//Case Studies/Steam Pipe Monitoring

Case Study

Steam Pipe Monitoring

CURRENTA expects to save up to 1.6 million euros annually through the use of a digital twin for its steam networks

IndustriesChemistryPharmaceuticals
Related IoT SolutionsSteam Pipe Monitoring

Challenge

A lack of transparency regarding steam temperatures leads to inefficiency

Solution

A digital twin of the steam networks enables optimized burner control

Highlights

  • 8 Steam Networks
  • 45 sensors
  • Expected annual savings in the seven-figure range

Starting Point

CURRENTA operates eight steam networks with three different steam quality levels across the three CHEMPARK sites. Network-wide monitoring of steam temperatures is critical to efficiency and, consequently, energy costs. Until now, temperatures at the respective points in the steam network were recorded only manually and not consistently.

Challenged problems

  • 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
Retrofit LoRaWAN sensors on steam lines
Retrofit LoRaWAN sensors on steam lines
Digital twin of the steam network with temperature visualization
Digital twin of the steam network with temperature visualization
Visualization of optimization potential in the dashboard
Visualization of optimization potential in the dashboard

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

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Used Services

IT Infrastuctures

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