Automated Prediction of Mineral Liberation Behaviour in Chalcopyrite and Pyrite Using Python-based Image Analysis of Sem Polished Sections

Quantitative prediction of mineral liberation behaviour is critical for optimizing mineral beneficiation processes, directly influencing metal recovery efficiency, comminution energy utilization, and downstream separation performance[1]. This study presents a fully automated, Python-based image analysis framework designed to predict liberation characteristics from Scanning Electron Microscope (SEM) images of polished sections containing chalcopyrite (CuFeS₂) and pyrite (FeS₂)[2]. Unlike conventional approaches that require pre-liberated particle samples, this methodology evaluates grain exposure patterns and textural features to estimate potential liberation trends from in-situ mineral textures[3]. The developed computational pipeline addresses critical limitations of manual and semi-automated techniques—including time constraints, labor intensity, and operator bias—by implementing a reproducible, high-throughput analytical framework[4]. The workflow incorporates advanced image processing techniques, including Gaussian filtering for noise reduction, adaptive thresholding for contrast enhancement, and morphological operations to isolate discrete mineral grains[5]. Connected component analysis quantifies grain parameters including count, area distribution, and estimated liberation percentage based on exposed grain surface area[6]. The system supports batch processing capabilities, enabling consistent analysis across multiple samples with minimal user intervention[7]. Results demonstrate that chalcopyrite exhibits heterogeneous liberation behaviour (21.26% to 46.17% liberation efficiency) while pyrite shows more consistent performance (28.42% to 30.68%) [8]. This predictive framework contributes to digital mineralogy advancement by enhancing early-stage liberation assessments and supporting data-driven decision-making in mineral processing operations.