A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Yanxia Wang, Hui Cao, Xingyu Yan, Yan Zhou*, Xueqin Liu, Sean McLoone

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)
218 Downloads (Pure)

Abstract

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.
Original languageEnglish
Pages (from-to)21-30
Number of pages10
JournalChemometrics and Intelligent Laboratory Systems
Volume158
Early online date07 Aug 2016
DOIs
Publication statusPublished - 15 Nov 2016

Keywords

  • Generalized fuzzy linguistic model
  • Optical gas sensing systems
  • Parameter optimization

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