Steam Stripping-Coupled Cycle for Thermodynamic Optimization and Carbon Cycle Intensification in CO/CO₂ Mixed-Gas Hydrogenation Methanol Synthesis

Abstract

Methanol synthesis, a crucial platform chemical and clean energy carrier, plays a significant role in the global energy transition. This study focuses on thermodynamic optimization and carbon cycle intensification of the CO/CO₂ hydrogenation process. A multidimensional reaction system model was developed to investigate the effects of the CO/CO₂ feed ratio, H₂/CO _x molar ratio, reaction temperature and pressure, catalyst efficiency, and gas–liquid mass transfer resistance on product distribution. To improve carbon utilization, an innovative steam stripping-coupled cycle process was proposed, enabling efficient recovery of dissolved CO₂ in the liquid phase through phase equilibrium regulation. This reduced the CO₂ content from 10.72 kmol·h^–1 before stripping to 1.69 × 10^–4 kmol·h^–1 after stripping. Under optimized operating conditions, the methanol yield reached 82.0%, and the single-pass yields of CO and CO₂ were 90.7% and 72.6%, respectively. After the novel stripping cycle was adopted, the loss of liquid-phase CO₂ became negligible, with carbon and hydrogen losses mainly caused by gas-phase relaxation. When the relaxation rate was set to 1.0%, the utilization of CO _x and H₂ reached 93.2% and 82.8%, respectively. This strategy established a dynamic reaction-separation-recycle balance, improving both resource efficiency and economic performance, and offering theoretical and technical guidance for green methanol industrialization.

1. Introduction

In the context of global energy transformation and environmental protection, methanol, as an important chemical raw material and a potential alternative energy source, has increasingly received widespread attention from academia and industry. Methanol’s unique physical and chemical properties, such as its existence as a stable liquid at normal temperatures and pressures, and its high energy density, gave it significant advantages in transportation, storage, and utilization of existing energy infrastructure. , More importantly, the production of methanol through the conversion of CO _x (CO and CO₂) not only showed important economic value but also provided a feasible way for the high-value utilization of industrial waste, and at the same time provided large-scale and efficient energy storage. The construction of the system provided practical solutions. These varied prospects positioned methanol as a key contributor in future energy and chemical sectors.

However, the process of the hydrogenation of CO _x to methanol faced multiple challenges. These challenges were related to limitations in the supply of raw materials and the reaction process itself. First, the supply of H₂ was relatively limited. H₂ relied heavily on traditional fossil fuel conversion processes. These processes included steam methane reforming, coal gasification, partial oxidation of light oil residues, and dry reforming. In addition, some emerging production methods provided H₂. Examples included water electrolysis, the sulfur-alkali process, and the copper chloride process. H₂ was produced as a byproduct from the electrolysis of salt in the chlor-alkali industry. These sources of H₂ provided the necessary raw materials for methanol synthesis. However, they also highlighted the urgency of improving the efficiency of H₂ utilization. In contrast, CO₂ came from a wide range of sources, including carbon capture and utilization activities, and byproducts of processes such as bioethanol production. ,

Second, from the perspective of the reaction process, the process of CO _x hydrogenation to methanol was strictly limited by thermodynamic equilibrium, which led to the problem of generally low single-pass conversion rates, especially in traditional fixed-bed reactors. Although the nonhomogeneous kettle reactor was able to achieve high conversion rates under relatively mild conditions, the ensuing separation problem of solvent and product was an urgent technical issue that needed to be solved. , Theoretically, membrane reactors were able to effectively promote the reaction and break the limitations of thermodynamic equilibrium by quickly removing the reaction products. However, the conversion rate achieved under the existing technology was still not ideal, and there was still a lot of room for improvement. In this case, in order to improve the utilization rate of raw materials, it was particularly important to recycle unconverted raw gas. This not only improved the overall conversion efficiency but also reduced the raw material costs. Especially considering that H₂ costs accounted for a high proportion of the entire production process, improving the utilization of H₂ and CO _x was vital to enhancing the economics of the process.

With the growing demand for efficient low-carbon resource conversion in methanol synthesis, catalyst design expanded from traditional Cu-based systems to multicomponent cooperative mechanisms. Researchers focused on the evolution of microscopic reaction pathways and the precise regulation of catalyst interfaces. − Dong revealed the dynamic pathway of CO₂ hydrogenation to methanol on Cu catalysts using ReaxFF molecular dynamics simulations. He identified new mechanisms, including CH formation and methoxy hydrolysis, and clarified the synergistic effects of CO and H₂O on the intermediate evolution and methanol selectivity. This work deepened the understanding of the reaction mechanism. Han established the structure–activity relationships of ZrO₂ and CeO₂ in enhancing Cu catalyst performance for direct CO₂ hydrogenation, providing new strategies for catalyst design. This structure–activity-based approach not only deepened our understanding of catalytic mechanisms but also enabled the rational design of more efficient catalysts. Chang precisely tuned the ZnO/ZrO₂ catalyst structure through interfacial hydroxyl engineering. This modification enhanced both the CO₂ activation and proton transfer efficiency, enabling an efficient catalytic pathway. At a high space velocity, the system achieved 84% methanol selectivity and approximately 10% CO₂ conversion, providing key theoretical support for catalyst design. This body of research expanded the hi-tech landscape of methanol synthesis and offered valuable guidance for future surveys.

As the hydrogenation of CO _x to methanol advanced toward efficient, green, and large-scale applications, the systematic optimization of process parameters became a key approach to enhancing resource conversion efficiency and environmental performance. Recent research not only focused on the effects of operating variables-such as temperature, pressure, and feed ratio-on reaction performance but also emphasized the synergy of multidimensional optimization algorithms, dynamic simulation, and multiscale modeling in process control. , Rouhandeh developed a multidimensional optimization framework combining genetic algorithms and Aspen Plus to optimize the operating conditions of the bireforming of methane (BRM) process. The optimized conditions yielded 93% CH₄ conversion and an ideal H₂/CO ratio of 2.08, which enhanced methanol production (+11.8 t/h) and carbon conversion (+7.63 t/h of CO₂), while reducing steam consumption by 54.11 t/h. These results confirmed the environmental and economic advantages of BRM for clean hydrogen and methanol production. Kouzehli developed a robust kinetic model for CO₂ hydrogenation to methanol on Cu/ZnO/Al₂O₃ catalysts using a single-site LHHW mechanism. The model, which matched experimental data with 99% correlation, identified 200–220 °C and 80–100 bar as optimal conditions, improving methanol selectivity and guiding efficient reactor design and process optimization.

Given the research background and technological trends, this study investigated how key parametersCO/CO₂ feed ratio, catalyst type, reaction temperature, pressure, gas hourly space velocity, and H₂/CO _x feed ratioaffected CO and CO₂ conversion and methanol yield. It also examined their impacts on the off-gas volume and hydrocarbon utilization. To address sustainability goals, the concept of carbon cycle intensification was introduced, enabling a more systematic understanding of the reaction process and offering a solid basis for optimization. Based on these insights, the study proposed an optimized CO _x hydrogenation process using green hydrogen. A highly active Cu/Zn/Al/Zr fiber catalyst was employed to convert water-saturated hydrogen from brine electrolysis into methanol. This approach not only utilized green hydrogen effectively but also improved the reaction performance through catalyst design. Detailed parameter optimization and techno-economic analysis were performed to maximize carbon and hydrogen utilization while ensuring economic feasibility.

