Click for new scientific resources and news about Corona[COVID-19]

Paper Information

Journal:   INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING   2017 , Volume 51 , Number 1; Page(s) 29 To 35.
 
Paper: 

ESTIMATION OF COAL PROXIMATE ANALYSIS FACTORS AND CALORIFIC VALUE BY MULTIVARIABLE REGRESSION METHOD AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

 
 
Author(s):  BEHNAMFARD ALI*, ALAEI RASOOL
 
* FACULTY OF ENGINEERING, UNIVERSITY OF BIRJAND, BIRJAND, SOUTH KHORASAN, IRAN
 
Abstract: 

The proximate analysis is the most common form of coal evaluation that reveals the quality of a coal sample. It examines four factors including moisture, ash, volatile matter (VM), and fixed carbon (FC) within the coal sample. Every factor is determined through a distinctive experimental procedure under ASTM specified conditions. These determinations are time consuming and require various laboratory equipment. The calorific value is one of the most important properties of a solid fuel and its experimental determination requires special instrumentation and highly trained operator. This paper develops mathematical and ANFIS models for estimation of two factors of proximate analysis based on the other two factors. Furthermore, the estimation of calorific value of coal samples based on proximate analysis factors is performed using multivariable regression, the Minitab 16 software package, as well as ANFIS and MATLAB software package. The results indicate that ANFIS is a more powerful tool for estimation of proximate analysis factors and calorific value than multivariable regression method. The following equation estimates the calorific value of coal samples with high precision: Calorific value (btu/lb)=12204 - 170 Moisture+46.8 FC - 127 Ash.

 
Keyword(s): COAL, PROXIMATE ANALYSIS, CALORIFIC VALUE, DATA MODELING, REGRESSION AND ANFIS METHODS
 
References: 
  • ندارد
 
  pdf-File tarjomyar Yearly Visit 66
 
Latest on Blog
Enter SID Blog