Solvent-based Post-combustion CO2 Capture Research Articles

On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data

This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO2 capture process.

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes.

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

Intensification of Sono-Assisted CO2 Stripping/Carbon-Rich Solvent Regeneration by Fe2O3 Hydrophobic Micronized Particles

In 2020, the amount of CO2 generated by coal combustion was estimated to be 15.32 Gt CO2. In a solvent-based postcombustion CO2 capture (PCCC) process, the solvent regeneration/CO2 stripping process uses thermal energy to regenerate the solvent. It has an adverse impact on the solvent’s thermal stability, besides causing a significant quantity of solvent loss and a high energy demand. In recent times, one method for PCCC cost savings has emerged: sono-assisted solvent regeneration/CO2 stripping. The present work focuses on enhancing the high-frequency ultrasound-assisted aqueous carbon-rich 30 wt % monoethanolamine (MEA) solvent regeneration/CO2 stripping process using Fe2O3 hydrophobic micronized particles (concentrations varied in the ranges of 0.005, 0.01, 0.05, 0.1, and 0.2 wt %) in a controlled-temperature environment at 12 °C using 360 kHz, 470 kHz, and 1 MHz (streaming-dominant frequencies). From the investigation, the lowest concentration (0.005 wt %) of micronized particles shows maximum enhancements of 12.29, 20.93, and 33.62%, thereby decreasing the solvent sensible energy requirements by 1.26, 2.4, and 2.9 times than without micronized particles for the tested frequencies. In addition, the observed CO2 stripping rate is much higher when compared to the case without micronized particles. The stripping efficiency is observed to be high in the initial stages of sonication (5 min).

Amino-2-methyl-1-propanol-based Post-combustion Capture Process with Solvent Storage for Decarbonisation of Natural Gas Combined Cycle Power Plant

Meeting the Paris Agreement targets is challenging because a high proportion of the global energy generation is based on the combustion of fossil fuels. Carbon capture and storage is expected to provide a cost-effective means of reducing CO2 emission from the fossil fuel-fired power sector. In recent times, natural gas has become the most preferred fossil fuel due to the low prices and low carbon emissions associated with its combustion in comparison to coal and other fossil fuels. Among the commonly employed carbon capture technologies, amine solvent-based post-combustion CO2 capture is known as an appropriate option for retrofitting the natural gas combined cycle (NGCC) power plants. However, the energy requirement for solvent regeneration remains a major challenge due to less electricity sold and causing an energy penalty. One way to reduce this parasitic load on the NGCC is to use alternative solvents with superior performance than the widely used monoethanolamine (MEA). Another way to address this challenge is the addition of the solvent storage system, which enables the energy penalty to be deferred until times of low electricity prices. However, investing in solvent storage will only be profitable if there is a substantial difference between the high and the low electricity prices. In this work, a process model of the NGCC with the CO2 capture plant using 2-amino-2-methyl-1-propanol (AMP) is used to evaluate the feasibility of solvent storage and its effect on economic performance. The net efficiency of the process with solvent storage was obtained to be 46.7% compared to 45.6% LHV basis for the process without solvent storage. Despite this lower efficiency, the process with solvent storage was found to increase the profit by approximately 1% compared to that without solvent storage. Thus, this would result in a higher profit than the power plant if the carbon tax exceeds €25/tCO2.

Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture

Decarbonizing the energy intensive iron and steel industry is in urgent need to meet the ambitious environmental goal. Efficient and clean use of the blast furnace gas (BFG) through combined-cycle gas turbine (CCGT) power plant provides feasible pathway to realize a near-term CO2 reduction when integrated with carbon capture. This paper presents effective control strategies to coordinate the operation of BFG-fired CCGT plant and solvent-based post-combustion CO2 capture (PCC) process based on the in-depth understanding of the interactions among process dynamics in different time-scales for carbon, heat and electricity. The energy storage capability of the PCC process is explored in addition to the CO2 capture and the reboiler steam flowrate used for solvent regeneration is incorporated into the BFG-fired CCGT control loop. Considering this, two coordinated control strategies are developed for the BFG-fired CCGT-PCC, first based on the conventional PI control and then with advanced model predictive control (MPC) approaches. The coordinated strategies are demonstrated to improve the power ramping performance of the CCGT with little degradation on the PCC operation, thus providing better support for the reliability of the power system in the context of increasing penetration of renewable energy resources. Moreover, by considering the impact of disturbances into the predictive models, the MPC-based coordinated control can well alleviate the influence of BFG fluctuations, guaranteeing a stable operation of the integrated plant. This paper points to the new direction of using PCC for more flexible power regulation of adjustable sources in low-carbon energy systems with penetration of intermittent renewable powers.

