Machine learning-based prediction of swirl combustor operation from flame imaging

This study proposes a novel data-driven model to distinguish normal operation of a swirl combustor and predict key operation conditions using a flame image taken with a low-cost monochrome camera. The model, in the form of a convolutional neural network (CNN), is designed to take a flame image as input and provide either the total air flow rate (Q) or the fuel-air equivalence ratio (ϕ) as an output. However, since the type of problem in this study is regression, it is necessary to make predictions only on normal operation images, as it is not feasible to collect flame images for all unstable combustion modes. Thus, the stacked convolutional layers were first trained as a convolutional autoencoder (CAE) in an unsupervised manner using only flame images under normal operation modes, so that the CAE can perform well only on normal operation images. Then, a regressor that outputs either Q or ϕ is connected to the trained encoder and trained in a supervised manner. It was found that the model can predict Q and ϕ within ±5.17 L/min (equivalent to 3.4% of the total flow rate) and ±0.026, respectively, with a 96% probability, along with detecting abnormalities based on large reconstruction errors of input images. Predictions and image collection can be performed within 50 ms, demonstrating the potential for real-time monitoring of combustor status.