(729d) Real-Time Emission-Driven Modeling and Optimization Framework Under Uncertainty for Sustainable Hydrogen Production | AIChE

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(729d) Real-Time Emission-Driven Modeling and Optimization Framework Under Uncertainty for Sustainable Hydrogen Production

Authors 

De Sousa, M. - Presenter, Artie McFerrin Department of Chemical Engineering
Suresh, S., Texas A&M University
Montaño Flores, B. S. M., Artie McFerrin Department of Chemical Engineering
Kakodkar, R., Texas A&M University
Shah, H. B., Texas A&M University
Kenefake, D.
Pappas, I.
Fu, X., Shell
Demirhan, C. D., Texas A&M University
Niknezhad, S., Texas &M University
Pistikopoulos, E., Texas A&M Energy Institute, Texas A&M University
Mutlu, M., Ss
The U.S. hydrogen economy produces 10 million metric tons of hydrogen annually, which emits 41 million metric tons of carbon dioxide equivalent emissions [1]. As the demand for hydrogen increases and new emission regulations take effect, there is an emerging need to investigate the integration and evolution of both conventional and innovative hydrogen production systems [2]. To facilitate a smooth transition during the expansion of the hydrogen production infrastructure, these hybrid systems must be meticulously designed and operated with due consideration for future uncertainties.
To this end, we present a real-time emission-driven optimization framework, which is implemented through a mixed-integer linear programming (MILP) formulation to determine optimal design configurations and operation schedules while simultaneously mitigating emissions by utilizing electricity price forecasts, sporadic weather data, and supply and demand variability [3]. Life cycle assessment (LCA) criteria are directly incorporated in the optimization model to evaluate different environmental concerns that are segmented into on-site emissions (Scope 1), indirect emissions (Scope 2), and upstream and downstream emissions (Scope 3) [4]. The methodology avoids assuming perfect information by implementing a rolling-horizon optimization approach that allows decision-makers to investigate the sensitivity of capacity expansion plans concerning different look-ahead periods. The framework, illustrated by a detailed hydrogen production case study, results in Pareto optimal solutions that capture the trade-offs between environmentally friendly and economically competitive designs and operational profiles.

References
[1] Z. Guiyan, G. Edward J., M. Darik, H2 production through natural gas reforming and carbon capture: A techno- economic and life cycle analysis comparison, International journal of hydrogen energy (2023) 1288–1303.
[2] M. Wappler, D. Unguder, X. Lu, H. Ohlmeyer, H. Teschke, W. Lueke, Building the green hydrogen market - current state and outlook on green hydrogen demand and electrolyzer manufacturing, International Journal of Hydrogen Energy 47 (79) (2022) 33551–33570.
[3] R. Kakodkar, G. He, C. Demirhan, M. Arbabzadeh, S. Baratsas, S. Avraamidou, D. Mallapragada, I. Miller, R. Allen, E. Gençer, E. Pistikopoulos, A review of analytical and optimization methodologies for transitions in multi-scale energy systems, Renewable and Sustainable Energy Reviews 160 (2022) 112277.
[4] A. Hugo, E. Pistikopoulos, Environmentally conscious long-range planning and design of supply chain networks, Journal of Cleaner Production 13 (15) (2005) 1471–1491.

Topics 

Hydrogen Production & Storage
Process Design & Development
Energy (Sustainability & Environment)

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