Process Simulation and Evaluation of Carbon Separation Technology from High-CO₂ Gas Wells in Indonesia Using a Solvent and Adsorbent

3.1. Absorption and Thermodynamic Model

The most common and proven method for removing CO₂ is through the absorption process in a solvent containing amine, followed by desorption. The simplest and most used amine for CO₂ removal is MEA (monoethanolamine). In its application, MEA concentration is limited to 30% due to its corrosive nature.

In the study by Luo and Wang [12], the PC-SAFT EOS was used to calculate vapor phase properties, and the eNRTL method was used to model the electrolyte system of the MEA-H₂O-CO₂ mixture. Most of the eNRTL model parameters used in that study were obtained from the SRK-ASPEN databank, and some were updated by recent studies through regression using new experimental data.

The liquid-phase chemical reactions involved in the MEA-H₂O-CO₂ system can be expressed as follows:

R1: water dissociation

R2: dissociation of CO₂

R3: dissociation of carbonate

R4: dissociation of the protonated amine

R5: carbonate formation

The traditional method for modeling absorption columns is by using equilibrium methods. Each stage can be calculated by assuming equilibrium between the CO₂ concentrations in the gas and the liquid leaving that stage. This equilibrium stage model can be represented with efficiency for each stage. The Murphree efficiency for stage number n is defined in Eq-1 as follows.

(1)

where y is the mole fraction of CO₂ in the gas leaving the stage, Yn-1 is the mole fraction leaving the stage below, and y* is the mole fraction of CO₂ in equilibrium with the liquid leaving the stage. Most process simulation programs have a model for applying Murphree efficiency in column modeling. Aspen HYSYS has a specific estimation method for predicting Murphree efficiency, which is based on experience with CO₂ removal from high-pressure natural gas.

3.2. Operating Condition

Gas streams containing a target component (e.g., CO₂) are passed through an absorption column where it contacts a liquid absorbent. The target component dissolves in the liquid, effectively removing it from the gas stream. The rich absorbent (now containing the absorbed target component) is then heated or otherwise treated in a regeneration unit to release the absorbed component. This step separates the target component from the absorbent, allowing the absorbent to be recycled for reuse. The process flow diagram typically includes the main units for acid gas removal, namely the absorption column, heat exchanger, and regeneration column, as illustrated in Fig. (1).

The fixed operating conditions for the three gas fields are presented in Table 3. The remaining details will be discussed in the following section.

3.3. Energy and Recovery

The absorber performance is represented by CO₂ recovery, which indicates how much CO₂ from the feed gas can be removed during the absorption process. CO₂ recovery is defined as the amount of CO₂ in the product gas compared to the amount of CO₂ in the feed gas, as shown in the following equation 2:

(2)

In addition to CO₂ recovery, energy consumption is another critical parameter for evaluating the performance of different technologies. To compare it with adsorption, it is necessary to standardize the units expressed in kJ based on a 2400-second time frame, which corresponds to the duration of the adsorption cycle. as shown in eq 3 and 4:

(3)

(4)

4.1. Pressure Swing Adsorption

Pressure Swing Adsorption (PSA) is a cyclic adsorption process primarily designed for gas separation and purification, and it is widely used for gas separation. Industrial processes typically employ more than one pair of adsorption and desorption columns operating in parallel. The Pressure Swing Adsorption (PSA) process involves multiple stages, where pressure changes within the adsorption bed, the duration of each stage, and the sequence of valve operations vary according to Hosseini and Denayer [13].

First, gas flows into the adsorption column (Bed1) and increases the pressure to the desired level (PR). The process enters the adsorption stage (AD), where the second adsorption column (Bed2) contains more CO₂, and the stream leaving Bed1 has a low CO₂ concentration (high CH₄ content). Then, part of the CH4 product is transferred to Bed2 to assist in adsorbent regeneration. After that, Bed1 becomes saturated, and Bed2 is regenerated. The process then proceeds with the depressurization equalization step (DPE), which is crucial for reducing energy consumption by minimizing gas compression. After this step, the process is repeated with the roles of the two adsorption beds reversed, as shown in Fig. (2). This basic process includes the following six steps:

1. Pressurization (PR);

2. Adsorption (AD);

3. Depressurization equalization (DPE);

4. Blowdown (BL);

5. Purging (PU);

6. Pressure equalization (PPE).

The Pressure Swing Adsorption (PSA) simulation using a dual bed, as shown in Fig. (3), was conducted using Aspen Adsorption. To represent the dual bed, the interacting single bed full flowsheet approach can be employed to simulate the complete cyclic system of interacting units. This approach is valid under the following assumptions:

