Advanced modeling of HPGR power consumption based on operational parameters by BNN: A Conscious-Lab development

This study, for the first time, is going to introduce the boosted neural network (BNN) as a robust artificial intelligence for filling gaps related to themodeling of energy consumption (power draw) in the industrial scale high-pressure grinding rolls (HPGR). For such a purpose, a new concept called Conscious Laboratory (CL) has been developed. CL would be the modeling of variables based on real databases that are collected from the industrial-scale plants. Although using HPGRs have been absorbed attention in many processing plants, a few in vestigations have been conducted to model the power draw of HPGRs. In this article, BNN was used for modeling relationships between HPGR operational variables, and their representative power draws based on an industrial database. This investigation indicated that the generated CL based on BNN could accurately assess the multivariable relationships between monitoring variables of an HPGR from an iron ore plant. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

This study introduces the Boosted Neural Network (BNN) for modeling energy consumption in industrial scale high-pressure grinding rolls (HPGR) and develops a new concept called Conscious Laboratory (CL). BNN is used to accurately assess multivariable relationships in an industrial database for HPGR monitoring variables.