2. Materials and Methods

2.1. Method Description

This study conducted a comprehensive process simulation and performance evaluation of the CO/CO₂ hydrogenation to the methanol system. To ensure the accuracy and reliability of the results, a steady-state plug flow reactor (RPlug) model was established based on the high-precision PSRK thermodynamic model. A series of steady-state simulations under systematically varied conditions were carried out, enabling full-process, multicondition analysis, and parameter optimization of the target system. Under high-pressure (5 MPa) and high-temperature (240 °C) conditions, the RK-Soave thermodynamic model was used to describe the thermodynamic behavior of components such as H₂, CO, CO₂, H₂O, and CH₃OH. For the H₂-free low-pressure distillation section, the methanol distillation unit used a combination of the nonrandom two-liquid thermodynamic model and the RK-Soave thermodynamic model to accurately describe the liquid phase behavior. All binary interaction parameters came from the pure component database of the Aspen Plus process simulator, further ensuring the reliability of the simulation results.

The feed gas compression unit employed a multistage compressor system consisting of compressors and interstage condensers to gradually increase the pressure to 5 MPa. The MCompr model was used for the compressors and the Heater model for the condensers. Based on the required CO and CO₂ feed rates (500 and 400 kmol·h^–1, respectively), the H₂ feed was set to 2200 kmol·h^–1. Methanol synthesis was carried out over a Cu/Zn/Al/Zr catalyst via CO/CO₂ hydrogenation. After reaching the target reaction temperature and pressure, the feed gas mixed with recycle gas and entered an RPlug reactor model governed by known kinetics and rate control. A multitubular fixed-bed reactor was adopted, with specifications based on Wang: tube length of 12 m, diameter of 0.06 m, 3000 tubes, and a catalyst bed porosity of 0.75. Due to the low single-pass conversion of the RPlug model, the reactor effluent contained unreacted CO, CO₂, and H₂. After being cooled, the stream was separated in a high-pressure separator. The vapor phase was split using an FSplit model: most was recycled to the reactor, while a small portion was purged to prevent component accumulation. Crude methanol collected at the separator bottom was depressurized and fed into the methanol distillation column. In the distillation section, the liquid feed was preheated to its bubble point before entering the column operated at 0.1 MPa, with a tray pressure drop of 0.0068 MPa. The methanol purity at the top was controlled below 0.1 wt %, with a distillate flow of 50 kmol·h^–1 and a methanol recovery of 99.9 wt %.

2.2. System Description

2.2.1. Traditional Craft System

An expressive diagram of a representative methanol synthesis route from CO and CO₂ with feed gas recirculation is presented in Figure . The feed gas was compressed to a reaction pressure and then mixed with circulating gas in a mixer. The mixed gas was heated to the reaction temperature by the heater and then entered the plug flow model Rplug methanol synthesis reactor. The reaction product was cooled by a cooler and then entered the flash tank for gas–liquid separation. The liquid phase after flashing was crude methanol, which required further purification, and the gas phase was divided into relaxation gas and circulating gas. The circulating gas was compressed and mixed with fresh raw gas, which improved the utilization rate of the raw materials.

1.

Traditional CO _x hydrogenation to methanol cycle process.

2.2.2. Process System Reconstruction

Process analysis revealed two major pathways of CO₂ and H₂ loss during CO _x hydrogenation to methanol: (1) CO formation in the reaction system led to the release of CO, CO₂, and H₂ in the gas phase as part of the purge stream; (2) CO₂ dissolution in crude methanol solution resulted in its loss in the liquid phase, while losses of CO and H₂ in the liquid phase were negligible. To maximize CO₂ and H₂ utilization, fresh hydrogen was used to strip dissolved CO₂ from methanol and subsequently mixed with CO, CO₂, and the recycle stream. When fresh wet hydrogen produced via brine electrolysis was used, it simultaneously extracted CO and CO₂ from methanol and dehydrated the feed gas. This significantly enhanced the CO _x conversion and reduced energy losses. The operating conditions of the steam stripping unit in this study were referenced from typical industrial parameters for CO₂ removal in alcohol–water systems documented in the literature. , Combined with sensitivity analyses conducted in this study, the optimized parameters included 37 theoretical stages, an operating pressure of 1.2 bar, and a reflux ratio of 5. Under these conditions, simulation results revealed that dissolved CO₂ in the liquid phase decreased from 10.72 kmol·h^–1 (in stream S4, Figure ) before stripping to 1.69 × 10^–4 kmol·h^–1 (in stream S10, Figure ) after stripping, indicating that the liquid-phase CO₂ loss was considered negligible.

2.

Hydrogenation of CO _x mixed gas to methanol via a steam stripping-coupled cycle process.

2.3. Kinetic Model

To simplify the model structure and enhance simulation stability, this study excluded byproduct formation reactions, such as those producing alkanes, alcohols, and dimethyl ether (DME), as well as inert gases potentially present in the system. The modeled system primarily involved three reactions: CO₂ hydrogenation to methanol (eq ), the reverse water–gas shift reaction (eq ), and CO hydrogenation to methanol (eq ). The validity of this assumption was supported by the following considerations: under typical industrial conditions (220–270 °C, 30–50 bar), methanol synthesis was the dominant reaction pathway. In contrast, side reactions such as alkylation or DME formation occurred primarily under specific conditions, including acidic catalysts, high temperatures, or low pressures. , Previous studies demonstrated that, under Cu-based catalysts and moderate reaction conditions, byproduct formation was minimal and had negligible effects on the gas-phase composition and energy balance. Therefore, their exclusion did not significantly compromise the simulation accuracy. Furthermore, the primary objective of this study was to evaluate how operating conditions affected the thermodynamics and performance of the main methanol synthesis reaction, thereby prioritizing the core reaction pathway. This simplified assumption facilitated a focused analysis of the primary reaction system and improved the efficiency of parameter optimization.

$${\mathrm{C}\mathrm{O}}_2+3{\mathrm{H}}_2={\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}+{\mathrm{H}}_2\mathrm{O}\mathrm{\Delta }{H}_{298\mathrm{K}}=-49.16\mathrm{k}\mathrm{J}·{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$$ 1

$${\mathrm{C}\mathrm{O}}_2+{\mathrm{H}}_2=\mathrm{C}\mathrm{O}+{\mathrm{H}}_2\mathrm{O}\mathrm{\Delta }{H}_{298\mathrm{K}}=+41.21\mathrm{k}\mathrm{J}·{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$$ 2

$$\mathrm{C}\mathrm{O}+2{\mathrm{H}}_2={\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}\mathrm{\Delta }{H}_{298\mathrm{K}}=-90.77\mathrm{k}\mathrm{J}·{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$$ 3

In this study, the reaction rate expressions were established based on the validated Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetic model to characterize the reaction kinetics of CO/CO₂ hydrogenation to methanol. During the modeling process, the original kinetic expressions were reparametrized and standardized in terms of units to ensure compatibility with the input format requirements of the simulation platform, enabling the effective integration of the model. The Redlich–Kwong–Soave equation of state was employed to describe the thermodynamic behavior, further enhancing the integrated simulation framework of reaction-separation-recycle. The two types of Cu-based catalyst models used were developed based on fixed-bed experimental data from refs , , , and , respectively, and were validated in terms of structural stability and catalytic activity through various characterization techniques including X-ray diffraction, BET, and X-ray photoelectron spectroscopy. The models demonstrated good fitting accuracy and reliable extrapolation capability.

The kinetic rate expressions for reactions – are given below, with the required parameters listed in Tables –. Due to the high reaction pressure, fugacity (f) was employed in the calculations.

