Serial Hybrid Modelling for a Gold Cyanidation Leaching Plant
Bibliographic record
Abstract
In this paper, a serial hybrid model of a gold cyanidation leaching process is proposed. The serial hybrid model consists of mass conservation equations of gold and cyanide as well as two kernel partial least square (KPLS) models, which are used as the estimators of the unknown kinetic reaction rates without the complicated kinetic model structures considered. The proposed serial hybrid model makes full use of both the a priori process knowledge and the ability of a data‐driven model to discover the information behind data sets. Moreover, before training the KPLS models, the proposed estimation strategy based on Tikhonov regularization is used to estimate the kinetic reaction rates, which can mitigate the effect of measurement noise on the estimation results effectively. The proposed serial hybrid model has been applied to a gold cyanidation leaching plant to predict the gold leaching rate. The prediction results show that the proposed serial hybrid model can track the real leaching rate of gold closely and has the best prediction accuracy at both dynamic and steady states compared with the pure KPLS and mechanistic models, thereby laying an important foundation for the successful implementation of optimization and control of the leaching process.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".