Qin, Li;
Lu, Gang;
Hossain, Md Moinul;
Morris, Andy;
Yan, Yong;
(2022)
A Flame Imaging-Based Online Deep Learning Model for Predicting NOₓ Emissions From an Oxy-Biomass Combustion Process.
IEEE Transactions on Instrumentation and Measurement
, 71
, Article 2501811. 10.1109/tim.2021.3132998.
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Abstract
To reduce NO x (nitrogen oxide) emissions from fossil fuel and biomass-fired power plants, online prediction of NO x emissions is important in the plant operation. Data-driven models have been developed to predict NO x emissions from various combustion processes with good accuracy. However, such models have initially been built based on known combustion conditions, which are historically “seen”. For new conditions, which are “unseen”, these models usually perform unwell. In this study, an online deep learning (ODL) model is proposed to predict NO x emissions from an oxy-biomass combustion process for “seen” and “unseen” combustion conditions based on source deep learning and condition recognition models. The ODL model is mainly built based on “unseen” combustion conditions. A new objective function that consists of regression loss and distillation loss is introduced in the ODL model to improve the prediction accuracy. The ODL model is examined using boiler operation data, flame temperature maps, and NO x data obtained under a range of oxy-biomass combustion conditions on an Oxy-Fuel Combustion Test Facility. Flame images acquired using a dedicated imaging system are used for computing the temperature distribution of the flame through two-color pyrometry. The results demonstrate that the proposed model is capable of predicting NO x emissions under “seen” and “unseen” conditions with a mean absolute percentage error of less than 3%, for the first, second, and third updates.
| Type: | Article |
|---|---|
| Title: | A Flame Imaging-Based Online Deep Learning Model for Predicting NOₓ Emissions From an Oxy-Biomass Combustion Process |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tim.2021.3132998 |
| Publisher version: | https://doi.org/10.1109/tim.2021.3132998 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Condition monitoring, flame temperature map, NOₓ prediction, online deep learning, oxy-biomass combustion |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10188666 |
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