Carbon Capture Simulation Initiative
The CCSI Toolset will accelerate the development and deployment cycle for bringing new Carbon Capture and Storage (CCS) technologies to market. Integrated Data Frameworks (and other Data Science & Technology Department) personnel are leading the Integration Framework and Software Development Support toolset development tasks. These tasks are implementing the communication between the various tools and well as data management, visualization, and software engineering.
|PI:||Deb Agarwal (Integration Framework Task Lead)
Paolo Calafiura (Software Development Support Task Lead)
David Brown (LBNL Lab Lead)
|Collaborators:||National Energy Technology Laboratory (Overall Project Leadership),
Lawrence Livermore National Laboratory, Los Alamos National Laboratory, Pacific Northwest National Laboratory, Carnegie Mellon University, and University of California (Berkeley). The CCSI's industrial partners provide representation from the power generation industry and the power equipment manufacturers. The initial industrial partners are ADA Environmental Solutions (ADA-ES), Alstom Power, Ameren, Babcock Power, Babcock & Wilcox, Chevron, the Electric Power Research Institute (EPRI), Eastman, Fluor, General Electric, Ramgen Power Systems, and Southern Company.
The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. By developing the CCSI Toolset, a comprehensive, integrated suite of validated science-based computational models, this initiative will provide simulation tools that will increase confidence in designs, thereby reducing the risk associated with incorporating multiple innovative technologies into new carbon capture solutions. The scientific underpinnings encoded into the suite of models will also ensure that learning will be maximized from successive technology generations.
This work is supported by the US Department of Energy under contract No. DE-AC02-05CH11231 (initial funding provided under ARRA Project DE09000060 Carbon Capture Simulation Initiative).
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