Refractory residues classification strategy using emission spectroscopy of laser-induced plasmas in tandem with a...

Area of Science:

• Materials Science
• Analytical Chemistry
• Environmental Science

Background:

• Refractory scraps were historically landfilled, representing a significant waste.
• The circular economy and zero-waste initiatives are driving demand for refractory recycling.
• Effective waste classification is essential for sustainable refractory reuse and recycling.

Purpose of the Study:

• To develop and evaluate a classification strategy for refractory residues.
• To enable the productive cycling of refractory waste through improved resource management.
• To support the industrial implementation of refractory scrap recycling.

Main Methods:

• Combined laser-induced breakdown spectroscopy (LIBS) with a decision tree algorithm.
• Utilized LIBS emission intensities and ratios of key elements (Al, Mg, C, Si, Zr).
• Trained a decision tree model for qualitative analytical classification of refractory residues.

Main Results:

• Successfully categorized refractory residues into up to 10 distinct groups.
• Achieved a classification accuracy of up to 75% for refractory scraps.
• Demonstrated the potential for superior performance with optimized decision tree parameters.

Conclusions:

• The LIBS and decision tree strategy offers a viable approach for refractory residue classification.
• This method supports the integration of secondary raw materials into the refractory industry.
• The findings advocate for industrial implementation in waste management and end-user sectors.