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Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques

Li, Nan, Lu, Gang, Li, Xinli, Yan, Yong (2016) Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques. Combustion Science and Technology, 188 (2). pp. 233-246. ISSN 0010-2202. E-ISSN 1563-521X. (doi:10.1080/00102202.2015.1102905)

Abstract

This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results.

Item Type: Article
DOI/Identification number: 10.1080/00102202.2015.1102905
Uncontrolled keywords: Biomass, deep learning, de-noising auto-encoder, flame radical imaging, image processing, NOx emission
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 22 Jan 2016 12:33 UTC
Last Modified: 29 May 2019 16:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53825 (The current URI for this page, for reference purposes)
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