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Mantel Secures $30 Million to Bring Low Cost, Energy Efficient Carbon Capture to the Heavy Industrial Sector
Mantel Capture, Inc. (Mantel), the technology provider of a carbon capture system using molten borates, announced it has raised $30 million in Series A funding co-led by Shell Ventures and Eni Next. Additional participating investors include Engine Ventures, New Climate Ventures, Hartree, bp Ventures, Arosa Ventures, Vale Ventures, Newlab, MCJ Collective, and others. The funding will be used to implement a demonstration project at an industrial site and help pave the way for full-scale commercial deployment of Mantel’s high-temperature carbon capture systems. The company’s technology has already demonstrated carbon capture at lab scale of half a tonne per day, and the upcoming project will be around 10 times larger, rated to capture 1,800 tonnes of CO2 emissions per year at an industrial site.
Mantel leverages molten borates, the only high-temperature liquid-phase carbon capture material, to capture CO2 at the source of emission. By operating at high temperatures Mantel’s systems recover high-grade heat when capturing CO2 (absorption), offsetting the energy necessary to regenerate the molten borate material (desorption). This enables Mantel to capture CO2 from industrial emissions efficiently, reducing capture costs by more than half compared to conventional amine-based carbon capture technologies, which helps render installing Mantel’s carbon capture technology at heavy industry sites economically feasible. As more carbon capture equipment is installed, it is expected this will prompt more investment in infrastructure, further driving down costs of carbon capture, transport and storage.
A Data Architecture to assist Geologists in Real-Time Operations
Data plays a crucial role in making exploration and drilling operations for Eni a success all over the world. Our geologists use real-time well data collected by sensors installed on drilling pipes to keep track and to build predictive models of key properties during the drilling process.
Data is delivered by a custom dispatcher component designed to connect to a WITSML Server on all oil rigs and send time-indexed and / or depth-indexed data to any supported applications. In our case, data is delivered to Azure ADLS Gen2 in the format of WITSML files, each accompanied by a JSON file for additional custom metadata.
The visualizations generated from this data platform are used both on the oil rigs and in HQ, with operators exploring the curves enriched by the ML models as soon as they’re generated on a web application made in-house, which shows in real time how the drilling is progressing. Additionally, it is possible to explore historic data via the same application.