Dataset for Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes
datasetposted on 05.02.2020 by Yongliang Yan, Peter Clough, Ben Anthony
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.