• Each adsorbent layer is identical
• Only one adsorbent layer needs to be modeled accurately
• A set number of interactions can be applied
• The material transferred to the interacting bed is reused (replayed) within the cycle

4.2. Simulation Flowsheet

4.2.1. Adsorber and Adsorbent Specifications

The adsorbent material is crucial to the PSA unit. All cycle properties (operating conditions and modes) depend on the initial choice of absorbent. A study from Djeridi [15] has demonstrated that not only the micropore volume but also the structural, textural, and electrical characteristics affect CO₂ adsorption capacity. Several materials can be used in PSA technology, and the selected material must meet at least one of two criteria:

• The material must have a higher selectivity for CO₂. This type of material is referred to as an equilibrium-based adsorbent because its selectivity is mainly due to differences in interaction forces between CO₂ and CH4 with the surface.
• Adsorbent pores can be arranged to allow CO₂ (kinetic diameter 3.4 Å) to easily penetrate their structure. This material is known as a kinetic adsorbent because its selectivity is primarily due to diffusion constraints. Carbon molecular sieves (CMS) are among the most widely used materials for CO₂ separation.

The adsorption equilibrium isotherms for CO₂ and CH₄ in CMS-3K are shown in Fig. (4). While this material clearly has selectivity for CO₂, the most important property of CMS-3K is not its equilibrium selectivity, but its kinetic selectivity according to Grande [16].

Grande and Rodrigues have studied the performance of CMS-3K adsorbent in a five-step process compared to zeolite adsorbent in a four-step process. The results show that both adsorbents achieved CH4 purity above 98%, but the CH4 recovery with CMS-3K was higher than with zeolite, reaching 80% compared to 60% for zeolite. The molecular-sized pores in CMS provide high kinetic selectivity and adsorption capacity for various gases. Therefore, CMS was selected for this study based on the parameters provided in Table 4, as described by Kottititum et al. [14].

Table 4.

Parameter Name	Value	Unit
Adsorbent bulk solid density	715.93	kg/m3
Adsorbent particle radius	9e-04	m
Porosity	0.33	-

The isotherm parameter values were obtained from experimental data by Kottititum et al. [14], and the Langmuir isotherm equation is provided in Table 5.

Table 5.

Description	IP1 (x10^-6)	IP2	IP3	IP4
CH4	6.31	1056	4.7e-05	1067
CO2	12.7	1187	1.23e-04	1210

Mass transfer coefficients and the equilibrium adsorption model are key parameters for validating the modeling results with experimental data. In this study, the values obtained from the simulation by Kottititum et al. [14] are used, and the corresponding values can be found in Table 6, along with several other parameters.

4.3. Adsorption Isotherm

The most found in the literature is the Langmuir isotherm, which also serves as the basis for other types of isotherms. The Langmuir isotherm is one of the simplest models available to represent the interaction between adsorbent and adsorbate. The Langmuir equation (5) is given as:

(5)

Where:

• q is the adsorption capacity, in kmol/kg,

• pi is the partial pressure of the component

• qs is the maximum adsorption capacity in kmol/kg,

• b is the Langmuir parameter in bar

Various extensions and advanced versions of the Langmuir isotherm have been developed to be applied in different situations. For instance, the Extended Langmuir is often used for multicomponent systems. The Langmuir variation applied in Aspen Adsorption is provided in Table 7, where the fitting parameters are denoted with the notation IP. Further details on this equation can be found in the help section of the simulation software, according to Maksimov, [17].

Table 7.

Isotherm Variation	Isotherm Equation
Langmuir 1	(6)
Langmuir 2	(7)
Langmuir 3	(8)
Extended Langmuir 1	(9)
Extended Langmuir 2	(10)
Extended Langmuir 3	(11)

4.4. Mathematic Model

In this study, the Peng-Robinson model was selected due to its suitability for non-ideal gas phases and its widespread use in CO₂/CH₄ separation via the PSA (Pressure Swing Adsorption) technique according to Kottititum et al. [14]. Meanwhile, the adopted momentum balance is based on the Ergun equation 12, which is expressed as follows:

(12)

In this study, the Extended Langmuir 2 model was used, expressed as a function of the partial pressures of various components, as presented in Aspen Adsorption, and capable of accounting for temperature dependence. Meanwhile, the energy equilibrium model used is the non-isothermal model with gas and solid conduction Kottititum et al. [14].

