CO2 Capture Level Research Articles

Efficient multi-objective optimization and operational analysis of amine scrubbing CO2 capture process with artificial neural network

Amine scrubbing processes for post-combustion CO2 capture have been extensively studied and significantly improved via various novel designs. However, the amine scrubbers implemented nowadays were usually not optimized according to a number of different evaluation criteria. This is often due to the fact that, for high dimensional design spaces, the rigorous simulation runs needed to facilitate process optimization always calls for huge numbers of simulation software accesses and overwhelming iterative calculations. Therefore, in this study, the well-trained surrogate model was adopted to replace its rigorous counterpart for the purpose of ensuring efficient optimization runs in practical applications. In current study, two objectives, i.e., the specific equivalent work and the CO2 capture level, were both rapidly and effectively optimized in various practical scenarios with different flue gas CO2 concentrations. The corresponding operational parameters and utility consumptions were also easily obtained without additional effort. The computation results obtained so far showed that the proposed surrogate-assisted approach can be utilized to significantly reduce the computational load in practice.

Operating optimization of biomass direct-fired power plant integrated with carbon capture system considering the life cycle economic and CO2 reduction performance

Biomass direct-fired power plant integrated with carbon capture system can achieve negative CO2 emission during the power generation, which provides an important technology pathway for the transformation of energy system towards carbon neutrality. Understanding the economic competitiveness and CO2 reduction potential of the plant, and identifying the optimal operating mode are the key to fully exert the advantages of the technology. For this reason, this paper develops life cycle economic and CO2 emission assessment models for the biomass direct-fired circulating fluidized bed boiler power plant (BCFBP) integrated with solvent-based post-combustion carbon capture (PCC) system. The levelized cost and CO2 emission of energy indexes are then applied to evaluate the performance of the BCFBP-PCC system. Based on the life cycle assessment, a novel economy and CO2 reduction dual-objective operating optimization approach is proposed for the BCFBP-PCC to find the optimal hourly power generation and CO2 capture level of the plant under given market conditions. The static investment payback period is employed as a constraint in the optimization to narrow down the range of the solution set and provide clearer guidance for system operation. This paper provides the first work pioneered the economic and negative carbon operation optimization of the BCFBP-PCC system.

Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality

The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal.

Multi-Criteria Screening of Organic Ethanolamines for Efficient CO2 Capture Based on Group Contribution Method

Amine solvent has attracted much attention due to its high CO2 capture level and wide application range, but its high energy consumption for recycling restricts its large-scale commercialization. In this work, a multi-objective optimization technology based on the group contribution method was used to select potential amine solvents for CO2 capture. This computer-aided molecular design method considers the thermodynamic and kinetic properties of the candidate solvent and evaluates the influence of relevant parameters on solvent performance. Compared with previous experimental methods used to optimize solvent, this method selects potential solvents from a large number of solvent databases based on group contribution. Firstly, a corresponding classification database was established for various kinds of amine solvents. Then, the traditional experiments were used to verify and screen solvents. At the same time, the method was applied to 31 amine absorbents concerning solubility, molar volume, surface tension, heat capacity, viscosity, pKa, saturated vapor pressure, and so on, and seven solvents were found to have comparable performance to MEA, with higher absorption rates and solubility. This method provides guidance for screening CO2 capture absorbents with economic viability, high efficiency, fast absorption rates, and low regeneration energy consumption.

Dynamic modeling and coupling characteristics analysis of biomass power plant integrated with carbon capture process

