Prediction of Wort Density with LSTM Network
Derk Rembold Bernd Stauss Stefan Schwarzkopf
Many physical target values in technical processes are error-prone,
cumbersome, or expensive to measure automatically. One example of a physical
target value is the wort density, which is an important value needed for beer
production. This article introduces a system that helps the brewer measure wort
density through sensors in order to reduce errors in manual data collection.
Instead of a direct measurement of wort density, a method is developed that
calculates the density from measured values acquired by inexpensive standard
sensors such as pressure or temperature. The model behind the calculation is a
neural network, known as LSTM.