Comparison of Solvent Development Options for Capture of CO2 from

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Comparison of Solvent Development Options for Capture of CO2 from Flue Gases Anggit Raksajati, Minh Ho, and Dianne Elizabeth Wiley Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00283 • Publication Date (Web): 27 Apr 2018 Downloaded from http://pubs.acs.org on April 27, 2018

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Industrial & Engineering Chemistry Research

Comparison of Solvent Development Options for Capture of CO2 from Flue Gases Anggit Raksajati 1, 2*, Minh T. Ho 3, and Dianne E. Wiley 3 1

2

Department of Chemical Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia

Indonesia Center of Excellence (CoE) of CCS-CCUS, Institut Teknologi Bandung, Bandung 40132,

Indonesia 3

The University of Sydney, School of Chemical and Biomolecular Engineering, Sydney NSW 2006, Australia

* To whom correspondence should be addressed. E-mail: [email protected]

ABSTRACT

Chemical absorption is widely regarded as the most commercially-ready technology for postcombustion CO2 capture from large industrial emission sources. The benchmark solvent is monoethanolamine (MEA). Alternate solvents to MEA have been developed with improved properties such as solvent loading, regeneration energy, and absorption rate. Improvements in solvent properties can be challenging because of possible adverse interactions between solvent properties. Ideally improving all solvent properties and process designs concurrently is desirable to reduce the total cost of CO2 capture.

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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The changes in cost of CO2 capture for post-combustion CO2 capture from a black-coal power plant using absorption are investigated using Monte Carlo simulations, where key solvent parameters are varied simultaneously. Different classes of solvents are considered covering aqueous and phase-change solvents in conventional and encapsulated solvent systems. The results show that it is not necessary for new solvents to have superior values for all properties. There are combinations of solvent properties where low total capture cost can be achieved because improvements in the more significant parameters offset smaller or negative improvement in other parameters. In particular, low total capture cost can be achieved when solvents have the following properties: good stability towards SOx and NOx; a low heat of reaction; a high absorption rate; a low water vaporization rate; and, a low price per unit of the solvent. The results also show that regardless of the solvent type, different solvent systems can potentially achieve almost the same lowest capture cost of approximately USD 37 to 39 per tonne CO2 avoided.

KEYWORDS: Absorption, CO2 capture, CCS, Economics, Monte Carlo simulation, Solvent

INTRODUCTION Various solvents have been developed for post-combustion CO2 capture in order to improve the performance of the absorption system compared to monoethanolamine (MEA) 1, which has deficiencies during operation including high energy penalty, corrosivity, and high solvent loss 2. The improved performance of the CO2 absorption system in usually due to the improved properties of these alternate solvents. Some of the key solvent parameters that affect performance are: •

Heat of reaction - A low heat of reaction for the solvent could potentially translate to low regeneration energy in the stripper of the absorption system provided that the reduction in heat of reaction is not coupled with high water evaporation 3.

•

Solvent concentration - High solvent concentrations could lead to a lower solvent circulation rate, and hence to a decrease in the dimensions (size) of heat exchangers, the absorber, the stripper, and ACS Paragon Plus Environment

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pipelines. Increasing the solvent concentration could also translate to a decrease in the regeneration energy because of a lower amount of water being fed to the stripper feed 4. •

Solvent working capacity – High solvent working capacity could lead to a reduction in the regeneration energy decreases due to a decrease in the solvent circulation rate 4. Increasing the solvent working capacity could also reduce the dimensions (size) of the absorber, heat exchangers, pumps, and piping 4.

•

Absorption rate – High absorption rate of the solvent could lead to a reduction in the dimensions (size) of the absorber 4.

•

Solvent stability – Low solvent loss could lead to a reduction in the need for solvent make-up 4.

