(485b) An Operational Optimization Approach for Supply Network Decarbonization for Energy-Chemical Co-Production

Motivated by the ambitious goal of net-zero economy by 2050, there has been burgeoning interest from the chemical and energy sectors string for the development of clean energy solutions using renewable energy sources, green production routes, carbon capture and utilization techniques, etc. [1-2]. Extensive efforts have also made by the process systems engineering to quantitatively identify a long-term strategic energy transition pathway via the advanced process optimization and multi-scale synthesis approaches [3-5]. However, the resulting solutions typically require future breakthrough advances in the cost-competitiveness of energy storage devices and carbon neutral technologies (e.g., water electrolysis, direct carbon conversion) for commercial scale production. Innovative process design and operation strategies leveraging current process infrastructure are in dire need to start paving the way from fossil fuel to zero-carbon.

In this work, we propose an operational optimization approach for multi-product scheduling in response to grid and renewable energy source intermittency. Particularly, we are interested in a co-production plant for hydrogen and methanol, two of the most important energy carriers and fundamental chemicals but challenged by very high energy and carbon intensity in the current industrial practices [6-7]. Multiple process routes are considered synergizing green and blue production, such as water electrolysis, direct carbon hydrogenation, natural gas steam reforming with carbon capture, etc. This also enables to take advantage of the different energy intensity of each route towards optimal and flexible operation in response to power grid peak demand, energy price, and renewable energy intermittency. The statistics on electricity, solar, and wind power are collected from the national or state-wise databases such as EIA, ERCOT, and NREL. A multi-period process synthesis and scheduling model using mixed-integer linear programming formulation is developed to optimize the co-production schedule. The dynamic hydrogen-methanol production routes and product distribution at each time of a day or week are determined using tailored data-driven algorithms to solve the resulting large scale optimization model with temporal considerations. The proposed approach is demonstrated to achieve: (i) evolutionary penetration of carbon-neutral technologies in combination with existing processes, (ii) improvements in both cost and sustainability metrics.

References

[1] World Resource Institute and World Business Council for Sustainable Development. Greenhouse Gas Protocol. https://ghgprotocol.org/about-wri-wbcsd

[2] U.S. Department of Energy. Hydrogen Program Plan. 2020. https://www.hydrogen.energy.gov/

[3] Kakodkar, R., He, G., Demirhan, C. D., Arbabzadeh, M., Baratsas, S. G., Avraamidou, S., Mallapragada, D., Miller, I., Allen, R. C., Gençer, E. & Pistikopoulos, E. N. (2022). A review of analytical and optimization methodologies for transitions in multi-scale energy systems. Renewable and Sustainable Energy Reviews, 160, 112277.

[4] Allman, A., Palys, M. J., & Daoutidis, P. (2019). Scheduling‐informed optimal design of systems with time‐varying operation: A wind‐powered ammonia case study. AIChE Journal, 65(7), e16434.

[5] Potrč, S., Čuček, L., Martin, M., & Kravanja, Z. (2021). Sustainable renewable energy supply networks optimization–The gradual transition to a renewable energy system within the European Union by 2050. Renewable and Sustainable Energy Reviews, 146, 111186.

[6] Harris, K., Grim, R. G., & Tao, Ling. National Renewable Energy Laboratory. A Comparative Techno-Economic Analysis of Renewable Methanol Synthesis Pathways from Biomass and CO2. 2021.

[7] Soltanieh, M., Azar, K. M., & Saber, M. (2012). Development of a zero emission integrated system for co-production of electricity and methanol through renewable hydrogen and CO2 capture. International Journal of Greenhouse Gas Control, 7, 145-152.