ArticleScientific reports2026
A synergistic deep learning framework integrating GWO-VMD and dual-channel TCN-GRU for cobalt ion concentration prediction in zinc hydrometallurgical purification.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- ROCK2 regulates a stress-responsive transcriptional state and calcium signaling in renal cell carcinoma.Molecular and cellular biochemistry · 2026Article
- Gasless endoscopic thyroidectomy via the transsubclavian approach: comparable safety and superior anterior cervical function preservation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
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7 authors.
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Abstract
Accurate prediction of cobalt ion concentration is important for maintaining electrolyte purity and process stability in zinc hydrometallurgical purification. However, conventional laboratory analysis is time-consuming and difficult to support timely process adjustment. This work proposes a GWO-VMD-TCN-GRU framework for one-step-ahead prediction of cobalt ion concentration in the second-stage purification process. GWO is used to optimize VMD parameters and decompose the non-stationary concentration series into trend and detail components, while the TCN-GRU network captures local fluctuations and temporal dependencies. Experiments were conducted using 720 hourly samples collected over 30 consecutive days. The final selected model, evaluated on the fixed chronological test set, achieved RMSE, MAE, R², and KGE values of 0.1349, 0.1095, 0.9922, and 0.9710, respectively. Compared with representative statistical, machine learning, and deep learning baseline models evaluated under the same fixed chronological testing protocol, the proposed GWO-VMD-TCN-GRU model reduced RMSE by 75.3% to 94.5% and MAE by 73.1% to 94.3%. Five-fold time-series validation was further conducted to evaluate temporal generalization across chronological folds, with average R
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