Lean Solvent Flow Rate Research Articles

Post-combustion carbon capture process modeling, simulation, and assessment of synergistic effect of solvents

Post-combustion carbon capture appears to be a promising solution to reduce the emission of carbon dioxide (CO2) from power plants that generate electricity using either coal or natural gas. In addition, carbon capture process efficiency, capacity, and energy consumption have become challenging against the performance of the capture process. However, synergistic effect due to solvents blend has gained attention to reduce the process energy consumption and enhance process efficiency. In this study, blends of methyldiethanolamine (MDEA) and piperazine (PZ) at different concentrations were investigated using a validated post-combustion capture process model using Aspen HYSYS. Results were compared with 30 wt% monoethanolamine (MEA) as reference case. The effective process variables are concentration of solvents, the amount of water and solvent in the makeup section, viscosity of solvent, energy consumed in different process stages, and the amount of lean solvent flow rate. These variables were studied against fixed process variables using rate-based model. The study shows that using (43 wt% MDEA/7 wt% PZ) for post-combustion carbon capture needs 2.53 MJ/kgCO2 regeneration energy for 88.5% process efficiency compared to 4.003 MJ/kgco2 for 30 wt% MEA without the need for any process modifications. In addition, it was found that solvents synergistic effect contributes to resolving the drawbacks of post-combustion capture that will enable the high utilization of the process and contribution to reduce the consequences effect of climate change. Therefore, the study will help policymakers, industries and encourage researchers towards the large-scale commissioning of blended solvent -based post-combustion capture process.

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.

Parametric study and optimisation of hot K2CO3‐based post‐combustion CO2 capture from a coal‐fired power plant

AbstractDetailed parametric analyses on a post‐combustion carbon dioxide (CO2) capture system using a hot potassium carbonate solution was completed using a rate‐based simulation study. The system was simulated in Aspen Plus® V10, adopting the electrolyte non‐random two‐liquid property package. A base case study of the system was validated using literature data. Parametric analyses were then performed to study the effect of vital system parameters on CO2 capture rate and stripper reboiler duty. These system parameters include: (i) reflux ratio of stripping column (ii) lean solvent flowrate (iii) flue gas flowrate (iv) lean solvent concentration (v) lean solvent temperature (vi) flue gas temperature and (vii) absorber operating pressure. Changes in the lean solvent concentration (from 25 to 45 wt%) and absorber operating pressure (from 0.5 to 2.5 MPa) were observed to have the most significant effects on the overall performance of the capture system. Increasing both parameters showed substantial declines in the reboiler duty and resulted in remarkable increases in the corresponding carbon capture levels, and vice versa. The performance of various packing types in the absorber and stripper columns were also studied. Among the packing types examined, Flexipac Koch Metal 1Y was observed to yield the best system performance. The results from the parametric analyses were used to propose an optimised model that was able to improve carbon removal rate by 12.61% and decreased stripper reboiler duty by 14.86%. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

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.

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.

Experimental study of a hybrid solvent MEA-Methanol for post-combustion CO 2 absorption in an absorber packed with three different packing: Sulzer BX500, Mellapale Y500, Pall rings 16 × 16

Abstract Effects of different operating conditions on the CO 2 capture and mass transfer performance of the absorption of CO 2 into a hybrid solvent MEA-Methanol were studied in a pilot-plant absorber packed with three different packing consist of Sulzer BX500, Mellapale Y500 and Pall rings 16 × 16. The results showed that the structured packing had an obvious advantage in the mass transfer performance. And Sulzer BX500 was better than Mellapale Y500 for it had a more uniform gas–liquid distribution on the packing surface. In addition, CO 2 lean loading, lean solvent temperature, lean solvent flow rate and flue gas flow rate all had great effects on the absorption performance of the MEA-Methanol absorption system.

CO2 Absorption from Natural Gas: Dynamic Study & Simulation

Rate -based non-equilibrium dynamic simulation model for CO2 removal at high pressure is presented in this research. To ensure the smoothness in the operation plant for natural gas process, therefore the process control need to be designed based on the dynamics model studied. In order to successfully design a dynamic model, steady state model simulation model need to be established and in this research, Aspen Plus simulator had been utilized. The input data and information for the simulation had been acquired from the real pilot plants that operate at 15bar using structured column of FLEXIPAC 1Y. Dynamic analysis is conducted to observe the effect of changing the lean solvent flow rate and thermodynamic state on the MEA absorption column behavior. It is observed that the model satisfactorily predicts the pilot plant behavior under multiple inputs and disturbances.

