Implementing Automated Inspection Systems in Copper Tankhouses
摘要
As global demandDemand for copperCopper rises, the refiningRefining industry faces pressure to improve efficiencyEfficiency, quality, and profitability. Traditional manual inspectionsInspection in copper tankhousesCopper tankhouse are labor-intensive and error-prone. This work presents an AIArtificial Intelligence (AI)-driven vision systemVision systems with machine learningMachine learning for real-time defect detectionDefect detection, quality controlQuality control, and process optimizationProcess optimization in copper tankhousesCopper tankhouse. Implemented at an operational facility, the system uses high-resolution cameras and deep learning to detect defects, showing early improvements in accuracy and efficiencyEfficiency. This case study aligns with CopperCopper 2025 themes of “Process ControlProcess control & OptimizationOptimization” and “ElectrowinningElectrowinning & ElectrorefiningElectrorefining,” showcasing AIArtificial Intelligence (AI)’s role in advancing copperCopper refiningRefining.