Design and Operation Optimisation of a MEA-based CO2 Capture Unit

Transcription

1 Design and Operation Optimisation of a MEA-based CO2 Capture Unit Artur Andrade artur.andrade@ist.utl.pt Instituto Superior Técnico, Lisbon, Portugal Supervisors: Prof. Dr. Carla Pinheiro; Dr. Javier Rodriguez November 2014 Abstract The present thesis has the objective of analysing the cost reduction obtained through a rigorous model-based optimisation of a post-combustion CO2 capture plant for carbon capture and storage applications. Presently, capture technology is mainly based on chemical absorption with alkanolamines. Even though this is a well-known technology, its application in power plants presents high costs, thus limiting its implementation. This way, a full scale capture plant model was developed considering MEA as the solvent. This model is based on a conventional flowsheet and was implemented in gproms, using the gccs libraries. It comprises an absorption section, in which CO2 is dissolved by reacting with the amine, and a regeneration section where it is stripped from the solvent. After its validation, the model was optimised by modifying the design and operation parameters. A cost estimation model was applied to the plant model, in order to determine capital and operational expenditures. From the total cost obtained, 69% is due to the steam required in the regeneration section. As for the equipment cost, the absorber packing is the most relevant fraction. Considering typical values for the capture rate, CO2 purity and MEA concentration in the solvent as constraints, the plant s model optimisation led to a reduction of 15% in the specific total cost. Without imposing these typical values, the total cost was further reduced. These results clearly show the potential of model-based optimisation in the reduction of the cost associated with CO2 capture, thus contributing to its effective implementation in power plants. Keywords: Carbon capture and storage, post-combustion capture, chemical absorption, MEA, cost optimisation, gproms CO2 obtained in power plants, in the preparation 1. Introduction CO2 is a naturally-occurring gas with a major influence on Earth s surface temperature. of natural gas, or in other chemical industries [2]. For a power plant, the capture cost is the main component of the overall CCS costs, mainly However, due to human invention and due to the process energy requirements. This industrialisation, its concentration in the implies an extra consumption of steam and atmosphere has greatly increased. According to the 2013 IEA report [1], between the years of 2001 and 2011, the worldwide CO2 emissions from fossil fuel combustion increased by 31%, reaching a value of 31 billion tonnes per year. From this value, approximately 42% are due to the electricity and heat production sector. According to the Global CCS Institute, carbon capture and storage (CCS) has the electricity, leading to a reduction in net efficiency and consequently an increase in the electricity costs [2]. The technology employed in the capture process applied in power plants depends on the characteristics of the gas mixture being treated. These are related to the kind of fuel and technology used in the production of electricity. This way, the capture processes can be divided potential to significantly reduce the in: post-combustion capture (PCC), precombustion capture and oxy combustion. CO2 emissions. The CCS process chain comprises several technologies involved in removal of carbon dioxide from a gaseous stream, its From the available technologies for CO2 capture, absorption is at the present the only one transportation and final sequestration in site away with commercially available processes. from the atmosphere. This can be applied to the Nevertheless, adsorption, membranes and cryogenic separation are currently being tested 1

2 for application in the CCS chain [3]. Even though the absorption technology has been applied in several other industries, due to the considerable costs associated with the application of the capture process to such high flow rates, its application to the flue gas obtained in a largescale power plant fuelled by coal or natural gas is currently on a demonstration phase [4]. The only full scale capture unit already operating is located on the Boundary Dam Power Station (Canada) and was designed for the capture of 1 MtCO2/year. Besides this operating capture unit, there are several others planned to start operating in the near future, all of them based on CO2 capture by absorption [5]. The improvement of the currently available processes should allow a reduction of its costs by 20 to 30%, being the option with more potential for the reduction of CO2 emissions in the near future [2]. A literature review was conducted in order to understand how the absorption is applied to CO2 capture from a power plant, which technologies are commercially available, and which are the current trends to reduce the capture cost. With the reduction of costs associated with the CO2 capture as the main objective, a conventional capture plant model was built in the gproms ModelBuilder environment using the recently developed gccs libraries. After the model validation, the optimisation features of gproms were used in order to minimise the costs associated with the unit s design and operation. 