Table 1 summarizes the key solvent properties of various solvent groups (based on their molecular structure) that have been reported in literature. For amine-based solvents, the values of absorption capacity and absorption rate are gathered from a study by Puxty et al. 5, who evaluated the performance of 76 different amines consisting of primary, secondary, tertiary, sterically hindered, and poly amines. For alkali carbonates 7-10, ammonia 11, 12, amino acid salts 13-17, ionic liquids 18, 19, and deep eutectic solvents 20, the level of information about solvent properties is not as extensive and properties for the most commonly studied solvents in each category are shown.

In our previous work

4, 21, 22

, the impacts of varying solvent properties and process configurations on

costs and energy required for aqueous and phase changes solvents were evaluated by varying one parameter at a time, with the other parameters held constant. For encapsulated solvent systems

23

, we

examined the individual impacts of capsule and solvent properties. In this paper, the changes in cost of CO2 capture for post-combustion CO2 capture using absorption are investigated using Monte Carlo simulations, where all key solvent parameters are varied simultaneously. The analysis enables identification of the key solvent properties and other process parameters that should be targeted for future solvent development. The Monte Carlo simulations are conducted for four sets of absorption systems; (1) ACS Paragon Plus Environment

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aqueous solvents in a conventional absorption system, (2) phase-change solvents in a conventional absorption system, (3) aqueous solvents in an encapsulated solvent system, and (4) phase-change solvents in an encapsulated solvent system. Analysis of other types of capture technologies such as solid adsorbents, membranes, low temperature or cryogenic methods are beyond the scope of this paper.

METHODOLOGY PROCESS DESCRIPTION A simplified schematic of the generic CO2 capture process using a conventional absorption system is shown in Figure 1, while Figure 2 shows the simplified schematic of CO2 capture process using an encapsulated solvent system. For the conventional absorption system, the process can be divided into the following sub-blocks; pre-treatment (DCC, blower, and optional FGD and SCR), absorption, rich solvent treatment including the solids handling system for phase-change solvent systems (heat exchangers and optionally a solid-liquid separator), solvent regeneration, and lean solvent treatment. Based on our previous work 21, we assume that a packed column with an effective surface area of 250 m2/m3 is used for both aqueous and phase change solvent absorption, and that the packing has been designed to handle solids. More details on the preferred absorption column for phase change solvent systems can be found in Raksajati et al.

21

.

For the encapsulated solvent system, the solvent fluid is enclosed in a polymeric-based membrane capsule (with a diameter of 100-600 μm and a wall thickness of 10-30 μm). In this scheme, the CO2 gas is absorbed through the capsule wall into the solvent. Based on our previous work

23

, the most

economically-attractive process configuration for this system uses a circulating fluidized bed (CFB) as the absorber and a bubbling fluidized bed as the regenerator with heat recovery applied between the rich and lean sorbent stream. More details on the operation of this system are described in Raksajati et al. 23.

TECHNICAL CALCULATIONS ACS Paragon Plus Environment

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The techno-economic calculations are performed using an in-house techno-economic model developed for CO2CRC. The detailed equations and correlations used in the process model including equations for solvent properties have been described in Raksajati et al. 4 (for aqueous solvent system), Raksajati et al. 21 (for phase-change solvent system), and Raksajati et al. 23 (for encapsulated solvent system). The model is used to calculate the thermodynamic/solvent properties, the mass and energy balance of each equipment, the dimensions of each equipment, and the energy consumption.

Methods for calculating the capture plant capital cost and operating cost, and the specific cost of CO 2 avoided have been described in Raksajati et al. 4. The cost year of this analysis is 2011, with cost reported in US dollars (US$). The costs are evaluated on a pre-tax basis using a real discount rate of 7 % assuming the project life is 25 years. The load factor of the power plant and the capture plant is 85 %. The cost for coal is US$1.5/GJ.

