Grant: Collaborative Research and Development Grants
McGill University
$499,776.00
Smart mining complexes: large-scale stochastic optimization, high-order simulation and self-learning decision support systems for sustainable development of mineral resources
FY 2020-21
Summary
Modelling and optimization technologies have evolved to become an essential tool for optimal mine designs and production schedules, aiming to maximize the economic value of mining assets from their initial assessment and development to closure [1-11]. Recently, including our CRDPJ Grant 411270-10, new methodologies and algorithms have been developed for complex mining systems that enable the explicit and joint management of orebody, mining, processing and market uncertainties [11-23], and show substantial increases in both NPV and metal production. It is evident in all past developments, that the following areas are still main open areas for research: (a) computational complexity of large scale mining optimization problems, (b) endogenous and exogenous uncertainties in the key parameters (geological/mining, financial), and (c) absence of 'holistic' optimization of the elements of a mining complex that intelligently adapts to unveiling information.
Our work contributes substantial developments to strategic stochastic mine planning optimization methods, specifically, by integrating and simultaneously optimizing critical elements of mining complexes under uncertainty using our newly developed, computationally efficient simulation and optimization approaches. Through these contributions, we have changed the mine planning paradigm in several ways, particularly by shifting the focus of the optimizers from the assessment of economic values of extracted materials to the economic values of the products generated and sold to customers, while accounting for major sources of uncertainty (geological/supply). This contribution opens new possibilities to develop an advanced, all-inclusive framework to deal with several critical issues that could not be previously addressed.
Program
Collaborative Research and Development Grants
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
500414-2016
Name nom
Dimitrakopoulos, Roussos - Georgios
Department département
Mining & Materials Engineering.
Organization id
61
Institution établissement
McGill University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2016
Award amount
499776
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
141
Committee name en
University/Industry
Committee name fr
Universités/industrie
Area of application code
202
Area of application group en
Natural resources (economic aspects)
Area of application group fr
Ressources naturelles (aspects économiques)
Area of application en
Mineral resources ( prospecting, exploration, mining, extraction, processing)
Area of application fr
Ressources minérales (prospection, exploration, mines, extraction et préparation)
Research subject code
1401
Research subject group en
Mining and mineral processing
Research subject group fr
Génie minier et minéral
Research subject en
Mining engineering
Research subject fr
Génie minier
Installment
inst:5 - 5
Partie
1
Nb partie
5
Application title
Smart mining complexes: large-scale stochastic optimization, high-order simulation and self-learning decision support systems for sustainable development of mineral resources
Keyword
NSERC
Application summary
Modelling and optimization technologies have evolved to become an essential tool for optimal mine designs and production schedules, aiming to maximize the economic value of mining assets from their initial assessment and development to closure [1-11]. Recently, including our CRDPJ Grant 411270-10, new methodologies and algorithms have been developed for complex mining systems that enable the explicit and joint management of orebody, mining, processing and market uncertainties [11-23], and show substantial increases in both NPV and metal production. It is evident in all past developments, that the following areas are still main open areas for research: (a) computational complexity of large scale mining optimization problems, (b) endogenous and exogenous uncertainties in the key parameters (geological/mining, financial), and (c) absence of 'holistic' optimization of the elements of a mining complex that intelligently adapts to unveiling information.
Our work contributes substantial developments to strategic stochastic mine planning optimization methods, specifically, by integrating and simultaneously optimizing critical elements of mining complexes under uncertainty using our newly developed, computationally efficient simulation and optimization approaches. Through these contributions, we have changed the mine planning paradigm in several ways, particularly by shifting the focus of the optimizers from the assessment of economic values of extracted materials to the economic values of the products generated and sold to customers, while accounting for major sources of uncertainty (geological/supply). This contribution opens new possibilities to develop an advanced, all-inclusive framework to deal with several critical issues that could not be previously addressed.