Freeze-drying modeling and monitoring using a new neuro-evolutive technique

This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too

Modelling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes

A numerical model based on artificial neural networks (ANN) was developed to simulate the dynamic behaviour of a three reactors network (or ring reactor), with periodic change of the feed position, when low-pressure methanol synthesis is carried out. A multilayer, feedforward, fully connected ANN was designed and the history stack adaptation algorithm was implemented and tested with quite good results both in terms of model identification and learning rates. The influence of the ANN parameters was addressed, leading to simple guidelines for the selection of their values. A detailed model was used to generate the patterns adopted for the learning and testing phases. The simplified model was finalised to develop a model predictive control scheme in order to maximise methanol yield and to fulfil process constraints