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Title: Using Advanced Modeling to Accelerate the Scale-Up of Carbon Capture Technologies

Carbon capture and storage (CCS) is one of many approaches that are critical for significantly reducing domestic and global CO2 emissions. The U.S. Department of Energy’s Clean Coal Technology Program Plan envisions 2nd generation CO2 capture technologies ready for demonstration-scale testing around 2020 with the goal of enabling commercial deployment by 2025 [1]. Third generation technologies have a similarly aggressive timeline. A major challenge is that the development and scale-up of new technologies in the energy sector historically takes up to 15 years to move from the laboratory to pre-deployment and another 20 to 30 years for widespread industrial scale deployment. In order to help meet the goals of the DOE carbon capture program, the Carbon Capture Simulation Initiative (CCSI) was launched in early 2011 to develop, demonstrate, and deploy advanced computational tools and validated multi-scale models to reduce the time required to develop and scale up new carbon capture technologies. The CCSI Toolset (1) enables promising concepts to be more quickly identified through rapid computational screening of processes and devices, (2) reduces the time to design and troubleshoot new devices and processes by using optimization techniques to focus development on the best overall process conditions and by using detailedmore » device-scale models to better understand and improve the internal behavior of complex equipment, and (3) provides quantitative predictions of device and process performance during scale up based on rigorously validated smaller scale simulations that take into account model and parameter uncertainty[2]. This article focuses on essential elements related to the development and validation of multi-scale models in order to help minimize risk and maximize learning as new technologies progress from pilot to demonstration scale.« less
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Resource Type:
Journal Article
Resource Relation:
Journal Name: Power Engineering, 119(6):30-34
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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Country of Publication:
United States