Akademik Veri Yönetim Sistemi
Journal of Sustainable Metallurgy, 2026 (SCI-Expanded, Scopus)
Hydrometallurgical copper production from chalcopyrite presents a significant challenge: the passive layer. This layer renders the dissolution behavior of chalcopyrite refractory, creating resistance to dissolution in acidic or alkaline media. To surmount this obstacle, various activation methods can be employed, with heat treatment being one of the most prominent for eliminating the passive surface of chalcopyrite. Depending on the applied temperature, heat treatment converts chalcopyrite into different mineral phases. Since the aim is to produce water-soluble phases, temperature monitoring and optimization are crucial. To achieve this, a novel methodology based on the optical properties of the heat-treated samples is proposed. In this context, chalcopyrite concentrates were subjected to heat treatment for 3 h at 10 different temperatures varying between 300 °C and 700 °C, in 50 °C increments. The resulting mineralogical transformations were subsequently determined by XRD analysis and quantified by their respective peak intensities. Image analysis was conducted to evaluate the relationship between mineralogical composition and visual appearance. Images of heat-treated samples were collected through an image acquisition session. The acquired images were converted into numerical data using image analysis and subsequently characterized as chromaticity. When the relationship between chromaticity and XRD results was examined, the following was observed: (I) Hematite peak intensity values were highly correlated with red chromaticity (r = 0.65 − 0.98). (II) Copper sulfate peak intensity and red chromaticity exhibited a very strong linear relationship (r = 0.94 − 0.95). (III) Combination of these results demonstrated, both numerically and statistically, that hematite had a catalytic effect on copper sulfate. (IV) Using the obtained correlation coefficients, the relationship with the intensity values for hematite and copper sulfate was successfully modeled (R2 = 0.71 − 0.90). Overall, the results show that the mineralogical change can be efficiently tracked and monitored by image analysis.