As industry 4.0 technology advances, it is possible to leverage existing data to construct machine-learning solutions that provide actual value, improve decision-making, boost flexibility, and attract top talent.
FREMONT, CA: Even before COVID-19, renewable energy sources, low gas prices, and ambitious decarburization objectives were disrupting fossil-fuel power plants, shifting customer choices. It is now vital for the power generation business to use the latest digital and sophisticated analytics technology.
Many industries started their digital transformations with data models, which help optimize set points, dispatch decisions, maintenance methods, and operating mode selection. Forward-thinking firms now use visualization tools to monitor real-time generation and digital control software to send forecasts to control rooms. These advancements, however, are only part of a digitally enabled, next-generation power plant.
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Data is a company's most important asset. Building a fact-based, data-driven culture is the first step for any firm to learn how new analytics can turn data into meaningful insights. Digital and advanced analytics tools have evolved to keep pace with new technologies like artificial intelligence and machine learning. These methods aim to identify hidden patterns and intricate interdependencies beyond typical multivariate regression analysis.
For instance, a next-generation power plant can use machine learning to model essential plant operational processes more precisely than anticipated. Previously, maximizing a plant's heat rate—the amount of energy required to create one kilowatt-hour—was thought to be feasible using thermodynamic models and OEM set points (kWh). Machine learning can now optimize heat rates more efficiently and in less time.
Power plants must enhance their unit efficiency and operational resilience during the next decade to remain competitive in the global energy supply.
The foundation for digital technologies has expanded at a breakneck pace.
Previous generation power plants used legacy systems that depended on "first principles" engineering insights and problem-solving methodologies, such as direct temperature or pressure monitoring without predictive or pattern-recognition algorithms. These applications required expensive systems that tracked only a few types of data to offer system alerts and operating bands.
Two trends point to it:
First, operators are more familiar with new data collection technologies, methodologies, and tools. This led to basic dashboards to track plant-specific metrics like high superheater temperature or turbine overpressure.
Second, a drop in data processing costs has expanded the availability of competent programmers and digital employees. As a result, more organizations now provide low-cost data analytics. An analysis of 40 companies supplying solutions for power plants showed a dramatic increase in operations-oriented technology and advanced mathematical models. Small software programs that optimize certain operations or support a constant decision point have grown, creating a technology ecosystem that allows power generators to embrace focused, value-adding digitalization.
These trends paved the way for today's digital revolution. New advanced analytics methodologies and end-to-end digital tools can be built on cheap processing power, abundant talent, and an extensive historical data store.
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