve the dynamic operability of the integrated power generation-CO2 capture plant. Nevertheless, high-level operation of the entire system is difficult to achieve due to the strong interactions between the CFPP and post combustion CO2 capture (PCC) unit. In addition, the control tasks of power generation and CO2 removal are in conflict, since the operation of both processes requires consuming large amount of steam. For these reasons, this paper develops a model for the integrated CFPP-PCC process and analyzes the dynamic relationships for the key variables within the integrated system. Based on the investigation, a centralized model predictive controller is developed to unify the power generation and PCC processes together, involving the key variables of the two systems and the interactions between them. Three operating modes are then studied for the predictive control system with different focuses on the overall system operation; power generation demand tracking and satisfying the CO2 capture requirement. The predictive controller can achieve a flexible operation of the integrated CFPP- PCC system and fully exert its functions in power generation and CO2 reduction

What is the Value of CCS in the Future Energy System?

Ambitions to produce electricity at low, zero, or negative carbon emissions are shifting the priorities and appreciation for new types of power generating technologies. Maintaining the balance between security of energy supply, carbon reduction, and electricity system cost during the transition of the electricity system is challenging. Few technology valuation tools consider the presence and interdependency of these three aspects, and nor do they appreciate the difference between firm and intermittent power generation. In this contribution, we present the results of a thought experiment and mathematical model wherein we conduct a systems analyses on the effects of gas-fired power plants equipped with Carbon Capture and Storage (CCS) technology in comparison with onshore wind power plants as main decarbonisation technologies. We find that while wind capacity integration is in its early stages of deployment an economic decarbonisation strategy, it ultimately results in an infrastructurally inefficient system with a required ratio of installed capacity to peak demand of nearly 2.. Due to the intermittent nature of wind power generation, its deployment requires a significant amount of reserve capacity in the form of firm capacity. While the integration of CCS-equipped capacity increases total system cost significantly, this strategy is able to achieve truly low-carbon power generation at 0.04 tCO2/MWh. Via a simple example, this work elucidates how the changing system requirements necessitate a paradigm shift in the value perception of power generation technologies