Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

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dc.contributor.authorYan, Yongliang
dc.contributor.authorMattisson, Tobias
dc.contributor.authorMoldenhauer, Patrick
dc.contributor.authorAnthony, Edward J.
dc.contributor.authorClough, Peter T.
dc.date.accessioned2020-02-06T19:59:21Z
dc.date.available2020-02-06T19:59:21Z
dc.date.issued2020-01-09
dc.description.abstractHeterogeneous, multi-component materials such as industrial tailings or by-products, along with naturally occurring materials, such as ores, have been intensively investigated as candidate oxygen carriers for chemical-looping processes. However, these materials have highly variable compositions, and this strongly influences their chemical-looping performance. Here, using machine learning techniques, we estimate the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping. Experimental data for 19 manganese ores chosen as potential chemical-looping oxygen carriers were used to create a so-called training database. This database has been used to train several supervised artificial neural network models (ANN), which were used to predict the reactivity of the oxygen carriers with different fuels and the oxygen transfer capacity with only the knowledge of reactor bed temperature, elemental composition, and mechanical properties of the manganese ores. This novel approach explores ways of dealing with the training dataset, learning algorithms and topology of ANN models to achieve enhanced prediction precision. Stacked neural networks with a bootstrap resampling technique have been applied to achieve high precision and robustness on new input data, and the confidence intervals were used to assess the precision of these predictions. The current results indicate that the best trained ANNs can produce highly accurate predictions for both the training database and the unseen data with the high coefficient of determination (R2 = 0.94) and low mean absolute error (MAE = 0.057). We envision that the application of these ANNs and other machine learning algorithms will accelerate the development of oxygen carrying materials for a range of chemical-looping applications and offer a rapid screening tool for new potential oxygen carriers.en_UK
dc.identifier.citationYan Y, Mattisson T, Moldenhauer P, et al., (2020) Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes. Chemical Engineering Journal, Volume 387, May 2020, Article number 124072en_UK
dc.identifier.issn1385-8947
dc.identifier.urihttps://doi.org/10.1016/j.cej.2020.124072
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15105
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learningen_UK
dc.subjectArtificial neural networken_UK
dc.subjectManganese oresen_UK
dc.subjectOxygen-carrier materialsen_UK
dc.subjectChemical-loopingen_UK
dc.titleApplying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processesen_UK
dc.typeArticleen_UK

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