1. Reaction Kinetic Parameter of A, B, and C .

kinetic constant	pre-exponential factor A i (kmol·kg cat–1·s·Pa–n )	activation energy E i (kJ·mol–1)
1	4.0638 × 10–6	11.695
2	9.0421 × 108	11.286
3	1.5188 × 10–33	266.01

3. K _i Factor for the Adsorption Term.

term	expression	a i	b i	Πc j vi
1	1	a 1 = 1	b 1 = 0	$$\sqrt{f_{{\mathrm{H}}_2}}$$
2	$$\frac{K_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	a 2 = 4.3676 × 10–12	b 2 = 1.1508 × 105	$$f_{{\mathrm{H}}_2\mathrm{O}}$$
3	K CO	a 3 = 8.3965 × 10–11	b 3 = 1.1827 × 105	$$f_{\mathrm{C}\mathrm{O}}\sqrt{f_{{\mathrm{H}}_2}}$$
4	$$\frac{K_{\mathrm{C}\mathrm{O}}{K}_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	a 4 = 3.6673 × 10–22	b 4 = 2.3335 × 105	$$f_{\mathrm{C}\mathrm{O}}{f}_{{\mathrm{H}}_2\mathrm{O}}$$
5	$$K_{\mathrm{C}{\mathrm{O}}_2}$$	a 5 = 1.7214 × 10–10	b 5 = 8.1287 × 104	$$f_{{\mathrm{C}\mathrm{O}}_2}\sqrt{f_{{\mathrm{H}}_2}}$$
6	$$\frac{K_{{\mathrm{C}\mathrm{O}}_2}{K}_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	a 6 = 7.5184 × 10–22	b 6 = 1.9727 × 105	$$f_{{\mathrm{C}\mathrm{O}}_2}{f}_{{\mathrm{H}}_2\mathrm{O}}$$

		A i  = ln(a i )	B i  = b i /R	Πc j vi
1	1	0	0	$$\sqrt{f_{{\mathrm{H}}_2}}$$
2	$$\frac{K_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	–26.1568	13,842	$$f_{{\mathrm{H}}_2\mathrm{O}}$$
3	K CO	–23.2006	14,225	$$f_{\mathrm{C}\mathrm{O}}\sqrt{f_{{\mathrm{H}}_2}}$$
4	$$\frac{K_{\mathrm{C}\mathrm{O}}{K}_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	–49.3574	28,067	$$f_{\mathrm{C}\mathrm{O}}{f}_{{\mathrm{H}}_2\mathrm{O}}$$
5	$$K_{\mathrm{C}{\mathrm{O}}_2}$$	–22.4827	9777	$$f_{{\mathrm{C}\mathrm{O}}_2}\sqrt{f_{{\mathrm{H}}_2}}$$
6	$$\frac{K_{{\mathrm{C}\mathrm{O}}_2}{K}_{{\mathrm{H}}_{2}0}}{\sqrt{K_{\mathrm{H}}}}$$	–48.6395	23,619	$$f_{{\mathrm{C}\mathrm{O}}_2}{f}_{{\mathrm{H}}_2\mathrm{O}}$$

2. Driving Force Constant.

kinetic constant	A	B
K 1	–52.096	11,840
K 2	5.639	–5285
K 3	–46.457	6555
K CO	–23.20	14,225
$$K_{{\mathrm{C}\mathrm{O}}_2}$$	–22.48	9777

Catalyst kinetics for catalyst Cu/Zn/Al/Zr (CZAZ-HF)

$$r_1=\frac{k_1{K}_{\mathrm{C}\mathrm{O}}[f_{\mathrm{C}\mathrm{O}}{f}_{{\mathrm{H}}_2}^{3/2}-f_{\mathrm{C}{\mathrm{H}}_3\mathrm{O}\mathrm{H}}/(f_{{\mathrm{H}}_2}^{1/2}{K}_{f1})]}{(1+K_{\mathrm{C}\mathrm{O}}{f}_{\mathrm{C}\mathrm{O}}+K_{\mathrm{C}{\mathrm{O}}_2}{f}_{\mathrm{C}{\mathrm{O}}_2})[f_{{\mathrm{H}}_2}^{1/2}+(K_{{\mathrm{H}}_2\mathrm{O}}/K_{{\mathrm{H}}_2}^{1/2})f_{{\mathrm{H}}_2\mathrm{O}}]}$$ 4

$$r_2=\frac{k_2{K}_{{\mathrm{C}\mathrm{O}}_2}(f_{{\mathrm{C}\mathrm{O}}_2}{f}_{{\mathrm{H}}_2}-f_{{\mathrm{H}}_2\mathrm{O}}{f}_{\mathrm{C}\mathrm{O}}/K_{f2})}{(1+K_{\mathrm{C}\mathrm{O}}{f}_{\mathrm{C}\mathrm{O}}+K_{{\mathrm{C}\mathrm{O}}_2}{f}_{{\mathrm{C}\mathrm{O}}_2})[f_{{\mathrm{H}}_2}^{1/2}+(K_{{\mathrm{H}}_2\mathrm{O}}/K_{{\mathrm{H}}_2}^{1/2})f_{{\mathrm{H}}_2\mathrm{O}}]}$$ 5

$$r_3=\frac{k_3{K}_{\mathrm{C}{\mathrm{O}}_2}[f_{\mathrm{C}{\mathrm{O}}_2}{f}_{{\mathrm{H}}_2}^{3/2}-f_{\mathrm{C}{\mathrm{H}}_3\mathrm{O}\mathrm{H}}{f}_{{\mathrm{H}}_2\mathrm{O}}/(f_{{\mathrm{H}}_2}^{3/2}{K}_{f3})]}{(1+K_{\mathrm{C}\mathrm{O}}{f}_{\mathrm{C}\mathrm{O}}+K_{\mathrm{C}{\mathrm{O}}_2}{f}_{\mathrm{C}{\mathrm{O}}_2})[f_{{\mathrm{H}}_2}^{1/2}+(K_{{\mathrm{H}}_2\mathrm{O}}/K_{{\mathrm{H}}_2}^{1/2})f_{{\mathrm{H}}_2\mathrm{O}})]}$$ 6

Catalyst kinetics about catalyst Cu/ZnO/Al₂O₃ (CZA-GM):

Due to the strict structural and unit requirements imposed by Aspen Plus on kinetic rate models, the original kinetic model was not directly implementable within the platform. Therefore, the model adopted in this study was reparametrized. The thermodynamic equilibrium equations were incorporated into the kinetic constants, and the units of the equations were modified to comply with the requirements. The rearranged kinetic model was presented in eqs –, where pressure was expressed in Pa and temperature in K. The model parameters are listed in Table .

4. Rearranged Kinetic Model Parameters.

term		value
k 1	A 1	–29.87
	B 1	4811.2
k 2	A 2	8.147
	B 2	0
k 3	A 3	–6.452
	B 3	2068.4
k 4	A 4	–34.95
	B 4	14928.9
k 5	A 5	4.804
	B 5	–11797.5
k 6	A 6	17.55
	B 6	–2249.8
k 7	A 7	0.131
	B 7	–7023.5

Methanol synthesis

$$r_{{\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}}=\frac{k_1{P}_{\mathrm{C}\mathrm{O}}{P}_{{\mathrm{H}}_2}-k_6{P}_{{\mathrm{H}}_2\mathrm{O}}{P}_{{\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}}{P}_{{\mathrm{H}}_2}^{-2}}{{(1+k_2{P}_{{\mathrm{H}}_2\mathrm{O}}{P}_{{\mathrm{H}}_2}^{-1}+k_3{P}_{{\mathrm{H}}_2}^{0.5}+k_4{P}_{{\mathrm{H}}_2\mathrm{O}})}^3}$$ 7

Reverse water gas shift reaction

$$r_{\mathrm{R}\mathrm{W}\mathrm{G}\mathrm{S}}=\frac{k_5{P}_{{\mathrm{C}\mathrm{O}}_2}-k_7{P}_{{\mathrm{H}}_2\mathrm{O}}{P}_{\mathrm{C}\mathrm{O}}{P}_{{\mathrm{H}}_2}^{-1}}{1+k_2{P}_{{\mathrm{H}}_2\mathrm{O}}{P}_{{\mathrm{H}}_2}^{-1}+k_3{P}_{{\mathrm{H}}_2}^{0.5}+k_4{P}_{{\mathrm{H}}_2\mathrm{O}}}$$ 8

$$\ln (k_i)=A_i+\frac{B_i}{T}$$ 9

The LHHW model has been widely employed under moderate to elevated temperatures (200–300 °C) and pressures (10–100 bar), with specific conditions tailored to catalyst properties and reactor configuration. To streamline the kinetic framework, several standard assumptions are often adopted: (1) reactions occur only on the catalyst surface, ignoring internal reactions and diffusion; (2) catalyst activity is treated as stable over time, ignoring deactivation effects; (3) diffusion within catalyst particles is assumed to occur instantaneously; (4) the presence of other alkanes, higher alcohols, and inert components was negligible in the kinetic modeling; (5) external mass transfer resistance is negligible, presuming uniform reactant access; (6) equilibrium is assumed, so yields stay below the theoretical maximum due to thermodynamic constraints and incomplete H₂ conversion. Reverse reactions and partial CO _x conversions are also considered. These assumptions simplify the model while capturing key features of methanol synthesis.

