Format And Type Fonts CHEMICAL ENGINEERING TRANSACTIONS VOL. 39, 2014 A publication of The Italian Association of Chemical Engineering www.aidic.it/cet Guest Editors: Petar Sabev Varbanov, Jiří Jaromír Klemeš, Peng Yen Liew, Jun Yow Yong Copyright © 2014, AIDIC Servizi S.r.l., ISBN 978-88-95608-30-3; ISSN 2283-9216 DOI: 10.3303/CET1439251 Please cite this article as: Fong J.C.L.Y., Anderson C., Hooper B., Xiao G., Webley P.A., Hoadley A., 2014, Multi-objective optimisation of hybrid CO2 capture processes using exergy analysis, Chemical Engineering Transactions, 39, 1501-1506 DOI:10.3303/CET1439251 1501 Multi-Objective Optimisation of Hybrid CO2 Capture Processes Using Exergy Analysis Jean C. Li Yuen Fong b , Clare Anderson a , Barry Hooper a , Gongkui Xiao d , Paul A. Webley c , Andrew Hoadley* b a Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) b Department of Chemical Engineering, Monash University, Clayton, VIC 3800 c Department of Chemical Engineering and Biomolecular Engineering, The University of Melbourne, VIC 3010 d School of Mechanical and Chemical Engineering, The University of Western Australia, WA 6009 andrew.hoadley@monash.edu Carbon dioxide (CO2) purification is an essential step in the carbon capture and storage (CCS) process. The leading technology consists of a solvent absorption carbon capture process followed by a multi-stage CO2 gas compression into supercritical state for sequestration. This study considers a hybrid system of vacuum swing adsorption (VSA), membranes and cryogenic separation. Replacing the multi-stage gas compression with the cryogenic separation has two main advantages: firstly, it further purifies the CO2 stream, which is valuable for both VSA and membrane processes since both processes struggle to achieve high purity product. Secondly, it produces liquid CO2 that can be pumped to the supercritical state, which is required for transport and sequestration. Due to the higher degree of freedom available in hybrid processes, a new methodology using multi-objective optimisation combined with exergy analysis was used to optimise the process. This allowed different decision variables to be considered to find the range of optimum operating conditions for each of the processes. It was determined that the refrigerant flow rate, multi-stage compression and cryogenic minimum temperature had the biggest impact on the recovery rate. Furthermore, it was observed that the total specific shaft work had a linear relationship with the specific exergy loss rate. 1. Introduction The negative effects of greenhouse gases (GHG) in the atmosphere have been widely reported. The IPCC fifth assessment report increased the scientific certainty that changes in the anthropogenic CO2 concentrations and atmospheric temperature are related (Edenhofer and Seyboth, 2013). Therefore, reduction of CO2 from major sources such as fossil fuel power generation is critical. Among a portfolio of technologies required to mitigate those emissions, integration of Carbon Dioxide Capture and Storage (CCS) into coal fired power stations is a technology that can significantly reduce the carbon emissions from stationary sources. Post-combustion CCS involves the separation and capture of the CO2 from the flue gas generated by the power plants, compressing the separated CO2 into a supercritical fluid and then storing it in geological structures such as deep saline formations. In order to implement CCS, equipment needs to be installed to capture and compress the carbon dioxide. Heat and electrical energy are required to operate the equipment, which reduces the efficiency of the power plant. The main post-combustion CO2 capture methods involve: solvent absorption, adsorption, membranes, cryogenic separation and hybrid processes. (Pires et al., 2011). Each of these technologies has different advantages and disadvantages. For example, solvent absorption can achieve the high CO2 purity and recovery rate by having a high solvent loading; however this comes at a high recovery heat energy required to regenerate the solvent. On the other hand, membranes and adsorption processes have a relatively low environmental impact due to the fact that they do not require solvents but both of these processes draw a large amount of electrical energy. It is possible that by combining two or more capture processes, the advantages and disadvantage of each process can complement each other to have a 1502 better overall performance. For example, adsorption and cryogenic separation could be a natural fit due to fact that adsorption produces low purity CO2 and cryogenic separation can further purify the stream. Several studies have investigated different configurations of hybrid process. Both Scholes et al. (2013) and Belaissaoui et al. (2012) proposed hybrid processes which involved a solvent and membrane system. Although the hybrid performed better in terms of both energy consumption and CAPEX than the membrane process, it could not match the base MEA solvent absorption. While energy analysis has been predominantly used to assess the performance of a process, exergy analysis has also been used (Hagi et al., 2013) and is beneficial as it allows the quality of energy being transferred in the process to be studied. This work aims to optimise the integration of post-combustion hybrid carbon capture with exergy analysis and CO2 recovery rate as the two main performance indicators. In order to do so, a