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


Golgiyaz S., TALU M. F. , Das M., ONAT C.

ALEXANDRIA ENGINEERING JOURNAL, vol.61, no.2, pp.1079-1089, 2022 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1016/j.aej.2021.06.022
  • Journal Name: ALEXANDRIA ENGINEERING JOURNAL
  • Journal Indexes: Science Citation Index Expanded, Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.1079-1089
  • Keywords: Excess air coefficient estimation, Flame image, Gauss model, Flame stability, Artificial neural network regression model, RADIATIVE ENERGY SIGNAL, EQUIVALENCE RATIO, IMAGE, PREDICTION, BOILER, VISUALIZATION, TEMPERATURE, DESIGN, SYSTEM, FLAMES

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. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.