making moves

Chevron supports 2 carbon emissions tech startups

Two startups have recently announced support from Houston-based Chevron Technology Ventures. Photo via Getty Images

Chevron Technology Ventures has added two startups to its portfolio — one to its startup accelerator and one via an investment.

Delaware-based Compact Membrane Systems closed an oversubscribed series A funding round of $16.5 million led by Pangaea Ventures. CTV also contributed to the round, along with GC Ventures, Solvay Ventures, and Technip Energies.

CMS's technology is targeting carbon capture in traditionally hard-to-abate sectors, such as steel, cement, etc., which represent more than a tenth of worldwide emissions. The CMS platform, which operates in a 10,000-square-foot lab and manufacturing facility in Delaware, is a fully electrified and low-cost solution.

“We are delighted to have secured such a strong group of investors who share our vision for delivering a revolutionary carbon capture technology for industrial applications,” says Erica Nemser, CEO of Compact Membrane Systems, in a news release. “This oversubscribed funding round catalyzes our ability to deliver large projects. Deployment of our commercial systems by 2026 will have measurable environmental and economic benefits to our customers and society.”

It's the latest investment from CTV's $300 million Future Energy Fund II, which specifically "focuses on industrial decarbonization, emerging mobility, energy decentralization, and the growing circular economy," says Jim Gable, vice president of innovation at Chevron and president of CTV.

“The technology that CMS has developed has the potential to drive further efficiencies and cost reduction along the CCUS value chain, supporting decarbonization of hard-to-abate sectors and complementing our existing portfolio of investments in this space,” Gable says in the release.

The company is planning to use its new funding to further develop and commercialize its product by 2026.

Another startup has announced support from Chevron last month. Calgary, Alberta-based Arolytics Inc. announced last month that its been accepted into CTV's Catalyst Program. The company has an emissions software and data analytics platform for the oil and gas sector, and the program will help it further develop and deploy its technology.

"Being selected for the Catalyst Program is an amazing opportunity for Arolytics," says Liz O'Connell, CEO of Arolytics, in a news release. "The interest from Chevron demonstrates the oil and gas industry's desire to reduce emissions. It aligns closely with Arolytics' mission to build and execute efficient emissions management programs that enable industry to become leaders in emissions management."

Arolytics' technology, which includes AroViz, an emissions management software, and AroFEMP, an emissions forecasting model, targets methane emissions specifically, per the release.

Launched in 2017, the CTV Catalyst Program accelerates early-stage companies that are working on innovations within the energy industry. Arolytics will use the program to make key connections, identify important use cases, and expand into the U.S. Market.

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A View From HETI

A View From UH

A University of Houston team looked into what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation. Photo via UH.edu

A team of researchers at the University of Houston are using machine learning to help guide pollution fighting strategies.

As reported in the journal Environmental Pollution last month, the team used the SHAP algorithm of machine learning (a game theory approach) and the Positive Matrix Factorization to pinpoint what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation.

The paper was authored by Delaney Nelson, a doctoral student at the Department of Earth and Atmospheric Sciences of UH, and Yunsoo Choi, corresponding author and professor of atmospheric chemistry, AI deep learning, air quality modeling and satellite remote sensing.

The team's research closely tracked nitrogen-based compound and volatile organic compound measurements from Texas Commission on Environmental Quality's monitoring stations in the Houston area. After importing measurements from The Lynchburg Ferry station in Houston's ship channel and the urban Milby Park station, the machine learning and SHAP analysis showed a chemically definitive difference between the two areas.

For example, at the industrial station, the most impactful sources of pollution were from oil and gas flaring/production. At the urban site n_decane and industrial emissions/evaporation had the most impact on ozone.

According to Nelson and Choi, this shows that the machine learning and SHAP analysis approach can be used to tailor more precise air quality management strategies in different areas based on the site's unique characteristics.

“Once we know the specific emission sources and factors, we can develop targeted strategies to reduce emissions, which will in turn reduce ozone in the air and make it healthier for everyone," Choi said in a statement.

“Pollution is a critical issue in Houston, where you have extreme high heat and high concentration of ozone in the summers. The types of insights we got are very useful information for the local community to develop effective policies. That’s why we put our time, effort and technological expertise into this project," he continued.

Next the team envisions applying their approach in different cities and across the country.

“Austin, San Antonio and Dallas all have different characteristics, so I expect (volatile organic compound) sources will also be different,” Choi said. “Identifying VOC sources in different cities is very important because each city should have its own unique pollution fighting strategy.”

This summer, the City of Houston released an updated report on its major strategies to combat climate change and build a more resilient future for its residents.

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