Novel Shortcut Estimation Method for Regeneration Energy of Amine

Article pubs.acs.org/est

Novel Shortcut Estimation Method for Regeneration Energy of Amine Solvents in an Absorption-Based Carbon Capture Process Huiyong Kim, Sung June Hwang, and Kwang Soon Lee* Department of Chemical and Biomolecular Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Korea ABSTRACT: Among various CO2 capture processes, the aqueous amine-based absorption process is considered the most promising for near-term deployment. However, the performance evaluation of newly developed solvents still requires complex and time-consuming procedures, such as pilot plant tests or the development of a rigorous simulator. Absence of accurate and simple calculation methods for the energy performance at an early stage of process development has lengthened and increased expense of the development of economically feasible CO2 capture processes. In this paper, a novel but simple method to reliably calculate the regeneration energy in a standard amine-based carbon capture process is proposed. Careful examination of stripper behaviors and exploitation of energy balance equations around the stripper allowed for calculation of the regeneration energy using only vapor−liquid equilibrium and caloric data. Reliability of the proposed method was confirmed by comparing to rigorous simulations for two well-known solvents, monoethanolamine (MEA) and piperazine (PZ). The proposed method can predict the regeneration energy at various operating conditions with greater simplicity, greater speed, and higher accuracy than those proposed in previous studies. This enables faster and more precise screening of various solvents and faster optimization of process variables and can eventually accelerate the development of economically deployable CO2 capture processes. used in the industry.5,9−11 However, the immediate deployment of the current absorption-based technology has been delayed because of the large amount of energy required to run the process,5,10,12 while a lot of research is being carried out to develop advanced energy-efficient solvents and processes. The adoption of any of the present absorption-based capture processes in a power plant is expected to result in an energy penalty of 20−30%,13−15 where the energy is consumed mostly for regeneration of absorbed CO2. With various absorbent candidates being competitively developed in many institutions worldwide, the performance (i.e., total required work including the regeneration energy) evaluation of developed solvents remains time-consuming and challenging. Typically, candidate solvents are pre-screened by comparing the absorption rate, absorption capacity, heat of absorption, degradation, viscosity, solvent cost, etc.16,17 However, the energy performance is not evaluated quantitatively at this stage, and only solvent characteristics are listed without having a clear selection guideline for the best candidate solvent. After pre-screening, pilot plant tests and rigorous modeling studies are usually conducted to observe solvent performance. Pilot plant testing is essential in the process

1. INTRODUCTION Preventing anthropogenic climate change by reducing emissions of CO2 without decreasing fossil fuel usage is a critical issue worldwide.1,2 Global energy usage is continuously increasing, and the International Energy Agency (IEA) predicts that the reliance on fossil fuels will account for 80% of the energy supply in 2050.3 With the increase in the consumption of fossil fuels, greenhouse gas emissions, which are thought to bring about severe climate change, are also expected to increase. Efforts to solve this problem have led to numerous initiatives concerning the reduction of CO2 emissions. Carbon capture and sequestration (CCS) technology is considered to be a promising means of addressing the CO2 emission problem. This technology includes the capture (and compression) of CO2 from fixed-point sources, its transportation, and its storage. According to the IEA Blue Map Scenario, the amount of CO2 emitted in 2050 could be reduced by 48 billion tons from the projected total CO2 emissions through the use of all possible efforts, and CCS is predicted to be responsible for 19% of this reduction.3 A large portion of carbon capture research is focused on absorption-based post-combustion CO2 capture technology.2,4−8 Other technologies, such as adsorption, solid sorbent-based absorption, membrane separation, oxy-fuel combustion, and pre-combustion capture, are not expected for near-term use because of their technical immaturity compared to the absorption process, which has long been © XXXX American Chemical Society

Received: September 24, 2014 Revised: December 9, 2014 Accepted: December 11, 2014

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DOI: 10.1021/es504684x Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

assumptions, a reliable estimation of Qregen was enabled using only VLE and caloric data. Calculated values of Qregen were compared to those from rigorous simulations for two wellstudied solvents, MEA and piperazine (PZ), at two different stripper operating pressures to verify the pertinence of the proposed method. In the remainder of the paper, section 2 describes the standard CO2 absorption process and stripper behaviors, section 3 describes the derivation of the proposed shortcut method, and section 4 demonstrates the performance of the shortcut method by applying it to four different cases.

