Bayesian Network-based Technical Index Estimation for Industrial Flotation Process under Incomplete Data

Due to the lack of detection instruments or long measurement cycles in the industrial flotation process, accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational adjustment. In the real-world flotation process, incomplete data is a widespread phenomenon owing to hardware sensor failures and other reasons. To this end, this paper proposes a Bayesian network (BN)-based concentrate grade estimation method under incomplete data. The real-time froth image information and the concentrate grades of the previous periods are taken as the input of the BN model, and the current concentrate grade is the output of the BN model. The expectation maximum (EM) algorithm is used to estimate the model parameters. The application results show the proposed method can accurately estimate the concentrate grade even if some data are missing.

[1]  Solomon Tesfamariam,et al.  Integrating failure prediction models for water mains: Bayesian belief network based data fusion , 2015, Knowl. Based Syst..

[2]  Zhiqiang Ge,et al.  Adaptive soft sensors for quality prediction under the framework of Bayesian network , 2018 .

[3]  Fuli Wang,et al.  Bayesian Network-Based Modeling and Operational Adjustment of Plantwide Flotation Industrial Process , 2020 .

[4]  Hui Li,et al.  A safe control scheme under the abnormity for the thickening process of gold hydrometallurgy based on Bayesian network , 2017, Knowl. Based Syst..

[5]  Luis M. de Campos,et al.  Clustering terms in the Bayesian network retrieval model: a new approach with two term-layers , 2004, Appl. Soft Comput..

[6]  Bir Bhanu,et al.  Dynamic Bayesian Networks for Vehicle Classification in Video , 2012, IEEE Transactions on Industrial Informatics.

[7]  Tao Tang,et al.  A Bayesian network model for prediction of weather-related failures in railway turnout systems , 2017, Expert Syst. Appl..

[8]  Hui Li,et al.  Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process. , 2018, ISA transactions.

[9]  Yan-Lin He,et al.  Novel Multiblock Transfer Entropy Based Bayesian Network and Its Application to Root Cause Analysis , 2019, Industrial & Engineering Chemistry Research.

[10]  Feng Yu,et al.  Estimation of copper concentrate grade for copper flotation , 2018 .

[11]  Heikki N. Koivo,et al.  Prediction of Concentrate Grade in Industrial Gravity Separation Plant – Comparison of rPLS and Neural Network , 2008 .

[12]  Zhang Yong,et al.  Flotation concentrate grade prediction model based on RBF neural network & immune evolution algorithm , 2012, Proceedings of the 31st Chinese Control Conference.

[13]  Weihua Gui,et al.  Integrated prediction model of bauxite concentrate grade based on distributed machine vision , 2013 .

[14]  Weihua Gui,et al.  Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade , 2014 .

[15]  Sameer H. Morar,et al.  An evaluation of factors affecting the robustness of colour measurement and its potential to predict the grade of flotation concentrate , 2009 .

[16]  M. R. Mosavi,et al.  Prediction of copper grade at flotation column concentrate using Artificial Neural Network , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[17]  Fuli Wang,et al.  An Operational Adjustment Framework for a Complex Industrial Process Based on Hybrid Bayesian Network , 2020, IEEE Transactions on Automation Science and Engineering.