Combustion process monitoring based on flame intensity time series

Abstract

The pulverised coal and its mixture with biomass are one of the most popular fuels in industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the other hand, maximum energy production efficiency, it is necessary to monitor the combustion process of these fuels. One way to do this is to monitor the flame intensity. This is an optical, non-invasive solution, and information on the status of the process is obtained with minimal delay. The article proposes a method for identifying undesired combustion states for which the excess air coefficient is greater or smaller than the value ensuring total combustion. Three deep recurrent neural network architectures for classifying the flame intensity time series were explored. The best results were obtained using the convolutional long short-term memory model, which offered the accuracy of 86.5%–99.8%, depending on the current thermal power. The prediction time of a single data sequence was about 0.6 ms. High accuracy and low time consumption of the proposed method create an opportunity for its use in industrial combustion systems of pulverised coal and its mixture with biomass.