The insights from this study can be leveraged to develop more sustainable and efficient recycling practices beyond ferrous scrap classification in the steel industry. One key application is in optimizing the recycling process by automating the sorting and separation of different types of scrap materials based on their characteristics. By integrating the classification models developed in this study into recycling facilities, the sorting process can be streamlined, leading to higher efficiency and reduced waste.
Furthermore, the explainability techniques used in the study can help identify areas for improvement in the recycling process, such as reducing contamination levels or enhancing material recovery rates. By understanding the key features that drive classification decisions, operators can make informed decisions to optimize recycling practices and minimize environmental impact.
Additionally, the conformal prediction approach can enhance the transparency and reliability of recycling practices by quantifying uncertainties in classification outcomes. This can build trust among stakeholders and ensure that recycling processes are conducted with accuracy and accountability, ultimately contributing to more sustainable and environmentally friendly operations in the steel industry.