How Industrial DataOps Optimize Energy Management

By Oliver Haya, Business Manager, Rockwell Automation

Rising energy costs and increasing demand, the digitalization of energy networks, the need to optimize efficiency, and increasing regulations are pressure points (see Figure 1) creating greater emphasis on energy management. As a result, optimizing energy management is a major focus for manufacturers and producers.

Across manufacturing, energy is one of the fastest-growing production costs, making it vital to improve efficiency. With worldwide power consumption predicted to increase by 300% by 2050, a stable, well-managed energy supply will be key to optimizing critical functions. As manufacturers move toward “net zero,” energy management has become critical.

Improved awareness of energy usage is the foundation for any strategic energy management program. For manufacturers that want to reduce energy costs, improve their sustainability footprint and position themselves to meet the demands of a changing regulatory environment, a solid energy management program helps them understand where, when and how they use energy.

This, in turn, helps establish the necessary scope of energy savings efforts and define key metrics.

Structuring Data

Historically, the distributed and siloed nature of networks and data sources presented challenges to gathering energy data. This lack of connectivity limited an organization’s ability to pull energy performance metrics from specific industrial equipment because the data in question lacked operational technology (OT) context.

For example, important details include production runs, the quantity and types of goods produced, and the specific energy used for each asset. Without OT context, it’s common for manufacturers to get only a monthly report of a site's entire energy usage.

A comprehensive energy management strategy requires a holistic view of energy consumption. This involves looking at energy usage from five sources, known as WAGES — water, air, gas, electricity and steam. Each of these elements has a different carbon footprint and associated costs, so it’s important to first identify greenhouse gas (GHG) emission sources, which involves dividing them into three categories:

• Scope 1 emissions. These are direct emissions, caused by the manufacturer operating items it owns or controls. Most commonly, this is operating company vehicles and heating buildings — places where fuel is burned.
• Scope 2 emissions. These indirect emissions caused by upstream processes typically include all electricity used in a facility, materials used in products, and the energy used to create them.
• Scope 3 emissions. These downstream indirect emissions relate to how the product manufactured will be used during its lifespan. This is the hardest for manufacturers to measure. For example, a car will have a certain carbon footprint that went into making it, but how much carbon will that car burn over its lifespan?

Companies must cut emissions across all three scopes to meet internationally agreed-upon targets related to sustainability. Creating a benchmark of existing energy consumption is key: assessing the current carbon impact, extrapolating where less carbon can be emitted and determining the energy impacts.

In tandem, a significant challenge manufacturers face is that they usually receive a monthly roll-up of a utility bill, and then must sort which products are generating that energy consumption.

So, they must review a month of product A data and product B data to determine which is more energy-intensive to produce. However, manufacturing doesn’t work like that, commonly having multiple product lines running simultaneously.

To solve this, it’s necessary to examine data more granularly by looking at individual devices such as flow meters, electrical meters, power monitors, and intelligent connected devices that generate information about water, air, gas, electricity and steam.

Collecting data from individual devices across a network isn’t enough, however. The data must be contextualized in the wider concept of the manufacturing plant.

For example, which meters measured energy use for which products, and which products were being produced at what time? What happens when products are lost due to quality or operational issues? This presents a different challenge.

Power of Industrial DataOps

Of course, data is only as valuable as the analytics behind it, so a comprehensive energy management strategy requires connecting and integrating disparate sources so the correct data can be collected, analyzed, contextualized and leveraged by manufacturers. Industrial data operations (DataOps) focus on breaking down silos and optimizing the broad availability and usability of industrial data to drive improvement.

Complex industrial data requires context for broader organizational use. Industrial DataOps can provide a central, contextualized source of truth with automated data pipelines, offering industrial users a common and simplified way to discover, understand and analyze industrial data.

This contextualization is vital, because it not only uncovers and identifies relationships between elements of connected data, but also where the relationships aren’t explicitly represented.

So, while manufacturers might previously have only collected information at the enterprise or site level, the convergence of data and analytics within industrial DataOps allows data to be parsed at a more granular or product level.

Consider a product such as wheat crackers. Manufacturers can examine and contextualize production data to find the energy consumed to produce a batch of, say, 100,000 boxes. Imagine 1,000 boxes were lost due to quality or operations issues. With the level of detail now available, the manufacturer can derive not only the cost of energy for the overall batch, but also the lost product.

How DataOps Work

This insight is achieved by using existing data connections and asset-centric data relationships from OT and IT data sources. These feed into a production data platform, which provides industrial DataOps capabilities to add value to data.

This, in turn, produces industry solutions, including energy management, batch operations, asset intelligence and custom applications. With the data platform at its core, new applications can be built and scaled rapidly, speeding time-to-value.

To classify factors such as energy intensity — how much energy went into this batch of wheat crackers on this day — requires information such as the raw materials used, the production data, the recipe used, quality information, and the energy source.

Industrial DataOps’ contextualization helps manufacturers gather and dissect that data methodically to establish WAGES usage. It’s possible to go a step further and understand, for example, how much of the electricity used came from solar, wind, natural gas and so on.

Having this verified information helps manufacturers provide energy source and stamp a product with, “this product consumed X kilograms of carbon dioxide as part of its production.” This is becoming a closer reality from a regulatory perspective, where a product’s Scope 1 and Scope 2 emissions must be disclosed in the European Union.

An additional area of opportunity lies in looking at other factors that can influence the cost of production beyond WAGES usage. For example, the depth of data available through correctly optimized industrial DataOps can help determine:

• What’s the net out where electricity is cheaper at night, but is offset by higher labor prices on the “third shift?”
• If a manufacturer uses hybrid ovens with both electric and natural gas options, which one should be used at a certain time?
• How do ambient conditions in a plant impact product quality? Does operating the air conditioner at a lower temperature in summer impact electricity costs to the point it offsets any quality gain?

Data contextualization also can help improve assets such as batch analytics. In the food and beverage industry, such data could help identify whether running a batch at a lower temperature for longer or at a higher temperature for a shorter period will reduce overall energy consumption without sacrificing quality or asset utilization.

Roadmap to Energy Optimization

The route to energy optimization isn’t linear. There are no shortcuts in the journey from energy monitoring and reporting to energy management and optimization. Manufacturers can’t simply jump to autonomous control of their energy consumption. Figure 2 shows the roadmap to energy optimization as a four-phased journey divided into two over-arching elements: visualize and analyze, and optimize and control.