Dynamic modelling, identification and preliminary control analysis of an amine-based post-combustion CO2 capture pilot plant

Solvent-based post combustion CO2 capture (PCC) is considered as a mature technology for dealing with CO2 emissions from fossil-fired power plants. In this study, a mathematical black box model is developed to analyse the dynamic responses of a PCC pilot plant. The model identification reported the dynamics of variables of the key units in the plant, the absorber, rich/lean heat exchanger and desorber. Pilot plant dynamic data were used to develop a data-driven model for each unit operation. Individual models were integrated to produce a simplified 4 × 3 PCC process model of the PCC plant. The fastest dynamic with a time constant ranging from 2 to 3 min featured in the relationship between power plant flue gas flow rate and CO2 concentration in the absorber off gas. Whereas, the slowest response with a process time constant between 9 and 27 min occurred in CO2 concentration at the top of the stripper due to changes in reboiler heat duty. Preliminary control analysis using relative gain array (RGA) analysis suggested that carbon capture efficiency, CC (%), and energy performance, EP (MJ per kg of CO2 captured), can be controlled by manipulating the lean solvent flow rate and reboiler heat duty, respectively. The proposed control structure was tested and tracked CC and EP random set point step changes in the range between 7–25 h and 4–5 h respectively. This study contributes to understanding transient variable behaviours in PCC plants; concurrent with current industrial requirements for controllability and flexible operation of PCC plants, in response to the dynamics of power plant load, electricity and carbon prices.

Dynamic model validation of the post-combustion CO2 absorption process

A general dynamic process model of the post-combustion carbon dioxide (CO2) absorption process based on aqueous monoethanolamine (MEA) is developed and implemented in MATLAB. The overall process model contains several unit models developed from first principle conservation laws for mass and energy, each representing individual process equipment. An equation based numerical integration method is used to solve the overall equation system simultaneously in MATLAB. Pilot plant data from specifically designed dynamic experiments with 30wt% MEA is collected from a pilot plant at NTNU and SINTEF laboratories. This includes steady state data for eight different conditions along with six dynamic data sets with relevant step changes in lean solvent flow rate and reboiler duty. The pilot plant data show a very good steady state mass balance which indicates that the generated data sets are reliable. Two of the dynamic data sets are used for model validation and the results shows adequate agreement between model and pilot plant data. An average of 0.3% and −2.8% deviation in absorbed CO2 is seen for the two simulated cases compared to pilot plant results and it is concluded that the model is able to capture the main dynamics of the experiments. The main cause of deviation is believed to concern uncertainties in mass transfer and effective mass transfer area correlations.

Modelling of a post-combustion CO2 capture process using neural networks

This paper presents a study of modelling post-combustion CO2 capture process using bootstrap aggregated neural networks. The neural network models predict CO2 capture 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. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO2 capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO2 capture process.

A study of new absorbent for post-combustion CO2 capture test bed

Capture and storage of post combustion CO2 from fossil fuel power plants is the best technique that can be rapidly and safely employed for the control of greenhouse gas emissions. Test bed studies with highly efficient amine CO2 solvent (KoSol−4) developed by KEPCO Research Institute was performed. For the first time in Korea, evaluation of post-combustion CO2 capture technology to capture 2t-CO2/day from a slipstream of flue gas from a commercial scale coal-fired power station was performed. Based on a chemical absorption/regeneration process with KoSol−4, we have studied the effects of lean solvent flow rate, input location and inter-cooling of absorbent, pressure and temperature of stripper for process optimization. This optimization aimed to reduce the energy for solvent regeneration. The optimum point in flow rate of lean absorbents was 700kg/h in 350Sm3/h flue gas. And the regeneration energy using new solvent was 3.02GJ/t-CO2 in 90% CO2 removal, flue gas temperature of 40°C and stripper pressure of 0.35kgf/cm2. In addition, corrosion rate of the pilot plant were measured in six points. It is the largest as 40.52mpy in the bottom of the stripper tower.

Rate based modeling of absorption and regeneration for CO2 capture by aqueous ammonia solution

A rate-based model, RateFrac, in Aspen Plus process simulator, is used to simulate the CO2 absorption and regeneration processes of a research scale pilot plant. The simulation results of the CO2 absorption and regeneration predicted by the rate-based model are in good agreement with the experimental data. The effects of lean solvent flow rate, NH3 concentration, CO2 loading, and temperature on the absorption process were analyzed. Optimization analysis for the CO2 regeneration process was carried out. The operation parameters include temperature of the rich solvent, temperature of the condenser, inner diameter and packed height of the stripper. Under the optimal operation conditions, the regeneration energy is 3.63MJ/kgCO2, which is much lower than that reported for the research scale pilot plant (4–4.2MJ/kgCO2).

Dynamic modelling and analysis of an amine-based post-combustion CO2 capture absorption column

A dynamic, non-equilibrium model of a packed column for the chemisorption of CO2 from dilute gas streams using a monoethanolamine solvent has been proposed. The model uses the SAFT-VR equation of state to describe the thermophysical properties and fluid-phase behaviour of this process. The SAFT-VR equation-of-state is used to account for all of the inter-species interactions in the fluid, including the reactions. In this way, we avoid the use of enhancement factors. Further it is not necessary to include a detailed description of the reaction mechanisms or the reaction products. Steady state validation of the proposed model is performed using pilot plant data. The position and extent of the mass transfer zone is found to be a function of competing interphase fluxes of H2O and CO2. We evaluate through dynamic simulation the effect of changing the lean solvent flowrate and thermodynamic state on column behaviour. The influence of flue gas humidity on the position and shape of the mass transfer zone is also highlighted and discussed.