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: There was already significant disruption to fossil-fuel power facilities from renewable energy, cheap gas costs, and ambitious decarbonization targets before the onset of COVID-19. This is shifting consumer preferences. Because of this, it is more important than ever for the power production business to utilize the most up-to-date digital and advanced analytics technologies.

In many cases, electric utilities began their digital transitions with technology solutions such as data models, which help optimize set points, improve dispatch decisions, and support maintenance plans and operating-mode selection. In the meantime, forward-thinking organizations have recently begun integrating visualization tools and digital control software to convey predicted data to control rooms in real-time. This next generation of power plants will be digitally enabled and rely on these advances to improve operational outcomes.

Data is one of an organization's most precious assets. And the first stages on any company's path are establishing a fact-based, data-driven culture and educating employees about how recent developments in analytics can translate data into actionable insights. The next generation of digital and sophisticated analytics tools has arisen with revolutionary technology such as artificial intelligence and machine learning. These approaches aim to go beyond the capabilities of typical multivariate regression analysis to find hidden patterns and complicated interdependencies.

For example, a next-generation power plant can employ machine learning to account for substantially more inputs, allowing for more exact modeling of essential plant operational functions than was previously allowed. A few years ago, performance optimization models based on thermodynamic models and OEM set points were deemed an appropriate method for improving a plant's heat rate—the amount of energy required to create one kilowatt-hour (kWh). Nowadays, machine learning is capable of optimizing heat.

The foundation for digital technologies has expanded at a breakneck pace.

Historically, power plants depended significantly on established legacy systems based on "first principles" engineering insights and problem-solving techniques, such as direct monitoring of temperature or pressure variations without the use of predictive or pattern-recognition algorithms. These initiatives necessitated the purchase of pricey systems that tracked just a limited set of data and depended on proprietary engineering knowledge to generate system alerts and operating bands.

Two tendencies illuminate the way forward. To begin, operators have better understood new technologies, processes, and instruments for data collection and storage. This has resulted in basic dashboards for monitoring plant-specific parameters such as high superheater temperature warnings or turbine overpressure.

Further, the availability of programmers and digital technology specialists has expanded while the cost of data processing has fallen. As a result, an increasing number of businesses now provide affordable data analytics solutions. Indeed, an examination of 40 businesses that offer solutions that are instantly relevant in power plants reveals a dramatic increase in the development of operations-oriented technologies and advanced mathematical models. Small software applications optimized for a single activity or supporting a consistent decision point have multiplied. Increasing specification levels have established a technology environment that enables power producers to embrace focused value-adding digitization.

These trends set the groundwork for today's digital transition. The combination of affordable processing power, abundant talent, and an enormous historical data store enable sophisticated analytics approaches and end-to-end digital solutions.