Develop adequate computational systems for modelling the design, start-up, scaling-up and continuous improvement of bioprocesses involving microorganisms Develop modelling systems that contain experimental multi-omics data on microbial responses to conditions in large fermenters and that combine know-how of metabolic networks and large-scale fluid dynamics into an integral, computation-driven framework to help in the design, scale-up and start-up of bioprocesses.The modelling approach should specify the ‘optimal’ use of the selected biomass – in terms of the environmental, economic and social sustainability of the value chain – and the resulting savings in cost and time during scaling-up and start-up. Proposals should therefore be developed in partnership with the operator of a (pre-)commercial-scale biorefinery or a pilot or demonstration plant, who should validate the results.To achieve a fair assessment, adequate metrics will be needed to compare the results of modelling from different perspectives. The models should also make connectivity to Industry 4.0 and The Internet of Things possible for future use in a complete value chain.Proposals should simulate a selected specific biomass feedstock and associated processing steps yielding targeted intermediary products.Proposals may include different processing routes for the selected feedstock to show how the developed models may be replicated, scaled up and used in different value chains. This experimental validation should also include a sensitivity analysis to assess the models’ ability to cope with disruptions and non-uniform reaction mixtures. The validation should also specify all included assumptions and should yield information to quantify sensitivity and uncertainties alike.The industry should actively participate to demonstrate the potential for integrating the developed concepts into current industrial landscapes or existing plants so that the concepts can be deployed more quickly and scaled up to apply industrial-wide.Proposals should specifically demonstrate the benefits versus the state-of-the-art and existing technologies. This could be done by providing evidence of new processing solutions and new products obtained.The technology readiness level (TRL)1 at the end of the project should be at least 3 for the bio-based value chain in question. Proposals should clearly state the starting TRL, which may be as low as 1 or 2.Proposals should seek complementarity with projects funded under Horizon 2020 to avoid overlap, promote synergies and advance beyond the state-of-the-art.Indicative funding:It is considered that proposals requesting a BBI JU contribution of between EUR 1 million and EUR 2,5 million would allow the specific challenge to be addressed appropriately. However, this does not preclude the submission and selection of proposals requesting other amounts.To be eligible for participation a consortium must contain at least one constituent entity of the Bio-based Industry Consortium not eligible for JU funding according to Commission Delegated Regulation (EU) No 623/2014.1 Technology readiness levels as defined in annex G of the General Annexes to the Horizon 2020 Work Programme: http://ec.europa.eu/research/participants/data/ref/h2020/other/wp/2018-2020/annexes/h2020-wp1820-annex-ga_en.pdf The state-of-the-art approach to designing, scaling up and starting up bioprocesses is governed by ‘trial and error’ and replicating traditional manufacturing methods. These methods often cause scaling-up losses and start-up delays or failures. There are many variables that have an impact on the design and scaling-up of bioprocesses, making this a very complex exercise. Among the major causes of these variables are:an increasingly wider range of biomass feedstock and their varied and heterogeneous composition; andrevolutionary developments in molecular biology producing more efficient microorganisms that can create a wider range of bio-products. Both developments demand reliable modelling systems to cope with many variables in simulating the full value chain, from feedstock to products, in search of the most effective combinations.The design phase should be long enough and have sufficient tools available to test different and radical concepts. And ultimately, in the scaled-up and (semi-)commercial operating phase, there should be guidelines for continuous improvement cycles.Today’s methods for scaling-up often take a more limited view and do not look at the bigger picture, so that optimisation takes place at lab level, not at plant operation level.Industry needs reliable modelling approaches, able to predict entire pathways from feedstock and energy intake to product output. This may mean designing tailor-made paths for each specific feedstock – from its intake and preparation, through the processing steps to the end-products.Recent developments in computation-driven frameworks can help cope with many variables in designing optimal feedstock-organisms-bioprocess configurations and simulating scaling-up. These computation approaches are already standard in fields other than microbial technology and industrial biotechnology.The specific challenge of this topic is to design and apply reliable and robust computational modelling approaches for bioprocesses. help shorten the time to market for bio-based products;help realise savings in large-scale implementation of bio-based value chains in time, costs, material and energy requirements, environmental impacts, etc.;help establish more efficient bioprocesses and a higher strain performance. Expected duration: 1 to 4 years.Type of action: Research and innovation action.The conditions related to this topic are provided in the chapter 2.3.3. of the BBI JU AWP 2018.