combination of steady- state mass and energy balance simulation and multi-objective optimisation is used. 2. Methodology and Framework The post-combustion flue gas properties were based on a 300 MW sub-bituminous coal-fired power station. The flue gas is pre-treated to remove the impurities and water by scrubbing, resulting in a stream composed of a binary mixture of CO2 and N2 as shown in Table 1. The pressure drop associated with the pre-treatment is accounted for as an energy penalty. The hybrid capture process investigated in this study was a combination of Vacuum Swing Adsorption (VSA), cryogenics and membrane, as shown in Figure 1. All simulations were performed using the Aspen HYSYS® software package, version 8.4, using the Peng-Robinson fluid package throughout the whole capture process plant. The VSA electrical power requirement, which uses the VSA CO2 recovery rate and the CO2 outlet purity as the input variable, but does not take into consideration the water removal, was used according to the following mathematical model represented in equations 1-3. Those equations were modelled by using unpublished simulation values obtained from Aspen Adsim® provided by Xiao and Webley (2013). Blower Power (GJ/t CO2)= 2.06 - 5.37E-02x + 1.08E-02y + 3.58E-04x 2 - 1.05E-04xy (1) Vacuum Pump Power (GJ/t CO2)= 4.55 - 1.21E-01x + 2.66E-02y + 8.18E-04x 2 - 2.61E-04xy (2) Total VSA power = Blower Power + Vacuum Pump Power (3) where x is the CO2 recovery rate in % mass fraction and y is the CO2 outlet purity in % mass fraction The CO2 outlet stream from the VSA was compressed in three stages with inter-cooling using cooling water. A mixed ethane/propane refrigeration system was used to achieve cryogenic temperatures in the cryogenic separation. The tail gas membrane process was modelled using mass transfer equations, specifically developed for applications in carbon capture simulations. (Scholes et al., 2013) There are a number of key operating conditions, known as decision variables, in capture processes that can be varied to observe their impact on two key objectives; the exergy loss rate and overall CO2 recovery rate of the hybrid process. As changes to the design variables may result in antagonistic results in the two objectives, MOO should be a useful tool as it provides a range of solutions, called the ‘Pareto-Optimal’ solutions that enable a balance between the two objectives to be considered. Using this range of solutions, the user can make the final selection by either Table 1: Post-combustion flue gas properties based on a 300 MW sub-bituminous coal fired power station after pre-treatment Feed Conditions Vapour Fraction 1.00 Temperature (°C) 50.3 Pressure (kPa) 103 Molar Flow (kmol/h) 5.78e4 Mass Flow (kg/h) 1.67e6 Composition (mol frac) CO2 0.64 N2 0.36 1503 Membrane VSA Multi-Stage Compression Cryogenic Separation Flue Gas Waste CO2 Storage Recycle Stream Waste Pump Figure 1: Schematic diagram of VSA, cryogenics and membrane hybrid carbon capture process looking at the impact the decision variables have on the optimised solution or by performing further analysis on the optimised solutions. In this work, the NSGAII genetic algorithm has been applied for this MOO(Rangaiah, 2009); seven decision variables were allowed to be varied including: refrigerant ethane molar fraction, refrigerant molar flow, cryogenic process stream outlet temperature, multi-stage compression pressure and the membrane stage cut (fraction of feed gas that permeates through the membrane). Table 2 shows the range of values that those decision variables were allowed to vary for the hybrid capture process. All other process unit conditions were specified using industry accepted parameters. The compression stage for both the multi-stage compression and refrigerant compression had an efficiency of 75 %, with inter-stage coolers using cooling water with an approach temperature of 40 °C and a pressure drop of 40 kPa. The heat exchangers in the cryogenic separation were plate-fin heat exchangers with a pressure drop of 50 kPa on both the process side and refrigerant side. 3. Results 3.1 Decision Variables Pareto Charts The Pareto Optimal Front is the solution obtained from the last generation of the Genetic Algorithm of the non-dominated solution set. The results shown are for a MOO using 100 individuals with 50 generations. The Pareto charts of the first six decision variables are shown in Figure 2 plotted against the objective 1: Maximum CO2 Recovery. 3.2 Objective Variables Pareto Charts The Pareto charts of the objective variables are shown in Figure 3(a). From the objective variables Pareto charts, it could be observed that with increasing recovery rate, there was an increase in exergy loss rate. Therefore, in order to better understand the exergy loss rate with respect to the amount of CO2 being captured by hybrid capture process, a new graph, using the Pareto-Optimal solutions, of specific exergy loss rate versus recovery rate was generated (Figure 3(b)), where specific exergy loss rate is the exergy loss rate per mass of CO2 being recovered by the process. 