Biomass direct-fired circulating fluidized bed boiler has distinguished advantages of strong fuel adaptability, high combustion efficiency and low NOX emission, thus has been regarded as one of the important pathways for biomass utilization. Retrofitting the biomass direct-fired circulating fluidized bed boiler power plant (BCFBP) with carbon capture can strongly support the goal of carbon neutrality, because negative CO2 emission is achieved during the power generation. Nevertheless, the biomass direct-fired circulating fluidized bed boiler is sensitive to the combustion condition, high in operating costs, slow in dynamic response and poor in regulating capability. The integration of carbon capture may pose significant changes on the operating safety, efficiency and flexibility. A comprehensive analysis is thus required to understand the interactions between the BCFBP and carbon capture under various operating conditions. To this end, this paper develops a plant-wide dynamic model for a 130 ton/h BCFBP integrated with solvent-based post-combustion carbon capture (PCC) system. Multi-stage models of biomass combustion, steam generation and steam turbine-feedwater heater systems are developed to represent the dynamic interactions among biomass, heat, electricity and carbon, so that the impacts of BCFBP operation on the PCC process can be evaluated. Detailed BCFBP-PCC connecting system model are also developed to reflect the dynamic changes of steam/water flowrate, pressure and temperature in the steam turbine system caused by the integration of PCC. The developed model is then used to analyze the system interactions, operating costs, negative carbon capability and load ramping flexibility of the integrated BCFBP-PCC. Results show that the integration of PCC 1) causes significant impacts on the water/steam parameters of the steam turbine-feedwater heaters behind the reboiler steam extraction point; 2) reduces the power output of BCFBP by 26.12 % under 95 % CO2 capture level condition; 3) expands the power output variation range of the BCFBP from 22.3 to 36.6 MWe and provides the negative CO2 emission capacity from 2.2 to 6.9 kg CO2/s; 4) offers a feasible way to improve the power ramping performance through flexible distribution of the water/steam in the steam turbine-feedwater heater system. This paper provides specific guidance for the design, retrofit and operation of the BCFBP-PCC plant.

Effect of stripper pressure and low lean loadings on the performance of a PCC plant for 99% CO2 capture level

Effect of stripper pressure and low lean loadings on the performance of a PCC plant for 99% CO2 capture level

Financing coal-fired power plant to demonstrate CCS (carbon capture and storage) through an innovative policy incentive in China

Traditional policy incentives for carbon capture and storage (CCS) mainly rely on fiscal subsidies, which tend to put an inordinate strain on public finances. This study attempts to explore a non-fiscal incentive policy, granting a time extension (extra electricity quota), to finance early CCS demonstration projects in China. We find that coal-fired power plant (CFPP) operate at a loss even without CCS retrofitting under the current electricity quota (4000 h per year), while it can make profits with CCS retrofitting if extra electricity quotas are provided. Specifically, the electricity quota needs to be roughly 4709–7260 h per year with the CO2 capture level ranging from 0.1 to 1 Mt per year in the demonstration stage. In particular, the levelized cost of electricity (LCOE) of CFPP with a capture level of 1 Mt per year is estimated at 298.8 CNY/MWh if the electricity quota reaches 7000 h per year, which is approximately equal to that of CFPP without CCS retrofitting and extra electricity quota (292.2 CNY/MWh). Thus, the extra electricity quota can be considered as an economically feasible policy incentive, and related results are able to provide useful information for electric power enterprises and government decision-makers.

Energy Performance Analysis of Post-combustion Carbon Capture using Piperazine for Large-scale Natural Gas Combined Cycle Power Plants

Post-combustion carbon capture (PCC) via chemical absorption has the potential to reduce CO2 emission significantly. The main challenges to this technology include the high energy consumption and high capital and operating costs. Alternative process configurations and new solvents are being pursued to reduce the energy consumption process. In this study, the rate-based model of the PCC process using piperazine (PZ) was developed in Aspen Plus® V8.4. The model was validated at pilot scale with experimental data. The model correctly predicted the CO2 capture level, rich loading and reboiler duty measured in the pilot plants. The validated model was scaled up to a PCC plant capable of processing flue gas from a 250 MWe natural gas combined cycle (NGCC) power plant. Three different configurations of the large-scale PCC process using 30-40 wt% PZ were simulated and the energy consumption was evaluated to determine which combination of configuration and solvent gives the best energy performance. The energy performance of the PCC process using PZ was also compared to the standard process using 30 wt% monoethanolamine (MEA) solvent. A regeneration energy of 4.97 GJ/tonneCO2 was obtained for the PCC process with 30 wt% MEA. Compared to the lowest regeneration energy obtained using the standard PCC process with PZ, the regeneration energy reduces by 35%, 41%, and 45% respectively for 30, 35 and 40 wt% PZ concentrations. The minimum regeneration energy of 2.41 GJ/tCO2 was achieved with the advanced flash stripper (AFS) process using 40 wt% PZ.

