Online Optimization of a Top-spray Fluidized Bed Granulation Process Based on a Three-stage Population Balance Model
نویسندگان
چکیده
A multi-scale three-stage population balance model (TSPBM) was developed in combination with the multivariate projection models to obtain the unknown kernel constants in population balance model (PBM), considering the batch and multi-stage characteristics of the top-spray fluidized bed granulation process. Partial least square (PLS) regression is adopted as the particle-scale modeling method in this work to describe the relationship between the manipulated operating variables and kernel constants used in PBM. Population balance model works as process-scale model describing the evolution of granule size distribution (GSD) according to kernel constants. By developing the relationship between the GSD and the manipulated operating variables, the developed multi-scale TSPBM is firstly established as a prerequisite for optimization strategy design. An online optimization strategy is proposed to improve the granule quality of top-spray fluidized bed granulation and to reduce the mismatch between the developed model and the actual system. By adjusting the granule growth trajectory on predefined sample intervals, the new optimal operating variables of pulse frequency, binder spray rate and atomization pressure are determined to ensure the granule size not deviating from desired trajectory. A differential evolution (DE) algorithm is used to solve the problem for online optimization problem and adjust the granulation operating variables. Experimental results and simulation tests are carried out to validate the effectiveness of the proposed TSPBM and the online optimization strategy. The developed TSPBM can accurately predict experimental GSD, which is carried out at randomly selected operating conditions. The proposed online optimization strategy can improve more than 50% prediction capability comparting with the offline optimization method.
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