A Machine Learning Based Intelligent Decision Support System for Flotation Dynamics in Simulation of Clay Minerals

In coal mines, process management is related with planning and flow of coal minerals from their mining points to journey's end. The process is dependent on the operational decisions, which should be completed during coal production. In these systems, simulation modelling is assumed a powerful tool for decision making. The simulation modelling can further be enhanced through applying artificial intelligence (AI) and machine learning (ML) methods. The communication of water with clay minerals upholds the water adsorption on the clay surface, which makes them complex systems. Therefore, evading the water absorption from the clay turns out to be a hard job. The computational trainings of clay minerals are needed to comprehend the dynamics of water distribution. Tradition of coal slurry treatment is completed by adding medicament of its mineral flocculation sedimentation. As a result of coal, slime water contains a lot of clay minerals that are rich in kaolinite versatility and it is difficult to settle. The flotation will be one of the kaolinite recycling. In this paper, clay minerals containing a variety of minerals were taken as samples, and sodium dodecyl sulfate and sodium oleate were used as collectors to explore the flotation effect through test and molecular dynamics simulation. A machine learning based intelligent decision support system is designed to improve the outputs of the simulation model. The results show that when the pH value is 8 and the amount of collector and sec-octanol are 150 g/t and 250 g/t, respectively, the flotation rate of fine mineral can reach 63.25%. According to the molecular dynamics simulation results, the addition of the collector can reduce the hydrophilicity of the kaolinite surface, and the physical adsorption of SDS only occurs on the (001) surface.