Rotary Adsorption: Selective Recycling of CO2 in Combined Cycle Gas Turbine Power Plants

A conceptual design assessment shows that the use of structured adsorbents in a regenerative adsorption wheel is technically feasible for the application of selective exhaust gas recirculation (SEGR) in combined cycle gas turbine (CCGT) power plants. As the adsorber rotates, CO2 is selectively transferred from a flue gas stream to an ambient air stream fed to the gas turbine compressor, increasing the CO2 concentration and reducing the flow rate of the fraction of the flue gases treated in a post-combustion CO2 capture system. It imposes an estimated pressure drop of 0.25 kPa, unlike a pressure drop of 10 kPa reported for selective CO2 membrane systems, preventing a significant derating of the gas turbine. An equilibrium model of a rotary adsorber with commercially available activated carbon evaluates the inventory of the adsorbent and sizes the wheel rotor. Two rotary wheels of 24 m diameter and 2 m length are required per gas turbine—heat recovery steam generator train to achieve an overall CO2 capture level of 90% in a CCGT power plant (ca. 820 MWe) with SEGR “in parallel” to the capture plant. Two to five rotary wheels are required for a configuration with SEGR “in series” to the capture plant. A reduction of 50% in the mass of the adsorbent would be possible with Zeolite 13X instead of activated carbon, yet the hydrophilicity of zeolites are detrimental to the capacity and upstream dehydration of the flue gases is required. A parametric analysis of the equilibrium properties provides guidelines for adsorbent development. It suggests the importance of balancing the affinity for CO2 to allow the regeneration of the adsorbent with air at near ambient pressure and temperature, to minimise the inventory of the adsorbent within practical limits. An adsorbent with a saturation capacity of 8 mol/kg, a heat of adsorption from 24 to 28 kJ/mol CO2 and a pre-exponential factor of the equilibrium constant from 2 × 10–6 to 9 × 10–6 kPa−1 would result in an inventory below 200 kg, i.e., approximately the limit for the use of a single rotary wheel system.

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.

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.

CO2/SO2 emission reduction in CO2 shipping infrastructure

There is an increased focus on the reduction of anthropogenic emissions of CO2 by means of CO2 capture processes and storage in geological formations or for enhanced oil recovery. The necessary link between the capture and storage processes is the transport system. Ship-based transport of CO2 is a better option when distances exceed 350 km compared to an offshore pipeline and offers more flexibility for transportation, unlike pipelines which require a continuous flow of compressed gas. Several feasibility studies have been undertaken to ascertain the viability of large-scale transportation of CO2 by shipping in terms of the liquefaction process, and gas conditioning, but limited work has been done on reducing emissions from the ship’s engine combustion.From 2020, ships operating worldwide will be required to use fuels with 0.5% or lower sulphur content (versus 3.5% now) or adopt adequate measures to reduce these emissions. This study explores the use of the solvent-based post-combustion carbon capture and storage (CCS) process for CO2 and SO2 capture from a typical CO2 carrier. A rate-based aqueous ammonia process model was developed, validated, then scaled up and modified to process flue gas from a Wartsila 9L46 F marine diesel engine. Different modes of operation of the carrier were analysed and the most efficient mode to operate the CCS system is during sailing. The heat recovered from the flue gas was used for solvent regeneration. A sensitivity study revealed that the 4 MWth supplied by the “waste heat recovery” system was enough to achieve a CO2 capture level of 70% at a solvent recirculation flowrate of 90–100 kg/s. The removal of SO2 by the ammonia water solution was above 95% and this led to the possibility of producing a value-added product, ammonium sulphate. The boil-off gas and captured emitted CO2 were recovered using a two-stage re-liquefaction cycle and re-injected into the cargo tanks, thereby reducing extra space requirements on the ship.

