Estimation of copper concentrate grade based on color features and least-squares support vector regression

Ren, Chuancheng; Yang, Jian; Liang, Chuan

doi:10.5277/ppmp150115

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The study aimed to accurately estimate the residual content of silica in the final concentrate, where the RF model achieved the best performance. Ren et al [16] approached the copper concentrate prediction with a least-squares support vector regression, where they investigated the relationship between the color features of minerals extracted from the microscopic images and concentrate grade. The results showed that there was a strong correlation between the concentrate grade and color features, and that the LS-SVR model can capture this relationship and produce accurate predictions.…”

Section: Predictive Models For Grade and Recoverymentioning

confidence: 99%

“…Figure 5 shows all the machine learning models that were employed in the reviewed publications, separated into categories. The regression models include models such as multiple non-linear regression (MNLR) [10], M5Prime [12], ridge regression (RR) [15], support vector regression (SVR) [19,43], and least-squares SVR (LS-SVR) [16]. The hybrid model presented by Cook et al [12] combined a random forest with a firefly algorithm, and the novel models were FlotationNet [14] and CAPNet [45].…”

Section: Summaries and Future Workmentioning

confidence: 99%

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Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Szmigiel,

Apel,

Skrzypkowski

et al. 2024

Minerals

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Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

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Section: Predictive Models For Grade and Recoverymentioning

confidence: 99%

Section: Summaries and Future Workmentioning

confidence: 99%

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Szmigiel,

Apel,

Skrzypkowski

et al. 2024

Minerals

View full text Add to dashboard Cite

show abstract

“…Foam image classification and condition recognition have become hot topics in university and corporate R&D. Research on modeling to predict indicators in the flotation process primarily falls into two categories: traditional machine learning methods and deep learning. For example, Ren et al [10] applied the least squares support vector regression (LS-SVR) model, utilizing color features of microscopic images to estimate the grade of copper concentrates. Tang et al [11] developed a BP neural network model based on the characteristics of coal slime flotation foam images for predicting the ash content of flotation concentrates.…”

Section: Introductionmentioning

confidence: 99%

Application of Stacking Ensemble Learning in Predicting Copper’s Flotation Concentrate Grade

Yin,

2024

Minerals

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Addressing issues such as a low operational condition recognition efficiency, strong subjectivity, and significant fluctuations in Outotec X fluorescence analysis data in copper flotation production, a copper concentrate grade classification model is constructed based on image processing technology and the Stacking ensemble learning algorithm. Firstly, a feature extraction model for copper concentration flotation foam images is established, extracting color, texture, and size statistical features to build a feature dataset. Secondly, to avoid redundancy in the feature data, which could reduce model accuracy, a combined correlation feature selection is employed for dimensionality reduction, with the filtered feature subset being used as the model input. Finally, to fully leverage the strengths of each model, a Stacking ensemble learning copper concentrate grade classification model is constructed with support vector machine (SVM), random forest (RF), and adaptive boosting (AdaBoost) as base models and logistic regression (LR) as the meta-model. The experimental results show that this ensemble model achieves good recognition for different grade categories, with a precision, recall, and F1 score of 90.01%, 89.85%, and 89.93%, respectively. The accuracy of this Stacking ensemble model, with a 7% improvement over Outotec X fluorescence analysis, demonstrates a potential to meet the daily production needs of beneficiation plants.

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Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades

2018

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Estimation of copper concentrate grade based on color features and least-squares support vector regression

Cited by 3 publications

References 14 publications

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Application of Stacking Ensemble Learning in Predicting Copper’s Flotation Concentrate Grade

Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades

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