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Bibliographic Details
Main Authors: Rembold, Derk, Stauss, Bernd, Schwarzkopf, Stefan
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.06458
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author Rembold, Derk
Stauss, Bernd
Schwarzkopf, Stefan
author_facet Rembold, Derk
Stauss, Bernd
Schwarzkopf, Stefan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_06458
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Prediction of Wort Density with LSTM Network
Rembold, Derk
Stauss, Bernd
Schwarzkopf, Stefan
Machine Learning
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.
title Prediction of Wort Density with LSTM Network
topic Machine Learning
url https://arxiv.org/abs/2403.06458