A Model-Based Analysis of Direct Methanol Production from CO₂ and Renewable Hydrogen ^†

Abstract

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of this study is to model and optimize the methanol synthesis process from CO₂, focusing on maximizing methanol yield while minimizing CO₂ content in the product stream. In this work, a detailed methanol synthesis process simulation was developed using the Soave–Redlich–Kwong equation of state in the Aspen Plus V11 commercial software environment. Pure CO₂ streams, which are produced from the post-combustion carbon capture process, and renewable hydrogen streams were used. The results are compared with open literature sources. In addition, a sensitivity analysis was employed to evaluate the effects of the pressure, temperature, and recirculation fraction on process efficiency. The results showed that the highest methanol yield of 76,838 kg/h was obtained at 80 bar, 276 °C, and a recirculation fraction of 0.9. The lowest CO₂ content in the final product (73 kg/h) occurred at 80 bar, 220 °C, and a recirculation fraction of 0.6. These findings demonstrate the trade-off between maximizing methanol output and reducing unreacted CO₂. In conclusion, optimal operating conditions for both the high yield and low CO₂ content were identified, providing a foundation for further process refinement. Future work will involve developing a more complex multi-reactor model and conducting economic assessments for large-scale industrial implementation.

1. Introduction

The rise in atmospheric carbon dioxide (CO₂) concentrations over the past century has been identified as one of the leading causes of global climate change. The increasing reliance on fossil fuels for power production and industrial activities has resulted in significant emissions of greenhouse gases, particularly CO₂, which contributes to global warming and disrupts natural ecosystems [1]. To address these challenges, carbon capture and utilization (CCU) has emerged as a promising approach to mitigate CO₂ emissions while enabling the transition to a circular carbon economy. CCU technologies provide a dual advantage by lowering atmospheric CO₂ concentrations while generating valuable products that fuel industrial advancements.

Methanol synthesis from CO₂ is a prominent pathway within the spectrum of CCU technologies [2]. Methanol, a simple form of alcohol, is widely used as a chemical feedstock and a clean energy carrier in various industries, including plastics, pharmaceuticals, and fuel production. The synthesis of methanol from CO₂ provides a sustainable solution by integrating renewable energy sources and utilizing waste CO₂ emissions, transforming them into economically valuable products [3]. Furthermore, methanol is considered an important intermediate in the transition towards a hydrogen-based economy, as it facilitates the storage and transport of hydrogen in liquid form [4,5].

Recent studies have highlighted the potential of methanol synthesis as a key component of sustainable energy systems. Borisut et al. [6] optimized methanol production via CO₂ hydrogenation using a response surface methodology. Their findings emphasized the importance of operating conditions such as temperature, pressure, and recycling ratios in minimizing production costs while achieving high yields. Similarly, Oles et al. investigated green methanol production using computational fluid dynamics to analyze catalytic reactor parameters. Their research demonstrated a yield improvement of up to 10% through optimized pressure and velocity settings, highlighting the role of advanced modeling tools in process refinement [7].

Another significant contribution comes from Milani et al., who developed a model for CO₂ integration in natural gas-based methanol plants. Their work demonstrated a reduction in methane usage by 25.6% and a combined CO₂ emissions decrease of 21.9% through effective process integration [8]. Rafiq et al. extended this understanding through a life cycle assessment comparing CO₂-based and conventional methanol production pathways. Their analysis revealed that renewable energy integration, particularly through co-electrolysis, significantly reduces greenhouse gas emissions, albeit with trade-offs in resource usage [9].

Despite these advancements, challenges remain in scaling up CO₂-to-methanol technologies for industrial applications. These include optimizing catalytic performance, ensuring process efficiency at varying scales, and addressing the economic feasibility of renewable hydrogen integration. This study aims to address these challenges by developing a comprehensive simulation model of the methanol synthesis process using Aspen Plus. The primary objective is to identify optimal operating conditions for maximizing methanol yield while minimizing the CO₂ content in the final product. By employing advanced thermodynamic models and conducting sensitivity analyses, this research seeks to provide actionable insights for scaling up methanol production processes and integrating them into industrial applications. The findings of this study contribute to the ongoing efforts to develop sustainable and economically viable CCU technologies, supporting global initiatives to combat climate change.