2. Post-combustion Capture Technology Post-combustion capture is applied in power plants based on the combustion of a fossil fuel (coal, natural gas or oil), where a capture unit is used to remove the CO2 present in the flue gas. This flue gas is mainly composed of N2, CO2, H2O and O2, and also of reduced amounts of SOx, NOx and ash [6]. The flue gas being treated have a considerably low CO2 content and is at atmospheric pressure, leading to a reduced CO2 partial pressure. Therefore, the chosen solvent has to be able to ensure acceptable loadings and kinetics these conditions. Due to this, chemical absorption poses a better option in PCC processes, when compared with physical solvents [2]. The processes by chemical absorption are based on the CO2 chemical dissolution in an alkaline solvent, through its selective reaction with one or several of the solvent components. Examples of these solvents, and the available processes in which there are applied are shown in Table 1. As can be observed, amines, more precisely alkanolamines, are the chemical solvents most used for CO2 capture, due its reactivity and adequate basicity. From these, monoethanolamine (MEA) is the one typically considered. This compound have a primary amine group, which confers a high reactivity to the molecule, allowing a strong reaction with CO2 at elevated rates. Due to this reactivity, MEA is highly prone to oxidative and thermal degradation, which limits the solvent concentration. The application of oxidation inhibitors allows the increase of MEA concentration to the typical value of 30 wt%, and even 40 wt% [7]. In order to cope with this high reactivity this amines can also be blended with sterically hindered or tertiary amines which are less reactive [8]. Concerning the thermal degradation, for MEA it is negligible if the regeneration temperature is kept below 110 to 120 C [9]. The capture process is based on the CO2 dissolution, conducted in a packed column (absorber). The solvent loaded with CO2 (usually called rich solvent) is pumped to the regeneration section. Before entering the regenerator, the rich solvent is heated through an integrated heat exchange with the regenerated solvent (also called lean solvent). The regeneration process typically occurs through stripping above atmospheric pressure. The heat demanded by this process is provided by a reboiler, which constitutes the main energy requirement of the capture unit. The lean solvent is pumped back to the absorber after being regenerated. Since the solvent is subject to losses during the process, a make-up is required before re-entering the absorber [2]. Table 1 Commonly used chemical solvents and processes in which they are applied. Solvent Type Processes Typical composition ABB Lummus Cress Up to 30 wt% with corrosion MEA Primary amine Fluor s Econamine FG Plus SM inhibitors KS1 TM Blend of sterically hindered Hindered amine MHI s KM-CDR amines Praxair s Amine Up to 40 wt%, blended with a MDEA Tertiary amine Shell s Cansolv CO 2 Capture System primary amine NH 3 Ammonia Alstom s Chilled Ammonia Up to 28 wt% Hot Potassium UOP s Benfield Blend of potassium carbonate and K 2CO 3 Carbonate Giammarco-Vetrocoke s Low Energy amine promoters

3 Deviation in Absorbed CO 2 (%) Simulated Absorbed CO 2 (kg/h) In these processes, it is typical to achieve capture rates between 80 and 90%, and a purity as high as 99.9% (in volume) for the recovered CO2 [2]. The inclusion of a PCC unit in a coal fuelled power plant leads to an increase of 29% in the energy input to achieve the same output, while for a natural gas fuelled power plant this increase is of 16% [4]. 