Reduce Emissions, Improve Efficiency and Identify Cost Savings

Michigan State University (MSU), one of the largest public research universities in the United States, operates its own large-scale power supply with multiple industrial steam boilers and turbines. 

Using Multivariate Data Analysis to create unique models, the university staff was able to uncover operational changes that could save more than $1 million USD a year in fuel costs and capital expenditures, while also gaining insights to extend the lifetime of equipment, reduce carbon emissions and support operator training.

With the help of data analytics MSU could save more than $1 million USD

The customer: Department of Power and Water, Michigan State University

The challenge: Reduce fuel costs and environmental emissions from power production, and improve the efficiency of operations

The solution: Develop multivariate models using data from CEMS, monitors, boilers, and turbines to identify key factors influencing fuel usage, NOX emissions, and equipment efficiency

The result: Uncovered surprising relationships between gas temperature, air temperature, pressure, on-off schedules, flame geometry, and other factors that affect NOX emissions, fuel usage, and equipment lifespan, with a potential savings of more than $1 million USD per year in fuel costs and equipment replacement

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