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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.06458 |
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| _version_ | 1866914709357199360 |
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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 |