TEMPERATURE CONTROL IN FERMENTERS: APPLICATION OF NEURAL NETS AND FEEDBACK CONTROL IN BREWERIES

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Abstract
The main objective of on-line quality control in fermentation is to perform the production processes as reproducible as possible. Since temperature is the main control parameter in the fermentation process of beer breweries, it is of primary interest to keep it close to the predefined set point. Here, we report on a model-supported temperature controller for large production-scale beer fermenters. The dynamic response of the temperature in the tank on temperature changes in the cooling elements has been modeled by means of a difference equation. The heat production within the tank is taken into account by means of a model for the substrate degradation. Any optimization requires a model to predict the consequences of actions. Instead of using a conventional mathematical model of the fermentation kinetics, an artificial neural network approach has been used. The set point profiles for the temperature control have been dynamically optimized in order to minimize the production cost while meeting the constraints posed by the product quality requirements.

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