Real Time Optimization for Freeze Drying Process

Abstract

Freeze drying process has different applications including pharmaceutical products, foodstuffs (whose organoleptic properties are important and have to be maintained) and other industrial bioproducts (conservation of living microorganism, dehydration or concentration of heat labile). The interesting factors to be analyzed are the structural rigidity, which facilitates rapid and almost complete rehydration at a later time, little loss of flavor, and minimization of degradative reaction, which, normally, occurs in ordinary drying processes, such as protein denaturation, non enzymatic browning and enzymatic reactions. The process variable interactions are complex, and the experimental approach for examining the various operational policies is hard, expensive and time consuming. Multivariable process requires exhaustive simulation to understand the system behavior as well as to identify which are the most important process variables. This information are important for developing of optimization strategies. A theoretical model was developed and solved in order to describe quantitatively the dynamic behavior of the primary and secondary drying stages. The solution procedure involves the discretization of the equations by orthogonal collocation in the axial direction and the integration with respect to time by an algorithm based on the Runge-Kutta method. For the validation procedure the result of the solution model were compared with real values (pilot plant for skimmed milk and industrial plant for soluble coffee) to validity the proposal. The model allows carrying out numbers of simulation, so that it is possible to observe which are the design operationally variables that more significantly impact the system behavior. The objective of this work is, also, to obtain a mathematical model that can predict the experimental data to simulate the process to aim at high quality dried product with the minimum time process. The results show that the proposed model presents better prediction than the existing mathematical model since a more detailed description is provided. The next step was to definy the operational strategies and the implementation of optimization algorithms. For the freeze drying optimization process solution, the option was to use an algorithm based on the non linear programming routine developed by Schittkowski (1985) . This routine solves general problems of non linear programming and it was adapted quitelly well to the proposed mathematical model.The optimization of the freeze dryer process showed the real benefits by decreasing the process time and the amount of residual water.