Open Access
Development of a video-based slurry sensor for on-line ash analysis. Sixth quarterly technical progress report, January 1, 1996--March 31, 1996
Author(s) -
G.T. Adel,
G.H. Luttrell
Publication year - 1996
Language(s) - English
Resource type - Reports
DOI - 10.2172/273262
Subject(s) - slurry , spectrum analyzer , calibration , process engineering , coal , environmental science , fly ash , engineering , remote sensing , computer science , waste management , mineralogy , chemistry , environmental engineering , electrical engineering , geology , mathematics , statistics
Automatic control of fine coal cleaning circuits has traditionally been limited by the lack of sensors for on-line ash analysis. Although several nuclear-based analyzers are available, none have seen widespread acceptance. This is largely due to the fact that nuclear sensors are expensive and tend to be influenced by changes in seam type and pyrite content. Recently, researchers at VPI&SU have developed an optical sensor for phosphate analysis. The sensor uses image processing technology to analyze video images of phosphate ore. It is currently being used by PCS Phosphate for off-line analysis of dry flotation concentrate. The primary advantages of optical sensors over nuclear sensors are that they are significantly cheaper, are not subject to measurement variations due to changes in high atomic number minerals, are inherently safer and require no special radiation permitting. The purpose of this work is to apply the knowledge gained in the development of an optical phosphate analyzer to the development of an on-line ash analyzer for fine coal slurries. During the past quarter, calibration tests were conducted on-site at the Middle Fork coal preparation plant owned and operated by Pittston Coal Company. After several plant visits, a consistent sensor calibration was obtained with approximately 95% of all samples analyzed falling within {plus_minus}2% ash of the best fit calibration line. The resolution of the optical analyzer was found to be approximately 0.5% ash per gray level increment. A linear neural network learning algorithm was found to be the most appropriate method for calibrating the sensor. The sensor now appears to be ready for installation and long-term testing at the Middle Fork test site