Feasibility Assessment of Knowledge-Based Decision Support Method and System for the Deployment of CO2 Direct Air Capture

Abstract

The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H “What, Who, Which, When, Why, and How” from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the ‘Tokyo Region’ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.