3.3 Additional Results In addition to the decision variables and objective variables, other key process performance variable were also recorded while performing the MOO. Two of those variables can be seen in Figure 4(a) and 4(b), Table 2: Table of decision variable range for MOO and optimum operating conditions of hybrid process Decision Variable Minimum Maximum Optimum Refrigerant Ethane Molar Fraction 0 0.4 0.22 Refrigerant Molar Flow (mol/s) 0.2 1.7 1.4 Cryogenic Process Stream Outlet Temperature (°C) -60 -30 -56.7 Multi-Stage Compression Pressure (kPa) 500 4,000 1,797 VSA CO2 Recovery Rate % 95 98 95.5 VSA CO2 Outlet Purity % 62 65 64.9 1504 Figure 2: Pareto chart of the six decision variables versus the CO2 recovery rate (a) Ethane molar fraction (b) Refrigerant molar flow (mol/s) ; (c)Multi-stage compression pressure (kPa); (d) Process stream minimum temperature (°C; (e) VSA CO2 Recovery Rate (f) VSA CO2 outlet purity (%) which were the ‘total shaft work required’ and the ‘total specific shaft work required’. This enabled a relationship between the exergy loss rate and total shaft work required to be examined as in Figure 5. 4. Discussion It can be seen from Figure 2 that most decision variables seem to have a scattered effect over the recovery rate except for two main decision variables; multi-stage compression pressure and the VSA CO2 recovery rate. The higher compression pressure increases the partial pressure of CO2 and therefore facilitates the separation of CO2 from nitrogen. Also important is the pressure of the stream, which lowers the CO2 freeze out temperature, which effectively allows the stream to be cooled to the lowest temperature possible before forming solid CO2. Finally, the VSA CO2 recovery rate dictates the overall recovery rate since the CO2 lost in the waste stream from the VSA cannot be recovered. As expected from Figure 3(a), the exergy loss rate increased with increasing recovery rate. That can be explained by the increase in exergy required in the compressors in the multi-stage compression of the 1505 Figure 3(a): Graph of objective variables exergy loss rate (kJ/s) versus recovery rate; 3(b): Graph of specific exergy loss rate (kJ/s) versus recovery rate process stream. Figure 3(b) shows that the rate of change of exergy loss is lower than the rate of change of CO2 being captured and thus the specific exergy loss rate decreases with increasing CO2 being captured up until a capture rate of 95 %. Figure 3(b) shows that the specific exergy loss rate has a minimum point at a recovery rate of approximately 95 % and total specific exergy loss rate of around 1.6 GJ/t (CO2 recovered). Figure 4(a) and 4(b) yielded results that were similar to Figure 3(a) and Figure 3(b), which means that the total shaft work required and exergy loss rate have a linear relationship. This was further proven in Figure 5, where the total specific exergy loss rate and total specific shaft work required showed a linear graph. This relationship can be explained by the fact that approximately 99 % of the exergy input is from the shaft work in the compressors. The optimum result is at a recovery of 94.4 %, which leads to the minimum specific exergy loss rate and shaft work. Figure 4(a): Graph of total shaft work required (kW) versus recovery rate. (b): Graph of specific shaft work required (kJ/t (CO2)) versus recovery rate 1506 Figure 5: Graph of total specific shaft work required (kJ/t (CO2)) versus specific exergy loss rate (MJ/t(CO2)) 5. Conclusion An overall exergy analysis of a hybrid carbon capture system has been performed while optimising the process using MOO. This allowed different key decision variables to be varied to understand the effect that they have on the overall recovery rate and exergy loss rate. It was determined that the multi-stage compression and the VSA recover rate had the biggest impact on the overall recovery rate. A minimum specific exergy loss of 1.6 GJ per tonne of CO2 was found at a recovery rate of 94.4 %. Furthermore, it was observed that the total specific shaft work had a linear relationship with the specific exergy loss rate. This is due to the fact that the compressors account for the majority of the exergy going into the system as well as the total shaft work. However, this can be further investigated by selecting an optimum point on the Pareto chart in Figure 3(b) and performing an advanced exergy analysis on each of the individual processes to break down the exergy loss rate to identify further potential improvements. Finally, an exergy analysis should be performed on a solvent absorption capture process, where the exergetic requirement would come from both compressors and thermal energy, and the results could then be compared to this hybrid process. Acknowledgement The authors would like to thank the Australian Government for funding provided through the CRC program to support this CRC for greenhouse gas technology (CO2CRC) research project. References Belaissaoui B., Le Moullec Y., Willson D., Favre E., 2012. Hybrid membrane cryogenic process for post- combustion CO2 capture. Journal of Membrane Science, 415–416, 424-434. Edenhofer O., Seyboth K., 2013. Intergovernmental Panel on Climate Change (IPCC). In: SHOGREN, J. F. (ed.) Encyclopedia of Energy, Natural Resource, and Environmental Economics. Waltham, United States: Elsevier. Hagi H., Nemer M., Le Moullec Y., Bouallou C., 2013. Assessment of the flue gas recycle strategies on oxy-coal power plants using an exergy-based methodology. Chemical Engineering Transactions, 35, 343-348. Pires J.C.M., Martins F.G., Alvim-Ferraz M.C.M., Simões M., 2011. Recent developments on carbon capture and storage: An overview. Chemical Engineering Research and Design, 89, 1446-1460. 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