CO2/SO2 Emission Reduction in CO2 Shipping Infrastructure

There is an increased focus on the reduction of anthropogenic emissions of CO2 by means of CO2 capture processesand storage in geological formations or for enhanced oil recovery. The necessary link between the capture andstorage process is the transport system. Ship-based transport of CO2 is a better option when distances exceeds 350 km compared to an offshore pipeline and offers more flexibility for transportation unlike pipelines which require a continuous flow of compressed gas. Several feasibility studies have been undertaken to ascertain the viability of large scale transportation of CO2 by shipping in terms of the liquefaction process, and gas conditioning, but limited work has been done on reducing emissions from the ship’s engine combustion. From 2020, ships operating worldwide will be required to use fuels with 0.5% or less sulphur content (versus 3.5% now) or adapt adequate measures to reduce these emissions. This study explores the use of the solvent-based post-combustion carbon capture and storage (CCS) process for CO2 and SO2 capture from a typical CO2 carrier. A rate based aqueous ammonia process model was developed, validated, then scaled up and modified to process flue gas from a Wartsila 9L46F marine diesel engine.Different modes of operation of the carrier were analysed and the most efficient mode to operate the CCS system is while it is sailing. The heat recovered from the flue gas was used for solvent regeneration. A sensitivity study revealed that the 4 MWth supplied by the “waste heat recovery” system was enough to achieve a CO2 capture level of 70% at a solvent recirculation flowrate of 90-100 kg/s. The removal of SO2 by the ammonia water solution was above 95% and this led to the possibility of formating a value-added product, ammonium sulphate. The boil-off gas and captured emitted CO2 were recovered using a two stage re-liquefaction cycle and re-injected into the cargo tanks; thereby reducing extra space requirements on the ship.

Flexible operation of post-combustion CO2 capture at pilot scale with demonstration of capture-efficiency control using online solvent measurements

Flexible post-combustion carbon capture and storage (CCS) has the potential to play a significant part in the affordable decarbonisation of electricity generation portfolios. PCC plant operators can modify capture plant process variables to adjust the CO2 capture level to a value which is optimal for current fuel cost, electricity selling price and CO2 emissions costs, increasing short-term profitability. Additionally, variation of the level of steam extraction from the generation plant can allow the capture facility to provide additional operating flexibility for coal-fired power stations which are comparatively slow to change output.A pilot-scale test campaign investigates the response of plant operating parameters to dynamic scenarios which are designed to be representative of pulverised coal plant operation. Online sensors continuously monitor changes in rich and lean solvent CO2 loading (30%wt monoethanolamine). Solvent loading is likely to be a critical control variable for the optimisation of flexible PCC operation, and since economic and operational boundaries can change on timescales 30 min or shorter, the development of methods for rapid, continuous online solvent analysis is key. Seven dynamic datasets are produced and insights about plant response times and hydrodynamics are provided. These include power output maximisation, frequency response, power output ramping and a comparison between two plant start-up strategies.In the final dynamic operating scenario, control of CO2 capture efficiency for a simple reboiler steam decoupling and reintroduction event is demonstrated using only knowledge of plant hydrodynamics and continuous measurement of solvent lean loading. Hot water flow to the reboiler is reduced to drop the capture efficiency. The “target” value for the minimum capture efficiency in the scenario was set at 30%, but a minimum CO2 capture efficiency of 26.4% was achieved. While there remains scope for improvement this represents a significant practical step towards the control of capture plant using online solvent concentration and CO2 measurements, and the next steps for its further development are discussed.

