Process observers and data reconciliation using mass and energy balance equations

Abstract

This chapter is devoted to data reconciliation for process audit, diagnosis, monitoring, modeling, advanced automatic control, and real-time optimization purposes. The emphasis is put on the constraints of mass and energy conservation, which are used as a foundation for measurement strategy design, measured value upgrading by measurement error filtering techniques, and unmeasured process variables estimation. Since the key variables in a mineral processing unit are usually flowrates and concentrations, their reconciliation with the laws of mass conservation is central to the discussed techniques. Tools are proposed for three different kinds of operating regimes: steady-state, stationary and dynamic. These reconciliation methods are based on the usual least squares and Kalman filtering techniques. Short examples involving grinding, flotation, leaching and thermal processes are presented to illustrate the problems of data reconciliation, sensor placement, fault detection and diagnosis. Strategies for coupling data reconciliation with real-time optimization and automatic control techniques are also proposed. A nomenclature section is included at the end of the chapter.