3. Materials and Methods In the present work, gproms ModelBuilder is the simulation platform used for flowsheet model simulation and optimisation. Starting from the existing gccs model library, it is possible to assemble a flowsheet, in which are also included any other auxiliary equations and custom sub models required. Another feature of gproms ModelBuilder is the optimisation tool, which can be used to optimise the steady state behaviour of a continuous flowsheet, considering both design and operation properties. Since the models considered in this thesis are non-linear, the optimisation problem constitutes a nonlinear programming problem (NLP). gproms ModelBuilder uses the SRQPD solver in the solution of NLP problems, with an Improved Estimation Based convergence criterion [10]. The models included in the gccs library use gsaft as physical properties package. This package is based on the Statistical Associating Fluid Theory (SAFT) equation of state, an advanced molecular thermodynamic method, based in physically-realistic models of molecules and their interactions with other molecules. For carbon capture, gsaft presents a modelling alternative to phase and chemical equilibrium in the CO2-MEA-H2O system, since the chemical bound between CO2 and MEA can be incorporated as a short-range association, being included in the molecular model. This way, the involved reactions are treated implicitly, thus greatly reducing the complexity of the model, thus increasing its robustness [11]. 4. Models Validation The validation of the models used in the simulation of a capture unit was achieved through the comparison of the simulation results with the experimental data publically available. For that purpose, were considered the papers by Tobiesen et al., [12], and by Notz et al., [13], whose flowsheets were implemented in gproms. Tobiensen et al. article [12] presents the experimental data for 20 non-equal runs in pilot scale absorber. From the presented data, it was possible to conclude that between Onda and Billet & Schultes correlations for the calculation of mass transfer coefficients, the first one is more accurate. Using this correlation were obtained deviations from -13% to 26%, comparing the simulated and experimental flow of captured CO2, as can be observed in Figure Figure 1 Parity diagram of the absorbed amount of CO2 (using Billet & Schultes correlation). These deviations are deemed acceptable when considering that predictive models that do not consider any fitting to the experimental data were applied. It was also verified that this deviation tends to increase with the decrease of the solvent lean loading (Figure 2) Experimental Absorbed CO 2 (kg/h) Lean Loading (mol CO2 /mol MEA ) Figure 2 Deviation between experimental and simulated absorbed CO2 with the considered lean solvent loading. Table 2 Experimental and simulation results for the process key parameters and respective variation Key parameter Experimental Simulated CO 2 capture rate (%) Specific heat requirement (GJ/t CO2) Example 1 Example 2 Deviation (%) Experimental Simulated Deviation (%)

4 In the Notz et al. article [13], two sets of experimental data for a complete capture pilot plant are presented. The key parameters used for comparison and the respective deviations are shown in Table 2. From these parameters, it was observed that for a complete capture plant model with a specified heat input, the CO2 capture rate tends to be under estimated, with deviations that can reach -25%, leading to an over prediction of the specific heat requirement of 32%. 5. MEA Full Scale Capture Plant Model 5.1. Base Case Assuming that the validation conclusions presented above are still valid for a full scale plant, a MEA-based capture plant model was developed. The flowsheet considered was based on a case study developed by PSE. The flue gas considered is characteristic of a natural gas fuelled power plant emitting approximately 2 million tonnes of CO2 per year. The lean solvent flow rate and MEA concentration were adjusted in order to meet a capture rate of 90% and a MEA mass fraction in the CO2 free lean solvent of 30%. This way, the base case is characterised by the parameters shown in Table 3. Table 3 Design parameters and operating conditions considered in the original capture plant model. Parameter Value Diameter (m) 20 Height (m) Absorber Sulzer Mellapak Packing 250Y TM Diameter (m) 8.5 Height (m) 10 Stripper Sulzer Mellapak Packing 250Y TM Lean-rich heat Cold stream outlet exchanger temperature ( C) Process stream Lean solvent outlet temperature cooler ( C) Reboiler Temperature ( C) Reboiler pump Pressure (bar) 1.79 Condenser Temperature ( C) 40 Flow rate (kg/s) Lean solvent MEA mass fraction (g/g) The referred conditions were applied in a conventional PCC flowsheet model, which topology is shown in Figure 3. Figure 3 MEA capture plant flowsheet as seen in gproms ModelBuilder. 4

