Design and Implementation of Machine Learningbased Monitoring System for Mineral Processing Flotation Reagent

Flotation, also known as froth flotation, is a method for separating minerals from powdered materials by altering their floatability through the use of flotation reagents. This paper proposes a flotation process control system for mineral processing based on machine learning. Addressing the issue of lack of precise detection methods in the flotation process of iron concentrate, a neural network regression method is used to predict the amount of reagents and the grade of the flotation concentrate. The flotation data in this paper come from the Key Laboratory of Multitechnology Resource Utilization of Bayan Obo Mine, Inner Mongolia Autonomous Region. The preprocessed data form the dataset used to create the production prediction model. The neural network model is constructed using the PyTorch deep learning framework. Finally, based on the established model, a comprehensive flotation dosing monitoring system is developed using the Django framework, which includes functions such as production indicator large screens, workshop personnel safety monitoring large screens, flotation reagent usage processing, flotation reagent procurement platform.