AI in Carbon Capture and Sequestration

At Xyonix, we build custom AI and machine learning models to improve CCS systems to more effectively mitigate climate change.

It can be time consuming and budget breaking to develop a carbon capture system. Finding the right absorbent alone can seem like a daunting task. Not to mention the high energy required for one system alone. At Xyonix, we can help reduce your overall energy needed while optimizing your carbon capture levels. We are experts in creating AI models to derive insights from your data. Whether you are trying to reduce amine degradation in thermal processes or working to develop carbon capturing systems compatible with transport, AI can give you cost-effective results fast.

AI To Assist Carbon Tracking

Image processing algorithms are used to detect carbon output for factories and power plants. AI uses satellite data to monitor and track these emissions globally.

AI To Assist Carbon Separation

Using AI and NLP technology to predict suitable molecules and materials to separate carbon from other molecules.

AI To Predict Emissions

AI can be used to predict future emissions of a company or region. Using this forecasting feature can help government policymakers plan emissions output targets to reduce future output.

The solution is simple. At Xyonix, we take your problem, analyze your data, and train your machine learning systems to generate insights and make predictions. After this initial step, your systems will automatically analyze data and generate insights.

Interested in using AI for carbon sequestration? Contact us and let’s dig in together.

Featured

Jul 16, 2021

“Xyonix helped us take our technology and product to the next level. What the team was able to accomplish in just a few months was extremely impressive.”

—Werner Koepf, CTO @ Conversica, Inc., PhD [more]

Jul 16, 2021

Want to dig deeper? Listen to our podcast episode on using machine learning to monitor carbon stocks with Kelsey Josund from Pachama or read one of our articles detailing applications of AI to environmental problems.

blog

Using AI To Optimize Mechanical Carbon Capture & Storage Systems

Since the first oil well was dug in 1859, we have been extracting carbon from the ground and pumping it into our atmosphere. Similar to the hundreds of thousands of oil wells around the world, what if we had many CCS systems capturing carbon, often in the form of oil, and injecting it back into the ground?

Improving Carbon Sequestration, Part 2: Organizations Using AI to Monitor Deforestation & Improve Decisions

Illegal logging is an environmental and economic loss for tropical regions like the Amazon. Illegal logging is associated with billions of dollars of lost revenue each year in addition to depressed timber prices, yet effective strategies to curb illegal deforestation are hard to find. Harnessing AI serves as a promising innovation for enhancing the accuracy of forest monitoring, with the potential to be adapted to the Amazon basin and other tropical regions.

Improving Carbon Sequestration, Part 1: Leveraging AI to Better Map and Monitor Stocks

In recent years, we’ve witnessed the shocking imagery of Australia, the western United States, Europe, and the Amazon rainforest engulfed in flames, the unprecedented intensity, duration, and extent of the fires inflicting irreversible damage on countries, economies, ecosystems, and the atmosphere. Managing and conserving forest resources remains a top priority, but mapping and monitoring global forests are crucial to understanding how to properly manage and protect them, especially in monitoring wildfires and illegal deforestation, as well as knowing which forests to prioritize preserving. Identifying and monitoring forests with strong conservation value and high carbon stock is also a huge leverage point for targeted intervention of Artificial Intelligence.

references

Andrea.d.steffen. “Scientists Have Now Produced Graphene from Carbon Dioxide.” Intelligent Living, 7 Sept. 2019, www.intelligentliving.co/graphene-from-carbon-dioxide/.

“Carbon Capture Quotes: Top 9 Quotes about Carbon Capture from Famous Authors.” More Famous Quotes, www.morefamousquotes.com/topics/carbon-capture-quotes/.

“Direct Air Capture of CO2: A Climate Solution with Limitations - TexasVox: The Voice of Public Citizen in Texas.” TexasVox, 13 Dec. 2018, www.texasvox.org/direct-air-capture-co2-climate-solution-limitations/.

“DOE Announces $12 Million for Direct Air Capture Technology.” Energy.gov, www.energy.gov/articles/doe-announces-12-million-direct-air-capture-technology.

Lebling, Katie, et al. “Direct Air Capture: Resource Considerations and Costs for Carbon Removal.” World Resources Institute, 6 Jan. 2021, www.wri.org/insights/direct-air-capture-resource-considerations-and-costs-carbon-removal.

Degot, Charlotte, et al. “Reduce Carbon and Costs with the Power of AI.” BCG Global, BCG Global, 21 Sept. 2021, https://www.bcg.com/publications/2021/ai-to-reduce-carbon-emissions.

Stone, Benjamin D. “Artificial Intelligence and Satellite Technology to Enhance Carbon Tracking Measures.” Lexology, Mintz, 29 July 2020, https://www.lexology.com/library/detail.aspx?g=36e9fdeb-00aa-4948-8bb0-a8967d9ec5b3.

Taylor-Smith, Kerry. “The Future of Carbon Capture and the Fight against Climate Change.” AZoCleantech.com, 14 Oct. 2020, https://www.azocleantech.com/article.aspx?ArticleID=1151.

Rahimi, Mohammad, et al. “Toward Smart Carbon Capture with Machine Learning.” Cell Reports Physical Science, Cell Press, 9 Apr. 2021, www.sciencedirect.com/science/article/pii/S2666386421000862.

Rinkesh. “Carbon Sequestration: How It Works, Types and Examples.” Conserve Energy Future, 17 Oct. 2020, www.conserve-energy-future.com/carbon-sequestration.php.

“The Story behind Carbon Engineering.” Carbon Engineering, 16 July 2021, carbonengineering.com/our-story/.

“Technological Carbon Sequestration.” Science and Climate, 13 May 2020, climatechange.ucdavis.edu/science/carbon-sequestration/technological/.