As the bioeconomy scales up from research to industrial reality, artificial intelligence is quietly reshaping how Europe designs and operates the biorefineries that are central to its success. From predictive optimisation to circular value-chain mapping, AI is becoming an operational necessity for the sustainable industries Europe needs to meet its climate goals. Two reports show how AI can and will continue to shape our bioeconomy future – one by Ana Arias and her colleagues at the University of Santiago de Compostela, writing in Environmental Technology and Innovation and the other by the Bio-based Industries Consortium (BIC) in its report AI as a Game Changer for the Bio-based Industries. Here we delve into them both to understand the opportunities and the challenges AI presents for the bioeconomy.
The first generation of biorefineries demonstrated that fossil carbon can be replaced; the next must show it can compete with all that fossil carbon can offer. That challenge lies at the heart of Europe’s bioeconomy transition – scaling up production while maintaining both environmental integrity and economic viability. According to Ana Arias and her colleagues at the University of Santiago de Compostela, writing in Environmental Technology and Innovation (2023), artificial intelligence could be the key to that transformation. By applying data-driven optimisation and predictive modelling to complex bio-based processes, AI can guide decisions that balance yield, cost, energy use and environmental performance in real time.
Arias described AI as a tool capable of turning conventional biorefineries into “smart biorefineries” – dynamic systems that learn and adapt rather than operate on fixed parameters. In her analysis, AI can identify the best operating conditions across hundreds of variables, improve product quality through precise monitoring, reduce waste and streamline logistics from feedstock to market. Coupled with Life Cycle Assessment (LCA), AI enables continuous evaluation of sustainability performance, closing the feedback loop between environmental data and industrial decision-making.
Yet Arias also cautioned against over-simplification. For AI to deliver meaningful sustainability gains, it depends on high-quality, well-structured data, robust ethical frameworks and awareness of its own energy footprint. “Smart” must not come at the expense of “sustainable”. Her paper argued for a balance between digital sophistication and social responsibility, where algorithms serve people and the planet’s wellbeing in equal measure.
From concept to industrial practice
If the 2023 study set the conceptual groundwork, the Bio-based Industries Consortium (BIC) has since shown how it looks in practice. Its 2024 report AI as a Game Changer for the Bio-based Industries captures a moment when artificial intelligence has started shifting from theory to implementation across Europe’s emerging bioeconomy.
In the Netherlands, the Process Design Centre (PDC) has created expert systems that optimise industrial process design – including pre-processing, fermentation and separation in bio-based production and chemical recycling. By comparing thousands of process configurations, the system identifies optimal reactor and separation setups in minutes. Founder Hans Keuken likens the tool to a medical diagnostic system: it asks questions, rules out poor options and narrows the field to the most efficient solutions. In nine out of ten cases, AI finds better configurations than those chosen by engineers relying on experience alone.
In Belgium, Ghent-based ML6 applies similar logic to biotechnology research. Working with partners in the Horizon Europe project DeCYPher, the company uses “active learning” AI models to predict the most promising genetic modifications and cultivation conditions for microbial production of terpenoids and flavonoids, compounds used in pharmaceuticals, fragrances and food additives. Instead of testing millions of bacterial strains in the laboratory, researchers now focus only on the few thousand that AI predicts will yield results. The combination of virtual and physical experimentation is cutting R&D time, costs and emissions while increasing discovery rates.
Further along the value chain, a project in Germany has developed its VCG.AI (R), which demonstrates how data intelligence can unlock new circular opportunities. Its BioLink(R) model uses large language and statistical algorithms to identify and match residual material streams with viable conversion technologies. By analysing techno-economic data, LCA datasets and regional resource maps, the system proposes profitable circular business models , transforming waste management into revenue generation. In one case, a medium-sized brewery turned an annual €200,000 disposal cost for spent yeast into potential profits exceeding €1.2 million through AI-driven valorisation scenarios.
These examples reveal a clear pattern: AI is making Europe’s bio-based industries more connected and more commercially competitive. It bridges the traditional gap between process engineering and systems thinking, enabling decision-making at both plant and regional scale.
Policy alignment
Europe’s forthcoming AI Act, part of the EU’s Digital Strategy, will soon provide the world’s first comprehensive regulatory framework for artificial intelligence. For bio-based industries, it will bring welcome clarity on standards for transparency, traceability and human oversight, all crucial factors as digital systems begin to influence resource use, product design and environmental compliance.
The integration of AI also aligns with the EU’s wider industrial goals: supporting the Green Deal, the Net-Zero Industry Act, and the Circular Bio-based Europe Joint Undertaking (CBE JU) in building a competitive, climate-neutral industrial base. CBE JU’s own communication echoes Arias’s early analysis: combining AI with LCA methodologies creates a powerful engine for sustainability, turning complex data into actionable intelligence.
However, the shift to data-driven biorefineries demands investment not only in technology but also in skills and interoperability. As BIC’s Marco Rupp observed during the consortium’s 2024 member webinar, smaller firms can already achieve significant efficiency gains with simple automation and off-the-shelf AI tools, but long-term competitiveness will depend on Europe’s ability to democratise access to data and digital expertise. SMEs, clusters and regional innovation ecosystems must all be equipped to participate, ensuring that AI strengthens industrial diversity rather than concentrating advantage.
Intelligent circularity
The next frontier is not merely optimising individual biorefineries, but connecting them into digital industrial symbiosis networks. Here, AI can map and match material, energy and data flows across entire regions, linking agriculture, energy, chemicals and waste management sectors. Combined with digital twins, predictive control and blockchain-based traceability, such networks could enable Europe to manage resources like a living system, adjusting production to market demand, feedstock availability and environmental thresholds in real time.
This vision may sound ambitious, but the building blocks are already visible. The convergence of AI, automation and the bioeconomy offers Europe a unique opportunity to rebuild industrial competitiveness around sustainability. The challenge now is to ensure that intelligence serves circularity, not simply adds more complexity. We need transparency to accompany every AI algorithm and that the benefits of smart manufacturing reach all sectors, large and small, in Europe’s growing bio-based community.
For Europe’s bioeconomy, AI provides the connections that will bring sustainability, resilience and competitiveness together. This will not only make biorefineries cleaner and more efficient; it will make them smarter and so help deliver on Europe’s most ambitious promise, a circular, climate-neutral industrial future.