In mineral processing the grinding comminution process is an integral part since it is often the bottleneck of the concentrating process, thus small improvements may lead to large savings. By implementing a Reinforcement Learning controller this thesis aims to investigate if it is possible to control the grinding process more efficiently compared to traditional control strategies. Based on a calibrated plant simulation we compare existing control strategies with Proximal Policy Optimization and show possible increase in profitability under certain conditions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-153273 |
| Date | January 2018 |
| Creators | Hallén, Mattias |
| Publisher | Umeå universitet, Institutionen för fysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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