Harnessing the power of emerging technologies is crucial for achieving long-term sustainability goals in the process industry. This is why, at the core of TRINEFLEX's development phase, lies the concept of digital retrofitting as a pivotal initial step in the project.


The term digital twins encompasses virtual replicas of physical assets or processes, providing a platform for simulations and modeling. TRINEFLEX's asset digitalization task facilitates the integration of energy-flexible technologies into these digital twins, allowing for virtual simulations that optimize performance and evaluate different scenarios. Importantly, changes and upgrades can be strategically planned within the digital twin before implementing them in physical assets. Furthermore, digital twins empower stakeholders in TRINEFLEX to actively engage in reducing energy and CO2 emissions. They can generate individualized technological configurations, test various scenarios, and predict the impacts of energy recovery and new technologies before implementation, all through the digital twin interface.
Digital twinning, however, would not be the valid strategy it is without being paired with digital retrofitting and big data.


Digital retrofitting involves upgrading legacy machines with sensors and integrating them into an IoT network. The process facilitates data acquisition and transmission in a digitalized format, laying the groundwork for the creation of digital twins — a concept central to the project's objectives but not the only one employed.

In the broader context of industrial revolutions and the emergence of what we can call Industry 5.0, the need for digital retrofitting becomes evident. Traditional machines, some dating back 30 years, still perform admirably but lack connectivity and data storage capabilities. Digital retrofitting addresses this by upgrading existing machinery into digitalized devices (without replacement). Once digitalized, these machines contribute to a centralized data repository, setting the stage for advanced analytics, machine learning, and model development. The resulting insights facilitate improved decision-making, reduced energy usage, and a deeper understanding of industrial processes.

In this era, the accumulation of Big Data plays a pivotal role in monitoring and optimizing production processes. Sensors, machinery configurations, and business-related systems contribute to the vast pool of data, with its scale directly tied to the level of digitalization in an industrial plant. The TRINEFLEX project acknowledges this reality and aligns its goals with the European Union's vision of achieving "zero net emissions" by 2050.

By combining digital retrofitting and Big Data integration, the project employs state-of-the-art Big Data technologies, interfacing data infrastructures and storage solutions to construct digital twins that act as decision support systems. These digital replicas aid in tackling key performance indicators such as energy demand, emissions, and costs.

In conclusion, our project wishes to epitomize the symbiotic relationship between digital retrofitting and Big Data integration, demonstrating their collective potential to shape the future of sustainable process industries. As industries embrace these advancements, they pave the way for smarter, more efficient, and environmentally conscious practices.