Pouya Nobahar Ghezeljehmeidan
Higher Degree by Research Candidate
School of Chemical Engineering
Faculty of Sciences, Engineering and Technology
The project is developing an integrated knowledge-based system based on data analytics, stochastic predictive modelling and/or machine learning that is being used for the monitoring, performance assessment and prediction of the mining and processing operations to contribute to optimising the entire value chain from mine to the final product.
Significant amounts of data are routinely collected on-site in mining operations and processing plants including, for example, drilling performance data, grade control and face samples, digging and hauling data, fleet management system data, crushing and milling performance data, and flotation and recovery rate. These data are rarely used in practice (e.g., mining operational data), or are used locally in isolation of the entire process (e.g., mineral processing data).
This research project is establishing a framework to explore the operational context and learn the dynamic performance relationships in the various stages of the operation and integrate these relationships into a single digital twin-like system. By collecting the data through the IoT (Internet-of-Things), the learning and integrating processes are being be done in near real-time so that the system can be used to help optimise the short-term operations including, for example, micro-adjustment of operational parameters, predicted maintenance or adapted design accounting for local conditions. The system is also being linked to potential long-term strategic optimisation of the value chain by examining the possibility and benefit of updating the resource model, altering the mine design and mine planning, and/or changing the mining and processing designs based on new information. The focus of the research is establishing the most significant performance components in the operational chain in terms of their impact on the entire mining system, not on the integration platform itself.
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Language Competencies
Language Competency Azerbaijani Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review Persian Can read, write, speak, understand spoken and peer review Turkish Can read, write, speak and understand spoken -
Education
Date Institution name Country Title Sahand University of Technology Iran Master of Science Lorestan University of Medical Sciences Iran Bachelor of Science Sahand University of Technology Iran Associate degree -
Research Interests
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Journals
Year Citation 2024 Nobahar, P., Shirani Faradonbeh, R., Almasi, S. N., & Bastami, R. (2024). Advanced AI-Powered Solutions for Predicting Blast-Induced Flyrock, Backbreak, and Rock Fragmentation. Mining, Metallurgy and Exploration, 41(4), 2099-2118.
2024 Nobahar Ghezeljehmeidan, P., Pourrahimian, Y., & Shirani Faradonbeh, R. (2024). Detection of Ore Type in Drilling Cores Using Machine Vision Algorithm. JOURNAL OF MINING AND ENVIRONMENT.
2024 Nobahar, P., Xu, C., Dowd, P., & Faradonbeh, R. S. (2024). Exploring Digital Twin Systems in Mining Operations: A Review. Green and Smart Mining Engineering.
2022 Nobahar, P., Pourrahimian, Y., & Mollaei Koshki, F. (2022). Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine. Mining, 2(3), 528-541.
Scopus9 -
Conference Papers
Year Citation 2024 Nobahar Ghezeljehmeidan, P., Xu, C., & Dowd, P. (2024). From resource to product: integrating advanced AI and data analytics for mine-to-product optimization. In Minexcellence 2024. Santiago, Chile. 2022 Nobahar, P., & Emami, A. (2022). Using Hierarchical Analysis, Determine the Best Method for Crushing Oversized Mineral Rocks. In 10th Iranian Mining Conference. Kerman.
Integrated Operations for Complex Resources, ARC Training Centre
Mine Automation 7115/4115 (TA)
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