The development of digital tools, namely digital twin, plays an important role in BioFibreLoop. We interviewed Victoria Caballero, our digital twin developer from our partner IDENER.
Set-up of a digital twin. ©IDENER
What is a digital twin and why is it useful for BioFibreLoop?
“A digital twin is a virtual replica of a physical process (in this case the BioFibreLoop process) that uses real-time data to simulate and predict outcomes. In this project, the digital twin focuses on processes like functionalisation of textiles, including coating, laser engraving, and embossing of bio-based fibre materials.
The digital twin continuously collects real-time data from the process, such as temperature, pressure, material composition, or laser intensity, and feeds this data into physics-based and machine learning (ML)-optimised models. These models simulate the dynamic behaviour of materials and processes, enabling prediction, monitoring, and optimisation of process performance in near real time It then delivers output back to the process line, helping to fine-tune parameters and optimise results in real-time. For example, changes in laser intensity or temperature can be used to predict how material properties like viscosity or thermal conductivity evolve during processing, allowing proactive adjustments to maintain product quality.
This is very important for the BioFibreLoop project given that it reduces the need for costly and time-consuming physical experiments. Instead of trial-and-error on the factory floor, the digital twin allows researchers and engineers to test different conditions virtually. This enables faster innovation, better resource efficiency, and metrics-based decision making. Ultimately, it supports more sustainable and high-performance bio-based materials by making the functionalisation process smarter, more predictable, and more precise.”
Once the digital twin has been constructed and finalised in BioFibreLoop, how will it actually look like?
“In BioFibreLoop, the digital twin will be a connected, intelligent system built on three main pillars:
So, the final digital twin in BioFibreLoop will be a dynamic interface, not just a screen with numbers, but an interactive platform that links the physical and digital worlds. It will empower users to explore “what-if” scenarios, make data-driven decisions, and accelerate development of sustainable, high-performance bio-based materials.”
How do you develop the digital twin for the BioFibreLoop processes?
“Building a digital twin for this project consists of several steps that will be developed over the BioFibreLoop project:
Identification of operational parameters
The first step is to define the key variables that affect the functionalisation processes—such as temperature, speed, material properties, laser intensity, and more. These parameters form the foundation of the digital twin design.
Design of the Digital Twin baseline
With parameters identified by the processes experts that are part of the BioFibreLoop project, the baseline model of the digital twin is created. This includes collecting pre-existing data, thinking about and coming up with the design and the architecture of the twin based on the real-life process, and integrating it with the physical systems (by using sensors).
Construction of the Digital Twin
At this stage, virtual models available in the scientific literature are integrated to build a tool that can replicate the real-world processes. These are linked with the information processing layer that enables real-time data flow between physical and virtual environments.
Optimisation with machine learning and data-driven models
The digital twin becomes smarter by learning from data. Using machine learning algorithms and database-driven models, the tool will be able to predict outcomes, adjust process settings, and support continuous improvement.
Simulation and Validation
Finally, the twin is tested through simulations and compared against real-life results. This ensures that it accurately reflects the physical system and can be trusted for decision-making and process optimisation.”
What you’ve done since project start was to identify the operational parameters. How did you do that exactly? Which challenges did you encounter? How do you develop the digital twin for the BioFibreLoop processes?
Since the start of the digital twin building, one of our focus areas has been the identification of operational parameters, i.e. the crucial variables that influence how the bio-based fibre functionalisation processes (coating, laser engraving and embossing) actually work, both in lab scale and also further upscaled, taking into account what industrial stakeholders work with the most.
To do this, we’ve collaborated closely with technical partners who are experts in these processes. Together, we reviewed each step of the process and identified the parameters that will be essential for accurate modelling. These include factors such as temperature, pressure, line speed, laser power, and material characteristics.
Importantly, we didn’t only focus on what affects the process efficiency, but we also considered how the parameters will support simulation of scenarios relevant to LCA (Life Cycle Assessment) and final product quality. This ensures the digital twin can serve both operational optimisation and sustainability assessment.
However, this stage came with its own set of challenges. Since the full-scale production processes are still being developed in parallel with the digital twin, there are elements that can only be tested and validated once experimental runs are carried out. For now, we’re relying on the best available technical knowledge from all partners to make informed decisions about which parameters to prioritise.
What are now the next steps for the construction of the digital twin?
“Now that the key operational parameters have been identified, the next phase in building the BioFibreLoop digital twin is all about integration and optimisation.
The immediate step is to bring together the models that have already been developed for individual unit operations (such as coating, laser engraving, and embossing). These models will be integrated into a single, unified framework that represents the full functionalisation process. The aim is to create a comprehensive digital twin that reflects the entire system and its interdependencies, based on the production parameters we’ve identified.
