Projects

AWAD: Agro-industrial Waste valorization through Machine-learning Accelerated Design

Enhancing agro-industrial waste with machine learning

Funding: ICSC Innovation Funds

Enabling technology: Artificial Intelligence, High Performance Computing, Machine Learning

Sustainable Development Goals

The AWAD project addresses the needs of waste management and sustainable materials, whose industrial use is often limited or precluded due to numerous compatibility issues. Through advanced HPC Machine Learning approaches, AWAD will design thermal stabilization strategies and customized biofiller selection and functionalization for targeted plastics, paving the way to commercial biocomposites with optimized properties.

National Centre for HPC, Big Data and Quantum Computing (ICSC), a project funded by the European Union – NextGenerationEU – and by Italy’s National Recovery and Resilience Plan (PNRR) – Mission 4, Component 2.

Objectives

The objectives are multiple:

Revealing the multi-scale degradation mechanisms of natural biofillers;
Developing Machine Learning-based regression models to predict the mechanical properties of final plastic biocomposites;
Rationally designing measures to minimize biofiller degradation;
Creating predictive models for the functional properties of agriculture-based biocomposites.

Initial challenge

AWAD’s main challenge is improving the agro-industrial waste management process. By designing thermal stabilization strategies and customizing biofiller selection and functionalization, the process can lead to commercial biocomposites with optimized properties.

Solution

The project aims to identify the degradation modes and mechanisms of natural biofillers. The results of this process will be used to create machine learning-based regression models for the mechanical properties of biofillers.

Benefits

AWAD will benefit agro-food industries by overcoming the limited industrial use of biofillers and creating processes to improve the thermal stability and functional properties of polymeric biocomposites. Machine learning models that link natural components to degradation and mechanical properties can be used in other research on materials science and engineering. Furthermore, the reuse and valorization of agro-industrial waste will result in the promotion of a more circular and sustainable economy.

IFAB’s role

IFAB participated in the project in the role of facilitator, working to create favorable conditions for its implementation. IFAB’s contribution was realized by activating connections between complementary stakeholders, opening channels for access to expertise and resources, and building the relational and operational context within which the project could take shape and develop.