An experimental investigation and predictive modeling using machine learning technique for reclamation of metal values from scrap NdFeB magnets

Abstract

Neodymium-Iron-Boron (NdFeB) magnets are widely used in various industries due to their exceptional magnetic properties, such as high coercivity, remanence, and maximum energy product. These magnets consist of rare earth elements (REEs) viz., neodymium (Nd), praseodymium (Pr), and Dysprosium (Dy), along with other metals. With the ever increasing demand for REEs and the need to bridge the supply gap, it is crucial to develop alternative methods for their extraction. Recycling metal from scrap magnets is a promising approach to address the pressure on the supply chain and work towards sustainable development. The primary goal of this study is to generate a predictive model based on machine learning to determine the optimal conditions for metal recovery from scrap NdFeB magnets through water leaching after chloridizing roasting. Bench-scale leaching experiments were carried out to generate a dataset for statistical process optimization and machine learning analysis. The leaching kinetics of neodymium was also explored, and mixed-controlled shrinking core model was found to be most suitable, with an activation energy of 58.11 kJ/mol in the temperature range of 25– 95 °C. This study is the first to utilize a machine learning approach to analyze the potential process variables and their impact on metal recovery from calcined NdFeB magnet powder. A comparative analysis between experimental and machine learning approaches is presented to predict the optimal conditions for selective recovery of metal values from scrap NdFeB magnets. The maximum efficiency of extraction of metal ions was found to occur at a temperature of 95 °C, solid to liquid ratio of 125 g/l, and leaching duration of 60 min.