Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions

Abstract

Accurate and reliable prediction of combustion emissions is essential for combustion optimization adjustment. Existing data-driven approaches are limited by insufficient labeled data and low robustness, resulting in low prediction accuracy. To address these limitations, a semi-supervised learning model consisting of adversarial denoising autoencoder and Gaussian process regression is proposed for combustion emissions prediction. The unsupervised adversarial denoising autoencoder is applied for feature extraction of the flame image, and the supervised Gaussian process regression is utilized for feature recognition to estimate the CO2 and NOx emissions concentrations. Especially, a structural similarity-based loss function is developed to improve the adversarial denoising autoencoder training efficiency. During the experiments, the heavy-oil flame images under variable operation conditions are captured to verify the performance of the semi-supervised learning model. Results indicate that the model provides accurate emissions prediction with a prediction time of 61.38 ms/f (milliseconds per frame), where the prediction accuracy for the CO2 and NOx emissions are R2=0.97 and R2=0.98, respectively. The confidence intervals generated by the model cover the actual observations and confirm the reliability of the predictions.