Utilizing advanced machine learning techniques for accurate prediction of oxygen quantity in gas-fired boiler combustion to enhance environmental pollution control

Abstract

PyTorch, an open-source machine learning framework built on the Torch library, is used in this application to apply deep learning to image classification in a boiler section and to design an entity algorithm for predicting the amount of oxygen available in the furnace section. The physical features of this flame are viewable using pictures obtained from a Charge Coupled Device (CCD). By removing the nonlinear elements, a multilayer CNN forecasts the amount of oxygen in the flue gas from a boiler. From the results of experiments conducted on-site in a real-time combustion system, images of boilers under various settings, including temperatures, air pressures, and gas conditions, have been obtained. Classification models are then applied. The precise quantity of oxygen content is calculated with these photos as input and comparing the outcomes with the test data set. More insightful information about the flame's physical features can be defined using a Convolutional Neural Network (CNN) model and a multilayer representation of the flame images. The flame images captured on-site from an actual combustion system are utilized to illustrate this notion. The oxygen content is predicted using a multilevel-based, unsupervised, and semi-supervised deep entity algorithm by taking 12 classes and training 4,203,592 images each flame image in the tests has a resolution of 24 bits per pixel and a size of 658*492 pixels. After training the model, the loss is as low as 3%, and the attained accuracy is 97%.