Using machine learning to forecast amine emissions

A power plant made with AI. Credit: Kevin Maik Jablonka. Scientists at EPFL and Heriot-Watt University have developed a machine learning approach to accurately predict potentially harmful amine emissions from carbon-capturing plants. Global warming is partly due to the vast amount of carbon dioxide that we release, mostly from power generation and industrial processes, such as making steel and cement. For a while now, chemical engineers have been exploring carbon capture, a process that can separate carbon dioxide and store it in ways that keep it out of the atmosphere. This is done in dedicated carbon-capture plants, whose chemical process involves amines, compounds that are already used to capture carbon dioxide from natural gas processing and refining plants. Amines are also used in certain pharmaceuticals, epoxy resins, and dyes.