Analysis of flexible operation of CO2 capture plants: Predicting solvent emissions from conventional and advanced amine systems

Charalambous, Charithea; Saleh, Ahmed A.; Spek, Mijndert van der; Wiechers, Georg; Moser, Peter; Huizinga, Arjen; Gravesteijn, Paul; Ros, Jasper; Monteiro, Juliana Garcia Moretz‐Sohn; Goetheer, Earl; García, Susana

doi:10.2139/ssrn.3812299

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…This figure shows that applying the different scenarios cause the plant to emit much more compared to its steady-state operation. A preliminary analysis of the data has been reported by Charalambous et al (12). a.u., arbitrary units.…”

Section: Causal Impact Analysismentioning

confidence: 91%

“…This requires tools that fully capture the dynamic and multivariate behavior of the plant away from its steady-state operation. The classical analysis techniques, such as response function fits ( 11 , 12 ), or chemometrics approaches ( 13 ) give some insights into the typical response to the different perturbations. However, these techniques cannot take the full multivariate, nonlinear nature of the time-dependent behavior of a complex plant into account.…”

Section: Introductionmentioning

confidence: 99%

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

Jablonka

Charalambous

Fernández³

et al. 2023

Sci. Adv.

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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.

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Section: Causal Impact Analysismentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

Jablonka

Charalambous

Fernández³

et al. 2023

Sci. Adv.

Self Cite

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show abstract

“…[2][3][4][5] This requires tools that fully capture the dynamic and multivariate behaviour of the plant away from its steady-state operation. The classical analysis techniques, such as response function fits, 6,7 or chemometrics approaches 8 give some insights into the typical response to the different perturbations. However, these techniques cannot take the full multivariate, non-linear nature of the time-dependent behaviour of a complex plant into account.…”

Section: Mainmentioning

confidence: 99%

“…1 for a schematic flow diagram) and therefore provides an ideal real-life example of the difficulties of understanding amine emissions. 7 This test involved a sequence of eight different scenarios (see Supplementary Note 1 for more details on the rationale of these scenarios). Extended Data Fig.…”

Section: Stress-test Campaignmentioning

confidence: 99%

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

Jablonka

Charalambous²,

Fernández³

et al. 2021

Preprint

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One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent and degradation products into the atmosphere. To mimic the mounting importance of intermittent operations of power plants we performed a stress test in which we measured the amine emissions from a pilot plant that has been in operation using a mixture of amines (CESAR1) in a slipstream from a coal-fired power plant. Understanding how changes in the operation far from the steady-state of the plant affect the emissions is key to designing emission mitigation strategies. However, conventional process modelling techniques struggle to capture the full dynamic, multivariate, and non-linear nature of this data. In this work, we report how a data-intensive approach can be used to learn the mapping between process and emissions from data. The resulting model can forecast the emissions, can be used to analyse the data and also perform in silico stress tests. By doing so, we reveal that emission mitigation strategies that work well for single component solvents (e.g. monoethanolamine) need to be revised for a mixture of solvents such as CESAR1. We expect that the combination of large amounts of data with flexible learning algorithms will impact the way we design and operate industrial processes, as we can now harvest information at conditions where conventional approaches fail.

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Advances in design, scale and operability of the Rotating Liquid Sheet (RLS) contactor: Pressure drop, flood-point and mass transfer – Comparison to random packing

Wardhaugh,

Corsi,

Fernandes

et al. 2024

Chemical Engineering Research and Design

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Analysis of flexible operation of CO2 capture plants: Predicting solvent emissions from conventional and advanced amine systems

Cited by 6 publications

References 21 publications

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

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

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

Advances in design, scale and operability of the Rotating Liquid Sheet (RLS) contactor: Pressure drop, flood-point and mass transfer – Comparison to random packing

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