Working Conditions Identification Method Based On Semi-supervised Learning And Copper Flash Smelting Process

Working-status of copper flash smelting process frequently changes, which contribute a lot to the poor output and quality of copper. Copper flash smelting process has accumulated lots of industrial operating data. It contains much underlying information between rules of working-status and production parameters. Accurately recognizing working-status and timely taking control are of very grand significance to ensure stable and reliable running of system and promote the economic benefits of enterprise. A method of working-status based semi-supervised learning by using labeled and unlabeled data has been studied in this paper.YATSI may suffer more from the common problem in semi-supervised learning, i.e. the performance is usually not stable due to the unlabeled examples may often be wrongly labeled. A semi-supervised k-nearest neighbor classifier named DE-YATSI is proposed. A data editing based on estimating the conditional probability of class is used to identify and relabel mislabeled examples of the pre-labeled data set. A k-nearest neighbor classifier with weights is trained by the labeled and the edited "pre-labeled" data set. Experiments on UCI datasets show that DE-YATSI could more effectively and stably utilize the unlabeled examples to improve classification accuracy than YATSI.Main factors of working-status of copper flash smelting are analysed in this paper. Semi-supervised algorithms (YATSI and DE-YATSI) and supervised algorithm (KNN) are used to recognize working-status of the copper flash smelting. Experimental results show that semi-supervised algorithm can better recognise working-status than supervised algorithm. Considering labeled samples with small number and unlabeled ones with large number of industrial historical data of copper flash smelting process, a framework of working-status recognizes based of semi-supervised are presented. It includes data acquisition and processing, semi-supervised off-line modeling and working-status on-line recognize. The working-status recognition system (WSRS) is designed which includes system theory, architecture design, subsystems decomposing, system function and steps of realization.