Dec-2021

Shrinking the carbon footprint

A digital transformation roadmap for green fuel producers

Craig Harclerode
AVEVA

Article Summary

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 optimise processes and systems.

Before any organisation can optimise processes and systems, though, first it must recognise 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. All of this data, though, is meaningless if it isn’t contextualised. In fact, a wealth and diversity of tags, without structure or context, becomes itself 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 contextualised, which is not at all an easy task in traditional 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 normalise 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 won’t always be available.

How operational intelligence drives optimisation
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 day to day. Smarter decision-making means improved efficiency, reduced operations costs, reduced maintenances costs, and fewer lost opportunities.

Digitally optimising 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, increase situational awareness, and allow operators to prevent and respond to abnormal operations and events. These improvements, in turn, promote safety, operational performance, and reduce carbon and greenhouse gas emissions.

Custom dashboards give 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 minimising carbon emissions, but they pose new challenges, too. 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 isn’t just to minimise greenhouse gases 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 in order to optimise efficiency and minimise the carbon footprint.

For example, some green fuel companies are now sharing their operations data with catalyst providers, which run near-real-time modelling 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 and 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 optimise enterprise- wide financial reporting.

Digital pitfalls to avoid
Some companies simply send all of their data to data lakes, typically hosted by cloud vendors. By this imperfect strategy, the resulting data lacks context and consistency. At the same time, 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, for instance, or a piece of refinery equipment, can generate tens of thousands of data points every few seconds. When we add concerns about cybersecurity and governance 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.

Here’s another common pitfall to avoid: many companies reach for Big Data applications before they have secured a strong data foundation. Digital twins, machine learning, and AI all reside in a layer of advanced analytics that can generate significant value, but typically produce poor results if a robust analytical framework isn’t 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 start using prescriptive and adaptive analytics that incorporate machine learning and AI.

Once green fuel companies reach this level of advanced analytics, it’s important to funnel those results directly back into operations to enable even more optimisation.

Here’s an example of how that happens: take, for instance, a furnace used for heating oil during the refining process; operators work to optimise heat output while minimising carbon emissions. They start by modelling the furnace and applying lower- level analytics to examine the correlations between pressure, feed and fuel composition, temperature, excess air, and oxygen content from analysers. 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 CO 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 make adjustments in near real time.

We can apply these same principles to projected versus actual results (i.e., projected greenhouse gases versus actual greenhouse gases)