MODELLING TOOL FOR GIVING VALUE TO AGRI-FOOD RESIDUAL STREAMS IN BIO-BASED INDUSTRIES

ended

Agri-food industry generates nearly 50% of the average composition of global waste in form of organic residual stream. Current management practices for waste management, including landfilling as the worst option, have to be improved to fulfil the targets of the EU. Agri-food residual streams are a potential substitute of fossil-based resources and have an important added value if used as feedstock in for the BBI, but present a high complexity and variables mixture of molecules, which makes difficult its valorisation. For this reason, the integration of a deeper knowledge on composition, logistics and volume of various organic streams could support the decision-making process to maximise profit from a specific feedstock and to select the most appropriate technologies for its optimal valorisation . The main project objective is to develop and validate at a relevant environment, the MODEL2BIO Decision-Support System tool. This will be an innovative concept that using predictive models, will estimate i) the agri-food industry residual streams composition (considering the seasonality, territorial features and origin); ii) the best recommended ways for valorising it. These recommendations will be made under a holistic perspective (technical, economic, environmental and social), with the prioritisation of the valorisation possibilities through technical and economic criteria and the final decision through holistic criteria. This innovative tool is based on the interconnection of three complementary elements: Simulation module, Optimisation algorithm and the LCA module. MODEL2BIO consortium consist on a multidisciplinary team of 11 partners from 5 different countries, covering the whole value chain and containing RTDs, universities, SMEs and clusters, and the active involvement of 27 companies providing residual streams, production process data and validating the MODEL2BIO tool. MODEL2BIO Budget is 5.970.976,16€ and request 4.730.393,8€ for funding.