development but is labor-intensive and costly. In addition, further scale-up is required to obtain an accurate energy assessment because of the heat loss problem in small-scale plants. Rigorous modeling is another labor-intensive job, demanding a lot of experimental work for thermodynamic and kinetic modeling. Thus, developing a method that can reliably estimate the regeneration energy at the pre-screening stage can reduce trial and error and act as an important agent that can accelerate carbon capture research. The total required work of a CO2 capture process is composed of the regeneration energy (i.e., reboiler duty of the stripper), compression energy to 150 bar, solvent pumping energy, and flue gas blowing energy, as in eq 1.18 The regeneration energy, Qregen, can be further decomposed into energies required for solvent heating (sensible heat, Qsens), water and amine evaporation (latent heat, Qlatent), and CO2 desorption (heat of reaction, Qrxn), as shown in eq 219 and also in Figure 2a. In this study, we restricted ourselves to amines that have much lower volatility than water, and the amine evaporation was neglected.

2. ABSORPTION PROCESS AND STRIPPER BEHAVIORS Before introduction of the proposed shortcut method, stripper behavior is investigated in this section. For the base case study, an absorption process with a typical absorber−stripper configuration is assumed for 30 wt % MEA solvent, and stripper behavior of the process is studied by rigorous simulation using Aspen Plus.23 Because Qregen is provided at the reboiler of the stripper, understanding the stripper behavior is crucial, and only the stripper is studied. Thermal energy balance of the stripper discussed in section 2.3 shows that knowing the top-stage temperature of the stripper is the key to estimating Qregen, and stripper behavior investigated in section 2.4 reveals that the top-stage temperature can be easily obtained. 2.1. Standard Carbon Capture Process. Figure 1 shows a standard configuration of the absorption-based carbon capture

Wtotal = Wcomp + Wpump + Wblower + 0.75Q regen Treboiler + 10 − Tambient Treboiler + 10

(GJelec /tCO2 )

Q regen = Q sens + Q latent + Q rxn

(GJth /tCO2 )

(1) (2)

Individual energy terms in eqs 1 and 2 can be easily calculated, except Qlatent, the latent heat for water vaporization. Required works for compression, pumping, and blowing can be easily and reliably estimated from the respective performance equations. Among the three energy terms comprising Qregen, Qrxn can be predicted quite accurately using the Gibbs−Helmholtz equation19,20 and a small number of vapor−liquid equilibrium (VLE) data. Consistency of evaluating Qrxn using the Gibbs− Helmholtz equation with VLE data was confirmed by Mathias and O’Connell.20 Qsens can also be estimated from a simple enthalpy balance relation. The most troublesome part of the energy assessment is the latent heat for water evaporation, Qlatent. There have been some studies dedicated to the estimation of Qlatent. Rochelle et al. proposed a method to calculate Qlatent when regeneration is conducted in a flash drum instead of a stripper.21 Notz et al. proposed a shortcut method to estimate Qregen in a stripper using a modified Kremser equation.22 This method can estimate Qregen for various solvent flow rates when VLE data at absorber and stripper temperatures and caloric data are available. The method can yield reasonable results in limited situations only because of underlying assumptions, such as constant temperature and vapor flow within the stripper. As demonstrated in Figures 2b and 3a for a monoethanolamine (MEA) case, the maximum temperature difference within the stripper is greater than 20 °C. The vapor flow rate also varies significantly along the stripper column. Such discrepancies between assumed idealities and practical situations have limited the dependability of the Qregen prediction. On the basis of the considerations mentioned above, the purpose of this research is to propose a novel shortcut method to estimate Qregen for an absorption-based CO2 capture process using a stripper. An examination of stripper characteristics revealed that the stripper operation can be separated into two regions. With the introduction of suitable energy balance equations for each region and some simplified but reasonable

Figure 1. Typical configuration of the absorption-based carbon capture process.

