Copper is one of the most demanded minerals by the global industrial sector, with approximately 20 million tons mined worldwide each year. Silver, another important technical and precious metal, sees production around 26 kt/y. The general trend of declining average grades in these deposits has made mining low-grade ores a reality for many mines worldwide. The sensor-based sorting has emerged as a significant pre-concentration solution for these cases. This study investigates the applicability of this technique to copper ore samples from the Cerro do Andrade deposit, located in Caçapava do Sul, southern Brazil. The primary product of interest is copper (Cu), with silver (Ag) as a by-product. Pre-concentration tests are ongoing at the UFRGS Mineral Processing Laboratory (LAPROM) using a dual-energy X-ray transmission (DE-XRT) sensor sorter. Were analyzed 32 ore samples (64-16 mm size fraction). Relative density histograms and false-color images were generated. This data, along with Cu and Ag grades, was assessed in Excel to estimate recoveries (metallurgical and mass), concentration factors, and Cu and Ag grades in tailings fractions. Some scenarios of tailings generation and reuse were also explored. The analyzed samples had an average of 0.83% Cu and 7.31 g/t Ag. Pre-concentration simulations yielded Cu grades in the product ranging from 0.9% to 1.0% and Ag grades of 7.8 to 8.8 g/t in the Range A. Waste grades varied from 0.02-0.20% Cu and 0.7-2.2 g/t Ag. Range B exhibited more stable Cu and Ag grades in the product (around 0.9% Cu and 11 g/t Ag). Mass recoveries ranged from 92-77% in the Range A and reached 70% in the Range B. Metallurgical recoveries remained high: 99-95% Cu in the Range A and above 94% in the Range B. Silver recoveries were also promising (99-93% in Range A, 90% in Range B). Considering a feed of 1,000 kt/y, estimated ROM mass after pre-concentration ranged from 833-675 kt/y of product and 167-325 kt/y of coarse tailings. Currently, these preliminary results hold great promise, demonstrating the potential for achieving significant outcomes through the implementation of sensor-based sorting pre-concentration in the Andrade Project.