Utilizing Artificial Neural Network for Optimizing HPGR Operation in a Closed Circuit for Preparing
Iron Ore Pelletizing Plant Feed
HPGR is nowadays also being used for iron pellet feed preparation as the regrinding stage of the iron
concentrate in order to increase the blain value of the iron concentrate. In line 5 of Gol-E-Gohar Iron
Concentrate Plant final concentrate dewatered by vacuum belt filters has a blaine value of 1150 cm2/gr. In order
to increase the bliane value of the concentrate to 1850 cm2/gr, suitable for pelletizing process, this concentrate is
reground in an HPGR. In order to achieve this final blaine value, concentrate ground in the center area of the
HPGR rolls (approx. 40% of the HPGR whole product) is considered as the final product and the part ground in
the edges of the HPGR (approx. 60% of the HPGR whole product) is recycled back to the HPGR The studies
revealed that two group of factors influence the HPGR`s performance; operating parameters of the HPGR itself
and qualitative and quantitative specifications of the feed. In this study samples were taken from the HPGR feed
and product at fixed time intervals and operating parameters of the HPGR at the sampling times were recorded.
Results showed that level of the feed hopper, power of the HPGR, rolls pressure and rolls Rpm are the most
influential operating parameters of the HPGR while blaine number and moisture of the dewatered concentrate
and amount of the recycled product are the most effective qualitative factors. It has also been revealed that the
feed qualitative and quantitative specifications affect the operating parameters of the HPGR as well. In order to
optimize these parameters and minimize the fluctuations in the feed specifications, the system has been modeled
by using an artificial neural network. The results indicated that the proposed model can accurately estimate the
effect of operating and qualitative parameters on the final blaine number.
KEYWORDS
HPGR, Pelletizing, Pellet Feed Regrinding, Blaine Value, Artificial Neural Network