process, which is most widely employed in industrial processes. The main equipment used in Figure 1 includes the absorber, the stripper, and the heat exchanger. The cool CO2-rich solvent stream (RICHOUT) exits from the bottom of the absorber, gaining energy by exchanging heat at the heat exchanger with the hot CO2-lean solvent stream (LEANOUT) exiting from the bottom of the stripper. As a result of the heat exchange, CO2 and water in the RICHIN stream, which enter the stripper at the top stage, are partially vaporized. The minimum temperature approach (ΔTMTA) is formed at the RICHOUT side of the heat exchanger because of a larger mass flow rate of the RICHOUT stream than that of the LEANOUT stream. The liquid portion in the RICHIN stream flows downward, transferring CO2 to and receiving water from the upward vapor flow inside the stripper. The CO2-stripped LEANOUT stream is withdrawn from the reboiler. 2.2. Simulation Conditions for a MEA-Based Process. Some basic assumptions and simulation conditions for the rigorous simulation of the MEA-based process are described in B


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If the heat of reaction, Δhrxn (kJ/mol of CO2), is a function of CO2 loading and the reference states associated with the four streams in eq 4 are unloaded liquid solvent, gaseous CO2, and liquid water at TR and 1 bar, the enthalpy terms would be reasonably expressed as follows:

this subsection. Numbers are assigned to process streams around the stripper in Figure 1, in addition to names used to simplify the designation of the associated heat and mass terms. Some basic assumptions of the process for simulation include the following: (i) The flue gas contains 15% CO2, saturated water vapor at 40 °C, and N2 for balance. It enters the process at a flow rate of 510 kg/h or equivalently 17.1 kmol/h. (ii) The solvent is an aqueous solution of 30 wt % MEA. (iii) Rate-based simulation is used for the absorber, and VLE-based simulation with 20 equilibrium stages is applied to the stripper. The operating pressures for the absorber and stripper are chosen as 1 and 2 bar, respectively. (iv) The temperature of the flue gas, the LEANIN stream, and the condensate recycle stream of the stripper is 40 °C. (v) The CO2 recovery rate (percentage of CO2 removed from the flue gas stream) is 90%, which corresponds to a CO2 capture rate of 100 kg/h. (vi) ΔTMTA for the main heat exchanger is 10 °C. The assumptions above are typical, and the resulting stripper behaviors would be generally applied to amine-based carbon capture processes. Rate and equilibrium models and data provided in Aspen Plus were used without modifications. The amount of CO2 absorbed in the RICHOUT stream (rich loading) and the LEANIN stream (lean loading) can have various values depending upon equipment design, operating parameters, and solvent properties. In this study, rich loading was fixed at 0.54 (mol of CO2/mol of amine), and lean loading was varied over a range. The chosen value of rich loading corresponds to CO2 vapor pressure of 5 kPa at 40 °C. Because the CO2 partial pressure in the flue gas is 15 kPa (15% CO2 in the 101 kPa flue gas), setting the CO2 vapor pressure of the solvent at 5 kPa can offer a reasonable driving force for mass transfer from the flue gas to the MEA solvent.18,19 The rich loading value of 0.54 was maintained during simulation by adjusting the absorber height and the amount of interstage cooling. The CO2 recovery was maintained at 90% by adjusting the solvent circulation rate. 2.3. Thermal Energy Balance around the Stripper. Equations for Qregen computation can be derived from the energy balance for the control surface around the stripper shown in Figure 1. It is convenient to write the energy terms based on molar amount of unloaded solvent wherever applicable. For this, let us define mam (mol/tCO2) as the molar amount of amines in the circulating solvent streams per tonne of CO2 product. In addition, let us define rw as the molar ratio of water to amines in the unloaded solvent. Also, let us define M and mw as the molar amount of CO2 and the molar amount of water vapor in stream 3 per tonne of CO2 product, respectively. Obviously, M = 2.2727 × 104 mol of CO2 (1 tonne of CO2). Finally, let α be the CO2 loading defined as moles of CO2 per mole of amines. Then, the following holds: m1 = mam(1 + rw + αrich), m3 = M + m w ,

