The process used Methyl Diethanol Amine (MDEA) solution as the gas absorption solvent (lean solution). According to the reaction mechanism between CO₂ and the amine solution, it is known that the reaction is reversible. Higher pressures can promote the forward absorption reaction, increasing the absorption of CO₂. The process simulation operated under the following assumptions [33]: (1)

The mole fractions of CH₄ and CO₂ in the feed gas were 67 mol% and 33 mol%, respectively.

The simulation was conducted using Aspen HYSYS 14.0 [34,35] to simulate and optimize the energy consumption and performance of the designed process. The gas–liquid reaction between CO₂ and MDEA is assumed to reach thermodynamic equilibrium within each tray, which is a reasonable approximation under typical industrial operating conditions due to the fast reaction rate of CO₂ with amines. Therefore, explicit kinetic parameters were not introduced. This assumption allows for reliable prediction of process performance and energy consumption, as validated by previous studies [26,28]. The feed biogas was simplified to a binary CO₂/CH₄ mixture, as trace components are typically removed by pretreatment and their inclusion is not central to the core CO₂/CH₄ separation mechanism studied here.

The absorption process consists of a gas compression unit, an absorption tower unit, and a desorption tower unit. The specific simulation flow is illustrated in Fig. 1. Biogas undergoes compression (Com-1, Com-2) and cooling (E-3, E-4) before entering the absorption tower unit (T1). The product gas, after the reaction, is discharged from the top ofT1, while the MDEA rich solution exits from the bottom of T1 and undergoes pressure reduction via a pressure control valve to 110 kPa. It then undergoes heat exchange with MDEA lean solution (E-1) before entering the desorption tower (T2) for regeneration of the absorbent solution. The regenerated MDEA lean solution is pumped (Pump-2), cooled (E-2), and returned to the absorption tower for further reaction. The desorbed CO₂ (Rich CO₂) is discharged from the top of T2. In this process, the amine loss in the desorption tower is relatively low; therefore, wash trays were not installed. To maintain the system amine balance, an amine makeup point was set in stream M9 to sustain absorption performance.

Higher pressure facilitated the reaction between CO₂ and the methyldiethanolamine (MDEA) solution, but it also increased compression energy consumption and equipment costs. Therefore, it was crucial to determine the appropriate absorption pressure. For simulation purposes, an MDEA absorbent concentration of 20 mol% [24,26], a volumetric flow rate of 3 kmol/h, a plate tower with 20 trays [28], and an internal pressure drop of 5 kPa within the tower were assumed [33].

The absorption process was simulated using Aspen software. Fig. 2 illustrates the effects of operating pressure in the absorption column on CO₂ absorption efficiency, methane (CH₄) concentration, and product gas yield. As the absorption pressure increases, both CO₂ absorption efficiency and CH₄ concentration in the product gas rise markedly in the medium-pressure range, while the improvement becomes progressively less significant at higher pressures. At an absorption pressure of 200 kPa, the CH₄ concentration in the product gas is 91.90 mol%, and the CO₂ absorption efficiency is approximately 88%. When the pressure is elevated to 400 kPa, the CH₄ concentration increases substantially to 96.03 mol%, accompanied by a clear enhancement in CO₂ capture capacity. However, beyond this point, the rate of improvement declines significantly. At 600 kPa, the CH₄ concentration reaches 96.81 mol%, and at 1100 kPa it increases marginally further to 97.31 mol%. Additional pressure increases yield only negligible gains in CH₄ concentration while sharply increasing compression energy consumption. This trend is consistent with the findings of Zhu et al. [26] and Deng et al. [28], who simulated natural gas decarbonization using MDEA absorbents and reported that higher pressures substantially improve CO₂ absorption efficiency and CH₄ purity. These results indicate that moderate pressures can effectively enhance CO₂ absorption due to stronger gas–liquid contact and higher mass transfer rates. However, once the system approaches equilibrium, further pressurization offers limited benefits and instead leads to higher energy consumption and capital costs. Considering the trade-off between product gas purity and energy demand, this study identifies 1200 kPa as the optimal absorption pressure for subsequent simulations. Under this condition, the CH₄ concentration in the product gas reaches 97 mol%, which meets the purity requirements for natural gas pipeline transmission.

When the concentration and flow rate of the absorbent solution are constant, increasing the absorption pressure to a certain value stabilizes the CO₂ absorption efficiency. Therefore, optimization of the monoethanolamine (MDEA) absorbent solution concentration and flow rate was carried out under an absorption pressure of 1200 kPa.

