Implementing Automated Inspection Systems in Copper Tankhouses

This chapter delves into the transformative potential of automated inspection systems in copper tankhouses, focusing on the integration of AI and machine learning technologies. It begins by addressing the outdated process technologies prevalent in the mining industry and the pressing need for enhanced efficiency and quality control in copper refining. The chapter then explores how AI-driven vision systems learn to detect defects, comparing the process to teaching a child and detailing the use of deep convolutional neural networks (DCNNs) for defect classification. The system's configuration, including high-resolution cameras and lighting systems, is thoroughly described, along with its capacity to detect various defects such as nodules, edge strip damage, and surface contamination. The chapter also highlights the perceived benefits of the system, including increased throughput, improved accuracy and consistency, and significant operational savings. Early findings from the implementation at a client's facility are presented, demonstrating the system's impact on defect detection accuracy and operational efficiency. The chapter concludes by discussing the broader implications of AI and machine learning in sustainable refining practices and the future applications of such technologies in the industry.