H1 = (cam + rwc w + αrichcCO2)(T1 − TR ) rich

Δhrxn(α) dα

H2 = (cam + rwc w + αleancCO2)(T2 − TR ) +

∫0

lean

Δhrxn(α) dα

H3 = (McCO2(Ttop − TR ) + m w c w(Ttop − TR ) + m w λ) /(M + m w ) H4 = c w(T4 − TR ) (5)

where λ (kJ/mol) denotes the latent heat of water and Ttop = T3. Note that the heat capacity, c, is molar-based, having the unit of kJ mol−1 K−1. It is assumed that c for a species has the same value for both gas and liquid states and c for a mixture has a molar-weighted average value of pure species, regardless of the existence of reaction products inside the mixture. Substitution of eq 5 into eq 4 yields the following: Q regen = Q sen + Q rxn + Q latent Q sen = mam(cam + rwc w + αleancCO2)(T2 − T1) + McCO2(Ttop − T1) + m w c w(Ttop − T4) rich

Q rxn = −mam

∫lean

Δhrxn(α) dα

Q latent = m w λ

(6)

It is interesting to note that Qlatent is determined by the amount of water content at the top of the stripper, mw, instead of the amount of water boil-up at the reboiler. In eq 6, T2 − T1 is set to have ΔTMTA because it minimizes Qsen and all the other terms, except Ttop and mw, are known or can be pre-specified. The value of Δhrxn can be obtained experimentally using an instrument, such as the reaction calorimeter, or using VLE data and the following Gibbs− Helmholtz equation: −

⎡ ∂ ln P* ⎤ Δhrxn CO2 ⎥ =⎢ R ⎣⎢ ∂(1/T ) ⎥⎦α

(7)

where P*CO2(T, α) is the CO2 vapor pressure. The value of mw can be estimated using the pressure relationship. The column pressure at the top is the sum of the CO2 vapor pressure, PCO * 2(Ttop, αtop), and the partial pressure of water.

m2 = mam(1 + rw + αlean)

m4 ≈ m w (3)

Let us denote Hi (kJ/mol) as the enthalpy of the ith stream per mole of amines for i = 1 and 2 and per mole of the stream itself for i = 3 and 4. According to the energy balance around the control surface, Qregen can be represented as follows:

* (Ttop , αtop) + Pw(Ttop) Ptotal = PCO 2

(8)

When P*CO2(Ttop, αtop) and Pw(Ttop) are known, mw can be computed as follows:

Q regen = mamH2 + (M + m w )H3 − mamH1 − m w H4 (GJ/tCO2 )

∫0

+

* (Ttop , αtop) m w = MPw(Ttop)/PCO 2

(4) C

(9)


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(region A), while they begin to deviate significantly as αlean becomes smaller than 0.2 (region B). In region A, the flow rate and enthalpy of the RICHIN stream dominate those of the other incoming streams to the top stage (condensed water and upward vapor from the lower stages) and Ttop inevitably follows TRICHIN. In contrast, in region B, a larger amount of thermal energy is provided to the reboiler to discharge CO2 by increasing the reboiler temperature and the upward vapor stream has a stronger effect on the top stage than in region A. Consequently, Ttop of region B is determined to be higher than TRICHIN, and the difference becomes more severe as αlean is reduced further. The stripper behavior demonstrates that Ttop, the important variable for estimation of Qlatent, can be simply approximated by TRICHIN for region A. Figure 3 illustrates the temperature, CO2 loading, and vapor mole fraction profiles inside the stripper to better comprehend

Pw(Ttop) can be approximated by Raoult’s law, xw,topPvap w (Ttop), where xw is the mole fraction of water, because xw is high (>0.8) for most aqueous amine solvents. It is observed from simulation studies that xw inside the stripper remains nearly constant around the mole fraction of water in the unloaded fresh solvent, xw,fresh, irrespective of the stage number, especially when the bicarbonate formation is not excessive. On the basis of this observation, eq 8 can be rewritten as follows: * (Ttop , αtop) + x w,freshPwvap(Ttop) Ptotal = PCO 2