After Aspen simulation calculations, the effects of flow rate on CO₂ absorption efficiency and CH₄ concentration were analyzed for lean liquid concentrations of 15%, 20%, 25%, and 30% as shown in Fig. 3. As depicted in Fig. 3, the trends of how flow rate affects CO₂ absorption efficiency and CH₄ concentration are similar across different concentrations. When the flow rate of the lean liquid is constant, increasing the MDEA concentration from 15 to 20 mol% results in increased CO₂ capture. However, further increases in the lean liquid concentration do not significantly raise CH₄ concentration; instead, there is a declining trend. This is because the higher MDEA concentration inhibits the ionization of water molecules, affecting the reactions of CO₂ and OH-, thereby reducing both CO₂ absorption efficiency and CH₄ concentration. Therefore, a lean liquid concentration of 20 mol% of MDEA is chosen for efficient CO₂ absorption. Similar non-linear effects of amine concentration on CO₂ solubility have been reported in recent thermodynamic studies [39].

When the lean liquid concentration is held constant, increasing the flow rate of the lean liquid results in an initial increase followed by stabilization of CO₂ absorption efficiency and CH₄ concentration. At lower flow rates of the lean liquid, the MDEA solution may not completely absorb CO₂ from the mixed gas. Increasing the flow rate of the lean liquid enhances the MDEA content, effectively promoting early-stage absorption of CO₂ by the lean liquid and rapidly increasing the CH₄ concentration. However, once the lean liquid flow rate reaches a certain threshold, mass transfer limitations restrict further reaction progress, leading to a balanced CO₂ absorption reaction. Continuously increasing the lean liquid flow rate does not significantly enhance CH₄ concentration. On the contrary, a larger amount of lean liquid increases regeneration energy consumption and circulation load, thereby raising overall operational energy consumption and CH₄ recovery energy. Under an absorption pressure of 1200 kPa, with a lean liquid of 20 mol% MDEA and a flow rate of 2.5 kmol/h, CO₂ absorption efficiency reached 94.28%, and the CH₄ concentration in the product gas was 97.04 mol%. These values meet natural gas pipeline transportation standards. Therefore, these parameters are selected as optimal for the concentration and flow rate of the lean liquid in the absorption process.

CO₂ absorption is an exothermic reaction, and the temperature of the lean liquid entering the absorption tower affects the efficiency of CO₂ absorption and the methane (CH₄) concentration in the product gas. Therefore, this section focuses on optimizing and analyzing the temperature of the lean liquid. Based on the optimized process parameters mentioned above—absorption pressure of 1200 kPa and lean liquid concentration of 20 mol%—simulation analysis was conducted to assess the impact of different flow rates and temperatures of the lean liquid on CO₂ absorption efficiency and CH₄ concentration.

Fig. 4 illustrates the effect of the lean solvent inlet temperature on CO₂ absorption efficiency and methane concentration. As the lean solvent temperature increases, both CO₂ absorption efficiency and CH₄ concentration exhibit a similar decreasing trend, which is consistent with the findings reported by Zhang et al. [40]. In addition, the most pronounced decline is observed when the lean solvent flow rate is 2 kmol/h. CO₂ absorption by the lean liquid is an exothermic reaction, where lower temperatures favor the equilibrium shift towards CO₂ absorption. As the lean liquid temperature rises, the occurrence of reverse reactions reduces CO₂ absorption efficiency, leading to an increased CO₂ content in the product gas discharged from the top of the absorption tower and a subsequent decrease in CH₄ concentration. According to the graph, when the lean liquid flow rate is 2 kmol/h, increasing the lean liquid temperature from 303.15 to 323.15 K results in CO₂ absorption efficiency and CH₄ yield decreasing to 59.95 and 82.68 mol%, respectively. Once the lean liquid flow rate exceeds a certain value (greater than 2.5 kmol/h), increased absorption of CO₂ by more MDEA reduces the saturation of the rich liquid. Therefore, effective CO₂ absorption can still be maintained within the temperature range of 293.15 to 308.15 K. Although lower temperatures of the lean liquid can enhance CO₂ absorption efficiency and CH₄ concentration, cooling the lean liquid to low temperatures requires substantial cooling water and pump power consumption at the bottom of the regeneration tower. Additionally, in practical production, the viscosity of the solution increases with decreasing reaction temperature, leading to reduced diffusion coefficients and absorption rates. Based on the analysis above, at a lean liquid flow rate of 2.5 kmol/h, a lean liquid temperature of 298.15 K is chosen as optimal for entering the absorption tower.

