Speaker
Description
The world is currently facing a lot of climate disasters, including droughts, floods, heavy rainfall, and heavy snowfall, because of climate change. In response, detailed implementation rules are being established and enforced by parties to the United Nations Framework Convention on Climate Change (UNFCCC) to reduce greenhouse gas emissions. The Republic of Korea, as a party to the UNFCCC, recently proposed the “1st National Carbon Neutrality and Green Growth Basic Plan” to establish detailed policies for achieving carbon neutrality by 2050 in various fields such as industry, energy transition, buildings, transportation, Carbon Capture Utilization and Storage (CCUS) and so on. As a result, strategies for carbon neutrality are urgently needed in various fields in Korea, particularly for industries with high emissions of greenhouse gases. However, most companies lack carbon emission inventories and databases, which are essential for establishing carbon reduction strategies. This is particularly true for small and medium-sized manufacturing companies.
In this study, we aimed to develop a model for calculating and evaluating the actual carbon emissions of these companies by building a carbon emission inventory and monitoring power usage in real-time. We also aimed to develop an artificial intelligence (AI)-based carbon emission prediction system based on this model. Additionally, we tried to develop a system capable of providing Environmental, Social, and Governance (ESG) management reports by conducting a Life Cycle Assessment (LCA) based on a detailed carbon emission inventory for each production processes and facilities. To achieve these goals, we applied edge-computing technology to the power meter to enable independent preprocessing of power usage data within the meter. This reduced the amount of computational processing required and enabled more accurate real-time predictions. The monitoring system was designed to check real-time current/power usage, carbon emissions/reductions, estimated carbon emissions according to production plans and schedules, and carbon reductions by period.
In conclusion, this study presents the development of a carbon emission monitoring system that assists small and medium-sized manufacturing companies in building a carbon emission inventory and database. This provides them with essential data on their carbon emissions and enables them to establish strategies for reducing their emissions. The carbon emission monitoring system developed through this research is expected to serve as a fundamental technology for preparing for national carbon neutrality policies and achieving carbon neutrality in the industries by applying it to companies that have not yet implemented leading responses to carbon neutrality and ESG management due to economic burdens.
References
none
| Keywords | Smart factory, AI, Carbon neutrality |
|---|
