Process Feature Change Recognition Based on Model Performance Monitoring and Adaptive Model Correction for the Gold Cyanidation Leaching Process

The gold cyanidation leaching process (GCLP) is the central unit operation in hydrometallurgy, and satisfactory gold recovery is highly significant in practice. However, GCLP faces the challenge of an irregular slow time-varying feature (STVF), which seriously affects gold recovery, and blind treatm...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.28955-28967
Main Authors: He, Dakuo, Wang, Zhengsong, Liu, Qing, Shi, Jiahui, Yang, Le, Wang, Qingkai, Zhao, Jianjun
Format: Article
Language:English
Subjects:
Adaptation models
adaptive model correction
Cyanidation
Feature extraction
Feature recognition
Gold
Gold cyanidation leaching process
Hydrometallurgy
Leaching
Mineral resources
model-based principal component analysis
Monitoring
Optical wavelength conversion
Ores
Principal components analysis
process feature change recognition
Recovery
Strategy
Support vector machines
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Summary:The gold cyanidation leaching process (GCLP) is the central unit operation in hydrometallurgy, and satisfactory gold recovery is highly significant in practice. However, GCLP faces the challenge of an irregular slow time-varying feature (STVF), which seriously affects gold recovery, and blind treatment for STVF also has drawbacks, which results in the need for the recognition of STVF for purposeful, rather than blind, treatment. Meanwhile, it also faces the problem of change of working condition (COWC) due to the variability of mineral resources. Both STVF and COWC may cause degradation of the soft-measuring model, which presents the need for model correction. Therefore, a coping strategy is proposed to solve these existing problems. First, an improved model-based principal component analysis monitoring is proposed to detect model mismatch and monitor the change of process feature. Next, a support vector machine-based process feature change recognition method is presented to recognize change type, which not only provides guidance in treating STVF but also makes it possible to implement targeted model correction for STVF and COWC. Finally, an adaptive model correction strategy that combines case-based correction and just-in-time learning-based correction is proposed. The simulation studies have verified the validity of the proposed coping strategy.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2895115