(10)

which has two unknowns, Ttop and αtop. Because Ptotal is given, αtop can be obtained from eq 10 if Ttop is known and VLE data for P*CO2(T, α) is available. This enables the computation of mw in eq 9 and then Qregen from eq 6. Additionally, when Δhrxn is given as a function of α only, P*CO2(Ttop, αtop) can be expressed, as in eq 11, by integrating the Gibbs−Helmholtz equation using VLE data at only 313 K. * (Ttop , αtop) = PCO * (313, αtop) PCO 2 2 ⎛ ⎞ Δh ⎛ 1 1 ⎞⎟⎟ exp⎜⎜ − rxn ⎜⎜ − R ⎝ Ttop 313 ⎟⎠⎟⎠ ⎝

(11)

In short, knowing one unknown, Ttop, is sufficient for calculating Qlatent and then Qregen. Examining stripper behaviors can provide a clue to estimate the unknown variable Ttop. 2.4. Stripper Behaviors. Stripper behaviors were investigated for different values of αlean, from 0.15 to 0.375, while T1 and αrich from the absorber were fixed at 40 °C and 0.54, respectively. Three energy terms and Qregen are shown in Figure 2a as αlean varies. Qrxn remains nearly constant over the

Figure 3. Profiles of (a) temperature, (b) CO2 loading in the liquid phase, and (c) mole fractions of CO2 and water in the vapor phase inside the stripper for selected values of αlean.

that region A is governed by the RICHIN stream while region B is governed by the vapor flow from the reboiler. Centering around αlean = 0.2, the temperature, CO2 loading in the liquid phase, and vapor mole fractions of CO2 and water at the bottom stage (reboiler) continue to a near-top stage when αlean < 0.2, and the reverse holds when αlean > 0.2. The column profiles are best balanced, and minimum Qregen is achieved, when αlean = 0.2.

3. SHORTCUT ESTIMATION METHOD FOR REGENERATION ENERGY In the previous section, it was revealed that the stripper behaves differently in high and low lean loading regions. On the basis of this observation, the shortcut estimation method for Qregen is developed separately for each region. 3.1. Shortcut Estimation Method for Region A. For region A, Ttop ≅ TRICHIN = T5 and accurate prediction of T5 is the key to the estimation of Qregen. T5 can be easily obtained from the energy balance around the main heat exchanger shown in Figure 4a. The RICHIN stream is in a partially vaporized state and can be expressed into vapor and liquid streams. The energy balance equation can be written as

Figure 2. (a) Individual energy terms contributing to Qregen and (b) comparison of Ttop with TRICHIN from rigorous simulation for different values of αlean when T1 = 40 °C and αrich = 0.5383.

considered αlean range. Qsens increases with αlean as the solvent circulation rate is increased to achieve 90% CO2 recovery. Qlatent increases sharply as αlean is lowered below 0.2 because of the increased boiling in the reboiler. Qregen is strongly affected by Qlatent and shown to have a minimum at αlean = 0.2. In Figure 2b, Ttop is compared to TRICHIN or equivalently T5. Ttop and TRICHIN agree quite closely in the region of αlean > 0.2

mam(H1 − H2 − H5,liq − H5,vap + H6) = 0

(12)

where H1 and H2 are as given in eq 5. Assuming that the vapor portion of the RICHIN stream is composed of only CO2 and water vapor, the remaining three enthalpy terms are expressed as follows: D


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Figure 5. (a) Temperature estimates and (b) estimates of the energy terms by the rigorous simulation and the shortcut method for region A (RIG, rigorous simulator; SC, shortcut method).

Figure 4. (a) Streams around the main heat exchanger and (b) energy balance boundary for region B.

from the proposed shortcut method for region A. Three energy terms that comprise Qregen calculated by the shortcut method are also displayed in Figure 5b together with Qregen from the rigorous simulation. Quite satisfactory agreement of the reboiler and RICHIN temperatures and the regeneration energy from the rigorous simulation and the shortcut method manifests for region A. 3.2. Shortcut Estimation Method for Region B. Accurate estimation of Qregen for region B is not important because it is not a recommended operating region as a result of high regeneration energy. Nevertheless, Qregen for region B, together with Qregen for region A, enables us to figure out the overall variation of Qregen with respect to αlean and, especially, to locate the region boundary, where Qregen is likely to have the minimum value. To estimate Qregen for region B, energy balance around the reboiler as shown in Figure 4b is used with an additional assumption that the temperature difference between the reboiler (stage 20) and the upper next stage (stage 19) is small, which can be observed from Figure 3a for αlean = 0.15. This assumption is valid, because upward vapor flow with a high enthalpy content from the reboiler renders the temperatures at stages above the reboiler similar. The control surface and streams around the reboiler are defined as shown in Figure 4b. Then, Qregen can be represented as follows:

