A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier
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A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifierDate
2022-06-05Citation
Aguado R, Casteleiro-Roca J-L, Vera D, Calvo-Rolle JL. A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier. International Journal of Hydrogen Energy 2022;47:20755–70. https://doi.org/10.1016/j.ijhydene.2022.04.174
Abstract
[Abstract] This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas.
Keywords
Biomass gasification
Green hydrogen
Artificial intelligence
Machine learning
Hybrid modeling
Virtual sensor
Green hydrogen
Artificial intelligence
Machine learning
Hybrid modeling
Virtual sensor
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/
ISSN
0360-3199