ASSUMPTIONS AND INPUTS This paper uses flue gas from a new build 500 MW super-critical black coal power plant in Australia as the emission source with a CO2 emission intensity of 0.808 tonne per MWh electrical. The flue gas is assumed to contain 13 %-mole of CO2, 75 %-mole of N2, 5 %-mole of O2, 7 %-mole of H2O, 400 ppm of SOx, and 450 ppm of NOx with a pressure and temperature of 1 atm and 130 °C. The analysis assumes that 90 % of the CO2 contained in the flue gas is recovered by the capture plant. In the post-treatment process, the separated enriched CO2 stream is dehydrated and then compressed to 100 bar ready for transport.

The following solvent systems are considered: 1. AQ: Aqueous solvents in a conventional absorption system. 2. PC: Phase-change solvents in a conventional absorption system. 3. ES AQ: Aqueous solvents in an encapsulated solvent system. ACS Paragon Plus Environment

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4. ES PC: Phase-change solvents in an encapsulated solvent system

The following benchmark technologies are used for comparison for each solvent system. For system AQ, the benchmark technology is MEA 30 %-wt. solvent in a packed-column absorber and stripper using Mellapak 250X 4. For system PC, the benchmark technology is promoted potassium carbonate 40 %-wt. solvent in a spray tower absorber in conjunction with a packed-column absorber using Mellapak 250X and with medium grade heat recovery applied (Mid HI) 21. For system ES AQ and ES PC, MEA 30 %wt. and sodium carbonate 30 %-wt. are used as the benchmark technologies, respectively, in a circulating fluidized bed (CFB) absorber and a multi-stage bubbling bed regenerator 23.

The process models for the different solvent systems have been validated as follows. For aqueous solvent systems, results from Raksajati et al. 4 for MEA were compared to results reported by Abu-Zahra et al. 24 and Oexmann et al. 25. This solvent system is well-established and reliable data from a variety of laboratory, pilot plant and commercial installations is accepted in the literature as providing baseline information for comparison purposes. For phase-change solvent systems, results from Raksajati et al. 21 for potassium taurate and potassium carbonate were compared to results reported by Sanchez-Fernandez et al.

13

and Endo et al.

26

respectively. The study from Sanchez-Fernandez et al.

13

provides empirical

expressions for solvent properties estimation, which are derived from experimental data (lab scale) such as vapor-liquid-solid equilibrium (VLSE), slurry test (to develop relationship between solids fraction with liquid loading, temperature, and solvent concentrations). The patent from Endo et al.26 provides solvent properties and process performance based on experimental data (lab and pilot scale) such as vapor-liquidsolid equilibrium (VLSE). For encapsulated solvent systems, results from Raksajati et al.

23

for sodium

carbonate were compared to results reported by Vericella et al. 27 provide estimates of solvent and capsule properties that are derived from experimental data (lab scale), such as vapor-liquid equilibrium (VLE) and capsule properties (thickness, diameter, mass transfer model).


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For each of the solvent systems, two series of Monte Carlo simulations are completed; one where pretreatment with FGD and SCR is not used, and another series where pre-treatment with FGD and SCR is used. For the series without pre-treatment (FGD and SCR) units, it is assumed that the new solvents tolerate high levels of SOx and NOx in the flue gas. To achieve this, the solvent system would need to simultaneously produce by-products with the SOx and NOx, while not affecting the CO2 capture efficiency or causing solvent degradation, e.g. in potassium carbonate and potassium taurate solvents the SOx and NOx form potassium sulphate and potassium nitrate that can be separated as by-products. This is explained in more detail in our previous study 21.

To investigate all of these cases, there are therefore eight Monte Carlo simulations in total, each with 100,000 iterations. When the solvent capacity and concentration are varied, the corresponding equilibrium partial pressure of CO2 in the solution at the bottom of the absorber is assumed to be constant at 10 kPa. The pressure of the stripper is assumed to be constant at 2 bar.

