Probabilistic methods for flotation circuit mass balance estimation

Abstract

Flotation control and supervision allow flotation circuits to maintain target concentrate grades and plant recoveries. The decision-making required for effective supervision of flotation control systems (e.g., selection of targets, setpoints, and setpoint limits) is enhanced by up-to-date information on total and component mass flows throughout the circuit. Limited instrumentation in flotation circuits typically yields a partial, noisy, and infrequently updated view of flotation conditions. Data reconciliation approaches have traditionally been used in conjunction with conservation balances to provide more consistent estimates of mass flows, although unmeasured and unobservable variables are either not fully characterized or at all. In this work, a probabilistic approach to mass balance estimation is presented, which can generalize different use cases (full redundancy, full observability, and partial observability with additional process assumptions) – using maximum a posteriori and marginal density estimation methods. This probabilistic approach is demonstrated on two case studies (a simple illustration and the Brunswick Mining flotation circuit).