Optimize Your Heat and Power Units Using Data Analytics 

Optimize your boiler and power generation with data analytics

Save 2 – 6% on Your Annual Fuel Bill Without Any Hardware Changes

Reduce energy cost and emissions of your power and heat generation plants without capital investment and permitting by using multivariate data analysis on your stored operational data.

Boilers have a lifetime of up to 50 years, turbines at least 12 years, depending on manufacturer. This means, most sites won’t be equipped with the latest, most efficient and environmentally-friendly equipment. Also, from a financial point of view it would not be viable to replace equipment in good condition with new and expensive units. 

Multivariate Data Analysis (MVDA) can greatly reduce fuel costs, improve efficiency and minimize emissions Every piece of equipment will have different operating variables which influence its production and pollution efficiency.  These can effectively and individually be determined with MVDA and subsequently used to optimize operations.

Typical benefits realized are:

  1. Fuel cost savings, typically in the range of 2 – 6% annually
  2. Reduced emissions, compliance with permits /environmental regulations
  3. Monitoring and controlling of operations and predicting emissions
  4. Increased equipment lifetime, 3 to 5 years is easily possible.
  5. The return on investment in MVDA can be in less than a month, without capital equipment expenditure.

The use of our solutions will also often lead to additional improvements beyond the above, as the following example shows.


Try our Boiler Benchmark

What potential optimizations could your data uncover?

Take a few minutes to complete this short questionnaire and uncover ways you can improve your boiler operations without capital expenditures.

Try the Boiler Benchmark 

boiler Benchmark Thumbnail


Case Study: Optimize Your Heat and Power Unit With the Help of Data Analytics

Cost, Reliability and Output Improvements in Boiler Operations

In this case study, we present the savings that can be made with the help of Data Analytics. 

Watch the customer testimonial video


Use Case: Michigan State University finds huge savings


  • MSU followed strict standard operating procedures, however generally looking for any possible economic and operational enhancements. 
  • Annual spend 24M USD in fuel; natural gas. 
  • Four steam boilers, one recovery steam generator. 
  • 5 steam turbines, one combustion turbine.  
  • Experience and seasoned operators, 15+ years on the job.  Many are approaching retirement which posts a challenge to the transfer of knowledge. 
  • Certificate allowing 10 ppm NOXachieved 8-9 ppm on rolling 24-hour average.

Fuel savings 1 070K-1570K USD annually:

  • Identified and maximized factors of significance with positive contribution and minimized those with negative contribution to the steam production (500K USD). 
  • Analyzed and improved feed-water heater efficiency (250K USD). 
  • Detected and exchanged feed water pump before actual failure (250K USD). 
  • Found turbine efficiency higher after weekend shutdowns, equivalent to washing cycle, decided to systematically shut down on weekends (70K USD). 
  • Evaluating practice of purchasing power off-peak to cover nights and weekends (potential 500K USD). 
  • Reduced emissions:

    • Discovered and corrected an air conditioner problem in the CEMS shelter, resulting in consistent 7ppm NOx emissions, a 12-22% reduction.  
    • Maintaining ignitor gas temperature constant and turning it off when not needed will reduce unburned fuel, NOx and CO2 emissions. 
    • Turned off the fuel chiller.  Warmer gas burns faster, reducing the NOx emission. 
    • Added a pressurized boiler feed water tank, as higher pressure and temperature of the feed water reduces N0x emissions.

Other improvements:

  • The routine weekend shutdown of turbine extends rotor life by at least 20% (specified lifetime 30 000 hrs). 


Boiler campaign analytics result

An analytical result: the score plot above summarizes all operational data from a boiler over a period of 2 years.

The coefficient plot of 1 point shows all variables positively contributing to operation (red), which are to  be maximized, and those negatively contributing (blue), which are to be minimized.

Get Case Story


Boiler Benchmark

What potential optimizations could your data uncover?

Take a few minutes to complete this short questionnaire and uncover ways you can improve your boiler operations —
without capital expenditures.

Try the Boiler Benchmark