Once the integrated model is in place, the focus will shift to optimising it specifically for the BioFibreLoop processes. This will be done using machine learning techniques, which allow the model to learn from real process data, predict outcomes, and suggest process improvements. This makes the twin not just a static model, but a smart, adaptable tool.
After optimisation, the model will undergo validation through comparison with experimental data from the actual production line. This ensures that the digital twin accurately represents real-world behaviour and can be trusted for simulations and decision-making.
Finally, to make the digital twin practical and user-friendly, a dedicated user interface will be developed. This will allow project partners and future users to interact with the model easily by testing scenarios, exploring outcomes, and using it to support both innovation and production.”
What will the impact of the development of the digital twin beyond the consortium be?
“The digital twin developed within BioFibreLoop has the potential to create impact far beyond the project consortium. BioFibreLoop will actually provide a simplified, open-source version of the Digital Twin, an Open Virtual Replication tool. This will allow the textile sector to replicate the BioFibreLoop process.
Furthermore, the impact might go beyond textile industry as several industries increasingly look for smarter, more sustainable production tools.
By creating a virtual replica of bio-based material functionalisation processes, the digital twin provides a scalable, adaptable solution that can be applied to other manufacturing lines, materials, and industrial settings even above the specific use cases of the project. This opens opportunities for other companies to reduce resource use, lower costs, and accelerate innovation without relying on extensive physical trials.
Importantly, the digital twin will also enable better integration of sustainability metrics, like those used in Life Cycle Assessment (LCA), into early design and process decisions. This supports more informed, metrics-based choices for product development and environmental performance across sectors working with bio-based or circular materials. In the long term, the BioFibreLoop digital twin could serve as a reference model for digitalisation in sustainable manufacturing, in accordance with the industry digitalization agenda.”
Victoria Caballero. ©IDENER
Who are you and what is your role in the project?
“I am a chemical engineer who was born in northern Argentina. After finishing my studies in engineering in Córdoba, Argentina, I moved to Dublin, Ireland to pursue my PhD. There, I worked on mathematical modelling of microorganisms in food to predict and assess food safety and shelf life. In the BioFibreLoop project, I am coordinating WP2 along my team in IDENER.AI, building the mathematical models and setting them to successfully represent the BioFibreLoop processes.“
What has been/will be your personal highlight in the project?
“I think it is interesting translating real-life, isolated functionalisation steps into mathematical models. It is very interesting to take a process governed by physics, like coating or laser engraving, and express its behaviour in a mathematical language that a computer can understand and work with.
This modelling work is not just theoretical; it’s what allows us to connect all the unit operations into a coherent, integrated digital twin. Once we have this shared mathematical foundation, we can use it to simulate, test, and ultimately optimise the entire process in a way that’s practical and impactful.
Seeing how this translation from the real world to the virtual one can drive smarter, more sustainable decisions is definitely a highlight and it’s what makes the digital twin more than just a model, but a real enabler of innovation.“
Can you share any funny or unexpected moments or learnings that you experienced while your worked on this project?
“One thing that that raised some eyebrows and brought a few laughs in our last consortium meeting was when my consortium peers discovered how I organize my personal leisure activities. Let’s just say I manage my weekends and holidays with the same enthusiasm and structure as I do EU projects: Using Excel sheets, Gantt charts, and even the occasional few codes to access things easily. Apparently, not everyone plans their hikes or dinner parties with task trackers and milestones! It sparked some fun conversations about how project management mindsets spill over into everyday life and how maybe, just maybe, there’s a hidden efficiency expert in all of us.“
What type of BioFibreLoop clothing (active wear, outdoor wear, work wear) are you personally most excited about?
“I am especially excited about the potential for outdoor wear. One of my favourite activities is hiking, but I’m constantly dealing with torn trousers from getting snagged on branches or rough terrain. So, the idea of having durable, high-performance outdoor clothing made from sustainable bio-based fibres really appeals to me, especially if it can hold up to the challenges of real trails!
I’m also a fan of running, so I’m looking forward to seeing functional textiles produced in Europe in a way that’s both innovative and environmentally responsible. It’s incredibly motivating to know that through BioFibreLoop, we’re helping make that a reality. Being part of a project that supports both sustainability and local production is something I’m genuinely proud of.
“
Project Coordinator
Thomas Fischer,
Deutsche Institute für Textil- und Faserforschung Denkendorf (DITF)
thomas.fischer@ditf.de
Technical Coordinator
Thomas Stegmaier,
Deutsche Institute für Textil- und Faserforschung Denkendorf (DITF)
thomas.stegmaier@ditf.de
Funded by the European Union Horizon Europe Research and Innovation Program under Grant Agreement No. 101130603. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor HADEA can be held responsible for them.
