Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

Correlation Analysis of Control Parameters of Flotation Process

The dosage of gold-antimony flotation process of 5 main drugs,including Copper Sulfate,Lead Nitrate,Yellow Medicine,No.2 Oil,Black Medicine,with corresponding visual features of foam images,including Stability,Gray Scale,Mean R,Mean G,Mean B,Mean Average,Dimension and Degree Variance,were recorded.Parameter correlation analysis showed that the correlation among Copper Sulfate,Yellow Medicine,Black Medicine,as well as the correlation among Gray Scale,Mean R,Mean G,Mean B,is strong,and the correlation among Dimension,Gray Scale,Mean R,Mean G,Mean B,as well as the correlation between Stability and each dosing parameter,is week.It also indicated a feasible way to decrease the complexity of flotation control system by reducing some parameters.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.

Upgrading graphite by flotation at Bogala Mines in Sri Lanka

Graphite is naturally floatable due to its hydrophobic pro pe rty and also soft and smears on other gangue particles, rendering the gangue mor e or less floatable too. Due to this reason it is important to concentrate on ar eas such as suitable flotation reagents, depression agents, pH modifiers, and pa rticle size to be fed during the process. The paper surveys and analyses the sui table particle size to be fed to achieve high-grade concentrate. According to t h e work carried out the author suggested the ideal cost effective flotation f low sheet for improved results at Bogala Mines in Sri Lanka.