Assessing Machine Learning and Deep Learning-based approaches for SAG mill Energy consumption

Pedro Lopez, Ignacio Reyes, Nathalie Risso, Cristhian Aguilera, Pedro G. Campos, Moe Momayez, Diego Contreras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Energy consumption represents a high operational cost in mining operations. Ore size reduction stage is the main consumer in that process, where the Semiautogenous Mill (SAG) is one of the main components. The implementation of control and automation strategies that can achieve production goals along with energy efficiency are a common goal in concentrator plants; however, designing such controls requires a proper understanding of process dynamics which are highly complex. This work studies machine learning and deep learning strategies that can be used to generate models for predicting energy consumption, using key process variables. In particular, the application of K-Nearest Neighbors Regressor (KNN-reg), Polynomial Regression (PR), Support Vector Regression (SVR) and Long-Short Term Memory (LSTM) strategies for energy prediction over SAG mill process data is developed in order to identify configurations suitable to be implemented for real-time prediction integrated over industrial data infrastructures. All techniques are compared in terms of Root Mean Square Error (RMSE) where, although all the models achieved acceptable performance, best results were obtained by a LSTM implementation which yielded an error of less than 4% associated to energy prediction.

Original languageEnglish (US)
Title of host publication2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665408738
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 - Virtual, Online, Chile
Duration: Dec 6 2021Dec 9 2021

Publication series

Name2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

Conference

Conference2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Country/TerritoryChile
CityVirtual, Online
Period12/6/2112/9/21

Keywords

  • Energy consumption
  • Machine learning
  • Mine automation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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