Automation of rock selection and aggregate quality for reuse in tunnelling and industry

Petitat, M.; Allmen, Katja von; Burdin, J. C.

doi:10.1002/geot.201500012

Cited by 4 publications

(1 citation statement)

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“…An implication that can be drawn from the results is the possibility of using the simulation modeling approach as an operational decision support. The proposed method can also be applied in other research areas such of waste management or recycling of material excavated from tunnels [15,16]. The next section concludes the main findings of this study.…”

Section: Experiments 2: Simulation Model With Stochastic Failure Modelsmentioning

confidence: 84%

Operational Decision Support for Material Management in Continuous Mining Systems: From Simulation Concept to Practical Full-Scale Implementations

Shishvan

Benndorf

2017

Minerals

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Material management in opencast mines is concerned with planning, organizing, and control of the flow of materials from their extraction points to destinations. It can be strongly affected by operational decisions that have to be made during the production process. To date, little research has focused on the application of simulation modeling as a powerful supportive tool for decision making in such systems. Practical experiences from implementing a simulation model of a mine for the operational support on an industrial scale are not known to the authors. This paper presents the extension of a developed stochastic simulation model by the authors from a conceptual stage (TRL4) to a new Technology Readiness Level (TRL 6) by implementing it in an industrially relevant environment. A framework for modeling, simulation, and validation of the simulation model applied to two large opencast lignite mines is presented in detail. Operational implementation issues, experiences, and challenges in practical applications are discussed. Furthermore, the strength of applying the simulation modeling as an operational decision support for material management in coal mining is demonstrated. Results of the case studies are used to describe the details of the framework, and to illustrate the strength and limitations of its application.

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Section: Experiments 2: Simulation Model With Stochastic Failure Modelsmentioning

confidence: 84%