A tray column was selected for the absorption process, and the number of trays directly affects the mass transfer efficiency between the gas and liquid phases. Fig. 5 illustrates the relationship between the number of trays, CO₂ absorption efficiency, and methane concentration in the product gas. Under constant operating conditions—namely a lean solvent concentration of 20 mol% MDEA, temperature of 298.15 K, flow rate of 2.5 kmol/h, and absorption pressure of 1200 kPa—increasing the number of trays enhances both CO₂ absorption efficiency and CH₄ concentration. This observation is consistent with common industrial practice. The vertical axis values correspond to separation parameters at each plate level. As the number of plates in the absorption tower increases (15, 20, 25), both CO₂ absorption efficiency and CH₄ concentration show an increasing trend. Specifically, CO₂ absorption efficiencies are 88.60, 94.29, and 97.21 mol%, respectively, while CH₄ concentrations are 94.46, 97.04, and 98.43 mol%, respectively. This is because increasing the number of plates enlarges the contact area between CO₂ and the lean liquid, thereby facilitating more CO₂ absorption by the lean liquid. However, the relationship between CO₂ absorption and the number of plates is not linear. At 25 plates, CO₂ absorption by the lean liquid approaches saturation, and both CO₂ absorption efficiency and CH₄ concentration stabilize without significant increases. In addition, an excessive number of trays increases both equipment manufacturing costs and operating expenses. From an economic perspective, the number of trays is directly related to the following aspects: (1) Equipment manufacturing cost: The more trays there are, the greater the column height and the higher the complexity of the internal structure; (2) Footprint and installation cost: Increasing the number of trays leads to larger column dimensions and higher infrastructure investment; (3) Operation and maintenance cost: A greater number of trays may result in increased pressure drop (although a per-tray pressure drop of 0.25 kPa is assumed in this study), thereby raising energy consumption. Therefore, selecting 20 trays represents a reasonable techno-economic compromise, as it achieves the required CH₄ concentration in the product gas to meet the natural gas pipeline transmission standard (≥97 mol%) while avoiding excessive investment and operational burden.

After the MDEA solution in the absorption tower absorbs CO₂, it undergoes heating regeneration in the regeneration tower. The regenerated MDEA lean solution is pressurized, cooled, and then returned to the absorption tower for the next absorption cycle. The performance of the reboiler in the regeneration tower determines the regeneration capacity of the absorbent solution.

Fig. 6 depicts the effect of reboiler power on CO₂ desorption rate in the rich solution and CH₄ concentration in the product gas. As shown in the graph, increasing reboiler power initially enhances both CO₂ desorption rate and CH₄ concentration in the product gas, followed by stabilization. At a reboiler power of 3 kW, the CO₂ desorption rate reaches 89.68%, indicating that most of the CO₂ absorbed in the MDEA solution has been removed. Increasing the reboiler power to 4 kW raises the CO₂ removal rate to 99.67%. However, since the residual CO₂ in the MDEA solution is minimal at this point, further increasing reboiler power does not significantly improve CO₂ removal rate. At this stage, the CH₄ concentration in the product gas discharged from the top of the absorption tower stabilizes at 97.01 mol%, with a methane recovery rate of 99.78%. Based on the above analysis, a reboiler power of 4 kW is chosen as optimal. The choice of 10 trays in the regenerator can be justified from the following perspectives. First, unlike the absorber, the main objective of the regenerator is to fully desorb CO₂ from the rich solvent. The performance of the regenerator is relatively less sensitive to the number of trays compared to the absorber, which requires highly efficient multi-stage gas-liquid mass transfer. Therefore, increasing the number of trays in the regenerator has a limited impact on overall performance. Second, as shown in Table 1 and Fig. 6, the current configuration requires only 4 kW of reboiler duty to achieve a CO₂ removal rate of 99.67%, while maintaining a CH₄ concentration of 97 mol% in the product gas, indicating that the system is already in an efficient regeneration state. The existing 10 trays are sufficient to meet the mass transfer and separation requirements. Reducing the number of trays further would result in a CH₄ concentration in the product gas that does not meet the required standard of 97 mol%. For instance, as shown in Table 1, when the tray number is reduced to 5, the CH₄ concentration in the product gas drops to 96.9 mol%, failing to meet the 97 mol% requirement. Finally, increasing the number of trays results in higher capital costs, larger equipment size, increased reboiler duty, and pressure drop. However, the simulation results indicate that with the current 10 tray configuration, the CO₂ concentration in the MDEA rich solvent is already close to complete desorption, and adding more trays would have negligible benefits in improving the product gas purity.