Modelling of a post-combustion CO2 capture process using deep belief network

This paper presents a study on using deep learning for the modelling of a post-combustion CO2 capture process. Deep learning has emerged as a very powerful tool in machine learning. Deep learning technique includes two phases: an unsupervised pre-training phase and a supervised back-propagation phase. In the unsupervised pre-training phase, a deep belief network (DBN) is pre-trained to obtain initial weights of the subsequent supervised phase. In the supervised back-propagation phase, the network weights are fine-tuned in a supervised manner. DBN with many layers of Restricted Boltzmann Machine (RBM) can extract a deep hierarchical representation of training data. In terms of the CO2 capture process, the DBN model predicts CO2 production rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. A greedy layer-wise unsupervised learning algorithm is introduced to optimize DBN, which can bring better generalization than a single hidden layer neural network. The developed deep architecture network models can then be used in the optimisation of the CO2 capture process.

Optimization of the design, operating conditions, and coupling configuration of combined cycle power plants and CO2 capture processes by minimizing the mitigation cost

This paper deals with the optimization of the coupling between a natural gas combined cycle (NGCC) plant and a post-combustion CO2 capture process by minimizing the mitigation cost – defined as the ratio between the cost of electric power generation and the amount of CO2 emitted per unit of total net electric power generated – while satisfying the design specifications: electric power generation capacity and CO2capture level. Three candidate coupling configurations, which differ in the place where the steam is extracted from, are optimized using detailed and rigorous models for both the NGCC and the CO2 capture plants. By comparing the mitigation cost of each configuration, the optimal integration configuration and the corresponding optimal sizes and operating conditions of all process units (steam turbines, gas turbines, heat recovery steam generators HRSGs, absorption and regeneration columns, reboilers and condensers, and pumps) are provided. In the computed optimal solution, the steam required by the CO2 capture plant is extracted from both the steam turbine and the HRSG (evaporator operating at low pressure), and the mitigation cost is 90.88$/t CO2. The optimal solution is compared with suboptimal solutions corresponding to the other two candidate coupling schemes. These solutions are compared in detail regarding capital investment and operating costs, HRSG configuration, process unit sizes, and operating conditions.

Thermodynamic Optimization and Part-load Analysis of the NET Power Cycle

This paper performs the thermodynamic optimization and part-load analysis of the NET Power cycle (also called Allam cycle), a natural-gas-fired oxy-combustion cycle featuring 100% CO2 capture level, very high net electric efficiency, and potentially near-zero emissions level. To determine the maximum achievable cycle efficiency and optimal cycle variables, an Aspen Plus flowsheet including accurate first-principle models of the main equipment units has been developed and combined with a black-box optimization algorithm. The corresponding maximum cycle efficiency is equal to 55.35% (with 100% CO2 capture). Optimization-based sensitivity analyses are performed to explore the neighborhood of the maximum efficiency cycle design with the aim of finding combinations of the cycle variables which lead to reduced costs and thermo-mechanical stress of the most critical components. Finally, the part-load performance of the optimized NET Power cycle has been analyzed. Results indicate that in the load range 100-40% the cycle (excluding the ASU) features a considerably lower efficiency decrease compared to a standard combined cycle. This result, showing the possibility of efficiently operating the cycle also at part-loads, further increases the attractiveness of the NET Power cycle.

Operation and Bidding Strategies of Power Plants with Carbon Capture

For the post-combustion carbon capture (PCC) process with MEA solvent, most of the relevant literature discussed the optimal operation under a cost-minimum target. A power plant integrated with the PCC process, however, prefer to maximize its lifetime cumulative profits. To maximize the profits, we may identify the following issues. For the power plant operation, trade-offs should be made between electricity output and the energy-intensive carbon capture process; for the CO2 allowance bidding, a fossil-fuel power plant should bid and win adequate allowances from the market to balance its demand of CO2 emission. We apply the Sarsa temporal difference (TD) learning algorithm to search for a strategy that maximizes profits during the power plant lifetime with the above issues. This strategy includes both the operation and the bidding of the power plant. Our results show it is better than the independently-designed bidding and operation strategy with a fixed CO2 capture level. In addition, the Sarsa TD algorithm can find a better strategy than Sarsa(λ) if training data can be generated cheaply.

Modelling of a post-combustion CO2 capture process using extreme learning machine

This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine (ELM). ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables: inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, lean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.