A Neural Network Tool for Brewery Fermentations
Fermentation is an important, if not the most impor tant process step in the production of beer. It is subject to alterations stemming from the variation in the yeas t, living organism, and due to the complex raw ma teri ls of biological origin. The variability may manifest in changes in the fermentation speed or cause off-flav ors in beer. Thus, there is a need for tools, both analytical an d computational, to help in monitoring the process and keeping it in a desired course. In this paper we describe a prediction tool to assist production management in the brewery. The system relies on a neural network that predicts the course of the fermentation based on yeast hist ory, fermentation recipe and raw material variables. Th e system is able to predict the fermentation time w ith an average error of 10 percent of the remaining fermen tation time, which enables the brewery to spot prob lematic batches days in advance and plan the correct yeast harvesting time with an increased accuracy. The sys tem has been in daily trial use in Hartwall’s brewery in La hti since March 2000 and the user experiences have been positive.