First, eight major solvent parameters that are involved in the estimation of key process performances of post-combustion CO2 capture units (such as energy requirement and dimensions of unit operations), are selected and varied. Then, the respective impact of each solvent property variation on capture cost is evaluated, as explained in the “Capture Cost Distributions from Monte Carlo Simulations” section. The final evaluation in this paper (presented in the “Comparison Against Selected Solvents” section) omits parameters that are classified as having a minimal effect on capture cost based on the earlier analysis, and hence the dimensionality of the parameter search space is reduced and a higher level of insight into process parameters is obtained.

The ranges of inputs for all parameters varied in the eight Monte Carlo simulations are shown in Table 2. The data distribution shape and range for each parameter are selected by examining the likeliest variation for each solvent property based on properties of well characterized solvent systems. There are three types ACS Paragon Plus Environment

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of distribution shapes used, namely triangular, normal, and discrete uniform distributions. To define the triangular distribution, three input values are required for each parameter, namely the minimum, the likeliest, and the maximum value. For the normal distribution, two input values for each parameter are required, namely the mean and the standard deviation. The standard deviation is set so that 95 % of the data falls within the maximum and minimum values shown in Table 2. For a discrete uniform distribution, the only input required is the number of variations (n) for a parameter, where a finite number of n variations are equally likely and every one of the n values has equal probability 1/n. The detailed justification of the minimum, average, and maximum values and the type of distribution used for each parameter in the Monte Carlo Simulations is provided in the Supporting Information.

The Monte Carlo simulation results reported in this paper show the statistical confidence level for estimated capture costs based on the P10/50/90 framework, which is the standard method to estimate oil reserves. We extend this framework by adding outcomes P1, P5, P95, and P99. Mathematically speaking, Px is a number such that there is an x % likelihood that the capture cost is below this number. The values of Px are obtained from a cumulative probability distribution of capture costs.

RESULTS AND DISCUSSION CAPTURE COST DISTRIBUTIONS FROM MONTE CARLO SIMULATIONS Figure 3a, Figure 4a, Figure 5a and Figure 6a show the frequency probability distribution of capture cost obtained from the Monte Carlo simulations for the AQ, PC, ES AQ and ES PC solvents respectively with and without FGD while Figure 3b, Figure 4b, Figure 5b and Figure 6b show the respective cumulative probability distributions. The capture cost of the benchmark technology for each case is also shown in these figures. Table 3 summarizes the key outputs from the Monte Carlo simulations.

Of the cases evaluated, aqueous solvents in a conventional absorption system without FGD and SCR have the lowest Px values across all cost regions. For this solvent system, the difference in estimated cost ACS Paragon Plus Environment

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between the baseline and the minimum value or between the minimum and maximum values is much smaller than the other cases. This reflects the smaller variability in properties used for this solvent system because much more is known about the characteristics of good low cost aqueous solvents. For case ES AQ, the gap between the minimum and maximum is the widest among all cases evaluated, while the gap between the baseline cost and minimum value for case ES PC is the widest reflecting the extra process variabilities for the capsule properties for this system.

Interestingly, all solvent systems have almost the same lowest capture cost. For example, for the cases where the process with FGD/SCR, the minimum costs are approximately USD 53 to 55 per tonne CO2 avoided, while the cases without FGD/SCR result in a significant reduction in minimum costs to values of USD 37 to 39 per tonne CO2 avoided.

To better understand the factors leading to low capture costs a more detailed evaluation of the data in the lowest 5 % (P5) and lowest 1 % (P1) capture cost regions (the outputs that fall in the P1 and P5) was undertaken. Table 4 presents the mode, relative mean |𝝁𝒓𝒍𝒕𝒗 | and relative skewness |𝒔𝒌𝒓𝒍𝒕𝒗 | of the distributions obtained as a result of variations in the key properties. The properties are also classified into five groups based on criteria shown in the caption to the table depending on whether the distribution is not affected, is slightly affected, or is significantly affected by property changes. The cut-off values for determining the size of the effect were selected to achieve sufficient discrimination between the different properties as follows: •