Based on the kinetic model, the process simulation platform of steam stripping-coupled recycling for CO/CO₂ mixed gas hydrogenation methanol synthesis developed in this study was able to accurately reflect the coupling behavior of the complex reaction system within the reactor, providing strong support for system–level parameter optimization. Through systematic sensitivity analyses of key process parameters, including temperature, pressure, feed composition, and GHSV, the intrinsic mechanisms that affected carbon conversion efficiency and hydrogen utilization were revealed, thereby offering a solid theoretical foundation and practical guidance for the design of low-carbon, high-efficiency methanol synthesis processes.

3. Results and Discussion

3.1. Effect of CO and CO₂ Feed Ratio on the Raw Material Conversion Rate and Methanol Yield

The influence of the CO/CO₂ molar ratio on feed conversion and methanol yield under the specified conditions of 5 MPa pressure, 240 °C temperature, an H₂/CO _x molar ratio of 2.5, and a GHSV of 500 h^–1 is depicted in Figure . The CO/CO₂ molar ratio was set at the beginning of the reaction by adjusting the proportion of CO and CO₂ in the feed and was maintained stable throughout the entire feeding process. When the CO/CO₂ molar ratio decreased from 1/0.3 to 1/0.8, the conversion rates of both CO and CO₂ increased significantly. This was mainly because the moderate introduction of CO₂ enhanced the reverse water–gas shift reaction, promoted the thermodynamic equilibrium of the reaction system, and simultaneously activated the optimal catalytic activity of the Cu-based catalyst within this range, thereby improving the reaction rate and methanol formation. However, when the CO/CO₂ ratio was further decreased (i.e., the CO₂ content continued to increase), side reactions such as direct CO₂ reduction gradually intensified, leading to the consumption of part of the hydrogen and CO through nontarget pathways, which caused a decrease in the feedstock conversion rate for the main reaction. The hydrogen conversion rate reached its maximum value of 82.2% at a CO/CO₂ ratio of 1/0.8, indicating a synergistic effect among the three main reaction pathways and optimal hydrogen utilization at this point. Further increasing the CO₂ content led to a rise in the proportion of side reactions and a gradual decline in the rate of hydrogen conversion.

3.

Effect of CO _x on raw material and methanol yield rates (T = 240 °C, P = 5 MPa, H₂/CO _x = 2.5, GHSV = 500 h^–1).

Based on these observations, it was concluded that under the given reaction conditions, the CO/CO₂ molar ratio of 1/0.8 was most beneficial to methanol production, and the raw material conversion rate and methanol yield were relatively high. This ratio provided an important reference for optimizing the CO _x hydrogenation to methanol process.

3.2. Effect of Process Conditions on the CO _x Conversion Performance

3.2.1. Catalysts on the CO _x Conversion Performance

Two catalysts were chosen for a comparative study to examine the impact of catalyst type and operational parameters, such as temperature, pressure, carbon–hydrogen feed ratio, and gas hourly space velocity, on the single-pass conversion of H₂, CO, and CO₂ (XH₂, XCO, and XCO₂) and the methanol yield (YCH₃OH). One catalyst was a highly active Cu/Zn/Al/Zr catalyst specifically developed for CO/CO₂ hydrogenation to methanol (denoted as catalyst CZAZ-HF), and the other was the commercial Cu/ZnO/Al₂O₃ catalyst widely used in many studies (denoted as catalyst CZA-GM). The plug flow model Rplug was employed to simulate the fixed-bed reactor for methanol synthesis. The reactor parameters were as follows: 3000 tubes, each 12 m in length and 0.06 m in diameter.

As shown in Figure a₁, the single-pass CO conversion initially increased with temperature and reached a peak of approximately 90.7% at 240 °C, after which it slightly declined. Although the rate of CO₂ conversion continued to rise, its rate of increase slowed noticeably beyond 240 °C. Figure a₂ illustrates the effect of temperature on the methanol yield. For the CZAZ-HF catalyst, the methanol yields significantly increased from 38.0% at 220 °C to 82.0% at 240 °C. In contrast, the CZA-GM catalyst exhibited a relatively stable yield between 47.6% and 50.7%, which remained notably lower than that of CZAZ-HF. Further increases in temperature resulted in limited improvement in product yield and potentially promoted side reactions, thereby reducing the yield of the desired product. This trend was largely attributed to the endothermic nature of CO _x hydrogenation, where a moderate temperature increase enhanced the reaction kinetics and favored methanol formation. However, excessive temperatures tended to accelerate side reactions, such as the deep reduction of CO₂, leading to a decline in the methanol yield. This behavior corresponded with the post-240 °C decrease in the level of CO conversion observed in Figure a₁. Based on a combined thermodynamic and kinetic analysis, 240 °C was identified as the most suitable temperature for achieving an optimal balance between reaction rate and methanol selectivity in this system.

4.

Effect of process parameters on the reactant conversion rate and methanol yield.

The single-pass conversion rates of both CO and CO₂ both increased with increasing reaction pressure (Figure b₁), with the catalyst CZAZ-HF showing a larger variation. The increase in reaction pressure could push the reaction equilibrium toward the product side by increasing the concentration of reactants, especially in gas-phase reaction systems. Increasing the pressure increased the frequency of intermolecular collisions, thereby accelerating the reaction rate and improving the conversion rate and yield. In Figure b₂, it is proven that when the pressure increased from 1 to 5 MPa, methanol yield increased sharply; after the pressure exceeded 5 MPa, the growth trend slowed. The methanol yield of the catalyst CZAZ-HF increased rapidly from 60.0% at 1 MPa to 82.5% at 5 MPa, then slowly increased to 84.3% at 10 MPa. This phenomenon was consistent with chemical equilibrium theory, meaning that under high pressure, gas molecules were more likely to react, promoting the conversion of CO and CO₂. However, when the pressure was too high, the vapor pressure of methanol also increased, which affected the conversion of the reaction and caused an unstable product yield.

As shown in Figure c₁, we observed that with the increase in the H₂/CO _x feed ratio, the single-pass conversion of CO and CO₂ both increased, particularly when the H₂/CO _x ratio exceeded 2.5. At this point, the single-pass conversion of CO and CO₂ over catalyst CZAZ-HF was significantly improved. This phenomenon was closely related to the reaction kinetics. Increasing the supply of hydrogen helped to increase the rate of hydrogenation reactions, promote the reduction of CO and CO₂, and increase the production rate of the target product, methanol. As the main raw material for the hydrogenation reaction, its excessive supply played an important role in improving the conversion rate of the reaction. Figure c₂ illustrates that with the increase in the hydrogen–carbon feed ratio, the methanol yield also increased. The methanol yield of the catalyst CZAZ-HF increased significantly from 43.5% to 82.7%, while the catalyst CZA-GM fluctuated only slightly between 43.0% and 50.1%. This shown that a sufficient supply of hydrogen played a vital role in taming methanol yield. Excess H₂ increased hydrogenation but diluted reactants, leading to saturation in the methanol formation rate; thus, overall conversion increased, while methanol yield declined.

