Industrial bioleaching processes often suffer from suboptimal yields and monitoring gaps due to the extreme acidity and corrosiveness of the environment. This article presents a lightweight, web-based digital twin (DT) framework for semi-industrial bioleaching optimization, in tegrating low-cost RGB sensing (transformed to hue, saturation, and value space for robustness), IoT connectivity, and support vector machine (SVM) regression within a Unity-WebGL platform. To ensure industrial-grade reliability, the predictive pipeline utilizes a group-based three way split strategy to eliminate data leakage and ensure generalization to unseen experimental batches. While traditional random-split approaches often yield overfit results,our SVM-based regressorachievesageneralized R2 =0.55,providing stable and physically consistent predictions of copper concentration. A targeted ablation study demonstrates that noncontact optical sensing independently outperforms traditional pH probes (R2 = 0.52 vs. R2 = 0.42),offering critical operational resilience in corrosive media(pH <2.0). Model transparency is further validated through residual diagnostics and response surface analysis, confirming homoscedastic behavior and chemical consistency. Furthermore, a pilot evaluation indicates that the DT enables 57.3% faster anomaly detection than legacy supervisory control and data acquisition systems, facilitating proactive intervention. The framework’s decoupled WebGL architecture ensures zero-install deployment, offering a scalable blueprint for real-time bioprocess monitoring in data-scarce industrial environments.
Puertas et al. (2026) studied this question.