Dynamic primary-drying control

The main drawback of all these model-based algorithms is that they require that the model describes perfectly the dynamics of the process and that all the parameters and all the variables of the process are known. As this seldom happens, it is necessary to use one of the techniques described in Section 4.3 to monitor the primary drying in order to know the value of the temperature of the product, that is the controlled variable, and, thus, to calculate the control action. 

This paper presents the application of a model based predictive control strategy for the primary stage of the freeze drying process, which has not been tackled until now. A model predictive control framework is provided to minimize the sublimation time. The problem is directly addressed for the non linear distributed parameters system that describes the dynamic of the process. The mathematical model takes in account the main phenomena, including the heat and mass transfer in both the dried and frozen layers, and the moving sublimation front. The obtained results show the efficiency of the control software developed (MPC CB) under Matlab. The MPC( CB based on a modified levenberg-marquardt algorithm allows to control a continuous process in the open or closed loop and to find the optimal constrained control. 

H Sadikoglu. Dynamic modeling and optimal control of the primary and secondary drying stages of freeze drying of solutions in trays and vials. PhD dissertation. University of Missouri-Rolla, Rolla, MO, 1998. 

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