A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy

• Main Authors: Dong Xiao, Ba Tuan Le, Zhichao Yu, Chenyi Liu, Hongzong Li, Qifei He, Hongfei Xie, Jichun Wang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Hydrometallurgy; density of thickening; support vector machines; particle swarm optimization; random forests; fault monitoring and fault diagnosis
• Online Access: https://ieeexplore.ieee.org/document/8849997/

Hydrometallurgy is a metallurgical method for processing complex ores and low-grade ores while reducing environmental pollution. The density of the thickening process in hydrometallurgical production is rather poor, and there are many interference factors, resulting in frequent failures in the density of the thickening process. The main focus of this paper is to propose a method of fault monitoring and diagnosis for the density of the thickening process in hydrometallurgy. First, through the support vector machine (SVM) algorithm, the fault detection model is established to monitor the blockage of the underflow pipeline of the thickener. Second, the fault diagnosis model is established by using the random forest algorithm, and particle swarm optimization is used to optimize the fault diagnosis model. The fault type is judged using the optimized diagnosis model, and the corresponding treatment measures are taken accordingly.