Affects the distribution: |𝝁𝒓𝒍𝒕𝒗 | > |0.15| 𝑜𝑟 |𝒔𝒌𝒓𝒍𝒕𝒗 | > |0.5|

•

Slightly affects the distribution: |0.05| < |𝝁𝒓𝒍𝒕𝒗 | < |0.15| 𝑜𝑟 |0.25| < |𝒔𝒌𝒓𝒍𝒕𝒗 | < |0.5|

•

Does not affect the distribution: |𝝁𝒓𝒍𝒕𝒗 | < |0.05| 𝑜𝑟 |𝒔𝒌𝒓𝒍𝒕𝒗 | < |0.25|

The relative mean and the relative skewness for the lowest 5 % and lowest 1 % of cost outputs are defined as follows. ACS Paragon Plus Environment

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𝜇𝑙𝑜𝑤𝑒𝑠𝑡 5% − 𝜇𝑎𝑙𝑙

Lowest 5 %: |𝝁𝒓𝒍𝒕𝒗 | = |

𝜇100%

𝜇𝑙𝑜𝑤𝑒𝑠𝑡 1% − 𝜇𝑙𝑜𝑤𝑒𝑠𝑡 5%

Lowest 1 %: |𝝁𝒓𝒍𝒕𝒗 | = |

𝑠𝑘𝑙𝑜𝑤𝑒𝑠𝑡 5% − 𝑠𝑘𝑎𝑙𝑙

| ; |𝒔𝒌𝒓𝒍𝒕𝒗 | = |

𝜇𝑙𝑜𝑤𝑒𝑠𝑡 5%

𝑠𝑘𝑎𝑙𝑙

|

𝑠𝑘𝑙𝑜𝑤𝑒𝑠𝑡1% − 𝑠𝑘𝑙𝑜𝑤𝑒𝑠𝑡 5%

| ; |𝒔𝒌𝒓𝒍𝒕𝒗 | = |

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𝑠𝑘𝑙𝑜𝑤𝑒𝑠𝑡 5%

|

For each solvent system, the properties of the low-cost region are almost identical between the series where pre-treatment with FGD and SCR is not used and the other series where pre-treatment with FGD and SCR is used. For each solvent system, the values presented in Table 4 are applicable for both series. The complete results of Monte Carlo Simulations including histograms of different solvent properties for all solvent systems evaluated can be found in Raksajati et al.

28

. The histograms shows the distribution of

the solvent properties for all the outputs in this case, as well as the distribution of the solvent properties for the regions that make up the lowest 5 % and lowest 1 % of costs.

Solvent properties The results in Table 4 and Figure 7 show that the mode values of almost all of the parameters for the outputs that fall into the lowest 5 % and 1% cost regions are better than the mode values of the input data (“all” rows). This suggests that to achieve low capture cost improving all parameters is desirable; however, it is not always necessary that new improved solvents must have superior values for all of their properties.

Of the different solvent properties investigated, the vaporization rate of water is one of the most important parameters that drives the capture cost. The water to CO2 molar ratio typically has values between 0.3 and 0.4 for the lowest 1 % region and values between 0.5 and 0.6 for the lowest 5 % region. Thus, to achieve low cost, new solvents with a low water vapor pressure should be developed.


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Development of solvents with a low heat of reaction is also an important feature for all solvent systems. In order to achieve a low capture cost, new solvents should have a heat of reaction of 50 kJ/mole CO 2 or lower.

For all cases, improving the solvent working capacity will often reduce the capture cost, but only up to a point. Once a ‘good’ working capacity has been reached for a particular solvent, improvements in other properties are likely to have a larger effect of cost than further improvements in working capacity. For the conventional process systems (cases AQ and PC), for the cost to fall within the lowest 5 % cost region, the mode value of this parameter is approximately 10 % higher than the mode value of all the outcomes in these cases. For the encapsulated solvent system (cases ES AQ and ES PC), the mode is approximately 20 % higher than the mode value of all the outcomes in these cases because one of the main drawbacks of an encapsulated solvent system using the CFB configuration is the low solvent working capacity due to the gas and capsule streams flowing co-currently 23. Hence, improvement of solvent working capacity becomes more critical for encapsulated cases than for conventional cases.

