Improving Soft Sensor Reliability in the Mining Industry Using Incremental Learning

This work investigates the structure and potential of incremental learning methods to improve the mining industry by enhancing long-term soft sensor reliability, despite the frequent and dynamic changes in operating conditions, typical of this sector. Several incremental learning methods were evaluated using operational data from a case study involving mass flow rate estimation on a crushing circuit. Results showed that these models achieved better long-term accuracy; while requiring a computational cost adequate for industrial application, supporting their suitability for real-world deployment in mining environments.