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.
@article{arxiv.2403.06458,
title = {Prediction of Wort Density with LSTM Network},
author = {Derk Rembold and Bernd Stauss and Stefan Schwarzkopf},
journal= {arXiv preprint arXiv:2403.06458},
year = {2024}
}