Sustainability Catalyzes Future Automation

As refineries seek new ways to reduce CO2 emissions, they rely on a wide variety of tools that must be seamlessly interconnected to drive sustainability and efficiency in tandem. Courtesy Emerson

To tackle the challenge of increasing sustainability and efficiency in tandem, operators are turning to a silo-free vision for flexible, data-centric operations.

By Claudio Fayad, Emerson and Steve Williams, AspenTech

In the past decade, more companies have recognized the need to increase the sustainability of their organizations. As they have made changes, the lion’s share of the work has fallen on already overburdened operations teams. Simultaneously, the ever-present pressure for increased efficiency and performance loomed in the background. Operators were expected to continue to do even more with less, all while reducing their emissions, waste, and energy use.

As time has passed, increasing sustainability of operations has become a greater imperative. In some parts of the world, process manufacturers face steep fines for emissions overages. In others, national programs provide compelling financial incentives to ensure operations meet sustainability benchmarks. As a result, many companies are now required to submit sustainability metrics alongside financial reports, continuing to increase the pressure to perform. In response, plant personnel have been driven to operate their facilities in a different way.

Today, many operations teams realize they can optimize sustainability and profitability together, which results in inherent improvement in both areas. One approach is to use a Boundless Automation strategy that moves data seamlessly from the field, through the edge, and to the cloud to seamlessly integrate flexible and powerful modeling and optimization techniques.

Driven by data

For several decades, a range of technologies has been applied to drive reliability, production, quality, and/or supply-chain performance improvements. Typically, these improvements have been achieved in silos. The optimized, sustainable operations of the future will be built on powerful automation platforms with rich modeling and analytics, fed by thousands or even millions of data points. These platforms will feature an extensive range of autonomous workflows, closed-loop automation, and self-optimizing capabilities.

To develop the data-centric platform that enables self-optimizing operation, successful digital-transformation teams integrate by design when prioritizing technologies. Such solutions seamlessly and securely pass contextualized data to repositories, where it’s available to high-level analytics tools without the need for external manipulation or complex architectures for data transmission. By ensuring that easily consumable data is delivered to the right people and systems at the right time, and with full context, teams can unlock the higher-level automation that drives the more flexible operation necessary to simultaneously meet all their goals.

Old operations new

Refining is one industry where companies are tackling the challenge of improving sustainability and efficiency. Historically, many refineries planned for operational performance as their primary goal, but new regulations and incentives have made it essential to develop strategies to reduce emissions at the same time. Effectively improving operations to reduce CO₂ without affecting
production requires many layers of technology to work in tandem. Planning, optimization, and enterprise analytics tools must work side-by-side with scheduling software and advanced process control, all effectively factoring CO₂ into all their operational models in a consistent way.

Refinery hydrogen production processes were significant CO₂ generators. Now, there are new approaches to generating hydrogen with electrolyzers, fed by green energy. The teams developing these approaches must consider many variables to stay competitive while meeting sustainability goals. Today, some operators have realized that they can charge a premium for products if they take advantage of green hydrogen in production, or they can elect to sell their green hydrogen or excess green electricity at a premium.

Ultimately, there are far greater options than in the past. Finding the sweet spot of when and what to sell, and when to re-use, requires deep analysis that is only possible with reliable contextualized data from the control layer, combined with powerful models and optimization.

Agile plants

Process manufacturers are building more agile facilities designed to quickly scale production up or down, or even to manufacture different ranges of products to meet changing needs in the marketplace. Traditional plant design and operation often focused on closed-loop systems where the plant operated the same way—typically full throttle—most of the time, with exceptions for unusual activities such as startup and shutdown. Today’s operations commonly find they need to adjust production from 20% to 80%, and back again.

Consider a green hydrogen manufacturer with limited storage. If that manufacturer relies on solar and wind energy for production, it will want to maximize output when the sun shines and the wind blows. When weather conditions are poor, however, they may need to quickly ramp down to lower production. Simple control strategies struggle to maintain closed-loop performance over such a wide operating range, but these companies are not using simple control strategies.

Instead, they rely on advanced process control, tied into digital-twin simulation tools that, in turn, are seamlessly connected to forecasting tools to integrate process and environmental variables. Using such a configuration, the team can receive a new demand or incorporate a weather forecast, and then generate an optimal operational strategy based on those variables, followed by an update to the advanced control execution system to achieve these goals.

Tools supporting flexible operating strategies are also useful for traditional manufacturers as they navigate the new green-energy economy. Many plants will soon need to shift energy-use strategies between fossil and renewable supplies, based on availability and market price. As these needs increase, organizations will require the ability to consume and handle large amounts of external and internal data for analysis, modeling, and optimization. Implementing these technologies with a Boundless Automation vision in mind will enable these teams not only to break free from silos, but to also move the data as quickly as possible, and with as much context as possible, to analytics on the edge, and in the enterprise cloud. EP

Claudio Fayad is Vice President of Technology in the Process Systems and Solutions Business at Emerson, St. Louis (emerson.com).

Steve Williams is Vice President of Product Management, Portfolio Product Strategy, at AspenTech, Bedford, MA (aspentech.com). He previously led product management for manufacturing and supply chain products.