(431f) Investigating the Design of Global-Scale Power-to-Methanol Production Systems: Large- or Small-Scale Chemical Plants?

Today, the chemical industry relies predominantly on fossil resources for feedstock and energy supply. The concentrated nature of these resources facilitates the operation of large-scale chemical plants and even complex production systems of various products, which benefit from the economies of scale and material/energy integration. However, with the goal to mitigate the negative consequences of climate change, the chemical industry faces a transition towards renewable sources of material and energy (e.g. wind and solar) that require larger land areas compared to fossil sources [1]. Consequently, the costs and losses associated with the energy/material supply to the production system increase, potentially negating the cost reductions achievable through the economies of scale by increasing the capacity. This would imply the existence of a threshold capacity beyond which a further increase ceases to be economically viable.

We investigate these scale dependencies within the context of methanol production, an important platform chemical with a global production capacity on the order of 100 Mt/a, which can be used for plastics production or directly as a shipping fuel [2]. Current fossil-based methanol synthesis processes can have a single-train capacity as high as 2,600,000 t/a with reports indicating installations of up to five such trains within a single production system [3]. In contrast, the newly emerging Power-to-Methanol processes using renewable sources of electrical energy, hydrogen and carbon dioxide, which are currently under construction, have capacities of up to 100,000 t/a [4]. Such disparity in scale leads to the following questions: To what extent would future solar and wind-based Power-to-Methanol processes be economically beneficial at scales approaching their fossil counterparts, given the aforementioned considerations? Alternatively, would a spatially distributed fleet of a greater number of small-scale plants be a more effective strategy to produce large quantities of methanol?

To offer quantitative insights into these aspects, we propose an optimization-based production system design method, in which the costs of energy/material supply from a given land area with solar/wind energy availability and the economies of scale of the chemical plant are modeled. Our study also includes the option to distribute the sub-processes away from the demand location of the system to benefit from reduced costs of energy/material transport through electricity transmission lines and pipelines. Importantly, heat integration/waste-heat utilization, which can lead to higher efficiencies for the Power-to-Methanol process as quantified in our previous modeling studies [5-7], are also considered. However, benefitting from such integration also constrains the sub-processes (e.g. water electrolysis and direct air capture of CO₂) to be installed in close proximity to the chemical plant, making the design problem even more interesting.

The proposed design approach is subsequently applied to assess case studies with different renewable energy conditions, representing promising, often remote [8], locations for greenfield Power-to-Methanol production systems. In this way, we contribute a modeling study, which can help in determining the economically optimal design, including the energy/material supply, sub-process localization for the production of methanol in large quantities, and is readily adjustable to other Power-to-X products.

[1] Nøland, J. K., Auxepaules, J., Rousset, A., Perney B., Falletti G. (2022) Spatial energy density of large-scale electricity generation from power sources worldwide. Sci Rep, 12, 21280, 10.1038/s41598-022-25341-9

[2] Tabibian S.S., Scharifzadeh M. (2023) Statistical and analytical investigation of methanol applications, production technologies, value-chain and economy with a special focus on renewable methanol. Renew Sustain Energy Rev, 179, 113281, 10.1016/j.rser.2023.113281

[3] Johnson Matthey (2020) World’s largest single train methanol plants to use Johnson Matthey technology. URL: https://matthey.com/documents/161599/166306/Johnson-Matthey-Press-Release-Baofeng-IV-Methanol-Plantv07FINAL.pdf/7fbe5fa0-f118-832e-7d88-0b1668c9962a?t=1650968256207 (accessed: 03.04.2024)

[4] Methanol Institute (2024) Renewable Methanol: E-methanol and Biomethanol Plants. URL: https://www.methanol.org/renewable/ (accessed: 03.04.2024)

[5] Svitnič, T., Sundmacher K. (2022) Renewable methanol production: Optimization-based design, scheduling and waste-heat utilization with the FluxMax approach. Applied Energy, 326, 120017, 10.1016/j.apenergy.2022.120017

[6] Svitnič, T., Beer K., Sundmacher K., Böcher, M. (2024) Optimal design of a sector-coupled renewable methanol production amid political goals and expected conflicts: Costs vs. land use. Sustainable Production and Consumption, 44, 123-150, 10.1016/j.spc.2023.12.003

[7] Svitnič, T., Sundmacher K. (2024) Identifying standard and simple designs of Power-to-Methanol processes: The costs of complexity reduction. Energy Conversion and Management, 307, 118325, 10.1016/j.enconman.2024.118325

[8] Pfennig M., Böttger D., Häckner B., Geiger D., Zink C., Bisevic A., Jansen L. (2023) Global GIS-based potential analysis and cost assessment of Power-to-X fuels in 2050. Applied Energy, 347, 121289, 10.1016/j.apenergy.2023.121289