2. Methodology

This study developed a comprehensive simulation model for methanol synthesis using CO₂ and renewable hydrogen in Aspen Plus, leveraging advanced thermodynamic modeling and process design techniques. The objective was to optimize the process for maximum methanol yield while minimizing CO₂ emissions. This methodology details the modeling and simulation setup, process assumptions, reactor design, and sensitivity analysis.

2.1. Model Development

The methanol synthesis process developed in this study involves three primary steps: CO₂ compression for transportation, methanol production, and the dewatering of the final methanol product. These steps were simulated in Aspen Plus to evaluate the process’s technical and operational feasibility under varying conditions (see Figure 1).

In the first step, CO₂ from the capture plant undergoes multi-stage compression with intercooling to reach the pressure required for transport to the methanol plant. For the transportation of CO₂ over long distances, it is essential to pressurize the captured CO₂ to a high level [10]. However, single-stage compression becomes impractical and inefficient at such pressures due to the excessive temperature increase during compression. This rise in temperature can lead to several drawbacks, including higher energy consumption, material wear, and complications related to liquefaction. To overcome these challenges, this study utilizes a multi-stage compression system with intercooling. The Peng–Robinson equation of state is employed for thermodynamic modeling to ensure the accurate simulation of the process. The number of compression stages was determined based on the criterion that the pressure ratio for each stage should not exceed 3, as recommended in [11] (Equation (1)). Taking into account the maximum allowable discharge temperature and using the pressure ratio equation, a total of four stages was selected, resulting in an overall pressure ratio of 2.78.

$$R_{stage}=\sqrt[N_s]{R_{total}}$$

(1)

Here, R_total is the overall pressure ratio of the CO₂ compression unit, R_stage is the pressure ratio of each stage, and N_s is the number of compression stages.

The thermodynamic calculations were based on the Soave–Redlich–Kwong equation of state, which is widely used for systems involving hydrocarbons and non-ideal gases such as CO₂. The feedstock included a pure CO₂ stream sourced from a post-combustion carbon capture unit and renewable hydrogen generated through electrolysis. The hydrogen feed stream was configured at 25 °C and 8 bar with a flow rate of 22.88 tons/h, while the CO₂ stream was set at 35 °C and 1.1 bar with a flow rate of 139.8 tons/h. These conditions were selected to replicate typical industrial scenarios.

The second step focuses on methanol production, where compressed CO₂ is reacted with renewable hydrogen. The synthesis reaction occurs in an RPlug flow reactor, a plug flow reactor model in Aspen Plus, chosen for its capability to accurately model the kinetics of heterogeneous catalytic reactions (see Figure 1). Kinetic parameters for the methanol synthesis reaction were obtained in this study from the AspenTech reference database, ensuring consistency with industrial practices. The reaction mechanism included four primary reactions: the reverse water–gas shift reaction (R1), the hydrogenation of CO₂ to methanol (R2), the hydrogenation of CO to ethanol (R3), and methanol dehydration to dimethyl ether (R4). These reactions were modeled using appropriate reaction kinetics based on the Langmuir–Hinshelwood–Hougen–Watson mechanism for reversible reactions and the power-law model for irreversible reactions. The reactor temperature was assumed to remain constant, facilitated by a tubular cooling system, to maintain optimal reaction conditions.

CO₂ + H₂ ⇋ CO + H₂O

(R1)

CO₂ + 3H₂ ⇋ CH₃OH + H₂O

(R2)

2CO + 4H₂ → C₂H₅OH + H₂O

(R3)

2CH₃OH → CH₃OCH₃ + H₂O

(R4)

The reactor effluent flows into a separator, where unreacted gases, such as CO₂ and H₂, are separated and recycled back into the process via a recycled loop. The remaining stream, containing methanol and water, is directed for further separation. In this unit, methanol is purified to meet industrial-grade specifications while water and other impurities are removed. The purified methanol is then directed at a RadFrac column, where additional refinement ensures the final product’s purity. This process concludes with the collection of methanol as the primary product and water as a by-product.

2.2. Sensitivity Analysis

Sensitivity analysis was conducted to evaluate the effects of pressure (60–70 bar), temperature (220–260 °C), and recirculation fraction (0.6–0.9) on methanol yield and CO₂ content in the final product. A fixed feed flow rate of 160 tons/hour with mass fractions of 0.857 CO₂ and 0.143 H₂ was maintained. The exclusion of energy consumption considerations for compression and heating allowed for an isolated focus on reaction and separation efficiencies.

