Steam Pipe Monitoring

Challenge

Lack of data

The lack of an overview of real-time temperatures in the steam network prevents the identification of unnecessarily high temperatures

Unnecessary energy consumption

Excessively high feed-in temperatures due to a lack of data, resulting in increased energy consumption and reduced power plant efficiency

Delayed adjustment

Manual evaluations prevent the timely optimization of burner settings

Solution

A digital twin of the steam networks enables the automated optimization of burner settings based on real-time sensor data.

Retrofitting

Conneqtive is equipping the steam networks with 10 to 15 high-temperature sensors each to monitor steam temperature

Automated data transfer

The live data is transmitted via Conneqtive LoRaWAN®

Visualization

A dashboard displays real-time temperature trends for all areas of the steam networks

Data-driven optimization

The data provides the basis for automated fine-tuning of the burner settings at the power plant

Outcome

• Feedwater temperature reduced by up to 20°C while maintaining steam quality
• Expected annual savings of €10,000 per °C reduction per network
• Annual savings in the seven-figure range are possible
• Expected reduction of 9,200 tons of CO₂ per year
• Further efficiency improvements in the future through machine learning