Rapid Identification of Hazardous Heavy Metal-Containing Waste by Combining Edxrf with Machine Learning: Taking Zinc Smelting Waste as an Example

Abstract

To address the time-consuming identification and poor timeliness of environmental pollution tracing problems of traditional chemical detection methods for hazardous solid waste, a rapid identification and characterization methodology for hazardous waste containing heavy metals is established by using the EDXRF spectroscopy principle combined with an optical intelligent optimization algorithm. The results show that the spectral line signals corresponding to heavy metal elements such as Fe, Zn, Cu, Cd, and Ag can be used as important factors, and the optimized random forest model can be implemented to accurately identify and characterize nine typical types of hazardous waste containing heavy metals, and an accuracy rate of 100% is achieved. This method provides an efficient tool for the environmental management of hazardous waste and the rapid tracing of environmental risks in the future.

Keywords: EDXRF, Machine learning, hazardous waste, rapid identification, environmental monitoring and management