Data-Driven Grinding Control Using Reinforcement Learning
- Submitting institution
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University of Central Lancashire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30733
- Type
- E - Conference contribution
- DOI
-
10.1109/HPCC/SmartCity/DSS.2019.00395
- Title of conference / published proceedings
- 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
- First page
- 2817
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The goal for Grinding Control (GC) optimisation is to ensure the outputs of the controlled processes best follow the control actions and to ensure that the grinding product quality and efficiency are well controlled within the optimal ranges. In this paper, we present our work using a pure data-driven and reinforcement learning-based approach that optimises GC processes. Using our method, the experiment results show evident enhancement with regards to both product quality and grinding process efficiency, which generate considerable economic impacts for the mineral industry.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -