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Title: Appraising lignite quality parameters by linguistic fuzzy identification

Abstract

Lignite quality parameters have had central importance for power plants. This article addresses a comparative study on fuzzy and regression modeling for estimating the calorific value of lignite, which is one of the quality parameters from the other parameters: moisture, ash, volatile matter, and sulphur content. For the estimations, data driven models were designed based on linguistic fuzzy modeling structures. In addition, estimations of the fuzzy models were compared with linear regression estimations. The great majority of performance evaluations showed that the fuzzy estimations are very satisfactory in estimating calorific value of lignite.

Authors:
 [1]
  1. Inonu University, Malatya (Turkey). Dept. of Mechanical Engineering
Publication Date:
OSTI Identifier:
20885781
Resource Type:
Journal Article
Resource Relation:
Journal Name: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects; Journal Volume: 29; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT; LIGNITE; FUZZY LOGIC; REGRESSION ANALYSIS; CALORIFIC VALUE; CALCULATION METHODS; MOISTURE; ASH CONTENT; SULFUR CONTENT; VOLATILE MATTER

Citation Formats

Tutmez, B. Appraising lignite quality parameters by linguistic fuzzy identification. United States: N. p., 2007. Web. doi:10.1080/15567030600828933.
Tutmez, B. Appraising lignite quality parameters by linguistic fuzzy identification. United States. doi:10.1080/15567030600828933.
Tutmez, B. Thu . "Appraising lignite quality parameters by linguistic fuzzy identification". United States. doi:10.1080/15567030600828933.
@article{osti_20885781,
title = {Appraising lignite quality parameters by linguistic fuzzy identification},
author = {Tutmez, B.},
abstractNote = {Lignite quality parameters have had central importance for power plants. This article addresses a comparative study on fuzzy and regression modeling for estimating the calorific value of lignite, which is one of the quality parameters from the other parameters: moisture, ash, volatile matter, and sulphur content. For the estimations, data driven models were designed based on linguistic fuzzy modeling structures. In addition, estimations of the fuzzy models were compared with linear regression estimations. The great majority of performance evaluations showed that the fuzzy estimations are very satisfactory in estimating calorific value of lignite.},
doi = {10.1080/15567030600828933},
journal = {Energy Sources, Part A: Recovery, Utilization, and Environmental Effects},
number = 4,
volume = 29,
place = {United States},
year = {Thu Mar 15 00:00:00 EDT 2007},
month = {Thu Mar 15 00:00:00 EDT 2007}
}