2024 AIChE Annual Meeting
(364ao) The Design and Operation of Carbon Capture, Utilization, and Storage (CCUS) Supply Chain Networks Under Uncertainty
In this work, we study how the uncertainties in geological storage capacities, demands for CO2 utilization and future CO2 selling prices affect the design of regional, state-wide and national CCUS supply chains in the U.S. We consider a two-stage model, where the first stage decisions involve strategic planning in terms of the selection of CO2 sources and the scale of the CO2 capture plants to be built along with the topology in terms of the pipeline network, and the second stage decisions include the CO2 transportation and storage/utilization amounts to selected sinks averaged over a yearly basis. A framework has been developed which uses real-world emission data from the Greenhouse Gas Reporting Program (GHGRP) and the storage/retention volumes for the sequestration/utilization sites from the US Geological Survey (USGS)[4,5] to find an optimal CCUS network design by minimizing its total network construction and operational costs and minimizing future emissions that cannot be captured due to uncertainties in the geological volumes. The geological storage volumes are reported in terms of their cumulative distribution functions, and with 335 uncertain parameters, we essentially have an infinite number of possible scenario realizations. We use Sample Average Approximation[6] (SAA) to generate an approximate problem based on a sampling method that captures the underlying probability distributions of the uncertain parameters. We solve this approximate problem multiple times with different sets of independent and identically distributed scenarios to obtain guaranteed statistical lower and upper bounds on the true optimal of the original problem along with a good solution vector that will guarantee minimum infeasibility in terms of meeting a capture requirement.
For the nationwide and regional instances, for different capture requirements ranging from 50-70%, we report the annual overall CCUS network investment and operational (first stage) costs for the best network as well as the recourse (second stage) costs for different realizations of uncertainty in the storage volumes. We also report the sources selected for capture based on the industry types and the states they are located in. Most importantly, we report the values of the stochastic solutions for each of these instances which may also be interpreted as the advantage we have in capturing CO2 in all future scenarios by building a network according to the uncertain storage values, over building a network according to expected storage values.
References:
[1] Hasan, M. M. F., Boukouvala, F., First, E. L., Floudas, C. A. Nationwide, Regional and Statewide CO2 Capture, Utilization and Sequestration Supply Chain Network Optimization. Industrial & Engineering Chemistry Research, 2014, 53(18), 7489–7506.
[2] He, Y. J., Zhang, Y., Ma, Z. F., Sahinidis, N. V., Tan, R. R., & Foo, D. C. (2014). Optimal source–sink matching in carbon capture and storage systems under uncertainty. Industrial & Engineering Chemistry Research, 53(2), 778-785.
[3] Li, Y., Wei, J., Yuan, Z., Chen, B., & Gani, R. (2022). Sustainable synthesis of integrated process, water treatment, energy supply, and CCUS networks under uncertainty. Computers & Chemical Engineering, 157, 107636.
[4] Blondes, M.S., Schuenemeyer, J.H., Drew, L.J. and Warwick, P.D., 2013. Probabilistic aggregation of individual assessment units in the US Geological Survey national CO2 sequestration assessment. Energy Procedia, 37, pp.5110-5117.
[5] Warwick, Peter D., Emil D. Attanasi, Ricardo A. Olea, Madalyn S. Blondes, Philip A. Freeman, Sean T. Brennan, Matthew D. Merrill et al. A probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention. No. 2019-5115. US Geological Survey, 2019.
[6] Kleywegt, Anton J., Alexander Shapiro, and Tito Homem-de-Mello. "The sample average approximation method for stochastic discrete optimization." SIAM Journal on optimization 12, no. 2 (2002): 479-502.
Research Interests: Mathematical modeling, Systems engineering, Stochastic optimization, Supply chain design, Operations research