5 In these model, the absorption section is composed by an absorber model (A-301) in which the Billet & Schultes correlation is used the prediction of mass transfer coefficients and pressure drop. The heat exchange section is composed by the lean-rich heat exchanger (HX- 301) and the lean solvent cooler (HXU-301), which are used for the rich solvent cooling and the lean solvent cooling. In both these models, it was assumed a constant overall heat transfer coefficient of 5 kw/(m 2.K) [14] and pressure drop of 0.62 bar [15]. The regeneration section is composed by a stripper model (ST-301), associated with the reboiler (R-301) and condenser (C-301) models, in which were assumed heat transfer coefficients of 1.14 and 0.85 kw/(m 2.K) [15], respectively. The model P-304 is used to deliver the pressure required by the stripper model, while the model P-305 is used to specify the reboiler pressure. The lean solvent flow rate and MEA concentration are set in the model RB-303, which allows the calculation of the required solvent make-up. Besides these conventional components, the flowsheet model also comprises a saturation section (flash model (F-301), PID controller model (PID-301) and pump model (P-301)). In order to keep the absorber pressure above atmospheric, the pump model P-302 acts as a gas blower, in order to increase the flue gas pressure to 1.1 bar. In the columns models the Billet & Schultes correlation was used for the calculation of mass transfer coefficients and pressure drop. At last, were also added several flow multiplier models (FM-301 to FM-310) at the inlets and outlets of the absorber, regeneration section and reboiler to simulate the existence of several equipment working in parallel. Based on these model, the key parameters shown in Table 4 were obtained. Table 4 Results obtained from the simulation of the base case. Parameter Value Capture rate (%) 89.9 CO 2 purity (vol%) 95.8 MEA mass fraction in the CO 2 free lean 30.0 solvent (wt%) Number of absorption trains 2 Number of striping trains 2 Number of reboilers per stripping train 4 Specific heat consumption (GJ/t CO2) 5.66 Lean loading (mol CO2/mol MEA) Rich loading (mol CO2/mol MEA) Cost Estimation Model To determine the costs associated with the design and operation of a capture plant a cost estimation model was implemented. For that purpose, it was considered the procedure described in [14]. Considering that the objective of the present economic model is the optimisation 5 of the total cost associated to a carbon capture plant, only the cost fractions that depend on the plant design parameters and operation conditions were considered, in order to simplify the optimisation process. Using this costing model, it is possible to obtain the capture plant annualized investment, or CAPEX, and the annual operating cost, or OPEX, both in a euro bases referred to year In the case of the investment annualization, it was considered a linear amortization over a period of 10 year (equation (1)). CAPEX = Investment (1) 10 years Both CAPEX and OPEX were also expressed as functions of the captured amount of CO2, designated specific CAPEX (scapex) and specific OPEX (sopex), respectively (equations (2) and (3)). scapex( ton CO2 ) = CAPEX ( year) (2) = Absorbed CO 2 (ton CO2 year) sopex( ton CO2 ) = OPEX ( year) (3) = Absorbed CO 2 (ton CO2 year) For an approximate calculation of the investment required, it was used the factorial method, which is based on the cost of the main process equipment [14]. For that, were considered the columns (shell and packing), heat exchangers, condenser, reboiler and pumps (including drivers). Considering that amine solvents are corrosive, stainless steel 304 (SS- 304) is required in all the equipment that contacts directly with the capture solvent. The cost of the referred equipment is calculated through cost correlations [14], which upper bounds were used to determine the number of equipment working in parallel. Besides this, were also considered installation factors, and other expenses, which can be estimated as percentages of the main equipment costs [14]. For the calculation of the operational expenses, were considered fixed and variable production costs. The fixed production also can be approximated to a percentage of the main equipment cost. For the variable production costs were considered the annual consumption of utilities (steam, cooling water and electricity) and solvent, which unitary costs were retrieved from [16]. Concerning the steam consumption, it was considered low pressure steam (saturated at 3 bar). The cooling water requirements are given by the utility consumption, considering that the used water presents inlet and outlet temperatures of 29 and 49 C, respectively [15]. For the estimation of the electricity consumption, it was considered the

