Prediction of Circulation Load of Side-Flanged High-Pressure Grinding Rolls Closed-Circuit Crushing
Bibliographic record
Abstract
To enhance the performance of the combined high-pressure grinding roller (HPGR) and tower mill (TM) process for −1 mm particle size, this study addresses the key technical challenges of insufficient material quantity (<100 kg) and complex experimental procedures in HPGR closed-circuit crushing tests by proposing a novel circulating load prediction method based on the principle of mass balance and first-order crushing kinetics. Using a side-flanged HPGR WGM 6020 installation, systematic −1 mm HPGR closed-circuit crushing tests were conducted on seven different ore samples under three specific pressing forces, with detailed characterization of the dynamic variations in product size distribution, specific energy consumption, and circulating load during each cycle. The results demonstrate that within the specific pressing force range of 3.5 N/mm2 to 4.5 N/mm2 when the crushing process reaches equilibrium, the circulating load stabilizes between 100% and 200%, while the specific energy consumption is maintained within 1–2.5 kWh/t. Notably, at the specific pressing force of 4.5 N/mm2, both the circulating load and specific energy consumption rapidly achieve stable states, with ore characteristics showing no significant influence on the number of cycles. To validate the model accuracy, additional samples were tested for comparative analysis, revealing that the deviations between the model-predicted −1 mm product content and circulating load and the experimental results were less than ±5%, confirming the reliability of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".