Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant. Jablonka, K. M., Charalambous, C., Sanchez Fernandez, E., Wiechers, G., Monteiro, J., Moser, P., Smit, B., & Garcia, S. Science Advances, 9(1):eadc9576, January, 2023. Publisher: American Association for the Advancement of Science![Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{jablonka_machine_2023,
title = {Machine learning for industrial processes: {Forecasting} amine emissions from a carbon capture plant},
volume = {9},
shorttitle = {Machine learning for industrial processes},
url = {https://www.science.org/doi/10.1126/sciadv.adc9576},
doi = {10.1126/sciadv.adc9576},
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.},
number = {1},
urldate = {2023-01-05},
journal = {Science Advances},
author = {Jablonka, Kevin Maik and Charalambous, Charithea and Sanchez Fernandez, Eva and Wiechers, Georg and Monteiro, Juliana and Moser, Peter and Smit, Berend and Garcia, Susana},
month = jan,
year = {2023},
note = {Publisher: American Association for the Advancement of Science},
pages = {eadc9576},
}
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