In this case, a lumped resistance model with a linear form was chosen, which simplifies the analysis by considering only one resistance per component. Therefore, as previously mentioned, the analysis was simplified by using a single kinetic parameter for CH4 and CO₂. This kinetics model was adjusted using the linear driving force model, which is commonly applied in kinetic studies shown in eq 13.

(13)

4.5. Cycle Organizer

To create a cyclically operated process, a tool called Cycle Organizer can be used in the simulation with Aspen Adsorption. In this study, it is necessary to set the time for each stage (control variable), as shown in Table 8.

In addition to setting the time in the Cycle Organizer, manipulated variables must also be specified. These variables include the valve opening and closing settings, as well as the valve specifications. The valve operation schedule is arranged using the numbering system, as shown in Table 9.

4.6. Energy and Recovery

The performance of the adsorber is represented by CO₂ recovery, which indicates how much CO₂ from the feed gas can be removed during the adsorption process. CO₂ recovery is defined as the amount of CO₂ in the product gas compared to the amount of CO₂ in the feed gas, as shown in the following equation:

(14)

In addition to CO₂ recovery, which is a key performance parameter in this experiment, energy consumption is also an important parameter for evaluating the effectiveness of the technology.

5.1. Absorption using Aspen HYSYS

This flowsheet is used to simulate data from the three gas fields involved in the study, as shown in Fig. 5. However, adjustments in pressure and temperature are made before the gas enters the absorber column, which is designed for a pressure of 51 bar and a temperature of approximately 40°C. Although the flowsheet for the three gas fields appears similar, the design specifics, such as the number of trays and the solvent used, will be adjusted according to the conditions of each field to achieve recovery values comparable to those of other technologies, such as adsorption. Additionally, it is crucial to ensure that the temperature of the liquid product from the absorber, which contains MEA, does not exceed 120°C to prevent degradation.

With varying CO₂ compositions in the three gas fields, adjustments are made to the absorber operating conditions, such as the number of trays and the amount of solvent for each field, as shown in Table 10. There is a relationship between CO₂ concentration, solvent amount, and the outlet temperature of the amine (rich amine). Since the reaction between CO₂ and amine solvent is exothermic, a higher CO₂ concentration leads to a higher temperature of the product exiting the absorber. This can be mitigated by increasing the amount of solvent, which lowers the outlet temperature of the absorber. As explained, the temperature of the rich amine must be maintained around 120°C to prevent degradation. For more details, the operating conditions of the absorber and stripper are provided in Table 10.

Before applying the model to scenarios with relevant CO₂ concentrations, a preliminary simulation based on the work of Wang et al. [18] was conducted to validate the assumptions outlined earlier. Wang et al. [18] studied CO₂ absorption using MEA and validated their model using real operational data.

After making several adjustments, the CO₂ recovery values achieved are as follows: for the Masela field, CO₂ recovery is 93% with CH₄ purity at 99%; for the JTB field, CO₂ recovery is 95.43% with CH₄ purity at 97.24 mol%; and for the Natuna field, CO₂ recovery is 93.68% with CH₄ purity at 85.2 mol%. From the absorption process, the energy consumption is detailed in Table 11. The highest energy usage is observed in the condenser and reboiler, which are part of the stripper. As the separation process in the stripper becomes more efficient and the amount of solvent increases, the energy consumption also rises.

5.2. Adsorption using Aspen Adsorption

In the simulation, a PSA approach with a single bed is used, as shown in Fig. 6, with the composition and flow rates adjusted according to the natural gas conditions produced by each field. In the PSA cycle, adsorption occurs at a pressure of 10 bar, while purging takes place at a pressure of 6 bar.

In this study, a single PSA cycle using dual beds consists of 6 steps controlled by valves. These valves can be adjusted using the Cycle Organizer, with specifications provided in Table 12. The use of these 6 steps is expected to help reduce energy consumption.

The adsorption simulation is conducted by dividing the feed gas flow rate, as in real-world applications where adsorption is arranged in parallel with multiple pairs of PSA beds. The recovery value in the adsorption process is influenced by several factors, such as the type of adsorbent and the duration of adsorption. Longer adsorption durations result in greater CO₂ absorption by the adsorbent; however, there is a limit to the adsorbent's capacity to absorb CO₂ due to saturation levels. Further information about the operating condition of the adsorption column is shown in Table 13.

