Machine learning for industrial processes

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

Jablonka, K.M.
Charalambous, C.
Sanchez Fernandez, E.
Wiechers, G.
Garcia Moretz-Sohn Monteiro, J.
Moser, P.
Smit, B.
Garcia, S.

2023

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.

http://resolver.tudelft.nl/uuid:a9b9c69f-491e-4052-bb51-b67d7aa6510a

https://doi.org/10.1126/sciadv.adc9576

981295

NLM (Medline)

2375-2548

Science advances, 9 (9)

article