Integrated simulation and optimisation tools for production scheduling using finite element analysis caving geomechanics simulation coupled with 3D cellular automata

Abstract

Key challenges integrating caving geomechanics simulation into mine planning processes start with the considerable effort to build realistic models and to map production schedules and cave back geometries into the simulation. Currently, calibrating parameters for the complex failure mechanisms that define the interface (cave back, possible airgap, muck pile) between solid and flow domain can be extremely time consuming. This also often requires a high level of expertise in modelling. This work investigates the numerical efficiency of automated mesh and model building strategies and advantages of using high-performance computing on regular fine grids for non-linear finite element simulation. This allows direct mapping of cellular automaton results, and in return, predictions of rock mass failure without loss of accuracy at a higher frequency, maximising the use of information available from calibrated flow models for production scheduling. An important goal for such models must be the ability to simulate cave growth in complex geological settings and replicate realistic behaviour for relevant benchmark problems that reflect industry experience in block caving. These automated processes will not just accelerate the cave modelling processes and reduce manual processing time but also allow use of the full simulation cycle in case studies, sensitivity analysis and optimisation in an environment of uncertainty and constant changes to the available data. Integrated simulation and optimisation tools significantly improve understanding of realistic geomechanics behaviour driven by the inherent characteristics of the rock mass and structural geological setting, by the extraction strategy and by other engineering decisions (interaction with underground infrastructure). This greater level of understanding reflects in key performance indicators related to safety, revenue maximisation (strategy on how best to exploit the mineral resource), and operation excellence (productivity).

Solving rock mechanics issues through modelling: then, now, and in the future?

“…Steps towards this are already being taken; e.g. the workflow outlined by Arndt et al (2018). This workflow also involved aspects of machine learning.…”

“…CAVESIM simulates gravity flow of caved rock in block and sublevel caves, with accurate representation and control of material bulking, as well as modelling material flow against static faces and around or against disturbing features such as cave sidewalls, overhangs, hang-ups and pendants (Hebert & Sharrock 2018). A similar approach with an integrated coupled solution between a cellular automaton extraction and flow simulation and a finite element geomechanics simulation was described by Arndt et al (2018), using PCBC for the flow simulation and Abaqus for the finite element analysis. This automated process was claimed to both accelerate the cave modelling processes and reduce manual processing time, as well as to allow use of the full simulation cycle in case studies and sensitivity analysis.…”

Investigating economic and risk metrics using design of experiments in fully coupled caving geomechanics simulation

“…This paper presents work in continuation of the methodologies for caving geomechanics simulation coupled with 3D cellular automata (Arndt et al 2018), with further developments over the last four years contributing to the maturity of solutions. Experience was gained in different environments, ranging from early stage studies to established caving operations.…”

“…The key drivers providing the foundation for the coupled caving solution as adopted in this work, GEOVIA PCBC and SIMULIA Abaqus, are summarised below. For a detailed description see (Arndt et al 2018).…”

Procedure for estimating broken ore density distribution within a draw column during block caving

“…This extends to dilution entry estimations and computation of the Mixing Horizon (MH), which is the distance up the column in which mixing is assumed to occur. Accordingly, swell factor has been identified as a key parameter with respect to influencing PCBC results (Arndt et al 2018). Another example is the porosity index used in REBOP to determine the downward flow of material.…”