The effect of absorption rate on cost is found to be much more significant in conventional absorption systems (AQ and PC cases) than in encapsulated solvent systems (ES AQ and ES PC). For case AQ, the minimum absorption rate observed in the lowest 5 % cost region is approximately 30 % slower than that of MEA 30 %-wt., while the average value is about 40 % faster than MEA. Solvents with mass transfer rate at least equivalent to MEA dominate the lowest 5 % and 1 % of cost outputs contributing to 95 % and 98 % respectively of the total outputs in these regions. Similar trends for the mass transfer rate are also observed for case PC. In contrast, it is found that the use of slow solvents can be economically attractive for the encapsulated solvent systems. For these cases, about 20 % of the outputs in the lowest 5 % cost region have instances where the absorption rate is less than half that of MEA. This analysis suggests that cost reductions are best achieved by developing fast solvents. Such improvements in solvent properties


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affect the size of the absorber, making it smaller and cheaper. However, the use of slower solvents may still be economic if they are applied in a process that provides high surface area to aid mass transfer.

The results in Table 4 and Figure 8 show that improved solvent properties should not be obtained at the expense of high solvent price. It is important to ensure that the price of the solvent itself is kept low and at least as cheap as MEA. For case AQ, to achieve low capture costs that fall within the lowest 5% cost region, the average solvent price is about USD 4/kg, with the majority of outputs (62 %) having a solvent price below this value. However, for some outcomes of case AQ, the use of expensive solvents (e.g. USD 10/kg or higher shown in the tail of Figure 8) may still be economic if the corresponding solvent loss is below 0.4 kg solvent/tonne CO2 captured. These scenarios make up 7 % of the total outputs for the lowest 5 % cost region and 5 % of the outputs for the lowest 1 % cost region. Similar trends for the solvent price are also observed for case PC.

Improvements in solvent properties such as solvent working capacity, heat of reaction, solvent concentration, and water vaporization rate also affect the regeneration energy. For the lowest 5 % and 1 % of cost, the mode values of the regeneration energy for case AQ are 2.4 MJ/kg CO 2 and 2.1 MJ/kg CO2 respectively compared to the mode of the regeneration energy for all of the outcomes of 3.2 MJ/kg CO2. Perhaps not surprisingly, there is also a strong correlation between the capture cost and the regeneration energy, as shown in Figure 9. In order to achieve capture cost equal to the lowest 1 %, the regeneration energy must lie in the range 1.4 – 2.6 MJ/kg CO2. To fall within the lowest 5 % of costs, the regeneration duty can increase to a maximum value of 3.0 MJ/kg CO2. In order to achieve capture cost equal to the lowest 1 % for cases PC, ES AQ, and ES PC, the range of regeneration energy is 1.4 – 3.0 MJ/kg CO2, 1.5 – 2.9 MJ/kg CO2, and 1.3 – 2.8 MJ/kg CO2, respectively.


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Process configuration for phase-change solvents Apart from improving solvent properties, cost reductions for phase-change solvents in a conventional absorption system can be achieved by selecting a suitable process configuration 21. This includes utilising different levels of low-grade heat recovery for the dissolution heat exchanger and adding a solid-liquid separator. As shown in Table 5, outputs with large amounts of heat recovery/integration (High HI) dominate the lowest 5 % and 1 % cost regions by contributing approximately 55 % of the total outputs in these regions. In contrast, the scenarios where Low HI is used account for less than 5% of all the outputs in these low-cost regions. For almost all outcomes with Low HI, the amount of low grade heat available from the power plant is not enough to supply the heat required by the dissolution HE, and some heat is provided by LP steam from the power plant. In these circumstances, the cost to install the heat recovery system is outweighed by any benefits in recovering the low-grade heat. Thus, the development of phasechange solvents with improved properties should be done in conjunction with developments in process design, especially heat utilization.

