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Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors
RISE - Research Institutes of Sweden, Bioeconomy, ETC Energy Technology Center. Luleå University of Technology, Sweden.
RISE - Research Institutes of Sweden, Bioeconomy, ETC Energy Technology Center. University of Miskolc, Hungary.
University of Miskolc, Hungary.
RISE - Research Institutes of Sweden, Bioeconomy, ETC Energy Technology Center.ORCID iD: 0000-0003-2253-6845
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2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 226, p. 450-460Article in journal (Refereed) Published
Abstract [en]

A combination of image processing techniques and regression models was evaluated for predicting equivalence ratio and major species concentration (H2, CO, CO2 and CH4) based on real-time image data from the luminous reaction zone in conditions and reactors relevant to biomass gasification. Two simple image pre-processing routines were tested: reduction to statistical moments and pixel binning (subsampling). Image features obtained by using these two pre-processing methods were then used as inputs for two regression algorithms: Gaussian Process Regression and Artificial Neural Networks. The methods were evaluated by using a laboratory-scale flat-flame burner and a pilot-scale entrained flow biomass gasifier. For the flat-flame burner, the root mean square error (RMSE) were on the order of the uncertainty of the experimental measurements. For the gasifier, the RMSE was approximately three times higher than the experimental uncertainty – however, the main source of the error was the quantization of the training dataset. The accuracy of the predictions was found to be sufficient for process monitoring purposes. As a feature extraction step, reduction to statistical moments proved to be superior compared to pixel binning.

Place, publisher, year, edition, pages
2018. Vol. 226, p. 450-460
Keywords [en]
AI, Gasification diagnostics, Gaussian process regression, Image processing, Machine learning, Neural network, Process monitoring
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33999DOI: 10.1016/j.apenergy.2018.06.007Scopus ID: 2-s2.0-85048807165OAI: oai:DiVA.org:ri-33999DiVA, id: diva2:1230511
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-07-11Bibliographically approved

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Sepman, AlexeyWiinikka, Henrik

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