Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant

• Published in: Science advances Vol. 9; no. 1; p. eadc9576
• Main Authors: Jablonka, Kevin Maik, Charalambous, Charithea, Sanchez Fernandez, Eva, Wiechers, Georg, Monteiro, Juliana, Moser, Peter, Smit, Berend, Garcia, Susana
• Format: Journal Article
• Language: English
• Published: United States American Association for the Advancement of Science 04-01-2023
• Subjects: Applied Sciences and Engineering; Engineering; Physical and Materials Sciences; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.adc9576

One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent into the atmosphere. To understand how these emissions are affected by the intermittent operation of a power plant, we performed stress tests on a plant operating with a mixture of two amines, 2-amino-2-methyl-1-propanol and piperazine (CESAR1). To forecast the emissions and model the impact of interventions, we developed a machine learning model. Our model showed that some interventions have opposite effects on the emissions of the components of the solvent. Thus, mitigation strategies required for capture plants operating on a single component solvent (e.g., monoethanolamine) need to be reconsidered if operated using a mixture of amines. Amine emissions from a solvent-based carbon capture plant are an example of a process that is too complex to be described by conventional process models. We, therefore, expect that our approach can be more generally applied.