6 power required in both lean solvent and rich solvent pump drivers. The solvent (MEA) consumption is estimated based on the required make-up and amine degradation. According to [17], the washing sections in a capture plant are able to reduce the MEA concentration to 1 ppm. Therefore it was considered that the MEA concentration in the treated flue gas is reduced to this value, and the washed MEA used to reduce the required make-up. Also in reference [17], it is referred that MEA has a degradation rate of 1.5 kgmea/tco Cost Estimation Results for the Base Case By applying the cost estimation model in the capture pant model, it was possible to obtain a CAPEX of M /year and an OPEX of M /year, which correspond to a total cost of M per year, equivalent to per tonne of captured CO2. As can be observed in Figure 4, the CAPEX only represents 20% of the total cost. Figure 4 Total cost distribution in the base case (Total = /tco2). Considering the distribution of equipment costs shown in Figure 5, it is observable that the columns packing represents the major fraction of the equipment costs, from which 87% are due to the absorber s packing. Figure 5 Distribution of main equipment costs for the base case. From the distribution show in Figure 6, it is observable that utilities represent the main fraction of the OPEX, from which the steam consumption corresponds to 98%. In fact the annual steam consumption is 69% of the plant annual cost. 6 80% 84% 20% 5% 9% 2% CAPEX OPEX Columns Shell Heat Transfer Equipment Pumps and Drivers Packing 8% 5% 87% Figure 6 Distribution of the OPEX (Total = /tonco2). This way, it is expected that the optimisation of the capture plant flowsheet will mainly rely on changing the absorbers size and the steam consumption. 6. Optimisation Problem Formulation Total Utilities Cost Solvent Cost Fixed Production Costs 6.1. Objective Function For the optimisation of the capture plant, both the specific CAPEX and specific OPEX should be minimised. Therefore, Equation (4) was defined as objective function. OF = scapex + sopex (4) 6.2. Decision Variables The decision variables define which of the capture flowsheet variables are used to minimise the objective function. In this case, the variables which define the equipment design and the process operating conditions have been chosen (Table 5). According to Zahra [17], given the lean solvent flow rate, capture rate and lean loading, the optimal steam consumption is obtained for the combination of stripping pressure and reboiler temperature with higher values. Therefore, before proceeding to the process optimisation, the base flowsheet was modified in order to start from the maximum temperature (120 C [9]). This way, this variable was not directly included in the optimisation problem. Table 5 Decision variables. Model Variable Diameter (m) A-301 Height (m) C-301 Temperature (K) HX-301 Cold outlet temperature (K) HXU-301 Process outlet temperature (K) Source flow rate (kg/s) RB-303 Source MEA mass fraction (g/g) P-305 Pressure (bar) Diameter (m) ST-301 Height (m) 6.3. Constraints The process constraints are used to keep the standard conditions initially imposed, such as the capture rate, the MEA mass fraction in the CO2 free solvent and the minimum CO2 purity in

7 CO 2 molar flux (kmol.m -2.s -1 ) Temperature (K) CO 2 molar flux (kmol.m -2.s -1 ) the product stream, and to keep technical specifications, such as preventing the columns flooding. These constraints are divided in equality and inequality constraints, which are listed in Table 6 and Table 7, respectively. Table 6 Equality constrained variables, with respective constrained value. Constrained variable Imposed value Capture rate (g/g) 0.90 CO 2 molar fraction in the CO stream (mol/mol) MEA mass fraction in the CO free lean solvent (g/g) Table 7 Inequality constrained variables, with respective upper and lower bounds. Constrained variable Lower bound Upper Bound Vapour velocity/vapour flooding velocity in the top of the absorber Vapour velocity/vapour flooding velocity in the bottom of the stripper Process stream ΔT in the lean solvent cooler ( C) The process stream temperature difference (ΔT) in the lean solvent cooler is required, since in some of the optimisation cases it tended to achieve un-realistic values below 0. Therefore, this variable was included in the flowsheet model, where a very small positive number was defined as a lower bound (10-10 ), while the upper bound was considered an excessively large number (10 10 ). The ratio between the vapour operating velocity and the vapour flooding velocity should be kept below 70% to 80% in order keep the column s efficiency [18]. 