Title of article :
Grindability soft-sensors based on lithological composition and on-line measurements
Author/Authors :
Casali، نويسنده , , A. and Gonzalez، نويسنده , , G. and Vallebuona، نويسنده , , G. and Perez، نويسنده , , C. and Vargas، نويسنده , , R.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2001
Pages :
12
From page :
689
To page :
700
Abstract :
The grinding efficiency evaluation can be performed through the comparison of the operational work index with the ore work index Wi. In this work, the development of an ore grindability soft-sensor (ESTMOL) is presented. The ore work index is estimated on the basis of its lithological composition. Also addressed is the experimental development of a lithological composition sensor (ACOLITO) for ores on a conveyor belt. The lithological composition is determined from image analysis on samples obtained by a color video camera. Finally, a global operational work index for a complete grinding section is defined here, and its on-line estimation (PREDIMOL) is addressed, including the required soft-sensors to overcome the measurement problems. The experimental work is done with samples obtained from the CODELCO - Andina grinding plant. All the sensors have given up to now good results.
Keywords :
NEURAL NETWORKS , Modelling , Grinding , Artificial Intelligence , on—line analysis
Journal title :
Minerals Engineering
Serial Year :
2001
Journal title :
Minerals Engineering
Record number :
2273685
Link To Document :
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