Table 5 also shows that using a solid-liquid separator for phase-change systems is not always necessary to achieve very low cost. In the lowest 5 % and 1 % cost regions, 62 % and 79 % respectively of the total outputs in this region do not include a solid-liquid separator. This finding also supports results from our previous paper 21. In these cost regions, the solvents tend to already have low regeneration energy because of high working capacity, high concentration and low water vaporization rates or combinations of all three. Using a solid-liquid separator to reduce the amount of solvent being sent to the stripper is therefore not necessary because the benefits of a slight reduction in regeneration energy is counterbalanced by an increase in the capital cost due to an increase in the loading at the top of the absorber.

Capsule properties For encapsulated solvent systems improving the capsule permeability does not significantly reduce capture cost as shown in Table 4. In contrast, improving the capsule thickness has a much more significant ACS Paragon Plus Environment

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effect and is the most important feature among all capsule properties. This is consistent with the results in our previous paper 21. For the ES AQ and ES PC cases, 84 % and 89 % of the outputs in the lowest 5 % cost region have a capsule thickness below 10 μm. For outcomes where the solvents have poor properties (for example low working capacity below 0.2 mole/mole or solvent concentration below 30 %wt.), a very thin capsule ( |𝝁𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒅 |

and

𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡 − 𝜇𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑

0.1 < |

𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡

| < 0.2,

and red indicates

𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡 − 𝜇𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡

| < 0.1,

yellow indicates

𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡− 𝜇𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑

|𝝁𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒅 | and |


or

|𝝁𝒔𝒐𝒍𝒗𝒆𝒏𝒕 | >

| > 0.2

- For absorption rate, working capacity, and concentration, green indicates |

|𝝁𝒔𝒐𝒍𝒗𝒆𝒏𝒕 | < |𝝁𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒅 |

|𝝁𝒔𝒐𝒍𝒗𝒆𝒏𝒕 | < |𝝁𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒅 |

and

𝜇𝑠𝑜𝑙𝑣𝑒𝑛𝑡 − 𝜇𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑

0.1 < |


| < 0.2,

|𝝁𝒔𝒐𝒍𝒗𝒆𝒏𝒕 | > |𝝁𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒅 |

and red indicates

or

|𝝁𝒔𝒐𝒍𝒗𝒆𝒏𝒕 |
0.2

- For the need of FGD and SCR, absorber design, and internal heat recovery, green indicates that current designs are compatible with improved case, yellow indicates unknown compatibility, and red indicates that current designs are not compatible with improved case (or further testing is needed).


28

Page 29 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURES

To atmosphere

Flue Gas

Condenser Q Condenser

CO2 to transport 100 bar Post-treatment & Compressor

Q Cooler

Stripper

Lean Solvent Cooler

Pre-treatment

Absorber Cross HE

Q Stripper Solvent Pump

Reboiler

Figure 1


29


Page 30 of 46

Q condenser Condenser

Condenser feed 90-120 °C

Post-treatment 12

3

Q cooler Solvent Cooler

Hot flue gas > 120 °C

10

By-products removal

18

11

CO2 to compressor

17

Stripper

Pre-treatment 1

7 16 2

14

9

Steam condensate 130-150 °C

Solid-Liquid Separator (optional)

Absorber

13

5 4

Solvent Pump 6

8

Cross HE

15

Reboiler Q stripper

LP Steam from power plant

Dissolution HE (optional) Qdis HE

Figure 2


30

Page 31 of 46

AQ FGD

AQ No FGD

Baseline

Frequency (%)

10

5

0 30

40

50

60

70

80

90

100

110

120

110

120

Capture Cost (2011 USD/tonne CO2 avoided)

