Operators in the processing and manufacturing industries constantly seek to improve efficiency and minimize energy use by optimizing existing operations. Now, many of these actions must also align with sustainability goals to reduce a facility’s carbon footprint.
Fortunately, it’s possible to seek decarbonization while continuing to reach traditional operations optimization targets. Operations can be analyzed for a production line, at the plant level and even throughout the value chain. This requires important digital capabilities but results in the ability for users to reduce emissions while improving margins.
Whether companies are seeking the optimal operating setpoints for a specific asset, the best running conditions for a plant, or other business opportunities, the answers can be non-intuitive and difficult to obtain using traditional methods. That’s why companies need the tools to implement data gathering, analytics and digital twins to drive optimizations that align business and decarbonization goals.
Road to digital decarbonization
Although existing operations are likely to be digitalized to some extent, the most common applications have been focused on automation and safety. Applying digital transformation for efficiency gains and decarbonization can build upon these existing efforts, but additional data and analysis is generally required.
A priority here is to establish a fundamental platform that can connect to all sources of information to provide immediate access to high-fidelity historical, real-time and predictive data. From this starting point, hierarchies can be applied that reflect operating and reporting environments with built-in analytics to support deeper analysis.
Once a solid strategy for gathering and sharing data is in place, simple calculations and basic analytics configured on the operational data management system can help workers to monitor key performance indicators (KPIs) and quickly identify areas of improvement to increase efficiency and reduce greenhouse gas (GHG) emissions.
An important aspect of any decarbonization strategy is to ensure maximum asset reliability and safety. Optimizing the asset maintenance strategy prevents excessive fuel consumption as well as accidents that could lead to undesired emissions. Using historical data, it is possible to apply machine learning (ML) and artificial intelligence (AI) techniques to train models capable of predicting abnormal asset behavior. Predictive analytics applications provide early warnings that enable the operations team to plan corrective actions and allocate the best possible resources for corrective actions. The most advanced predictive analytics solutions combine ML with rigorous modeling, increasing the accuracy and early-response of anomaly identification.
Another approach is to use advanced models to recommend adjustments to the control system set points or optimize the production planning strategy. Advanced models can handle several aspects such as operational assets, process behavior, resource consumption and economic factors.
The goal is to investigate various scenarios to determine the optimal operation of scopes 1, 2 and 3 emissions: minimizing operational CO2 emissions, maximizing clean energy use and better managing the value chain (see chart).
By building emissions into the modeling, a unified and carbon-aware strategy can be developed for running equipment, production plants, and the enterprise.
Implementing carbon-aware optimization
One of the world’s largest auto manufacturers chose to pursue green goals by reducing energy consumption at its production plants, thereby also reducing CO2 emissions. However, there was no standard for data monitoring capabilities and, in some cases, data was still being processed in a manual, paper-based method at the plants.
The team implemented a unified data model at each facility capable of connecting process and energy data to create a centralized energy monitoring system. With this data model they were able to easily build an asset framework for monitoring and contextualizing available data allowing them to analyze gas, electricity and compressed air use.
Through the combination of real-time data with reporting and visualization tools, the team can now quickly evaluate different electricity usage scenarios, and even reveal the impact of weather on operations to choose the right energy strategy and power source selection for reducing overall energy use.
In another case, a large international energy company wanted to use advanced cloud computing to better manage its supply chain—including provisions for tracking and managing CO2 emissions. They wanted the ability to quickly evaluate strategies for improving margins and reducing carbon emissions at company facilities worldwide.
Using a unified approach for production planning in the cloud, they were able to realize fast data access and use optimization models to simplify and improve supply chain management. They also reduced decision time from two days to less than two hours and rapidly deployed new CO2 modeling functions for applications across their plants worldwide.
Standardizing this structure enabled meaningful comparisons and shared insights among multiple facilities, supporting the company’s sustainability goals.
Decarbonizing the future
By following and extending the data-driven approaches mentioned here, companies can reduce their entire carbon footprint throughout the value chain. This can include monitoring and managing business aspects, such as carbon offsets, carbon credits and carbon negative options.
These examples demonstrate the ability of digital twins and associated technologies to help manufacturers improve both profitability and sustainability. In some cases, this will be achieved by tuning equipment and processes to reduce CO2 emissions or maximize clean energy use. In other cases, advanced analytics may identify specific facility upgrades or other changes to achieve the desired results. In all situations, a robust data management approach with easy information access and sharing is the most reliable way to reveal the best path forward.
Fernanda Martins is the industry marketing director at Aveva and a chemical engineer from the University of São Paulo. She has more than two decades of engineering and industrial software experience. Today her work supports Aveva´s strategy in developing industrial software to build a sustainable connected future.