7. Optimisation Results 7.1. Specific Total Cost Minimisation with Standard Constraints The results obtained from this optimisation process show that the main variations occur in the absorber size (height and diameter are reduced in 29% and 12%, respectively), in the stripper size (diameter is reduced 15% and height is increased 69%) and in the lean solvent flow rate (reduced 18%). The reduction in the absorber size leads to a reduction of the packing volume, as well as the shell s mass. This reduction is possible due to the reduction in the lean loading (reduced to molco2/molmea) and the approach to the flooding velocity in the absorber, which now reaches 70% in the top of the column. Both these factors improve the column s efficiency, as can be observed in Figure Volume of packing from the absorber top (m 3 ) Base Optimal Figure 7 CO2 molar flux to the liquid phase across the absorber (top of the absorber equivalent to 0), before and after the specific total cost minimisation with standard constraints. An increase by 5% in the outlet temperature of the rich solvent in the lean-rich heat exchanger and by 1% in reboiler temperature led to an increase of the overall temperature in the stripping column, as can be observed in Figure Relative position from the stripper top Optimal Base Figure 8 Axial temperature profiles in the stripping columns for the liquid phase, before and after the specific total cost minimisation with standard constraints. The increase in the temperature profile and in the columns height caused an increase of its stripping efficiency. This can be observed in Figure 9, where the CO2 flux from the gas phase to the liquid phase across the stripper is always negative Volume of packing from the stripper top (m 3 ) Base Optimal Figure 9 CO2 molar flux to the liquid phase across the striper (top of the stripepr equivalent to 0), before and after the specific total cost minimisation with standard constraints. Considering the improved efficiency and the increase in the lean loading, the heat required in the reboiler is decreased. This way, the specific 7

8 Specific total cost ( /t CO2 ) heat requirement shows an optimal value of 4.87 GJ/tCO2 (less 14%). Considering this main factors, it is observed a reduction in the CAPEX of 21% and in the OPEX of 13%, which lead to a specific total cost of /tco2 (-15%) Effect of the Initial Guesses Considering that in the formulated optimisation problem exist ten decision variables (Table 5) and only three equality constraints (Table 6), it is expectable that multiple solutions can lead to the same optimal solution. The initial guesses of a non-linear optimisation problem may influence the results obtained, and even determine the convergence or not of the problem. Since the lean solvent CO2 loading as a main influence in the capture model, all the optimizations performed started from a lean loading of 0.2 molco2/molmea. In order to evaluate the influence of this parameter in the process convergence, the optimisation described in the previous section was conducted starting from a lean loading of 0.1 and 0.3 molco2/molmea, by adjusting the lean solvent flow rate and MEA mass fraction and the reboiler pressure. Through the optimal results obtained it was observed a variation of less than 1% in the total cost. It was also possible to conclude that the lean solvent temperature after the cooler isn t a relevant parameter in the considered conditions, and the lean solvent cooler wouldn t be required Effect of the Number of Absorption Trains The number of absorption trains was chosen considering the construction limitations regarding the column s diameter and the vapour flooding velocity. This variable was changed in the base case and optimised for each value. From the obtained results, it was possible to conclude that the decision variable mainly affected is the absorber diameter. Considering only one absorber would be technically unfeasible due to the elevated optimal absorber diameter. The use of two absorption trains conditioned by the possibility of constructing absorbers with a diameter above 15 m. Nevertheless, it was observed that increasing the number from 1 to 4 only increased the specific total cost in 0.4%, since the total volume of packing tends to be the same Specific Heat Requirement Minimisation The steam consumption is directly proportional to the heat required in the regeneration reboilers, and represents 69% of the capture plant annual cost. The minimisation of the specific total cost allowed a reduction in specific heat requirement in 14%. In order to evaluate if a greater reduction in this parameter is possible, and its effect in the capture total cost, the described optimisation procedure was conducted using the specific heat consumption as objective function. A further reduction of the specific heat requirement to 4.46 GJ/tCO2 was obtained. Nevertheless, this was achieved by greatly increasing the columns height and therefore the required packing volume. This led to an increase in specific CAPEX by 674% and in the specific OPEX by 23%, when compared to the initial optimisation, which is equivalent to a specific total cost is increased to /tco Effect of the Imposed Capture Rate This variable was initially imposed as 