H5,liq = (cam + rw,5lc w + α5lcCO2)(T5 − TR ) +

∫0

α5l

Δhrxn(α) dα

H5,vap = ((rw − rw,5l)c w + (αrich − α5l)cCO2)(T5 − TR ) + (rw − rw,5l)λ H6 = (cam + rwc w + αleancCO2)(T6 − TR ) +

∫0

lean

Δhrxn(α) dα (13)

Rearranging eq 12 using T2 − T1 = ΔTMTA and m5,vap = mam((rw − rw,5) + (αrich − α5)) yields the following relationship: 0 = (cam + rwc w )(ΔTMTA + T6 − T5) + cCO2(αlean(T6 − T2) − αrich(T5 − T1)) − (rw − rw,5l)λ +

∫α

rich

Δhrxn(α) dα

5l

(14)

Because the column pressure varies only slightly from top to bottom, the same pressure relationship as in eq 10 holds at the reboiler with respect to αlean and T6. * (T6 , αlean) + x w,freshPwvap(T6) Ptotal = PCO 2

(15)

Q regen = −mamH6 − m8H8 + mamH7

T6 can be determined from eq 15. Consequently, eq 14 has three unknowns, T5, rw,5l, and α5l. They can be uniquely determined using the following two more relationships: * (T5 , α5l) + x w,freshPwvap(T5) Ptotal = PCO 2

* (T5 , α5l) PCO αrich − α5l 2 = rw − rw,5l x w,freshPwvap(T5)

(18)

where H6 is in eq 13 and H7 = (cam + rwc w + α7cCO2)(T7 − TR )

(16)

+

∫0

α7

Δhrxn(α) dα

H8 = ((m w,R c w + mCO2,R cCO2)(T6 − TR ) + m w,R λ)/m8

(17)

m8 = m w,R + mCO2,R

Figure 5a compares temperatures at the reboiler, the top stage, and the RICHIN stream from the rigorous simulation to those

(19) E


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Environmental Science & Technology where mw,R and mCO2,R represent molar flow rates of water vapor and CO2 gas from the reboiler. They can be estimated by solving the following:

Figure 7 compares the estimates of regeneration energy from the shortcut method and rigorous simulation under various

mCO2,R = mam(α7 − αlean) mCO2,R m w,R

=

* (T6 , αlean) PCO 2 x w,freshPwvap(T6)

(20)

Finally, α7 can be computed from the VLE relationship if T7 is known. * (T7 , α7) + x w,freshPwvap(T7) Ptotal = PCO 2

(21)

In Figure 3a, we can see that T7 (at stage 19) is slightly lower than T6 (at stage 20) for region B. The difference is approximately a few degrees Celsius at best and becomes smaller as αlean becomes smaller. If T7 can be reasonably assumed, Qregen for region B can be estimated using eqs 18−21. Figure 6 compares Qregen estimates for both regions. In the shortcut estimation for region B, four different T6 − T7 values

Figure 7. Estimates of regeneration energy of MEA and PZ systems from the shortcut method and rigorous simulation under various operating conditions.

operating conditions. Estimates of the regeneration energy by the shortcut method agree well with those from rigorous simulation, especially in region A. Qregen in region B was calculated for various values of T6 − T7, between 0 and 5 °C, and the highest value at each αlean was selected. For PZ, rigorous simulation was prone to fail in region B, and the results could not be provided in Figure 7. VLE data for PZ available in the literature are limited for αlean < 0.2, and the associated VLE model can be unreliable in region B. This might be the cause for the failure of simulation. Three energy terms comprising the regeneration energy calculated by the shortcut method and rigorous simulation are compared in Figure 8. Because caloric data such as the heat of

Figure 6. Comparison of the estimate of regeneration energy from the shortcut method with that from the rigorous simulation.

were assumed. The results for 0.3, 1.0, and 3.0 °C show consistency in that αlean at the region boundary becomes smaller, while the Qregen curve becomes steeper. The result for 5.0 °C of T6 − T7 is out of the consistency mentioned above. This may indicate that a 5.0 °C temperature difference is unrealistic for this system. On the basis of this observation, we suggest a method to estimate Qregen such that Qregen is computed for various values for T6 − T7 (between 0 and 5 °C, for example) simultaneously and select the highest value at each αlean.

