Nonlinear Moving Horizon Estimator for Online Estimation of the Density and Viscosity of a Mineral Slurry

Abstract

This paper proposes a moving horizon estimator for nonlinear systems with unknown inputs, which do not comply with the model structures proposed in the literature for the design of nonlinear observers. The estimator is designed as an optimization problem over a moving horizon, constrained to process model equations and considering the unknown inputs as random inputs among their operating bounds. This proposal is applied to the transport of mineral slurries among process units, typically present in chemical and biological processes. There, to have the slurry properties as on-line measurements is vital to an efficient control of those processing units. The performance of proposed estimator is evaluated by simulation with data from a real processing plant, and its performance is compared with a linear estimator executing the same estimation task. Better results are obtained using the proposed estimator by considering the nonlinearities of the process.

Inverse identification of viscosity coefficient for Newtonian and non‐Newtonian slurries during the turbulent pipeline transportation

“…For example, to avoid the problem of port siltation, the fluid properties need to be monitored in the process of the reservoir dredging . In chemical engineering, the concentration of slurry must be maintained in a proper range to ensure stable supply and high reaction efficiency . When slurries comprised very fine particles, the presence of these particles can have a significant effect on the mixture properties, usually resulting in a sharp increase in the mixture viscosity .…”