Published September 2005 | Version v1
Journal article

Detection of element content in coal by pulsed neutron method based on an optimized back-propagation neural network

  • 1. College of Information Science and Engineering, Northeast University 131, Shenyang 110004 (China)
  • 2. Department of Physics, Northeast Normal University, Changchun 130024 (China)
  • 3. The College of Information Science and Engineering, Northeast University 131, Shenyang 110004 (China)

Description

This paper introduces the detection method of principal element content (carbon, hydrogen and oxygen) in coal by using pulsed fast-thermal neutron analysis method (PFTNA). A system for the measurement of coal by PFTNA is also presented. The 14 MeV pulsed neutron generator and a Bi4Ge3O12 (BGO) detector with a 4096 Multi-Channel Analyzer (MCA) were applied in this system. The detection model of element content in coal based on back-propagation (BP) neural network which is optimized by Genetic Algorithms (GAs) was put forward, and the research of verifying the model was made by comparing the practical measured data of coal in power plant. The results show that the detection precision of carbon, hydrogen and oxygen using this model is 0.3%, 0.2% and 0.4%, respectively

Additional details

Identifiers

DOI
10.1016/j.nimb.2005.04.071;
PII
S0168-583X(05)00590-2;

Publishing Information

Journal Title
Nuclear Instruments and Methods in Physics Research. Section B, Beam Interactions with Materials and Atoms
Journal Volume
239
Journal Issue
3
Journal Page Range
p. 202-208
ISSN
0168-583X
CODEN
NIMBEU

Optional Information

Copyright
Copyright (c) 2005 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.