January 2022

Special Focus: Sustainability

Shrinking the carbon footprint: A digital transformation roadmap for green fuel producers

From ethanol and renewable diesel to biofuels and gas-to-liquids, the world is moving toward low-carbon energy sources to mitigate climate change and boost energy security.

From ethanol and renewable diesel to biofuels and gas-to-liquids, the world is moving toward low-carbon energy sources to mitigate climate change and boost energy security. These new sources of energy have their own challenges; the key to meeting those challenges lies in the ability to optimize processes and systems.

However, before any organization can optimize processes and systems, it first must recognize that data is a critical asset and, as such, requires proper management. Many green fuel companies already have existing or planned ecosystems of control and data systems across their operations based on tags. These tags contain sensor data such as temperature, flows and vibration. However, all this data is meaningless if it is not contextualized. In fact, a wealth and diversity of tags, without structure or context, becomes a roadblock to discovering valuable insights. A high quantity of data is only as useful as its quality allows. All the promises of big data and the digital transformation will remain out of reach if data is not structured and contextualized, which is not an easy task in traditional information technology (IT) data lakes.

Because consistency in calculations and operations data transformation is key to generating actionable intelligence from data, the most progressive companies are adopting operations data infrastructures that normalize disparate data sources and enable subject matter experts (SMEs) to add context and lower–level analytics. This generates maximum efficiencies and profitability in a competitive environment where subsidies and incentives will not always be available.

How operational intelligence drives optimization

An operational data infrastructure gives site operations SMEs the ability to configure performance dashboards, which SMEs can then use to make proactive, better informed decisions to keep plants running smoothly. Smarter decision-making means improved efficiency, reduced operations costs, reduced maintenances costs and fewer lost opportunities.

Digitally optimizing work processes also grants engineers access to previously inaccessible data, with exceptional accuracy and little-to-no delay. Even at a very basic level of implementation, digital transformation projects reduce operators’ rounds, increases situational awareness and allows operators to prevent and respond to abnormal operations and events. In turn, these improvements promote safety, operational performance and reduce carbon and greenhouse gas (GHG) emissions.

Custom dashboards provide end users better visibility into operations, which allows them to both improve their business relationships and increase their return from the digital value chain.

Carbon accounting: A new currency

Green fuels may be the solution to minimizing carbon emissions, but they also pose new challenges. By the old energy paradigm, the oil and gas industry focused single-mindedly on achieving the lowest production cost. Today, that focus is complicated by a second, competing priority to achieve the smallest carbon footprint.

The goal is not just to minimize GHGs at one refinery but across the entire supply chain. One of the biggest opportunities that an operational data infrastructure affords is the creation of a digital value chain—the ability to securely share operations data with key stakeholders across the supply chain to optimize efficiency and minimize carbon footprint.

For example, some green fuel companies are now sharing their operations data with catalyst providers, which run near-real-time modeling of production processes to identify possible improvements. In other cases, green hydrogen producers purchase electricity needed in the refining process from green vendors, such as wind and solar farms. These types of collaborations, which contribute significantly to overall net-zero goals, are only made possible by the accessibility and shareability of critical carbon accounting data.

In the same way that companies exchange dollars and euros, green fuel producers are now exchanging carbon credits as part of their daily operations. This ability is an increasingly important factor in risk management and investment decision-making. Exchanging carbon credits requires transparency, consistency and verifiability. As blockchain and similar technologies evolve, operations data infrastructures are becoming more essential in calculating those carbon credit values in such a way that they can be bought, sold and traded. An operations data infrastructure also produces insights that can help optimize enterprise-wide financial reporting.

Digital pitfalls to avoid

Some companies simply send all their data to data lakes, typically hosted by cloud vendors. By this imperfect strategy, the resulting data lacks context and consistency. Simultaneously, the volume, velocity and variability of data output in an era of increasingly smart devices can quickly become overwhelming. Just one data source—such as a wind turbine or a piece of refinery equipment—can generate tens of thousands of data points every few seconds. When concerns about cybersecurity and governance are added to the equation, it becomes clear that the data lake is a less-than-ideal solution. For significantly better results, businesses should consider a hybrid approach that pairs a purpose-designed operations data infrastructure—securely in a single-tenant cloud—with a cloud-based software as a service (SaaS) solution, so that data is accessible and shareable.

Another common pitfall to avoid is that many companies reach for Big Data applications before they have secured a strong data foundation. Digital twins, machine learning (ML) and artificial intelligence (AI) all reside in a layer of advanced analytics that can generate significant value but typically produce poor results if a robust analytical framework is not already in place.

Once a green fuel company has installed an operations data infrastructure, they should implement descriptive, diagnostic and simple predictive analytics. From there, operators can begin using prescriptive and adaptive analytics that incorporate ML and AI. Once green fuel companies reach this level of advanced analytics, it is important to funnel those results directly back into operations to enable even more optimization.

The following is an example of how that happens. A furnace is used for heating oil during the refining process. Operators work to optimize heat output, while minimizing carbon emissions. They start by modeling the furnace and applying lower level analytics to examine the correlations between pressure, feed and fuel composition, temperature, excess air, and oxygen content from analyzers. Once operators have a foundational understanding of the process, more advanced technology can be added that uses laser-based sensors to increase the accuracy of the oxygen and carbon monoxide content in the excess air. Finally, AI pulls all those factors together to calculate the changes necessary to further improve processes. That information is then sent back into the system so that operators can adjust in near real time.

These same principles can be applied to projected vs. actual results (i.e., projected GHGs vs. actual GHGs).

The key to implementation

Those who deal in operational technology (OT), which is designed to run refineries safely and optimally, and IT, which focuses more on streamlining back-office operations, often clash when it comes to digital investments. The IT department—which is expected to be the steward of technology across the company—typically applies a traditional IT mentality to operations, but IT departments rarely appreciate the nuances of operations data and systems.

A typical IT-style solution to data management might see all operations data dumped into a data lake. Operators will then apply AI to this reservoir of unstructured, decontextualized data. This is, in other words, a combination of two mistakes discussed earlier. Not only does this practice produce very little in the way of valuable insights, but it can also result in several lost opportunities.

Companies that excel at digital transformations have redefined this OT-IT relationship by installing a Chief Digital Officer or Chief Transformation Officer, with a deep operational background. By overseeing both OT and IT, a Chief Digital Officer has a unique vantage from which to normalize operations across both departments. Such an officer is also well-positioned to drive sustainable business change by leveraging new digital technologies.

No digital transformation initiative should be an all-or-nothing commitment. Until a company learns what works, the best advice is to start small, simple and strategic. The key is to start.

Companies should begin with a relatively bounded operational area—one that is a likely candidate for success. For example, companies might begin by increasing the runtime of heat exchangers before fouling, or they might compare several installations of a given asset type in search of outliers. Starting small helps green fuel companies apply their digital strategies appropriately and demonstrate business value clearly. To prove the success of the business case, companies will need to identify key performance indicators by which to measure and document conditions before and after. In a sense, this is using data to show the value of that data.

To remain competitive over the long term, green fuel producers must continually strive for operational excellence; they must have a business model that is viable even without subsidies; and they must embrace modern carbon accounting. A digital-enabled business transformation, underpinned by an operations data infrastructure, is the key to achieving these goals. HP

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