To ensure the accuracy of the assumptions described above, a preliminary simulation was conducted for validation before applying the model to scenarios with relevant CO₂ concentrations. The validation is based on the work of Kottititum [14], which investigated adsorption for biogas purification and validated their model using real operational data.

Before entering the adsorption bed, the feed gas undergoes pretreatment to reduce the temperature and pressure to 25°C and 10 bar. The total energy consumed during the PSA process to achieve CO₂ recovery values of 93% for the Masela field, 95.15% for the JTB field, and 93.06% for the Natuna field is noted. The energy required for regeneration in this process is significantly lower compared to solvent regeneration in absorption, as shown in Table 14. This value will increase with higher CO₂ absorption and when aiming for higher recovery values.

5.3. Energy Comparison of Absorption and Adsorption for Each Field

From the simulation results of CO₂ separation for the Masela, Jambaran Tiung Bitu, and Natuna gas fields, it is observed that adsorption technology performs better in terms of energy consumption compared to absorption technology, as shown in Table 15. These findings are supported by Zanco et al. [7], who stated that among absorption, adsorption, and membrane technologies for a 12% CO₂ feed, absorption has higher energy consumption. However, this comparison is based solely on energy consumption, and the results may differ when considering the costs associated with both technologies. According to Zanco et al. [7], while adsorption offers lower energy consumption and can handle relatively high CO₂ concentrations, its application is typically limited to small-scale plants. In contrast, absorption, despite its higher energy consumption, has been proven effective in several large-scale plants. Therefore, it is important to note that a comprehensive evaluation should also include cost analysis, which will be addressed in future work.

Table 15 also shows that although the Masela field has a CO₂ concentration of 10% and a flow rate of 1200 MMSCFD, and the JTB field has a CO₂ concentration of 35% and a flow rate of 340 MMSCFD, the energy consumption for CO₂ separation from natural gas in both fields can be similar. This is due to the total mass of CO₂ that needs to be removed from the gas stream being nearly identical in both fields. The Masela field, despite having a lower CO₂ concentration, processes a significantly larger gas volume, resulting in a substantial total mass of CO₂. Conversely, the JTB field, with a higher CO₂ concentration but a lower flow rate, also produces a comparable total mass of CO₂. Thus, the energy required for CO₂ separation in both fields is almost the same despite differing operational conditions and CO₂ concentrations.

5.4. Effects of CO₂ Concentration, Pressure, and Temperature

The three gas fields used in this study exhibit different operating conditions in terms of CO₂ concentration, temperature, flow rate, and pressure. However, among these four variables, CO₂ concentration has the most significant influence on determining the CO₂ separation technology. Therefore, an analysis was conducted to assess the impact of CO₂ concentration on the separation performance (recovery) of the absorber and adsorber.

To ensure a fair comparison, simulations were performed with the same flowrate, pressure, and temperature for all three fields. The selected conditions were a flowrate of 340 MMSCFD, pressure of 51 bar, and temperature of 40°C. Additionally, the operating conditions of each piece of equipment within the CO₂ separation unit were kept constant, ensuring that the energy consumption across the fields remained comparable. This approach provides a clearer understanding of how varying CO₂ concentrations influence the performance of both absorption and adsorption processes.

On the other hand, from the absorber simulation using Aspen Adsorption, the performance of the absorber for the three fields is shown in Fig (7). It was concluded that the adsorber performed best in the JTB field with a gas concentration of 35%. For the Masela gas field with 10% CO₂, the performance was the lowest, followed by the Natuna gas field with 71% CO₂. This is due to the characteristics of the adsorbent, which has an optimal capacity at medium concentrations, like in the JTB field.

At low concentrations, such as in the Masela field, the adsorbent does not work as efficiently because there is insufficient gas to reach optimal saturation. Meanwhile, at very high concentrations, such as in the Natuna field, the adsorbent quickly reaches its saturation point, reducing the efficiency of further CO₂ absorption. This highlights how the performance of the adsorber is strongly influenced by the concentration of CO₂, with mid-range concentrations providing the best balance between adsorption efficiency and gas availability.

To validate the results of the CO₂ separation technologies, the simulation outcomes were compared with previous studies and experimental data. The effect of CO₂ concentration on recovery rates in absorption and adsorption processes was evaluated against comparable studies by Mustafa et al. [19] for the absorption study and Kottititum et al. [14] for the adsorption study, which reported consistent trends.