As concerns about the impact of climate change continue to grow, businesses are under increasing pressure to reduce their carbon emissions. In response, a growing number of companies are turning to technology solutions to help them better understand and manage their carbon footprint. One such solution is Eugenie.ai, a platform that helps companies track and analyze their carbon emissions in real-time.
In early 2022, the United Nations released its climate report which indicated that “The amount of harmful carbon emissions from 2010-2019 was the highest ever recorded in human history.” Excess carbon emissions are a major contributor to climate change, as they are primarily released from the burning of fossil fuels such as coal, oil, and gas, but can also result from other human activities such as deforestation and agriculture.
“When excess carbon emissions are released into the atmosphere, they contribute to the greenhouse effect, which causes the Earth’s temperature to rise,” explains Dr. Soudip Roy Chowdhury, CEO of Eugenie. According to the EPA, greenhouse gas (GHG) emissions emitted from industrial operations — including power generation and other manufacturing activities — contribute more than 50 percent of emissions.
GHGs can get trapped in the environment for decades. As such, they can have a long-term impact on the environment, including air quality, rising temperatures rising, and other factors that contribute to climate change.
Eugenie.ai’s framework
Eugenie.ai is a software service that helps industries with a lot of equipment reduce their excess emissions. It uses artificial intelligence and digital twins to track the performance and emissions of each machine and process. This allows the system to predict potential issues and simulate how changes to operations could affect emissions.
By tracking the machines and processes in real time, Eugenie.ai can detect operational anomalies in the manufacturer’s performance. This is extremely important since industrial machines tend to contribute to higher-than-baseline emissions levels when they aren’t running in an optimized manner. Eugenie.ai flags these anomalies, classifies them by severity, and shows actionable recommendations to operations teams.
“Companies can use these recommendations to decide potential actions intended to resolve the anomalies,” Dr. Chowdhury suggests. “They can even delegate specifically actionable items to their colleagues based on their access level and hierarchy within the organization.”
Eugenie.ai supplements the machine and process-level performance and emissions data with satellite imagery for environmental emissions’ volumetric estimation. This enables their software to serve as a reliable co-pilot for CSOs and organizations serious about reducing their Scope I emissions.
Tracking and removing emissions at the source
Eugenie.ai uses public/private satellite data and other LIDAR data to track and measure the volume and composition of industrial emissions at a site. This helps a company understand its carbon footprint in real-time.
Based on historical and current emission data, Eugenie.ai calculates the average emission metrics — be it weekly, monthly, quarterly, or yearly — and derives emission benchmarks. These measures help Eugenie.ai track and compare the current emission numbers and determine any anomaly including early warnings from the emission patterns.
It is more dependable and sustainable to decrease emissions at the source instead of electrification. Electrification involves using electric equipment instead of conventional ones that run on fossil fuels in industrial processes. However, emissions are only reduced significantly if the electricity comes from a green energy grid, which is not the case currently.
Organizations can make a measurable difference in reducing emissions without negatively impacting their economic goals by adopting an approach that focuses on reducing emissions at the source. While options such as carbon sequestration or transitioning to renewable energy sources can be costly and have uncertain impacts, Eugenie.ai uses AI for process optimization to effectively reduce emissions at the source (Scope 1) for heavy emitters.
Green process lines and how Eugenie.ai uses them
According to Dr. Chowdhury, reducing carbon emissions at the source offers more than just sustainability benefits. It can also help organizations adopt new business models. For instance, green steel is sold at a 50% higher price than traditional steel. By reducing emissions at the source, existing steel manufacturing processes can be converted into greener alternatives.
Industries are feeling pressure to reduce their emissions faster and more efficiently due to the growing demand for green commodities in society. This is because the green commodity channel can improve both business and the world.
Furthermore, the use of digital twins can lead to improvements in both product quality and sustainability. The virtual replicas of industrial plants can be game-changing in the adoption of sustainable manufacturing processes. Digital twins can be used for various purposes such as scheduling maintenance, optimizing processes, and monitoring emissions.
Traditional AI-based digital twins implement the black-box approach where insights are generated without traceability and transparency. “Eugenie.ai’s product presents a different approach to enable industrial organizations to establish more sustainable process lines through machine data, satellite data, and physics-based models,” Dr. Chowdhury affirms.
There is no longer any doubt that sustainable business practices are necessary, particularly in the industrial sector. “The United Nations has declared that climate change is the most critical issue of our time,” warns Dr. Chowdhury. “To undo the harm that has been caused, it is crucial for industrial companies to step up their sustainability efforts, as industrial companies are responsible for nearly about 24 percent of all greenhouse gas emissions in the United States.”