LOGISTICAL CONTROL OF IRON ORE STREAMS UNDER GEOLOGICAL AND MARKET UNCERTAINTY
Notice bibliographique
Résumé
In recent years, the mining industry has been transformed by the advent of Industry 4.0 technologies, including the Industrial Internet of Things (IIoT) and automation. These advancements have brought significant improvements in efficiency and productivity (Rogers, Kahraman, Drews, Powell, Haight, Wang, Baxla & Sobalkar, 2019), yet mining still faces significant challenges. The iron ore sector continues to exemplify the industry's difficulties, grappling with declining ore grades, stringent environmental regulations, socio-economic issues, and volatile commodity prices. To sustain a competitive advantage throughout the entire value chain, iron mining companies must shift towards multiphase engineering methodologies that simultaneously optimize input and output streams. Within this context, Canadian iron ore producers must adapt their control strategies by establishing alternate operating modes that coordinate system-wide responses to changing feeds and market demand. In line with this imperative, Navarra et al. (2017) introduced a framework rooted in Discrete Rate Simulation (DRS) (a subtype of Discrete Event Simulation (DES)) and inventory theory. This framework has been developed to evaluate blending and stockpiling strategies under alternate modes of operation, aiming to mitigate processing plant feed uncertainty and can leverage simulation-based optimization techniques to enhance the competitiveness of mining operations.While strategic approaches that align with the increasing adoption of mine-to-mill integration, which aims to optimize both mining and processing operations simultaneously, have become increasingly prevalent (Valery, Duffy, & Jankovic, 2019), there has been little research on modelling processing plant output streams and the interrelation between mill product quality and dynamic market demand. This challenge stems from the difficulty in acquiring representative data to accurately model various conditions, including production outputs, customer specifications, greenhouse gas emission allowances, dynamic spot prices and freight costs. This thesis addresses the necessity for developing decision-making tools tailored to logistical control across the entire iron ore value chain. It introduces an adaptation of Navarra et al.’s (2017) two-mode DRS framework contextualized for a Canadian iron ore operation with heterogeneous plant feed and a mathematical model to optimize iron ore product flows in the global market, by modelling primary ironmaking processes. As such, the proposed approach encompasses management of stockpiling space and transportation networks in an integrated mine-to-mill-to-market approach. The mathematical model utilizes the tonnage of saleable products acquired from simulating production campaigns as its input. It then optimizes iron ore proportioning and distribution among customers based on diverse objectives, encompassing economic and environmental parameters
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».