In Figure d₁,d₂, the CO and CO₂ conversion rates and methanol yield both slowly decreased with the increase in GHSV. When the GHSV increased from 500 to 5000 h^–1, the methanol yield of the catalyst CZAZ-HF remained at around 80.6%–83.0%, with little change. In contrast, the methanol yield of the catalyst CZA-GM decreased significantly with the increase in GHSV, from 53.0% to 60.0%. This trend was related to the residence times of the reactants. At higher GHSV, the residence time of the reactants in the reactor was shorter, which led to insufficient contact time between the reactants and the catalyst, affecting the completeness of the reaction and subsequently leading to a decrease in the conversion rate and methanol yield. From the perspective of reaction kinetics, a higher GHSV meant a lower reactant conversion time, which might not have been enough for the reaction to proceed completely, thereby reducing the methanol yield.

In the CO and CO₂ hydrogenation to methanol reaction system (eqs –), when CO, CO₂, and H₂ were employed as feedstocks, methanol was the primary product. However, when CO₂ and H₂ were secondhand as the feed, side reactions occurred, producing CO and H₂O, which disrupted the optimal reaction ratio between CO/CO₂ and H₂, thus decreasing the methanol yield. This phenomenon could be further explained by reaction kinetics and chemical equilibrium. Since the hydrogenation reaction of CO₂ was usually an endothermic reaction, under conditions of high temperature and low hydrogen concentration, side reactions (reduction or methanation of CO₂) were stimulated, which consumed the reactants and reduced the methanol yield.

Therefore, under the same conditions, catalyst CZAZ-HF exhibited good catalytic effects on both CO and CO₂, while catalyst CZA-GM showed high catalytic efficiency for CO conversion but low catalytic efficiency for CO₂ (the CO₂ conversion rate was less than 30.0%). These results indicated that the catalyst CZAZ-HF effectively inhibited the occurrence of side reactions while enhancing the conversion rates of CO and CO₂, indicating its superior catalytic performance. The dedicated CO/CO₂ hydrogenation-to-methanol catalyst CZAZ-HF was superior to the catalyst CZA-GM with regard to the conversion rates of CO and CO₂ and methanol yield. Therefore, the subsequent discussion was mainly based on the performance of the catalyst CZAZ-HF.

3.2.2. Catalyst Stability Analysis

The stability of Cu-based catalysts was a critical factor influencing the reaction efficiency and process reliability during CO _x hydrogenation to methanol. Under prolonged operating conditions, copper particles were prone to deactivation due to thermal sintering, carbon deposition, or hydrothermal-induced structural degradation. Particularly in high-pressure, hydrogen-rich, and water-producing environments, their dispersibility and active site density tended to decrease. To enhance catalyst stability, this study employed a Cu/Zn/Al/Zr catalyst. The incorporation of Zr enhanced the copper particle dispersibility and resistance to sintering, thereby enhancing low-temp catalytic act and structural stability.

Based on the enhanced characteristics of the modified catalyst, this study reasonably assumed that the catalyst performance remained stable throughout the operating cycle. This assumption considered practical limitations in both software modeling and research objectives. On one hand, the RPlug model used in Aspen Plus was a steady-state reactor model and lacked the ability to simulate time-dependent activity decay. On the other hand, the study focused on the effects of operating conditions (temperature, pressure, and feed ratio) on methanol yield and energy efficiency. Therefore, neglecting catalyst deactivation during the simulation cycle was a common engineering simplification, and many related studies adopted similar treatments. Building on this, future studies are encouraged to incorporate surface area decay functions or deactivation kinetic models to more accurately simulate catalyst life and to integrate these with periodic low-temperature oxidation regeneration strategies to extend industrial longevity.

3.3. Factors Affecting Carbon and Hydrogen Losses

3.3.1. Effect of Reaction Process Parameters on Carbon and Hydrogen Losses in the Gas Phase

Based on reactions eqs and , CO could serve as both a reaction and a product. In the traditional CO₂ hydrogenation to methanol cycle procedure shown in Figure , following the modification of catalyst type and process parameters like temperature, pressure, hydrogen-to-carbon feed ratio, and gas hourly space velocity, CO showed a clear tendency for net generation. , This led to the accumulation of CO in the system, which disrupted the optimal hydrogen-to-carbon ratio, thereby affecting the reactant conversion and methanol yield. To maintain the optimal H₂/CO _x ratio, excess CO had to be discharged through process relaxation. Figure illustrates the effect of process parameters on the hydrocarbon loss in the gas phase relaxation stream S3 after flash separation. The design was based on a CO feed amount of 500 kmol h^–1 and a CO₂ feed amount of 400 kmol h^–1. The process parameter range was T = 220–300 °C, P = 1–10 MPa, H₂/CO _x feed ratio = 1.6–3.0, GHSV = 500–5000 h^–1, Rp = 1.0%.

5.

Effect of process parameters on carbon and hydrogen losses in gas phase emissions.

As illustrated in Figure a, with the increase in reaction temperature, the quantity of net CO produced within the reaction system kept increasing. The accumulation of CO caused two major problems: on one hand, the increase in CO hindered temperature control and may have accumulated on the catalyst surface, resulting in catalyst deactivation; on the other hand, the increase in CO may have disrupted the optimal hydrogen–carbon feed ratio, leading to incomplete reactions (1) and (3). The net CO produced was typically released from the system via stream S3, accompanied by the emission of CO₂ and H₂, with H₂ loss being especially notable. Since H₂ was a slightly excessive feed, its purpose was to suppress the formation of higher alcohols, higher hydrocarbons, and other reducing substances, thereby increasing the concentration and purity of methanol. Therefore, the group of CO not only increased production costs but also affected the conversion rate of the reaction.

When the reaction temperature exceeded 240 °C, the net CO generation dropped sharply and leveled off. This was because CO could accumulate on the catalyst surface at high temperatures, causing catalyst deactivation and reducing the reaction conversion. According to Le Chatelier’s principle, methanol synthesis was an exothermic reaction, so an increase in temperature shifted the equilibrium toward the endothermic direction (toward the reactant side), thereby reducing the yield of methanol. Additionally, 240 °C balanced the reaction rate and CO generation, reduced the occurrence of side reactions, and helped to maintain a stable hydrogen-to-carbon ratio. This temperature also facilitated the timely removal of water generated during the reaction, preventing moisture from negatively impacting the reaction equilibrium. Therefore, 240 °C was the optimal temperature for achieving efficient methanol synthesis.

Figure b demonstrates that as the reaction pressure rose, the net CO produced within the reaction system consistently declined. Specifically, when the pressure exceeded 5 MPa, the net CO production was lower than 5 kmol h^–1, and the effect of continued pressurization on reducing relaxation tended to slow down. Due to the material conservation law, like the impact of temperature on relaxation amount, although the net CO produced by the system was small, the emissions of CO₂ and H₂, especially the loss of H₂, could not be ignored. However, increasing pressure also increased the compression energy consumption. Since the change in net CO production after 5 MPa was minimal, 5 MPa could be regarded as the optimal operating pressure.

