Fuzzy optimization of multi-period carbon capture and storage systems with parametric uncertainties

•Uncertainties in CO2 storage site properties cause technical risk.•A fuzzy optimization model to balance CO2 reduction and technical risk is developed.•Case studies illustrate the efficacy of the model in planning CO2 capture and storage. Carbon capture and storage (CCS) is an important technology...

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Bibliographic Details
Published in:Process safety and environmental protection 2014-11, Vol.92 (6), p.545-554
Main Authors: Tapia, John Frederick D., Tan, Raymond R.
Format: Article
Language:English
Subjects:
Air pollution
Carbon capture and storage
Carbon dioxide
Data uncertainty
Emissions control
Fuzzy
Fuzzy logic
Fuzzy mixed integer linear programming
Fuzzy set theory
Optimization
Risk
Source-sink matching
Technical risk
Uncertainty
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Summary:•Uncertainties in CO2 storage site properties cause technical risk.•A fuzzy optimization model to balance CO2 reduction and technical risk is developed.•Case studies illustrate the efficacy of the model in planning CO2 capture and storage. Carbon capture and storage (CCS) is an important technology option for reducing industrial greenhouse gas emissions. In practice, CO2 sources are easy to characterize, while the estimation of relevant properties of storage sites, such as capacity and injection rate limit (i.e., injectivity), is subject to considerable uncertainty. Such uncertainties need to be accounted for in planning CCS deployment on a large scale for effective use of available storage sites. In particular, the uncertainty introduces technical risks that may result from overestimating the limits of given storage sites. In this work, a fuzzy mixed integer linear program (FMILP) is developed for multi-period CCS systems, accounting for the technical risk arising from uncertainties in estimates of sink parameters, while still attaining satisfactory CO2 emissions reduction. In the model, sources are assumed to have precisely known CO2 flow rates and operating lives, while geological sinks are characterized with imprecise fuzzy capacity and injectivity data. Three case studies are then presented to illustrate the model. Results of these examples illustrate the tradeoff inherent in planning CCS systems under parametric uncertainty.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2014.04.012