The data from TCM is for some cases given in units that needs to be converted to be implemented in Aspen HYSYS and Aspen Plus. Some necessary decisions and fittings needed to be done.
Only the absorber is simulated
Experimental data from TCM is converted to units that can be used as parameters in the simulation program
The pressure loss over the absorber is assumed to be zero
The main goal is to achieve the same CO2 removal grade, temperature profile and rich loading as in performance data for the five scenarios.
The second goal is to compare the reliability in predicting performance data for equilibrium-based model with estimated EM-profile and rate-based model with estimated IAF.
3.1.1 Simulation tools
Several simulation programs can be used to calculate CO2 removal by absorption, such as Aspen HYSYS, Aspen Plus, Pro/II, ProTreat and ProMax. In this thesis, the process simulation tool that have been used to perform simulation of CO2 absorption into amine solution are the equilibrium-based models in Aspen HYSYS and Aspen Plus, and the rate-based model in Aspen Plus. The equilibrium-based models are based on the assumption of equilibrium at each stage. By introducing a Murphree efficiency, the model can be extended. Rate-based models are based on rate expressions for chemical reactions, mass transfer and heat transfer.
3.1.2 Murphree efficiency
There are few tools available for the estimation of stage efficiencies in CO2 absorption columns. There is a model available for estimation of Murphree efficiency for one plate in a plate column. The estimation model is based on the work of Tomcej et al., (1987) [35], modified later by Rangwala et al., (1992) [36]. This model is based on the assumption that a pseudo first order absorption rate expression is valid. However, there is no model for estimation of Murphree efficiency for a specific packing section height in a structured packing column.
The calculation of necessary column height for CO2 removal is an important design factor in CO2 absorption using amine solutions. A simple way to improve the available estimation model is to use Murphree efficiencies for a specific packing height. In a plate column, an efficiency value is estimated for each tray based on the ratio of change in mole fraction from a stage to the next, divided by the change assuming equilibrium. In a packed column, a packing height
of e.g. 1 meter could be defined as one tray with a Murphree efficiency. The Murphree efficiencies can be estimated outside the simulation program, before it is implemented to the simulation program. The overall tray efficiency is defined in equation 3.1, as the number of ideal equilibrium trays divided by the actual number of trays.
The Murphree tray efficiency related to the gas side for tray number “n” is traditionally defined by equation 3.2 [37].
Where y is the mole fraction in the gas from the tray, yn+1 is the mole fraction from the tray below and y* is in equilibrium with the liquid at tray n. This is illustrated in figure 3.1.
The Murphree efficiencies of each stage in the 24m high packed column we have at TCM, is estimated for 24 stages of 1m height, the Simulations have been done with both constant and varying efficiency for all stages.
Table 3.1 presents some estimated Murphree efficiency profiles from earlier simulations of TCM data. EM = 0.1 was simulated in Zhu (2015) [27] to see how constant Murphree efficiency impacts the simulating results. He simulated data from Hamborg et al., (2014) [25], and found that the best fit for removal grade and temperature profile was EM = Zhu.
EM = Zhu were later used for several scenarios by Sætre (2016) [28]. EM = Lin, was the best fit, according to Røsvik (2018) [33] where he simulated data from Faramarzi et al., (2017) [32].
The mentioned EM-profiles have been simulated in this report to verify earlier work, and new EM-profiles have been estimated based on these results. EM = SF1 and EM = SF2 have been estimated in this thesis to fit scenario H14, and also scaled to fit the other scenarios by introducing an EM-factor.(See 3.2.2)
𝐸𝑂 =𝑁𝐼𝐷𝐸𝐴𝐿
𝑁𝑅𝐸𝐴𝐿 (3.1)
𝐸𝑀 = (𝑦 − 𝑦𝑛+1)
(𝑦∗− 𝑦𝑛+1) (3.2)
Murphree efficiencies for each meter of the packed column from top to bottom
efficiency, inspired by Øi (2012) [9].
Table 3.1 Murphree efficiencies used in this thesis
3.1.3 Converting Sm
3/h to kmol/h
The inlet gas flow is given in Sm3/h and needs to be given in kmol/h. In 2016, Sætre [28]
created a formula to calculate the mole flow, this is given in equation 3.3. The factor 0.023233 is calculated based on standard conditions chosen by TCM to be 15°C and 1 atm, and the ideal gas law.
He commented that the results from using this formula deviated from measured data for some of the scenarios, where inlet gas flow was given in both volume flow and molar flow. He concluded that these deviations probably occurred due to uncertainties in the measured data of the experimental data at TCM. Therefore he decided to use the calculated molar flow instead of the measured molar flow, for those scenarios. This decision have also been used for this thesis.
3.1.4 Calculating composition of lean amine
The lean amine is specified in the reports from TCM [7] [32], by the following parameters:
Lean MEA concentration in water [wt%]
Lean CO2 loading [mol CO2 / mol MEA]
Lean amine supply flow rate [kg/h]
Lean amine supply flow temperature [oC]
Lean amine density [kg/m3]
To get the most accurate result, it is desired to implement the mole fractions of the lean amine in to the simulations. To accomplish this, some calculation is necessary.
Sætre used a method where he found the molar flow of MEA by using the weight%, mass flow and molar weight, implemented in equation 3.4.
𝑘𝑚𝑜𝑙 𝑀𝐸𝐴
Following, the H2O molar flow can be found with the same method, shown in equation 3.5.
Finally, the CO2 molar flow can be found by implementing the MEA molar flow and Lean CO2 loading into equation 3.6.
𝑘𝑚𝑜𝑙 𝐶𝑂2
ℎ = 𝑀𝐸𝐴 𝑚𝑜𝑙𝑎𝑟 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 [𝑘𝑚𝑜𝑙
ℎ ] × 𝐶𝑂2 𝑙𝑜𝑎𝑑𝑖𝑛𝑔 [𝑘𝑚𝑜𝑙 𝐶𝑂2 𝑘𝑚𝑜𝑙 𝑀𝐸𝐴]
(3.6)
When all the tree molar flows are found the molar fractions is easily calculated and can be implemented to the simulations.
3.1.5 Calculating CO
2removal grade
The CO2 capture efficiency can be quantified in four ways as described in Thimsen et al., (2014) [8] and shown in table 3.2, in addition CO2 recovery calculation is given in table 3.2, and is a measure of the CO2 mass balance [7].
Table 3.2: Methods for calculating CO2 removal grade and CO2 recovery
Method 1 Method 2 Method 3 Method 4 CO2 Recovery
𝑃 𝑆
𝑃 𝑃 + 𝐷
𝑆 − 𝐷
𝑆 1 − 𝑂𝐶𝑂2
1 − 𝑂𝐶𝑂2
(1 − 𝐼𝐶𝑂2) 𝐼𝐶𝑂2
𝐷 + 𝑃 𝑆 S = Flue gas supply OCO2 = Depleted flue gas CO2 content, dry basis
D = Depleted flue gas ICO2 = Flue gas supply CO2 content, dry basis P = Product CO2
In this report method 3, from table 3.2, is used to calculate removal grade. This method is only dependent on the CO2 flow in the flue gas supply and the depleted flue gas, the CO2 flow from the desorber is not included in these calculations. The uncertainty of this method was calculated to be 2,8% in Hamborg et al., (2014) [7], but it was stated that it might be even higher.