Project description and overview
Mineral processing comminution circuits typically contain grinding mills. These are usually the largest single pieces of equipment in the plant, the largest consumers of power, they dictate the plant production rate and the utilisation and mineral liberation that bears directly on recovery and project economics. Many SAG mills have limited instrumentation and rely on a few simple PID control loops and a lot of operator experience. Many plants with advanced process control are either too constrained or have the APC turned off as the ore parameters have drifted outside the control constraints. As a result, most grinding circuits are operated sub optimally and inconsistently with the full potential of the asset underutilised and inefficient.
PIQ and OMC have entered into a Joint Venture contract arrangement (PIQ-OMC). Data streaming from clients’ processing plant historians will be set up to a secure cloud-based storage facility. Experts in different fields will filter, analyse and interpret the data to understand the status of the comminution circuit. A collaboration with Curtin University on advanced machine learning analytics will be used to develop a dynamic model of the comminution circuit to predict optimal set-points and operating parameters. Data-analytics and process modelling will be done on live data and will lead to automation of mill operation, reducing the dependence on skilled personnel on site. Seamless feedback as well as regular reporting will be implemented to various plant personnel to ensure the circuit is operating optimally.
The live and historical operational data collected can be used by other METS companies for a variety of purposes from remote training to demonstration of value proposition of new products. This could deliver major value to METS engineers, consultants, trainers, data scientists and product developers.