As the H₂/CO _x feed ratio increased, the net CO generated in the reaction system decreased, leveling off when the ratio exceeded 2.5, as shown in Figure c. However, the effect of the H₂/CO _x feed ratio on relaxation differed from the effects of the temperature and pressure and was more complex. When the H₂/CO _x feed ratio was ≤2.5, the relaxation of the entire system depended on net CO generation. When the H₂/CO _x feed ratio exceeded 2.5, although increasing the ratio improved the carbon utilization and methanol yield of the system, the H₂ entering the system became excessive. The entire system needed to emit both the net CO generated and the excess H₂, resulting in an increase in the system’s relaxation amount. For example, when the H₂/CO _x ratio was 3.0, the H₂ loss reached 378.94 kmol h^–1, accounting for 14.0% of the 2700 kmol h^–1 H₂ feed. Therefore, an appropriate H₂/CO _x feed ratio had to be selected. A ratio of 2.5 was considered optimal and was set at the process inlet to maximize the system’s carbon utilization and methanol yield. In actual operation, due to gas circulation, the H₂/CO _x ratio at the reactor inlet changed. To prevent CO accumulation and ensure simulation convergence, gas relaxation was used. Under gas relaxation, the H₂/CO _x ratio at the reactor inlet could change and would not be strictly fixed at 2.5. When using the design specification (Design Spec) convergence simulation, the H₂/CO _x ratio was usually fixed by setting the molar ratio of hydrogen and CO _x in the feed flow as a target constraint in the design specification. The design specification optimized the feed flow rate to ensure reaction process convergence while maintaining a stable H₂/CO _x ratio, keeping it at 2.5 throughout the reactor. This control strategy effectively avoided CO accumulation, prevented excessive H₂ from appearing in the system, and ensured the efficient operation of the entire reaction system.

Figure shows that, within the GHSV range of 500–5000 h^–1, net CO production remained stable, but the relaxation of CO₂ and H₂ in stream S7 continued to increase, leading to a continuous decrease in the methanol yield of the entire system. This was because, at Rp = 1.0%, higher GHSV reduced CO₂ single-pass conversion and increased circulation, leading to greater CO₂ and H₂ losses. Lowering GHSV improved methanol yield, while decreasing the circulation volume of S8.

Therefore, to maximize the CO conversion rate, CO₂ conversion rate, and methanol yield while minimizing hydrocarbon losses, the recommended optimal process parameters were T = 240 °C, P = 5 MPa, H₂/CO _x = 2.5, and GHSV should be set to a minimum of 500 h^–1. The selection of these parameters achieved a good balance among reaction efficiency, energy feasting, and raw material utilization, improving the overall efficiency of the system.

3.3.2. Effect of Reaction Process Parameters on Carbon and Hydrogen Losses in Liquid Phase

Apart from the depletion of CO, CO₂, and H₂ through gas purge stream S3, a minor portion of CO₂ dissolved in crude methanol stream S4 also served as a notable contributor to carbon loss. Figure offers the effect of process parameters on carbon loss in the liquid phase. The parameters studied ranged from T = 220–260 °C, P = 1–10 MPa, H₂/CO _x = 1.6–3.0, GHSV = 500–5000 h^–1, and Rp = 1.0%.

6.

Effect of process parameters on carbon and hydrogen losses in liquid phase emissions.

In Figure a, as the reaction temperature increased, the net CO production in the system continued to rise. Although dissolved CO in stream S9 increased with the temperature, its total amount remained small. In contrast, the loss of dissolved CO₂ in S9 was significantly greater than that of CO, exhibiting a trend of first increasing and then decreasing, peaking at 240 °C. This phenomenon showed that temperature had a significant impact on CO₂ solubility in the liquid phase, but the overall effect of temperature on reaction kinetics and product separation had to be considered during process optimization.

As shown in Figure b, increasing pressure led to a continuous decline in liquid-phase CO, with the net CO yield dropping below 5.0 kmol h^–1 above 5 MPa. Meanwhile, CH₃OH production increased, although the growth rate slowed beyond 5 MPa. According to Henry’s law, CO solubility rose with partial pressure, making it more likely to dissolve into the crude methanol–water mixture under high-pressure conditions. Simulation results confirmed that elevated pressure increased the CO concentration in the liquid phase, contributing significantly to carbon loss.

As shown in Figure c, increasing the H₂/CO _x ratio reduced dissolved CO₂ losses in stream S9 and improved the methanol yield by enhancing carbon utilization. However, excessive H₂/CO _x ratios led to surplus H₂ consumption, indicating the need to optimize the feed ratio for balanced efficiency and resource use. As the GHSV range was between 500 h^–1 and 5000 h^–1, the net CO production remained constant, with little impact on the contents of CO, CO₂, and CH₃OH in stream S9, as shown in Figure d. This indicated that within the studied GHSV range GHSV had no significant effect on liquid product composition, likely due to the reaction being near equilibrium.

The solubility of CO₂, governed by Henry’s law, exhibited a linear proportionality to its partial pressure. This relationship was markedly enhanced under high-pressure reaction conditions, resulting in preferential dissolution of unreacted CO₂ into the crude methanol–water solution. As shown in the figures, the CO₂ concentration in the crude methanol liquid phase markedly increased with an elevated reaction pressure, establishing dissolved CO₂ as one of the primary contributors to liquid-phase carbon loss. Temperature had a dual effect on CO₂ solubility and reaction equilibrium as CO₂ dissolution loss first increased and then decreased with rising temperature. At intermediate temperatures (e.g., 240 °C), the gas–liquid equilibrium was not fully disrupted and the coupling of reaction kinetics with dissolution behavior led to higher CO₂ retention in the liquid phase. Although further heating could reduce the CO₂ solubility and help minimize liquid carbon loss, it might adversely affect the kinetics and selectivity of methanol synthesis. Therefore, process optimization required a trade-off among multiple factors.

Liquid-phase carbon loss mainly existed as unreacted, dissolved CO₂ and was primarily sensitive to the temperature and pressure. In separation units such as flash towers, the high solubility of CO₂ prevented complete removal of unreacted or byproduct CO₂, resulting in “hidden carbon loss” in the liquid phase. If not effectively removed by downstream degassing or stripping units, this CO₂ exited the system with crude methanol, thereby reducing the overall carbon utilization efficiency. To minimize liquid-phase CO₂ loss and improve methanol production, the process was recommended to operate at elevated pressure (≥5 MPa) and moderate temperature (240 °C), with a balanced H₂/CO _x ratio and an efficient gas–liquid separation system. Studies showed that within the studied GHSV range, GHSV had minimal impact on liquid-phase carbon loss and could be flexibly adjusted to meet other process ratios.

3.3.3. Effect of the Gas Relaxation Rate on Net CO Production and Circulation

In addition to reaction conditions, the gas relaxation rate, the ratio of relaxed gas to total feed, critically influenced carbon and hydrogen utilization. As shown in Figure , lowering the relaxation rate significantly reduced net CO production. When the rate approached zero, the net CO output dropped to 0.035 kmol·h^–1, balancing with dissolved CO₂ in the liquid phase. This indicated that under zero relaxation, net CO accumulation was nearly eliminated, resolving the H₂/CO _x ratio imbalance and theoretically removing the need to purge CO. However, reducing the gas relaxation rate posed the challenge of significantly intensifying the system’s internal recycle load.

7.

Effect of the gas relaxation rate on net CO generation and amount of recycle reaction of the conventional cycle process (T = 240 °C, P = 5 MPa, H₂/CO _x = 2.5, GHSV = 500 h^–1).

Simulation data display showed that when the gas relaxation rate was 20.0%, the circulation volume was 287.2 kmol·h^–1, and the corresponding carbon utilization rate from CO and CO₂ to methanol was 79.2%. When the gas relaxation rate dropped to 2.5%, the circulation volume increased to 399.5 kmol·h^–1, the carbon utilization rate increased to 93.3%, and the circulation volume rose by 39.1%. In the extreme case, when the gas relaxation rate was 0, the circulation volume reached 489.3 kmol·h^–1, 1.7 times that at the 20.0% gas relaxation rate. While reducing the gas relaxation rate could pointedly improve carbon utilization, it also markedly increased the system’s circulation load.

