A knowledge based method for estimating the material content value of end of life PCBs

End-of-life printed circuit boards (PCBs) are a high-value but complex waste stream, particularly for small and medium-sized enterprises (SMEs) that lack access to cost-effective recovery technologies. Manual sorting remains the industry norm, hindered by the absence of scalable, automated tools for real-time characterization. This limits material recovery and reduces profitability. This study introduces a novel, knowledge-based method for PCB characterization that estimates the recoverable value of gold, silver, and copper, metals which account for approximately 90 % of a PCB's total material value, based on the number, size, and geometric features of surface-mounted components. While individual metal content estimations show a mean absolute percentage error of 23.3 %, the method correctly predicted the grade classification of all but one PCB types in a representative validation sample. This is the first approach to enable low-cost, inline estimation of PCB material grade using component-level visual analysis. By supporting automated, value-based sorting, the method offers a practical and scalable solution for SME recyclers, improving both economic viability and material recovery in electronic waste management.