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

Kevin Maik Jablonka, Charithea Charalambous, Eva Sanchez Fernandez, Georg Wiechers, Juliana Monteiro, Peter Moser, Berend Smit, Susana Garcia

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
96 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article numbereadc9576
JournalScience Advances
Volume9
Issue number1
DOIs
Publication statusPublished - 4 Jan 2023

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