Experimental studies on CO2 absorption and solvent recovery in aqueous blends of monoethanolamine and tetrabutylammonium hydroxide

Solvent-based post-combustion CO2 capture process is recently carried out using chemical absorption with aqueous blends of Monoethanolamine (MEA) and Ionic Liquids (IL) as promising solvents. In the present work, the blends of MEA and TetraButylAmmonium Hydroxide [TBA][OH] have been used for CO2 absorption and desorption process. The solubility of CO2 is investigated with aqueous mixtures for various carbon loading time by varying compositions of MEA and [TBA][OH] as 30 wt%, 28 wt%, 25 wt%, 20 wt% and 0 wt%, 2 wt%, 5 wt%, 10 wt% respectively. It increases with increasing IL concentration for all the aqueous mixtures. The solvent regeneration was also studied at different temperatures in order to recover and reuse the solvent for cyclic absorption. The slight decrease in CO2 solubility was noted for 20 wt% MEA +10 wt% [TBA][OH] mixture. However, this mixture exhibits higher absorption/desorption rate and regeneration efficiency than other mixtures. The regeneration energy of this mixture was also calculated as 28.6 kJ/mol of CO2, which is 32% less than that of baseline 30 wt% MEA. Furthermore, the physicochemical properties such as density, viscosity and surface tension for all the solvent blends were studied experimentally.

Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization

High investment and operating costs are two barriers hindering the commercial deployment of solvent-based post combustion CO2 capture (PCC) technology in power industry. Since upstream power plants are always in flexible operation, conventional approaches which carry out the design based on a fixed condition fail to achieve the best overall performance. To this end, this paper proposes a simultaneous size and operation parameters optimization strategy for the PCC process, in which the flexible operation of the CFPP is taken into account during the design stage. The technique of particle swarm optimization (PSO) is used in a hierarchical manner to find the design and operating conditions of the PCC with minimal total annualized cost. To further explore the optimization potential, the average CO2 capture level in one day is taken as the environmental constraint rather than the conventional instantaneous one. The proposed approach is tested on the PCC design for an existing 660 MW supercritical coal-fired power plant. The optimization results of four different design approaches are compared with the economic, efficiency and environmental (3E) performance being fully assessed. The simulation results show that by using the proposed optimization method, 14.45% reduction in TAC can be attained compared with the conventional approach.

Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process

The flexible operation capability of solvent-based post-combustion capture (PCC) process is vital to efficiently meet the load variation requirement in the integrated upstream power plant. This can be achieved through the deployment of an appropriate control strategy. In this paper, a nonlinear model predictive control (NMPC) system was developed and analysed for the solvent-based PCC process. The PCC process was represented as a nonlinear autoregressive with exogenous (NARX) inputs model, which was identified through the forward regression with orthogonal least squares (FROLS) algorithm. The FROLS algorithm allows the selection of an accurate model structure that best describes the dynamics of the process. The simulation results showed that the NMPC gave better performance compared with linear MPC (LMPC) with an improvement of 55.3% and 17.86% for CO2 capture level control under the scenarios considered. NMPC also gave a superior performance for reboiler temperature control with the lowest ISE values. The results from this work will support the development and implementation of NMPC strategy on the PCC process with reduced computational time and burden.

A methodology for the heuristic optimization of solvent-based CO2 capture processes when applied to new flue gas compositions: A case study of the Chilled Ammonia Process for capture in cement plants

Solvent-based post-combustion CO2 capture technologies are key to timely decrease industrial CO2 emissions. However, the flue gas composition differs among different industries so that different optimal operating conditions are expected. This work provides a methodology to determine the operating conditions that minimize energy consumption and maximize productivity of the capture process, for given flue gas composition and process specifications, while keeping the time required for process development at a minimal level. Firstly, it carries out a comprehensive selection and calibration of the model. Secondly, it applies a step-wise heuristic optimization procedure. In this work, this methodology has been demonstrated by means of the Chilled Ammonia Process (CAP) applied to cement plants. The optimal CAP operation has led to reboiler duties as low as 2.1 MJthkgCO2captured-1, while maintaining the productivity of the CO2 absorber, thus the column height, at values similar to those typical of the power plant application.