4. APPLICATIONS OF THE SHORTCUT METHOD TO OTHER CASES Reliability of the proposed shortcut method was confirmed by applying the method to additional cases and comparing the results to those from rigorous simulations. As additional cases, 7 mol of MEA at 1 bar of stripper pressure and also 8 mol of piperazine (PZ) at 2 and 5 bar of stripper pressure were considered. The proposed method is applicable to other aqueous amine solvent systems as long as VLE and caloric data are available and volatility of amine is much lower than water. Rate and equilibrium models for PZ necessary for rigorous simulation are provided in Aspen Plus and were used without modifications.24 The VLE information from Aspen Plus was used for shortcut simulation. The rich loading corresponding to CO2 vapor pressure of 5 kPa was assumed for all cases, while lean loading was varied.

Figure 8. Estimates of three energy terms comprising regeneration energy from the shortcut method and rigorous simulation for (a) MEA at 2 bar, (b) MEA at 1 bar, (c) PZ at 5 bar, and (d) PZ at 2 bar.

reaction and heat capacity are available or can be predicted easily from VLE data, precise estimates of the sensible heat and the heat of reaction can be performed without much difficulty. Evaluation of the latent heat, the most troublesome part, could also be reasonably performed using the proposed method, as shown in Figure 8. Some deviation exists in the estimation of the latent heat between the two methods. Underlying assumptions, such as m4 ≈ mw in eq 3, xw ≈ xw,fresh in eq 10, F



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and non-volatility of amines, may have led to the uncertainty of estimation. Reliable estimation of the latent heat by the shortcut method in region A can be achieved when top and bottom temperatures of the stripper are correctly predicted and top and RICHIN temperatures agree well with each other, which is one of the key prerequisites of the shortcut method. Figure 9 exhibits the estimates of top, bottom, and RICHIN temperatures from the two calculation methods and shows that the required performances are satisfactorily fulfilled.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +82-2-705-8477. Fax: +82-2-3272-0319. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Korea Carbon Cpature and Sequestration Research and Development Center (KCRC) Grant funded by the Korean Government (Ministry of Education, Science and Technology, 2012-0008886) and the Sogang University Research Grant (201214007).

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REFERENCES

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Figure 9. Prediction of top and bottom temperatures of the stripper and RICHIN temperature from the shortcut method and rigorous simulation for (a) MEA at 2 bar, (b) MEA at 1 bar, (c) PZ at 5 bar, and (d) PZ at 2 bar.

Performance evaluation of a solvent is perhaps the most important but a quite complex and time-consuming step in the development of an economically feasible CO2 capture process. Through this study, a novel but simple method to reliably estimate the regeneration energy, Qregen, for an aqueous amine solvent in a typical absorption-based CO2 capture process with a stripper is proposed. The method requires only VLE and caloric data at various CO2 loadings and temperatures and can provide estimates of Qregen at various conditions of lean loading, rich loading, and stripper pressure. The method was derived from energy balance equations around the stripper under some ideal assumptions plus some crucial approximations drawn from careful examination of stripper characteristics. The assumptions and approximations include non-volatility of amines, two distinctive stripper behaviors depending upon the value of lean loading, coincidence of feed and top-stage temperatures, invariance of water content in the liquid phase, Raoult’s law for water, heat capacity of a mixture as a molar average heat capacity of pure components, etc. Reliability of the proposed shortcut method was verified by comparing it to the results for MEA and PZ systems from rigorous simulation using Aspen Plus. It is believed that the proposed method enables more reliable solvent screening with minimum experimental data, much faster “shortcut” calculation compared to rigorous process simulations, easier optimization of operating conditions, and eventually acceleration of the development of economically feasible absorption-based CO2 capture processes. G


Article

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Novel Shortcut Estimation Method for Regeneration Energy of Amine

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