Enhancing Operational Safety with Conformal Prediction in Soft Sensors

This study proposes a method to quantify uncertainty in soft sensors’ measurements for estimating ore mass flow rate on conveyor belts in mining. A linear regression model previously implemented in a PLC is extended with Conformal Prediction (CP) and a sliding window to generate adaptive prediction intervals. Residuals are updated incrementally for efficiency and adaptability. One-way ANOVA and Tukey’s HSD showed that window size significantly affects interval coverage and width. Larger windows (W100) yielded wider intervals (286.62 t/h) and higher coverage (95.3%), while smaller windows (W40) were narrower (243.97 t/h) with lower coverage (84.9%) but greater responsiveness. Processing time stayed under 0.1 seconds across all configurations, confirming suitability for real-time PLC use. The approach balances robustness and responsiveness, offering a lightweight, interpretable solution for uncertainty-aware control in industrial environments.