R
Roussos Dimitrakopoulos
Researcher at McGill University
Publications - 267
Citations - 6024
Roussos Dimitrakopoulos is an academic researcher from McGill University. The author has contributed to research in topics: Stochastic optimization & Open-pit mining. The author has an hindex of 39, co-authored 248 publications receiving 5116 citations. Previous affiliations of Roussos Dimitrakopoulos include University of Queensland & École Polytechnique de Montréal.
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Incorporating geological and equipment performance uncertainty while optimising short-term mine production schedules
TL;DR: Short-term production scheduling in open pit mining consists of defining the extraction sequence and process allocation of mineralised material over time-scales of either months, weeks, or days as mentioned in this paper.
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Implementation of conditional simulation by successive residuals
TL;DR: Two different implementations of conditional simulation of ergodic and stationary Gaussian random fields using successive residuals to overcome the size limitations of the LU decomposition algorithm as well as provide fast updating of existing simulated realizations with new data are discussed.
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Geologic heterogeneity representation using high-order spatial cumulants for subsurface flow and transport simulations
TL;DR: In this paper, the effects of geological heterogeneity representation on the hydraulic properties of two-dimensional flow and transport simulations are studied using various stochastic simulation algorithms, such as HOSIM, FilterSIM, and SGS.
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Stochastic production phase design for an open pit mining complex with multiple processing streams
TL;DR: In this paper, a stochastic network flow approach is proposed that jointly integrates uncertainty in supply of ore and multiple ore destinations into the development of production phase design and ultimate pit limit.
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Testing geological heterogeneity representations for enhanced oil recovery techniques
TL;DR: In this article, the effects of geological heterogeneity representation in a producing reservoir, when different stochastic simulation methods are used, so as to assess the consequent effects on flow responses for different enhanced oil recovery (EOR) techniques employed.