In process design and operation, multiple factors, such as carbon utilization efficiency, energy consumption, equipment investment, and operational complexity, had to be comprehensively considered to determine the optimal gas relaxation rate. In practice, a balance needed to be struck between carbon utilization efficiency and system complexity. According to Figure , a relaxation rate in the range of 1.0% to 5.0% may have been a good compromise, ensuring both a high carbon utilization rate and control over the increase in flow volume within an acceptable range.

3.4. Reconstructed Process System Performance Analysis

3.4.1. CO _x Hydrogenation to Methanol Cycle Process Including Stripping

In the CO/CO₂ hydrogenation methanol synthesis process with stripping, the gas relaxation rate served as a key parameter that controlled the release of unreacted gases within the system and significantly influenced both the circulation rate and feedstock utilization efficiency. As shown in Figure , the net CO production decreased markedly with a lower gas relaxation rate, indicating that excessive release of unreacted gases led to carbon source loss. In contrast, appropriately reducing the gas relaxation rate enhanced carbon recycling, improved system closure, and increased the resource utilization efficiency. This phenomenon reflected the regulatory role of the gas relaxation rate in maintaining the dynamic balance of reactants and optimizing the carbon conversion efficiency. Correspondingly, the circulation rate rose significantly as the gas relaxation rate decreased. When the gas relaxation rate was 20.0%, the circulation flow was 318.35 kmol·h^–1; as the rate decreased to 2.0%, the flow increased to 330.38 kmol·h^–1; and when the rate dropped to 0%, the circulation flow reached 389.53 kmol·h^–1 about 1.22 times that under the baseline condition. These results suggested that although a lower gas relaxation rate improved feedstock utilization, it also introduced additional burdens on equipment throughput and energy consumption, requiring a trade-off between the system load and economic performance.

8.

Effect of the gas relaxation rate on net CO generation and amount of recycle reaction of the new process (T = 240 °C, P = 5 MPa, H₂/CO _x = 2.5, GHSV = 500 h^–1).

To further quantify the impact of the gas relaxation operation on the loss of key reactants, this study conducted a quantitative analysis of the hydrogen content in the purge stream based on process simulation results. As shown in Table , the simulated data indicate that the hydrogen loss in the purge gas decreased significantly with a reduction in the gas relaxation rate. When the gas relaxation rate was set to 1.0%, the molar flow rate of H₂ in the purge stream (stream S7) was only 0.62 kmol·h^–1, which was negligible compared to the total hydrogen feed of 2200 kmol·h^–1. This result strongly demonstrated that the stripping operation effectively recovered gaseous reactants and enabled an almost closed-loop circulation within the system. However, as the gas relaxation rate decreased, challenges arose in terms of operational stability and capital investment. In practical engineering applications, excessively low gas relaxation rates may increase reactor load and lead to higher compression energy consumption. Therefore, it was recommended to select a moderate gas relaxation rate, within the range of 1.0–5.0%, to balance improved feedstock utilization with manageable system complexity and cost risks, while ensuring efficient conversion performance.

5. Process Simulation Results of CO _x Hydrogenation to Methanol Cycle Process Including Stripping.

current stock	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	S12
temperature (°C)	20	20	35	35	42.29	240	91.69	91.65	240	69	58	107
pressure (MPa)	0.1	0.1	0.15	0.15	1	5	1	1	4.5	4.5	0.14	0.15
gas phase fraction	1	1	1	1	1	0.9	1	1	0	0	0	0
molar flow (kmol·h–1)	500	400	2200	200	3242.1	1586.8	4.44	383.7	198.4	1153.9	839.3	314.9
CO (kmol·h–1)	500	0	0	0	500	7.63	0.74	7.5	0.20	1 × 10–5	0	0
CO2 (kmol·h–1)	0	400	0	0	400	53.5	0.42	52.9	10.7	0.03	0	0
H2 (kmol·h–1)	0	0	2200	200	2200	0.70	0.62	6.2	0.14	6 × 10–6	0	0
H2O (kmol·h–1)	0	0	0	0	283.7	630.2	3.43	283.6	315.1	314.9	0.6	314.9
CH3OH (kmol·h–1)	0	0	0	0	0	838.8	0	0	872.2	838.8	838.7	0.03

3.4.2. Process Economic Analysis

Although reducing the gas relaxation rate was beneficial for improving the utilization of CO, CO₂, and H₂, it also led to an increase in system circulation, which impacted equipment investment and operating costs. In addition, the H₂ loss in the gas phase could be recovered through the pressure swing adsorption technology, necessitating a balance between hydrocarbon utilization and economic considerations. This study focused on analyzing two key economic indicators: the total investment cost and unit production cost.

The techno-economic analysis followed the calculation methods from refs – assuming 8000 operating hours per year and a 15 year project lifetime. Total capital investment (PC) included fixed capital investment (FCI) covering equipment, piping, electrical systems, civil works, and installation-and working capital. Detailed costs are provided in the Supporting Information (Tables S1–S4).

$$\mathrm{E}\mathrm{I}=\sum _j\theta ·\mathrm{E}{\mathrm{I}}_j^r·{\left(\frac{S_j}{S_j^r}\right)}^{\mathrm{s}\mathrm{f}}$$ 10

$$\mathrm{F}\mathrm{C}\mathrm{I}=\mathrm{E}\mathrm{I}·(1+\sum _{\mathrm{i}=1}^n\mathrm{R}{\mathrm{F}}_i)$$ 11

$$\mathrm{P}\mathrm{C}=C_{\mathrm{r}}+C_{\mathrm{u}}+C_{\mathrm{o}\&\mathrm{m}}+C_{\mathrm{d}}+C_{\mathrm{p}\mathrm{o}\mathrm{c}}+C_{\mathrm{a}\mathrm{c}}+C_{\mathrm{d}\mathrm{s}\mathrm{c}}$$ 12

where θ represented the domestic production index; EI _j denoted the benchmark performance of equipment j; S _j referred to the production scale of equipment j; s _f indicated the benchmark scale; sf was the scale index; R _Fi was the proportional factor; C _r represented the raw material cost; C _u was the utility cost; C _o&m denoted the maintenance and operation cost; C _d was the depreciation cost; C _poc was the manufacturing cost; C _ac referred to the administrative cost; and C _dsc was the supply and sales cost.

The impact of the gas relaxation rate on equipment investment, production cost, and total investment cost is clearly shown in Figure . As the gas relaxation rate increased, system circulation volume decreased, resulting in a smaller equipment size and a gradual decrease in equipment investment costs. This trend reflected the positive impact of lower cycle volumes on capital costs.

9.

Impact of the gas relaxation rate on equipment investment, production costs, and total capital investment.

The unit production cost changed with the gas relaxation rate following a U-shaped curve that first decreased and then increased. This complex relationship could be divided into two stages: (a) when the gas relaxation rate was below 1.0%, increasing it reduced cycle power consumption and production costs. (b) Above 1.0%, cycle power stabilized but pressure swing adsorption increased power consumption, raising costs. The change in total investment cost with the gas relaxation rate showed a U-shaped trend similar to that of the unit production cost, reflecting the comprehensive impact of the relaxation rate on overall economics.

The research results demonstrated that the choice of the gas relaxation rate involved a complex balance of economy and efficiency. Although a very low relaxation rate improved the utilization efficiency of raw materials, it increased equipment investment and operating costs. On the other hand, a very high gas relaxation rate reduced raw material utilization efficiency and increased the energy consumption of the pressure swing adsorption process. Based on the economic analysis, this study determined the optimal gas relaxation rate to be 1.0%. At this point, both the unit production cost and total investment cost reached their minimum, achieving the optimization of economic benefits. During the process design stage, attention was focused on a gas relaxation rate of about 1.0% to achieve economic optimization. Considering fluctuations in raw material prices and changes in energy costs, it was recommended to establish a dynamic optimization model to adapt to market changes. Further research into efficient gas recovery technology may have helped achieve economic optimization at lower relaxation rates.

