Abstract
Crushing is one of the few remaining industrial processes that is currently being operated using belief-based manual control without the possibility to quantify the consequences of performed control actions. Current operating practice exposes crushing processes to inefficient and inconsistent production (overcrushing and undercrushing). This paper describes a novel size reduction control strategy for cone crushers to address this problem. The proposed control strategy is based on self-optimizing control structure (near-maximum performance using constant setpoint) to ensure the desired degree of size reduction in a long-term production. The ideal degree of size reduction is determined using empirically generated economic-valued crusher performance maps to maximize the average circuit performance. The results of the full-scale experiment revealed an enormous performance improvement as the result of decreased variability and increased average performance; introduction of automatic size reduction control resulted in 38–46 percent lower variation of circuit economic performance KPIs, which in turn enabled the potential to increase the average circuit performance by 12–16 percent. Developed methods enable the possibility to quantify the instantaneous performance of crushing circuits and to ensure consistent and efficient long-term production. These developments facilitate significant real-time performance improvements in crushing.
Original language | English |
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Article number | 107202 |
Number of pages | 11 |
Journal | Minerals Engineering |
Volume | 173 |
DOIs | |
Publication status | Published - 1 Nov 2021 |
Publication type | A1 Journal article-refereed |
Keywords
- Crushing
- Data reconciliation
- Key performance indicators
- Process control
- Self-optimizing control
Publication forum classification
- Publication forum level 2
ASJC Scopus subject areas
- Control and Systems Engineering
- General Chemistry
- Geotechnical Engineering and Engineering Geology
- Mechanical Engineering