90%. Its effect on the plant s specific total cost was evaluated by changing the imposed values to 70%, 80% and 99% (Figure 11) Capture rate (%) Figure 10 Variation of the optimal specific total cost with the imposed capture rate. Between 70% and 90%, the variation is achieved through an increase in the lean solvent flow rate and in the absorber and stripper height, associated with a slight decrease in the lean loading. This leads to an increase of 2% in the optimal total cost. On the other hand, between the capture rates of 90% and 99%, there is 28% increase in the specific total cost, due to a major decrease in the optimal lean solvent flow rate and lean loading, accompanied by a further increase in the columns height. In the case of a 99% capture it was also concluded that due to the heat released in the absorption process and the reduced solvent flow rate, the lean solvent cooler has higher relevance. In the same case, the rich solvent temperature entering the stripper is decreased, since the vapour flow rate is increased and the solvent flow rate is decreased, being the temperature across the columns kept at higher values Effect of the Imposed CO2 Purity This variable was initially imposed as 95%. Its effect on the plant s specific total cost 8

9 Specific total cost ( /t CO2 ) Specific total cost ( /t CO2 ) was evaluated by changing the imposed values to 75%, 85% and 99% (Figure 10) Figure 11 Variation of the optimal specific total cost with the imposed CO2 purity. Between the purities of 75% and 95%, it was observed an increase of only 0.2% in the specific total cost. The condenser temperature is related to this variation, since it defines the amount of water condensed, and therefore the purity of the final stream. On the other hand, for lower purities the amount of lost solvent in the regeneration process tends to increase, thus leading to a minimum in the sopex. When the purity is increased to 99%, the specific cost increases in 4%, associated with a major variation in the OPEX. This was due to the further reduction in the condenser temperature that required the use of chilled water, which was considered eight times more expensive [16], leading to an increase of 435% in the cooling/refrigerating water annual costs. Nevertheless, that it was verified that this parameter did not have a considerable influence in the specific heat consumption, thus the reduced effect on the total cost Effect of the Imposed MEA Concentration The MEA concentration was initially defined by the imposition of a MEA mass fraction in the CO2 free lean solvent of 30%. In order to evaluate its effect on the process total cost, the imposed value was modified to 20% and 40% (Figure 12) CO 2 Purity (%) MEA mass fraction in the CO 2 free lean solvent (%) Figure 12 Variation of the optimal specific total cost with the MEA mass fraction in the CO2 free lean solvent. The obtained optimal results showed a more significant variation in the specific total cost was than in the previous cases, showing a reduction in 5%, when changing this constraint from 20% to 30%, and in 4%, when changing it from 30% to 40%. These variations are mainly due to the possibility of reducing the solvent flow rate, with the increasing concentration, associated whit an increase in the optimal lean loading. Both these factors contribute to the reduction of the specific heat requirement, allowing the total cost reduction. Nevertheless, it should be considered that there are implications of using a more concentrate solvent, such as, the size of the water washing sections or the costs associated with the required oxidation inhibitors, which were not considered Specific Total Cost Minimisation with Inequality Constraints Since it was observed that both the capture rate and the CO2 purity tended to show a minimum specific total cost, the optimisation problem was conducted without any equality constraints. This led to the optimal capture rate of 75%, CO2 purity of 88% and MEA mass fraction in the CO2 free lean solvent of 40%, being the last one the variable upper bound. This allowed a specific total cost reduction to /tco2 (5% less when compared with the initial optimal results), associated with the specific heat requirement of 4.60 GJ/tCO2. 