A novel data reconciliation and gross error detection tool for the mineralprocessing industry

This paper describes a data reconciliation and gross error detection softwa re (RACE) to convert raw data into useful plant information for performance analysis, process control and circuit optimization. The conventional data reconciliation tools used in mineral processing industries generally focus on solid and component balances. RAGE uses Successive Quadratic Programming for data reconciliation and Generalized Likelihood Ratio Test with serial compensation technique for gross error detection. The software not only foc uses on solid and component balances but also gives liquid balance across t he entire circuit. (C) 2001 Elsevier Science Ltd. All rights reserved.