Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

TL;DR

This paper develops a model linking flame images to the excess air coefficient in coal combustion using Gaussian models and neural networks, aiming to improve combustion efficiency analysis.

Contribution

It introduces a novel three-stage approach combining image processing, Gaussian modeling, and neural networks for estimating excess air coefficient in coal combustion.

Findings

Successful correlation between flame image features and excess air coefficient.

Effective use of Gaussian mixture models for feature extraction.

Neural network model accurately predicts excess air coefficient.

Abstract

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient ({\lambda}) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, {\lambda} data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-{\lambda}.