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Assessment of Machine Learning algorithms for predicting autoignition and ignition delay time in microscale supercritical water oxidation process

hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorSHARMA, Deewakar
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorLECOUTRE-CHABOT, Carole
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorPALENCIA, Fabien
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorNGUYEN, Olivier
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorERRIGUIBLE, Arnaud
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorMARRE, Samuel
dc.date.issued2023
dc.identifier.issn0016-2361
dc.description.abstractEnWith recent advancements in space technology, there is a need to develop technologies to ensure a sustainable environment for human survival. Among these, treatment of human and organic waste aboard manned space missions is a challenging task for which supercritical water oxidation using hydrothermal flames has been proposed as a possible solution. The critical step in readily adopting this technology from established ground-based setups is scaling the process to microscale. In addition to the challenge of physical realization of the microreactors at these high pressure and temperature (P > 22 MPa, T>350°C) conditions, the need to explicitly analyze the process dynamics at microscale is inevitable owed to the size of the reactors under consideration, the physics being significantly different from meso/mini scale systems. One of the primary objectives is to identify the operating physical parameters for which formation of hydrothermal flames can be obtained. Before proceeding with an expensive computational or experimental approach to determine the exact ignition map, an initial estimate based on physical arguments can help in providing insights into the process. We address this problem using homogeneous ignition calculations to develop machine learning models to predict autoignition as well as ignition delay time. The ingenuity of the work lies in defining autoignition criteria in relation to flow time scales expected at microscale. Various classification models were trained and tested for predicting autoignition and regression models were demonstrated to predict the ignition delay time. While predicting autoignition is a straightforward process, a two-step approach is proposed for ignition delay time. Finally, how machine learning can be used more explicitly, particularly for understanding and designing efficient microreactors, is presented which highlights that machine learning approach is not merely restricted to prediction but can also have real implications on improving the process as a whole.
dc.language.isoen
dc.publisherElsevier
dc.subject.enSupercritical water oxidation
dc.subject.enhydrothermal flames
dc.subject.enmachine learning
dc.subject.enflame ignition
dc.subject.enmicroscale
dc.title.enAssessment of Machine Learning algorithms for predicting autoignition and ignition delay time in microscale supercritical water oxidation process
dc.typeArticle de revue
dc.identifier.doi10.1016/j.fuel.2023.129098
dc.subject.halSciences de l'ingénieur [physics]/Milieux fluides et réactifs
dc.subject.halChimie/Matériaux
dc.subject.halChimie/Chimie théorique et/ou physique
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
bordeaux.journalFuel
bordeaux.page129098
bordeaux.volume352
bordeaux.peerReviewedoui
hal.identifierhal-04153992
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04153992v1
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