Figure 3a AQ FGD

AQ No FGD

Baseline

100

Cummulative cases (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80 60 40 20 0 30

40

50

60

70

80

90

100


Figure 3b


31


PC FGD

PC No FGD

Baseline

Frequency (%)

10

5

0 30

40

50

60

70

80

90

100

110

120

110

120


Figure 4a

PC FGD

PC No FGD

Baseline

100 Cumulative cases (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 46

80 60 40 20 0 30

40

50

60

70

80

90

100


Figure 4b


32

Page 33 of 46

ES AQ FGD

ES AQ No FGD

Baseline

Frequency (%)

10

5

0 30

40

50

60

70

80

90

100

110

120


Figure 5a

ES AQ FGD

ES AQ No FGD

Baseline

100

Cumulative cases (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80 60 40 20 0 30

40

50

60

70

80

90

100

110

120


Figure 5b


33


ES PC FGD

ES PC No FGD

Baseline

Frequency (%)

10

5

0 30

40

50

60

70

80

90

100

110

120


Figure 6a

ES PC FGD

ES PC No FGD

Baseline

100

Cumulative cases (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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80 60 40 20 0 30

40

50

60

70

80

90

100

110

120


Figure 6b


34

Page 35 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ES PC - No FGD ES PC - FGD ES AQ - No FGD ES AQ - FGD PC - No FGD PC - FGD AQ - No FGD AQ - FGD 0

20

40

60

80

100

120

140

160

180

200

Capture Cost (2011 USD /tonne CO2 avoided)

Figure 7


35


1.6 Solvent loss (kg solvent/tonne CO2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 46

1.2

0.8

Lowest 5 % cost Lowest 1 % cost

0.4

0.0 0

5

10

15

20

Solvent price (USD/kg)

Figure 8


36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Regeneration energy (MJ/kg CO2 captured)

Page 37 of 46 4.0

3.0

2.0

Lowest 1% cost Lowest 5% cost

1.0

0.0 30

35

40

45

50

Capture cost (2011 USD/tonne CO2 avoided)

Figure 9


37

Industrial & Engineering Chemistry Research 0.4 Working capacity (mole/mole)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 46

0.3

0.2

Lowest 5 % cost Lowest 1 % cost

0.1

0.0 0

5

10

15

20

25

Capsule thickness (μm)

Figure 10


38

Page 39 of 46 5 Regeneration energy (MJ/kg CO2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


4 3 Lowest 5 % cost AQ No FGD

2

Rest of AQ No FGD Lowest 5 % cost AQ FGD

1 0 30

40

50

60

70

Capture cost (USD/tonne CO2,a)

Figure 11


39

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 40 of 46

TABLES Table 1 Solvent group

Examples

Working capacity Absorption rate (moles CO2/mole solvent) (min-1) 0.15-0.69 5 0.003-0.032 5

Primary amines

Monoethanolamine (MEA) Ethylenediamine (EDA) Secondary amines Diethanolamine (DEA) Diisopropanolamine (DIPA) Tertiary amines Methyldiethanolamine (MDEA) Triethanolamine (TEA) Hindered amines Aminomethylpropanol (AMP) 2-piperidineethanol (2-PE) Polyamines Piperazine (PZ) Hydroxyethylpiperazine (HEP) Alkali carbonates Potassium carbonate (K2CO3) Sodium carbonate (Na2CO3) Ammonia Ammonia (NH3) Amino acid salts Ionic Liquids Deep eutectic solvents

Solvent class Aqueous

0.10-1.0 5

0.004-0.031 5

65-75 6

Aqueous

0.07-1.1 5

0.001-0.008 5

50-65 6

Aqueous

0.25-1.0 5

0.003-0.02 5

50-75 6

Aqueous

0.50-1.8 5

0.003-0.3 5

66-78 6

Aqueous

0.001-0.002 9, 10

30-50 7, 8

0.35 11, 12

Comparison of Solvent Development Options for Capture of CO2 from

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