Development and Reconciliation of a Mine Operation Value Chain Flowsheet in IES to Enable Grade Engineering and Process Mass Simulations for Scale-up and Strategic Planning Analysis-Reference-Cited by-同舟云学术

Development and Reconciliation of a Mine Operation Value Chain Flowsheet in IES to Enable Grade Engineering and Process Mass Simulations for Scale-up and Strategic Planning Analysis

Published:2020-09-16 Issue:2 Volume:38 Page:721-730
ISSN:2524-3462
Container-title:Mining, Metallurgy & Exploration
language:en
Short-container-title:Mining, Metallurgy & Exploration

Author:

Amini E.,Becerra M.,Bachmann T.,Beaton N.,Shapland G.

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Metals and Alloys,Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42461-020-00303-9.pdf

Reference23 articles.

1. Bartlett J, Holtzapple A, Rempel C (2014) A brief overview of the process modelling/simulation and design capabilities of METSIM. Canadian Institute of Mining, Metallurgy and Petroleum: COM 2014 - Conference of Metallurgists Proceedings

2. Burger B, McCaffery K, Jankovic A, Valery W, McGaffin I (2006) Batu Hijau model for throughput forecast, mining and milling optimisation and expansion studies. Advances in comminution, Pages 461–479, SME Publications

3. Carrasco C, Keeney L, Napier-Munn TJ, Bode P (2017) Unlocking additional value by optimising comminution strategies to process Grade Engineering® streams. Miner Eng 2017(103–104):2–10

4. Carrasco C, Gahona M, La Rosa D, Shapland G, Beaton N, Amini E (2019) Augmenting Traditional Mine-to-Mill with Cloud-Based Simulation and Reconciliation Capabilities. SAG conference 2019

5. Cox J, Goldratt EM (1986) The goal: a process of ongoing improvement. North River Press. ISBN 0-88427-061-0

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Utilisation of geometallurgical predictions of processing plant reagents and consumables for production scheduling under uncertainty;International Journal of Mining, Reclamation and Environment;2022-12-05

2. An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models;Minerals Engineering;2022-11

3. Diversified Fiscal and Tax System Model Based on Improved Artificial Neural Network;2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS);2022-07

4. Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations;Minerals Engineering;2021-08