Initial Situation
CURRENTA operates eight steam networks across three CHEMPARK sites, with three different steam grades. Network-wide monitoring of steam temperatures is critical for efficiency and, consequently, energy costs. Previously, temperatures at the respective points in the steam network were only recorded manually and without continuous coverage.
Challenge
- Insufficient data availability — Lack of visibility into live temperatures across the steam network prevents the identification of unnecessarily elevated temperatures
- Unnecessary energy consumption — Excessively high feed-in temperatures due to insufficient data availability, resulting in increased energy demand and reduced power plant efficiency
- Delayed adjustment — Manual evaluations prevent timely optimization of burner settings
Solution
A digital twin of the steam networks enables automated optimization of burner settings based on live sensor data.
- Retrofitting — Conneqtive retrofits the steam networks with 10–15 high-temperature sensors each to capture steam temperatures
- Automated data transmission — Live data is transmitted via the Conneqtive LoRaWAN network
- Visualization — Temperature profiles across all sections of the steam networks are visualized in real time within a dashboard
- Data-driven optimization — The data provides the foundation for automated fine-tuning of burner settings at the power plant
Results
- Up to 20°C lower feed-in temperature at consistent steam quality
- €10,000 in annual expected savings per °C reduction per network
- Savings in the seven-figure range p.a. possible
- 9,200 tonnes of CO₂ per year in expected savings
- Further efficiency improvements in the future through machine learning
Text taken over from original and translated — Currenta Conneqtive – A Business Line of Currenta GmbH & Co. OHG
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