Solvent-based Post-combustion Capture Research Articles

Exploring the technical feasibility of carbon capture onboard ships

International shipping is crucial for freight transport, but it relies primarily on fossil fuels, contributing 3% of global greenhouse gas emissions. This calls for urgent action to decarbonize the shipping industry. While renewable-based e-fuels are a strong candidate for decarbonization of this sector in the long run, deploying these to the required scale will take significant time, considering technical modifications onboard the vessels, as well as the changes in fuel production and infrastructure for distribution. Carbon capture from ships is another route to emission reduction that can be implemented faster due to the relatively high maturity of the technology. Tankers, dry bulk carriers, and container vessels contribute a majority of global shipping emissions and are therefore prime candidates for carbon capture and storage. Solvent-based post-combustion capture is chosen for this study as it is mature and suitable for marine applications, though technical, economic, environmental, and practical challenges remain. This paper assesses the technical feasibility of the capture system for ships; both retrofit and newbuild vessels. While achieving zero-emissions presents a significant challenge, it is feasible to attain 70%–90% CO2 reduction through carbon capture in the near term. This reduction is crucial for transforming the industry into a more sustainable and environmentally friendly state. The limitation of space onboard is identified as a key factor in determining the viability, and the 70%–100% increase in energy consumption between existing ships and newbuild will be a substantial operational challenge. However, the high fuel consumption of the capture system could be economically acceptable if the price of alternative fuels remains high.

Impact of high capture rates and solvent and emission management strategies on the costs of full-scale post-combustion CO2 capture plants using long-term pilot plant data

Pilot plant campaigns are critical to our understanding of the performance of solvent-based post-combustion capture processes. During the ALIGN-CCUS project, long-term pilot plant runs in the Niederaussem lignite-fired power plant were conducted, that aimed to investigate solvent degradation and emissions management and capture rates beyond 90%. Based on these pilot runs, we here investigated the cost effect of amine degradation and emissions for two solvent systems, MEA and CESAR1, as well as the effect of incorporating degradation and emission mitigation strategies and the effect of higher carbon capture rates. We found that for lignite-fired power plants, CESAR1 is not per se a cheaper solvent system than MEA when actual solvent losses (or their mitigation measures) are considered, contrary to what earlier studies have suggested. It may, therefore, be just as good a benchmark as MEA would be, as long as all degradation and emission (mitigation) costs are included in their cost estimates. Second, MEA reclaiming with ion exchange resins and CESAR1 emission mitigation measures (dry-bed and/or turbulent flue gas pre-treatment) do not increase the cost of CO2 capture or electricity. If anything, our work suggests these measures reduce costs marginally, owing to lower solvent make up, offsetting the additional capital and operational costs. Finally, the cost effect of capture rates beyond 90% proves minimal (the cost of CO2 capture and the levelized cost of electricity increase by 3% and 5%, respectively, when moving from 90% to 98% CO2 capture rate), indicating solvent-based CO2 capture may also be a useful mitigation strategy in societies transitioning towards net-zero-CO2 emissions.

Optimization of Solvent-Based Post-Combustion Capture Processes for Waste-To-Energy

Optimization of Solvent-Based Post-Combustion Capture Processes for Waste-To-Energy

A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

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.

ExxonMobil to invest in Singapore site to produce lube basestocks and distillates

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

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.

Impact of CO2-enriched combustion air on micro-gas turbine performance for carbon capture

Power generation is one of the largest anthropogenic greenhouse gas emission sources; although it is now reducing in carbon intensity due to switching from coal to gas, this is only part of a bridging solution that will require the utilization of carbon capture technologies. Gas turbines, such as those at the UK Carbon Capture Storage Research Centre's Pilot-scale Advanced CO2 Capture Technology (UKCCSRC PACT) National Core Facility, have high exhaust gas mass flow rates with relatively low CO2 concentrations; therefore solvent-based post-combustion capture is energy intensive. Exhaust gas recirculation (EGR) can increase CO2 levels, reducing the capture energy penalty. The aim of this paper is to simulate EGR through enrichment of the combustion air with CO2 to assess changes to turbine performance and potential impacts on complete generation and capture systems. The oxidising air was enhanced with CO2, up to 6.29%vol dry, impacting mechanical performance, reducing both engine speed by over 400 revolutions per minute and compression temperatures. Furthermore, it affected complete combustion, seen in changes to CO and unburned hydrocarbon emissions. This impacted on turbine efficiency, which increased specific fuel consumption (by 2.9%). CO2 enhancement could therefore result in significant efficiency gains for the capture plant.

On the integration of CO 2 capture with coal-fired power plants: A methodology to assess and optimise solvent-based post-combustion capture systems

Amine and other liquid solvent CO 2 capture systems capture have historically been developed in the oil and gas industry with a different emphasis to that expected for fossil fuel power generation with post-combustion capture. These types of units are now being adapted for combustion flue gas scrubbing for which they need to be designed to operate at lower CO 2 removal rates – around 85–90% and to be integrated with CO 2 compression systems. They also need to be operated as part of a complete power plant with the overall objective of turning fuel into low-carbon electricity. The performance optimisation approach for solvents being considered for post-combustion capture in power generation therefore needs to be updated to take into account integration with the power cycle and the compression train. The most appropriate metric for solvent assessment is the overall penalty on electricity output, rather than simply the thermal energy of regeneration of the solvent used. Methodologies to evaluate solvent performance that have been reported in the literature are first reviewed. The results of the model of a steam power cycle integrated with the compression system focusing on key parameters of the post-combustion capture plant – solvent energy of regeneration, solvent regeneration temperature and desorber pressure – are then presented. The model includes a rigorous thermodynamic integration of the heat available in the capture and compression units into the power cycle for a range of different solvents, and shows that the electricity output penalty of steam extraction has a strong dependence on solvent thermal stability and the temperature available for heat recovery. A method is provided for assessing the overall electricity output penalty (EOP), expressed as total kWh of lost output per tonne of CO 2 captured including ancillary power and compression, for likely combinations of these three key post-combustion process parameters. This correlation provides a more representative method for comparing post-combustion capture technology options than the use of single parameters such as solvent heat of regeneration.