Development of a bench system with capacitive sensor, sample compression, and TinyML for iron ore moisture measurement.

In the mineral sector, many processes use water for ore beneficiation processes. A lack of sensing or control of water content can lead to operational problems in various mineral processing operations, especially in ore transport. Current instrumentation systems are either slow or inaccurate. Therefore, a novel bench system was developed to address this gap by achieving a fast response time and improved accuracy. The developed instrument measures the ore moisture by using the real-dual-frequency method (RDFM) to assess the ore's electrical conductivity and relative permittivity. Additionally, it takes into account the bulk density, the bench chamber level, and the compress torque. All these variables are used to create a tiny machine-learning (TinyML) model that evaluates the ore's moisture with a low time response. This process is done while the ore sample is compressed to reduce air bubbles inside the samples and improve measurement. Experiments were performed using the bench system in a mining company's physical analysis laboratory. The instrument was utilized to measure the moisture content in the ore, leading to the development of a dataset used to train and validate various tree-based tinyML models. The results indicate that ore compression enhances accuracy and that decision trees are effective for estimating moisture with a quicker response time.