Carbon-rich solvent regeneration in solvent-based post-combustion CO2 capture process (PCCC): Process intensification by megasonics

Global greenhouse gas emanations from fossil-fuel combusting for power-generation alone contribute 9.795 gigatonnes (Gt) in 2014. There is a tremendous focus on global carbon capture researchers to cut down these emissions significantly. Chemical absorption is widely used and the use of monoethanolamine (MEA) was generally preferred. However, MEA solutions were not without problems. For example, equipment deterioration due to O2 presence, high energy for solvent-regeneration, solvent degradation and formation of undesired harmful by-products inhibits the solvent's cyclic capacity. To address these issues megasonics-assisted (1 MHz - tank-type) solvent regeneration process had been investigated experimentally. The results revealed that low regeneration temperature (<45 °C), less condenser duty, the potential for sensible heat saving, no reboiler is required facilitates capital and maintenance saving, no significant colour change after regeneration and loss of absorption capacity after regeneration was 3.7 times lower than the conventional. In addition, similar regeneration energy (20.20 KJ/mol CO2) but with 3.5 times lower nominal power input than the conventional regeneration method. Furthermore, physicochemical characterization of the solvent before and after absorption and regeneration followed by 13C NMR spectra analysis was also discussed.

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.

Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization

This paper develops an intelligent predictive controller (IPC) for a large-scale solvent-based post-combustion CO2 capture (PCC) process. An artificial neural network (NN) model is trained to represent the dynamics of the PCC process based on an in-depth behavior investigation of the process under different operating conditions. The resulting NN model can portray the PCC characteristics very well in terms of dynamic trend, response time and steady-state gain. An intelligent predictive controller is thus developed based on the NN model to track the desired CO2 capture level and maintain the given re-boiler temperature, in which the particle swarm optimization (PSO) algorithm is applied to find the best future control sequence for the PCC process. A warm start scheme is proposed in the IPC to improve the quality of initial swarm in the PSO. Dynamic simulations to change CO2 capture level set-point and flue gas flow rate are carried out on the PCC process. The results show that the IPC can adjust CO2 capture level fast and significantly reduce the fluctuations in re-boiler temperature. It is concluded that the proposed IPC is helpful for flexible operation of the solvent-based PCC process.

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.

Dynamic Simulation and Plant-Wide Control for Flexible Operation of Subcritical Coal-Fired Power Plant Integrated with Solvent-Based Post-Combustion Co2 Capture

Dynamic Simulation and Plant-Wide Control for Flexible Operation of Subcritical Coal-Fired Power Plant Integrated with Solvent-Based Post-Combustion Co2 Capture

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.

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.

Attempting to Break the 2 GJ/tonne CO2 Barrier; Development of an Advanced Water-Lean Capture Solvent From Molecules to Detailed Process Design

Solvent-based post-combustion CO2 capture is an energy-intensive process primarily driven by the energy required to regenerate the CO2 capture solvent. Researchers are currently focused on developing drop-in solvent replacements for commercial amine solvents with lower regeneration energies. One approach to reducing the regeneration energy of a solvent is to reduce its water content, thereby reducing unnecessary condensing and consequent boiling in the process. There are a number of water-lean solvent formulations currently under development that allow for water contents below 10% by weight, versus more than 60% for commercial aqueous amines. One solvent class, CO2-Binding Organic Liquids (CO2BOLs), shows promise to reduce the parasitic load to a coal-fired power plant but has been impeded by high viscosities at high CO2 loadings. In this paper, we perform a preliminary modeling study of a new low-viscosity CO2BOL solvent and assess the energetics of different process stripper configurations. By tailoring the process configuration with the unique aspects of the solvent reboiler duties below 2 GJ/tonne CO2 could be achievable. Further, this study suggests that there is no one-size-fits-all process optimum configuration for solvents, and therefore optimal configurations will be solvent specific.

Non-linear system identification 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 design. Typically, first principle models are used, however they usually require comprehensive knowledge and deep understanding of the process. System identification approach is adopted to obtain a model that accurately describes the dynamics between key variables in the process. The nonlinear auto-regressive 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-models. 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.

CFD Modeling on Hydrodynamic Characteristics of Multiphase Counter-Current Flow in a Structured Packed Bed for Post-Combustion CO2 Capture

Solvent-based post combustion CO2 capture is a promising technology for industrial application. Gas-liquid interfaces and interactions in the packed bed are considered one of the key factors affecting the overall CO2 absorption rate. Understanding the hydrodynamic characterizations within packed beds is essential to identify the appropriate enhanced mass transfer technique. However, multiphase counter-current flows in the structured packing typically used in these processes are complicated to visualize and optimize experimentally. In this paper, we aim to develop a comprehensive 3D multiphase, counter-current flow model to study the liquid/gas behavior on the surface of structured packing. The output from computational fluid dynamics (CFD) clearly visualized the hydrodynamic characterizations, such as the liquid distributions, wettability, and film thicknesses, in the confined packed bed. When the liquid We (Weber number) was greater than 2.21, the channel flow became insignificant and flow streams became more disorganized with more droplets at larger sizes. The portion of dead zones is decreased at higher liquid We, but it cannot be completely eliminated. Average film thickness was about 0.6–0.7 mm, however, its height varied significantly.

Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties

This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJth/tonne CO2. Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO2 capture from the ideal cumulative CO2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties.