Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-9227-2470
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-5228-3888
2019 (Engelska)Ingår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 134, s. 156-165Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

A spatial model for process properties allows for improvedproduction planning in mining by considering the process variability ofthe deposit. Hitherto, machine-learning modelling methods have beenunderutilised for spatial modelling in geometallurgy. The goal of thisproject is to find an efficient way to integrate process properties (ironrecovery and mass pull of the Davis tube, iron recovery and mass pull ofthe wet low intensity magnetic separation, liberation of iron oxides, andP_80) for an iron ore case study into a spatial model using machinelearningmethods. The modelling was done in two steps. First, the processproperties were deployed into a geological database by building nonspatialprocess models. Second, the process properties estimated in thegeological database were extracted together with only their coordinates(x, y, z) and iron grades and spatial process models were built.Modelling methods were evaluated and compared in terms of relativestandard deviation (RSD). The lower RSD for decision tree methodssuggests that those methods may be preferential when modelling non-linearprocess properties.

Ort, förlag, år, upplaga, sidor
Elsevier, 2019. Vol. 134, s. 156-165
Nyckelord [en]
Data Integration, Spatial Model, WLIMS, Davis Tube, Iron Ore, Machine-learning, Geometallurgy.
Nationell ämneskategori
Mineral- och gruvteknik Metallurgi och metalliska material
Forskningsämne
Mineralteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-71579DOI: 10.1016/j.mineng.2019.01.032ISI: 000462107200015Scopus ID: 2-s2.0-85060907032OAI: oai:DiVA.org:ltu-71579DiVA, id: diva2:1263184
Anmärkning

Validerad;2019;Nivå 2;2019-02-11 (svasva)

Tillgänglig från: 2018-11-14 Skapad: 2018-11-14 Senast uppdaterad: 2019-04-11Bibliografiskt granskad
Ingår i avhandling
1. Bringing predictability into a geometallurgical program: An iron ore case study
Öppna denna publikation i ny flik eller fönster >>Bringing predictability into a geometallurgical program: An iron ore case study
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Skapande av predikterbarhet i ett geometallurgiskt program : en fallstudie med järbnmalm
Abstract [en]

The risks of starting, operating and closing mining projects have become higher than ever. In order to stay ahead of the competition, mining companies have to manage various risks: technical, environmental, legal, regulatory, political, cyber, financial and social. Some of these can be mitigated with the help of geometallurgy. Geometallurgy aims to link geological variability with responses in the beneficiation process by a wide usage of automated mineralogy, proxy metallurgical tests, and process simulation. However, traditional geometallurgy has neglected the non-technical aspects of mining. This has caused wide-spread discussion among researchers on the benefits of geometallurgy and its place in industry.

In order to improve predictability in geometallurgy, such programs should cover planning, and the testing of hypotheses, and only then should there be an attempt to develop suitable technical tools. Such approach would ensure that those tools would be useful and are needed, not only from the technical point of view, but also from the users’ perspective. Therefore, this thesis introduces methodology on how to decrease uncertainty in the production planning and thus determine how much effort to put into decreasing uncertainty in geometallurgical programs.

The predictability improvement of a geometallurgical program starts at the planning stage. The classification system developed here, through the survey (interviews) and literature review, indicates different ways to link geological information with metallurgical responses, and suggests areas where technical development is called for. The proposed developments can be tested before the start of the geometallurgical program with synthetic data. For the iron ore reference study (Malmberget), it was shown that implementation of geometallurgy is beneficial in terms of net present value (NPV) and internal rate of return (IRR), and building geometallurgical spatial model for the process properties (recovery and total concentrate tonnages), and that it requires fewer samples for making a reliable process prediction than concentrate quality. The new process and proxy for mineralogical characterisation models were developed as part of the geometallurgical program for the iron ore case study (Leveäniemi): an estimator of ore quality (ܺ௅்௎), a model for iron recovery in WLIMS, a model for iron-oxides liberation prediction. Additionally, it was found that DT may be applied only for studying marginal ores. The evaluation of different spatial process modelling methods showed that tree methods can be successfully employed in predicting non-additive variables (recoveries).

Ort, förlag, år, upplaga, sidor
Luleå: Luleå University of Technology, 2019
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Nyckelord
Additivity, Apatite iron ore, AIO, Block model, Change of support, Classification, Data integration, DT, Feed quality, Geometallurgical program, Geometallurgy, Iron ore, Iron recovery, Leveäniemi, Liberation, Machine learning, Magnetic separation, Malmberget, Mineralogical approach, Mineralogy, Prediction, Proxies, Proxies approach, Sampling, Simulation, Synthetic ore body, Traditional approach, WLIMS
Nationell ämneskategori
Mineral- och gruvteknik Metallurgi och metalliska material
Forskningsämne
Mineralteknik
Identifikatorer
urn:nbn:se:ltu:diva-71580 (URN)978-91-7790-266-9 (ISBN)978-91-7790-267-6 (ISBN)
Disputation
2019-02-04, D770, Lulea, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-11-14 Skapad: 2018-11-14 Senast uppdaterad: 2019-02-07Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Lishchuk, ViktorLund, CeciliaGhorbani, Yousef

Sök vidare i DiVA

Av författaren/redaktören
Lishchuk, ViktorLund, CeciliaGhorbani, Yousef
Av organisationen
Mineralteknik och metallurgi
I samma tidskrift
Minerals Engineering
Mineral- och gruvteknikMetallurgi och metalliska material

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 329 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf