Predicting iron ore fines shipment moisture

Jim Everett; D. Linden; P. Maney

doi:10.1179/1743275813Y.0000000034

Predicting iron ore fines shipment moisture

Jim Everett, D. Linden, P. Maney

Graduate Research School

Research output: Contribution to journal › Article

Abstract

In response to the implementation of a new schedule for iron ore fines in the International Maritime Organization's International Maritime Solid Bulk Cargoes Code, improved measures for the management of moisture have been developed. In particular, prediction of cargo moisture allows management of iron ore fines within the supply chain to facilitate the safe shipping of iron ore fines. An Autoregressive Integrated Moving Average model, augmented to allow for shipment tonnage, has been developed to predict the moisture level from previous shipments of the same product. The model explains about 60% of the moisture variance. It was found that inclusion of other available information, such as ore composition, train/rake assays and recent rainfall at port and mine did not add to the predictive power of the model. © 2013 Institute of Materials, Minerals and Mining and The AusIMM.

Original language	English
Pages (from-to)	113-121
Journal	Transactions of the Institutions of Mining and Metallurgy, Section B: Applied Earth Science
Volume	122
Issue number	2
DOIs	https://doi.org/10.1179/1743275813Y.0000000034
Publication status	Published - 2013

Access to Document

10.1179/1743275813Y.0000000034

Fingerprint Dive into the research topics of 'Predicting iron ore fines shipment moisture'. Together they form a unique fingerprint.

Cite this

@article{96869e44924d43c09ecdaa081cc1665f,

title = "Predicting iron ore fines shipment moisture",

abstract = "In response to the implementation of a new schedule for iron ore fines in the International Maritime Organization's International Maritime Solid Bulk Cargoes Code, improved measures for the management of moisture have been developed. In particular, prediction of cargo moisture allows management of iron ore fines within the supply chain to facilitate the safe shipping of iron ore fines. An Autoregressive Integrated Moving Average model, augmented to allow for shipment tonnage, has been developed to predict the moisture level from previous shipments of the same product. The model explains about 60% of the moisture variance. It was found that inclusion of other available information, such as ore composition, train/rake assays and recent rainfall at port and mine did not add to the predictive power of the model. {\textcopyright} 2013 Institute of Materials, Minerals and Mining and The AusIMM.",

author = "Jim Everett and D. Linden and P. Maney",

year = "2013",

doi = "10.1179/1743275813Y.0000000034",

language = "English",

volume = "122",

pages = "113--121",

journal = "Institution of Materials, Minerals and Mining. Transactions. Section B: Applied Earth Science",

issn = "0371-7453",

publisher = "Maney Publishing",

number = "2",

}

TY - JOUR

T1 - Predicting iron ore fines shipment moisture

AU - Everett, Jim

AU - Linden, D.

AU - Maney, P.

PY - 2013

Y1 - 2013

N2 - In response to the implementation of a new schedule for iron ore fines in the International Maritime Organization's International Maritime Solid Bulk Cargoes Code, improved measures for the management of moisture have been developed. In particular, prediction of cargo moisture allows management of iron ore fines within the supply chain to facilitate the safe shipping of iron ore fines. An Autoregressive Integrated Moving Average model, augmented to allow for shipment tonnage, has been developed to predict the moisture level from previous shipments of the same product. The model explains about 60% of the moisture variance. It was found that inclusion of other available information, such as ore composition, train/rake assays and recent rainfall at port and mine did not add to the predictive power of the model. © 2013 Institute of Materials, Minerals and Mining and The AusIMM.

AB - In response to the implementation of a new schedule for iron ore fines in the International Maritime Organization's International Maritime Solid Bulk Cargoes Code, improved measures for the management of moisture have been developed. In particular, prediction of cargo moisture allows management of iron ore fines within the supply chain to facilitate the safe shipping of iron ore fines. An Autoregressive Integrated Moving Average model, augmented to allow for shipment tonnage, has been developed to predict the moisture level from previous shipments of the same product. The model explains about 60% of the moisture variance. It was found that inclusion of other available information, such as ore composition, train/rake assays and recent rainfall at port and mine did not add to the predictive power of the model. © 2013 Institute of Materials, Minerals and Mining and The AusIMM.

U2 - 10.1179/1743275813Y.0000000034

DO - 10.1179/1743275813Y.0000000034

M3 - Article

VL - 122

SP - 113

EP - 121

JO - Institution of Materials, Minerals and Mining. Transactions. Section B: Applied Earth Science

JF - Institution of Materials, Minerals and Mining. Transactions. Section B: Applied Earth Science

SN - 0371-7453

IS - 2

ER -