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

posted on 05.02.2020, 16:24 by Yongliang Yan, Peter Clough, Ben Anthony
The training data for 19 manganese ores as potential oxygen carriers in the chemical-looping process from a fluidised-bed reactor. 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 the ANN models to achieve enhanced prediction precision.


PTC and EJA would like to thank the EPSRC and UKCCSRC for financial support through grant EP/P026214/1. YYL would like to acknowledge the financial support from the Cranfield University Energy and Power research bursary and Erasmus+ scheme. TM and YYL would like to thank the support from Chalmers Energy Area of Advances.


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