A Flame Imaging-Based Online Deep Learning Model for Predicting NOₓ Emissions From an Oxy-Biomass Combustion Process

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.