Figure 2 illustrates the assumptions used for the sensitivity analysis, highlighting key parameters such as the feed stream composition, constant feed rate, and controlled reactor temperature. These assumptions form the basis for analyzing how variations in process conditions affect methanol production and CO₂ reduction.

The simulation framework was designed to capture the complexities of industrial methanol synthesis while providing flexibility for iterative optimization. This methodological approach ensures the reliability of the findings and establishes a foundation for future studies in scaling up CO₂ utilization technologies.

3. Results and Discussion

The simulation results provide a detailed understanding of the methanol synthesis process, focusing on critical metrics such as methanol yield, CO₂ conversion efficiency, and the influence of operational parameters. Figure 3a illustrates the methanol conversion profile along the reactor length. Methanol conversion increases progressively with the reactor length, reaching a maximum at the outlet. The non-linear rise in conversion signifies the efficient utilization of the reactants, CO₂ and H₂, with a diminishing rate of reaction towards the reactor’s end due to equilibrium constraints. The maximum methanol yield achieved was 76,838 kg/h under conditions of 80 bar pressure, 276 °C temperature, and a recirculation fraction of 0.9.

An increase in the recycling ratio significantly improves methanol yield, driven by the enhanced availability of reactants (see Figure 3b). However, this is accompanied by a modest rise in CO₂ in the product stream, suggesting a delicate balance between maximizing yield and maintaining product purity. Optimizing the recirculation fraction is essential to achieve high conversion rates without incurring excessive operational costs. Methanol production increases with temperature up to an optimal point of 276 °C, beyond which a decline is observed (see Figure 3c). This decrease is attributed to the thermodynamic limitations of the exothermic methanol synthesis reaction, where higher temperatures shift the equilibrium unfavorably. The CO₂ content mirrors this trend, emphasizing the need for precise temperature control to ensure efficient conversion while avoiding thermal inefficiencies. Pressure has a positive impact on methanol yield, as higher pressures drive the equilibrium toward product formation. This trend highlights the importance of pressure in enhancing reaction performance. However, the associated increase in energy consumption for compression and the material costs for handling high-pressure systems necessitate careful economic evaluation to determine the most cost-effective operating pressure (see Figure 3d).

The conditions of 80 bar pressure, 220 °C temperature, and a recirculation fraction of 0.6 resulted in the lowest CO₂ content in the final methanol product, measured at 73 kg/h. At these operating parameters, the reaction equilibrium shifted in favor of CO₂ consumption, enhancing the efficiency of its conversion into methanol and reducing the amount of unreacted CO₂ in the output stream. The combination of relatively low-temperature and moderate recirculation provides sufficient time and conditions for the reaction to approach equilibrium while minimizing side reactions that could result in CO₂ retention. However, the lower recirculation fraction also implies a reduced reactant availability in the reactor, which may limit the overall methanol yield. This trade-off highlights the need for the precise optimization of reaction parameters to achieve both high product purity and acceptable yield levels.

4. Conclusions

This study developed and analyzed a comprehensive process model for methanol synthesis using CO₂ and renewable hydrogen in Aspen Plus, focusing on process simulation, optimization, and performance evaluation. The model incorporated multi-stage CO₂ compression, a plug flow reactor for methanol synthesis, and distillation-based product purification. The Peng–Robinson equation of state was employed for thermodynamic modeling, and kinetic reaction parameters were implemented to enhance accuracy.

Apart from this, several important generalized key conclusions can be drawn from this paper:

• The simulation accurately predicted methanol yield, CO₂ conversion efficiency, and reaction equilibrium behavior under varying process conditions.
• The model demonstrated a maximum methanol yield of 76,838 kg/h at 80 bar, 276 °C, and a recirculation fraction of 0.9.
• The lowest CO₂ content in the final product (73 kg/h) was achieved at 80 bar, 220 °C, and a recirculation fraction of 0.6, highlighting the trade-off between yield maximization and CO₂ reduction.
• Pressure and temperature optimization significantly impacted the process performance, with higher pressures favoring methanol synthesis but increasing compression energy demands.
• The recirculation fraction played a dual role, enhancing conversion efficiency but requiring additional energy for gas compression and separation.

These findings support the feasibility of CO₂-based methanol production as a sustainable carbon utilization strategy, demonstrating its potential for decarbonization efforts in the chemical industry.