摘要：Motivated by the lack of hardware analysers for particle size distribution (PSD) and solute concentration measurements in industrial crystallizers, this work investigates the feasibility of designing alternative monitoring tools based on state observers. The observability and detectability properties of the discretized population balance equation accounting for crystal growth, attrition and agglomeration coupled with energy and solute mass balances are studied. A systematic methodology for sensor selection based on nonlinear observability and detectability principles is proposed and applied. Results are corroborated by a machine learning technique (the self-organizing map), leading to the fact that the solute concentration is distinguishable with temperature measurements, while the PSD is not. The results represent the starting point for future detector design where temperature measurements are used to infer composition, while the estimation of the PSD is done in "open loop" fashion.