3.4.3. Raw Material Utilization Rate

Under the conditions of T = 240 °C, P = 5 MPa, H₂/CO _x = 2.5, GHSV = 500 h^–1, and a relaxation rate of 1.0%, the CO _x hydrogenation to methanol cycle with stripping, as shown in Figure , was modeled and calculated. Table presents the detailed process simulation results, including the temperature, pressure, vapor-phase fraction, and molar flow rates of the main components in each stream.

The utilization rates of carbon and hydrogen elements were key indicators of the CO _x hydrogenation to methanol process. Specifically, the H₂ utilization rate significantly impacted the production cost of methanol, while the carbon utilization rate directly affected the H₂ utilization rate. In this study, the carbon utilization rate was defined as the ratio of the molar flow rate of methanol in the product to the molar flow rate of CO _x in the feed. Similarly, the H₂ utilization rate was defined as twice the molar flow rate of methanol in the product to the molar flow rate of CO _x in the feed, as shown in the following equation.

$$E_{\mathrm{C}{\mathrm{O}}_x}=\frac{F_{\mathrm{C}{\mathrm{H}}_3\mathrm{O}\mathrm{H},\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}}}{F_{\mathrm{C}{\mathrm{O}}_{2,}\mathrm{F}\mathrm{e}\mathrm{e}\mathrm{d}}}\times 100\%$$ 13

$$E_{{\mathrm{H}}_2}=\frac{{2\times F}_{\mathrm{C}{\mathrm{H}}_3\mathrm{O}\mathrm{H},\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}}}{F_{{\mathrm{H}}_{2,}\mathrm{F}\mathrm{e}\mathrm{e}\mathrm{d}}}\times 100\%$$ 14

The results indicated that, compared to the theoretical maximum, the CO _x and H₂ utilization efficiencies achieved by this process exhibited an excellent conversion performance. The CO _x utilization efficiency of the CO _x hydrogenation methanol synthesis process with stripping reached 91.2%, reflecting the highly efficient use of the carbon source and effectively reducing carbon emissions. The H₂ utilization efficiency was 82.8%, which was particularly significant, given the critical role of hydrogen cost in methanol production economics. Compared with conventional processes, this stripping-integrated strategy markedly reduced feedstock waste and enhanced overall reactor conversion efficiency by efficiently recovering unreacted components. In addition, the performance of the separation system was also fully demonstrated. As shown in Table , the molar flow rate of methanol at the reactor outlet (S2) was 738.83 kmol·h^–1, while that in the final product stream (S11) was 738.78 kmol·h^–1, indicating nearly identical values. This consistency confirmed the high separation efficiency and negligible product loss. Moreover, the recycled gas stream (S8), which contained retained Raw gas, continued to participate in the reaction, effectively supporting the efficient operation of the closed-loop system.

By coupling low gas relaxation rate operation with a stripping-based recovery strategy, this study systematically evaluated the circulation potential of CO _x and H₂ under the simulation framework. The results demonstrated that this approach not only enabled deep recovery of reactants but also pointedly suppressed the ineffective emission of hydrogen in the tail gas. Thus, it provides both a theoretical basis and a process optimization direction for developing a low-carbon, high-efficiency methanol synthesis route.

4. Conclusions

This study analyzed the process pathways for methanol production via CO/CO₂ hydrogenation using a thermodynamic simulation system. Two catalysts were selected for comparative evaluation. The effects of process conditions on gas- and liquid-phase carbon/hydrogen losses were systematically investigated. A cyclic process integrating steam stripping recovery and gas-phase relaxation was proposed, which effectively enhanced the feedstock conversion efficiency and process economics.

• (1)The specialized Cu/Zn/Al/Zr catalyst (CZAZ-HF) exceeded the commercial Cu/ZnO/Al₂O₃ catalyst (CZA-GM) in both CO _x conversion and methanol yield. The optimized process parameters were: a temperature of 240 °C, pressure of 5 MPa, CO/CO₂ ratio of 1.0 to 0.8, H₂/CO _x ratio of 2.5, and the GHSV was maintained as low as possible. Under these conditions, the methanol yield reached 82.0%, with single-pass conversions of CO and CO₂ at 90.7% and 72.6%, respectively.
• (2)The dissolved CO₂ in the liquid phase decreased from 10.72 kmol·h^–1 before stripping to 1.69 × 10^–4 kmol·h^–1 after stripping. The liquid-phase CO₂ loss was negligible, while hydrocarbon loss mainly depended on gas-phase relaxation. H₂ in the relaxed gas stream was recovered using pressure swing adsorption. As the relaxation ratio increased, equipment investment costs gradually decreased, while unit production costs first decreased and then increased. The relaxation ratio significantly influenced the economic efficiency of the CO _x -to-methanol process.
• (3)An innovative CO/CO₂ hydrogenation-to-methanol cyclic process incorporating steam stripping was proposed, which effectively resolved the CO₂ loss issue in the liquid phase. Under optimized conditions with a 1.0% purge rate, the process achieved 93.2% CO _x utilization and 82.8% H₂ utilization, closely approaching the theoretical maximum values.
• (4)Economic analysis indicated that both the unit production cost and total capital investment were optimized at a relaxation ratio of 1.0%. Compared with conventional processes, the proposed system demonstrated superior raw material utilization and atom economy.

This study presented an efficient pathway for the hydrogenation of CO/CO₂ to methanol. Through systematic analysis of reaction conditions and process parameters, both feedstock conversion and methanol yield were successfully enhanced. The proposed stripping-integrated cycle process effectively addressed CO _x losses in the liquid phase and enhanced the overall process economy. The integration of carbon cycle intensification offered new design concepts and evaluation criteria for advancing low-carbon conversion processes. However, the simulation was based on idealized assumptions, neglecting catalyst deactivation, mass transfer resistance, and kinetic limitations. Actual industrial performance may be lower than simulated results, and further experimental validation is required for gas recovery and cycle efficiency. Future work should focus on developing more efficient catalyst systems, constructing dynamic optimization models responsive to market fluctuations, and advancing gas recovery technologies to maximize economic performance at reduced relaxation rates.

Glossary

Nomenclature

K _fi

equilibrium constant for reaction i

A _i

kinetic model constant, kmol·kg^–1 cat·s^–1·Pa^–1

f _i

fugacity of component j, Pa

K _j

equilibrium constant for adsorption of component j

E _i

activation energy, J·mol^–1

P

pressure, MPa

r _i

reaction speed j, mol·g^–1 cat·s^–1

T

temperature, K

C _i

methanol (CH₃OH) yield

X _i

CO _x conversion

X _j

H₂ conversion

GHSV

gas hourly space velocity, h^–1

All relevant data underpinning this study are included in the main manuscript and the Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c05883.

• Calculation formula and data for economic analysis, summary of investment data for main equipment components, ratio factors for fixed capital investment, raw material and utilities for techno-economic analyses, and assumptions for the estimation of total product cost (PDF)

X.Y.: conceptualization, methodology, formal analysis, investigation, data curation, and writingoriginal draft preparation; Y.G.: conceptualization, methodology, formal analysis, resources, writingoriginal draft preparation, supervision, project administration, and funding acquisition; G.Z.: writingreview and editing; S.C.: validation and supervision; C.W.: resources; C.T.: validation; A.B.: validation, and writingreview and editing; and J.Y.: formal analysis, investigation; and X.Z.: supervision.

The authors declare no competing financial interest.