Table 2 presents the relationship between reboiler power and CO₂ desorption gas flow rate. As reboiler power gradually increases, the amount of CO₂ desorbed in the regeneration tower also increases, leading to a decrease in the CO₂ content in the product gas discharged from the absorption tower. Increasing the reboiler load in the regeneration tower can raise the temperatures and CO₂ removal efficiency of the MDEA-rich solution before and after the heat exchanger. Therefore, enhancing the temperature of the MDEA-rich solution entering the heat exchanger can improve CO₂ removal efficiency under the same reboiler power. It should be noted that although the inlet and outlet temperatures of the lean–rich heat exchanger (E-1) vary under different reboiler duties (Table 2), the heat-exchanger configuration itself was kept constant, and the minimum temperature approach remained above 5 K. The temperature variations arise solely from process coupling rather than changes in heat-exchanger design. Therefore, optimization of heat-exchanger parameters is not included in this system-level study.

Based on the optimization of key absorption parameters in previous sections, the determined absorption process parameters are as follows: absorption pressure of 1200 kPa, MDEA lean liquid concentration of 20 mol%, lean liquid flow rate of 2.5 kmol/h, temperature of 298.15 K, 20 plates in the absorption tower, 10 plates in the regeneration tower, and a reboiler power of 4 kW. The initial pressure of the feed gas is 110 kPa, with a compression ratio of 10.91 before and after single-stage compression, hence employing two-stage compression with intercooling for pressurization before entering the absorption tower for reaction. Table 3 lists the optimized logistics information and Table 4 presents the parameters of the absorption process equipment units and energy consumption values, while Fig. 7 illustrates the energy consumption structure of this process. The overall energy consumption is 5.29 kW, with gas compression unit, pump transport unit, and reboiler energy consumption at 1.24, 0.667, and 3.99 kW, respectively. These correspond to 23.38%, 1.22%, and 75.40% of the total energy consumption, highlighting the reboiler and gas compression units as key energy-consuming components. The methane (CH₄) content in the product gas is 5.98 kg/h, with CH₄ recovery energy consumption at 0.8852 kWh/kg CH₄. The minimum separation work in the separation process is 0.1297 kWh/kg CH₄, resulting in an ECI (Energy Consumption Index) value of 6.82. Increasing the temperature of the high-pressure feed gas after compression allows this heat to be utilized for preheating the MDEA-rich solution, thereby reducing reboiler power and lowering the overall energy consumption of the absorption process.

To further evaluate the energy consumption performance of this study, the optimized unit methane recovery energy consumption (0.8852 kWh/kg CH₄) was compared with recently published small-scale amine-based biogas upgrading systems (Table 5). It can be observed that the energy consumption achieved in this study at a scale of 300 Nm³/d is lower than most reported MEA or mixed-amine systems, and the Energy Consumption Index (ECI = 6.82) is also at a relatively low level. This demonstrates that the MDEA solvent and parameter optimization strategy adopted in this work offer good energy consumption advantages for small- and medium-scale applications, particularly for urban distributed biogas projects sensitive to operational costs.

Simulation results indicate that the reboiler accounts for approximately 75.4% of the total energy consumption (see Fig. 7), making it the most energy-intensive unit in the absorption–regeneration process. In addition to the previously optimized operating parameters (e.g., number of trays, solvent flow rate, and temperature), further analysis was conducted on potential strategies such as waste heat recovery and alternative solvent formulations, as outlined below: (1)

Feasibility and Integration Pathways of Waste Heat Recovery: In the current process, the feed gas is compressed from 110 to 1200 kPa. After two-stage compression, its temperature increases significantly (see Section 3.6). This high-temperature gas can be utilized as a low-grade heat source to partially replace the reboiler duty. Previous studies have shown that using hot compression exhaust or product gas to preheat the rich MDEA solution can substantially reduce the reboiler power demand and thereby alleviate the overall energy burden. Application recommendation: A waste-heat exchanger module can be integrated into the simulation system, where the compression exhaust is used for secondary heat exchange with the rich solvent (e.g., by adding an auxiliary heat exchanger downstream of E-1). This would reduce the amount of external steam or electric heating required [19]. Although waste-heat recovery has strong energy-saving potential, its quantitative evaluation involves additional system modeling requirements and heat-integration design that fall outside the scope of the present study. The research will be reported in detail in future work

Application recommendation: In follow-up simulations, part of the MDEA concentration could be replaced with aMDEA while keeping the total amine concentration constant, in order to evaluate the reduction in energy consumption. Alternatively, experimental data could be coupled with the simulation to generate regressed thermophysical parameters for mixed-solvent systems.