Friday, May 24, 2024

Machine Learning To Reduce Greenhouse Emissions

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Researchers have developed a way to reduce greenhouse emission by using machine learning to predict the emissions from plant’s data.

A power plant made with AI. Credit: Kevin Maik Jablonka (EPFL)

Global warming is one of the most significant factors in today’s world when it comes to development and sustainability. Every product we use in our day to day life can affect the environment in one way or another. Manufacturing, in the first place, leaves a carbon footprint.

There are various measures taken to reduce, if not eliminate, the greenhouse emissions. 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.

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A group of scientists has come up with a machine learning solution for forecasting amine emissions from carbon-capture plants using experimental data from a stress test at an actual plant in Germany. The work was led by the groups of Professor Berend Smit at EPFL’s School of Basic Sciences and Professor Susana Garcia at The Research Centre for Carbon Solutions of Heriot-Watt University in Scotland.

“We wanted to know what the emissions would be if we did not have the stress test but only the operators’ interventions,” explains Smit. This is a similar issue as we can have in finance; for example, if you want to evaluate the effect of changes in the tax code, you would like to disentangle the effect of the tax code from, say, interventions caused by the crisis in Ukraine.”

Researchers used powerful machine learning to predict future amine emissions from the plant’s data. He says, “With this model, we could predict the emissions caused by the interventions of the operators and then disentangle them from those induced by the stress test. In addition, we could use the model to run all kinds of scenarios on reducing these emissions.”

The conclusion was described as “surprising.” As it turned out, the pilot plant had been designed for pure amine, but the measuring experiments were carried out on a mixture of two amines: 2-amino-2-methyl-1-propanol and piperazine (CESAR1). The scientists found out that those two amines actually respond in opposite ways: Reducing the emission of one actually increases the emissions of the other.

Reference :  Kevin Maik Jablonka et al, Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant, Science Advances (2023). DOI: 10.1126/


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