8. Conclusions The gccs capture library allowed the development of a capture plant model in the gproms ModelBuilder environment. The full scale capture plant optimisation was conducted using as base case a PSE s case study, which was modified in order to meet a 90% capture rate and a MEA fraction in the CO2 free lean solvent of 30%. The application of a cost estimation model to the base case, allowed the calculation of a specific total cost of /tco2, from which the OPEX represents 80% and is associated to a specific heat requirement of 5.66 GJ/tCO2. The cost estimation allowed to conclude that the equipment with more influence in the CAPEX is the absorber s packing (73% of the equipment total cost). For the OPEX, the main fraction is steam consumption, which in fact represents 69% of the total annual cost. The base case specific total cost minimisation, considering a capture rate of 90%, a CO2 purity of 95 vol% and a MEA fraction in the CO2 free lean solvent of 30%, led to its reduction in 15%, associated to a specific heat requirement of 4.87 GJ/tCO2. Considering the specific heat requirement as the objective function to be minimised, this variables was further reduced to 4.46 GJ/tCO2. Nevertheless, this led to an increase in the total 9

10 cost (143% when compared with the initial optimal results). The optimisation without any equality constraints led to the optimal capture rate of 75%, CO2 purity of 88% and MEA mass fraction in the CO2 free lean solvent of 40%, being the last one the variable upper bound. This allowed a specific total cost reduction to /tco2 (less 5% when compared with the initial optimal results), associated with the specific heat requirement of 4.60 GJ/tCO2. The analysis presented shows that the total cost is greatly affected by the trade-off between the solvent s flow rate, the absorber height and the lean solvent s loading. Increasing the MEA concentration in the solvent has a positive effect in all this variables. This is also verified for the capture rate in a limited range of values. It was also observed that at moderate capture rates, increasing both the stripper s height and the rich solvent temperature at its inlet also tend to reduce the steam consumption, and therefore the total cost. The interconnection between these variables shows the relevance of a model-based full plant optimisation for the reduction of the capture cost, which is an important step for the effective CCS implementation in real power plants. 9. Main Bibliography [1] International Energy Agency, CO2 Emissions from Fuel Combustion, Paris, [2] Working Group III of the Intergovernmental Panel on Climate Change, IPCC Special Report on Carbon Dioxide Capture and Storage, Cambridge University Press, New York, [3] A. B. Rao and E. S. Rubin, A Technical, Economic, and Environmental Assessment of Amine-Based CO2 Capture Technology for Power Plant Greenhouse Gas Control, Environmental Science & Technology, vol. 36, no. 20, pp , [4] E. S. Rubin, H. Mantripragada, A. Marks, P. Versteeg and J. Kitchin, The outlook for improved carbon capture technology, Progress in Energy and Combustion Science, vol. 38, pp , [5] Carbon Capture and Sequestration MIT, Carbon Capture and Sequestration Project Database, [Online]. Available: /. [Accessed 30 September 2014]. [6] Global CCS Institute, CO2 CAPTURE TECHNOLOGIES - POST COMBUSTION CAPTURE (PCC), EPRI, [7] B. Delfort, P.-L. Carrette and L. Bonnard, MEA 40% with improved oxidative stability for CO2 capture in post-combustion, Energy Procedia, vol. 4, pp. 9-14, [8] P. D. Vaidya and E. Y. Kenig, CO2- Alkanolamine Reaction Kinetics: A Review of Recent Studies, Chemical Engineering & Technology, vol. 30, no. 11, pp , [9] J. Davis and G. Rochelle, Thermal degradation of monoethanolamine at stripper conditions, Energy Procedia, vol. 1, no. 1, pp , [10] Process Systems Enterprise Limited, gproms Documentation, Release 3.7, London, [11] C. V. Brand, CO2 capture using monoethanolamine solutions: Development and validation of a process model based on the SAFT-VR equation of state, Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, [12] F. A. Tobiesen, H. F. Svendsen and O. Juliussen, Experimental validation of a rigorous absorber model for CO2 postcombustion capture, AIChE Journal, vol. 53, no. 4, pp , [13] R. Notz, H. P. Mangalapally and H. Hasse, Post combustion CO2 capture by reactive absorption: Pilot plant description and results of systematic studies with MEA, International Journal of Greenhouse Gas Control, vol. 6, pp , [14] G. Towler and R. Sinnot, Chemical Engineering Design : Principles, Practice and Economics of Plant and Process Design 2nd ed., Oxford: Butterworth- Heinemann, [15] A. K. Coker, Ludwig s Applied Process Design for Chemical and Petrochemical Plants, Burlington: Gulf Professional Publishing, [16] R. K. Sinnot, Coulson & Richardson's Chemical Engineering Series, Volume 6, Fourth Edition, Chemical Engineering Design, Oxford: Butterworth-Heinemann, [17] M. R. M. A. Zahra, Carbon Dioxide Capture from Flue Gas: Development and Evaluation of Existing, [18] R. Billet and M. Schultes, Prediction of Mass Transfer Columns with Dumped and Arranged Packings: Updated Summary of the Calculation Method of Billet and Schultes, Chemical Engineering Research and Design, vol. 77, no. 6, pp ,