The vision of COCOP is that complex process-industry plants are optimally run by operators with the guidance of a coordinating, real-time optimisation system. COCOP will strengthen the global position of the European process industry, which represents 20 per cent of the European manufacturing base with around 450,000 companies generating €1.6 billion in turnover and 6.8 million jobs. The project’s objective is to enable plant-wide monitoring and control by using the model-based, predictive, coordinating optimisation concept in integration with plant’s automation systems. This ambitious approach will be developed and verified in co-operation of European universities, research institutes and industry. The Consortium comprises two universities, three research organisations, the leading copper-plant technology provider, two large companies from the process industry (steel and special chemicals) and four SMEs providing automation solutions. Technical objective is to define, design and implement a concept that integrates existing industrial control systems with efficient data management and optimisation methods and provides means to monitor and control large industrial production processes. The plant-wide monitoring and control comprehend computationally intensive data analysis and large scale optimisation. The social objective is to improve operator plant-wide awareness and reduce mental workload. COCOP will liaise with standardisation bodies (automation) to ensure a sustained impact of the project’s results. Commercialisation of the solution by its process-automation industry partners will allow plant operators to approach optimal production and result in reduced energy and resource consumption, and decreased on-site material handling time and greenhouse gas emissions.

Inno4Vac proposes an ambitious programme that will harness the latest advances in immunology, disease modelling, and modelling for tackling persistent scientific bottlenecks in vaccine development and for de-risking and accelerating this process. To reach this aim the project is divided into four interlinked subtopics. In Subtopic 1, artificial intelligence in combination with big data analysis and computational modelling will be used to build an open-access and cloud-based platform for in silico vaccine efficacy assessment and development. Subtopic 2 will develop new and improved controlled human infection models (CHIM) against influenza, RSV and C. difficile that will enable early vaccine efficacy evaluation. Subtopic 3 will contribute to the development of cell-based human in vitro 3D models that resemble the in vivo situation of an infection at the mucosa and more reliably predict immune protection. These models will be combined with the development of related functional immune assays for clinically relevant (surrogate) endpoints. Finally, Subtopic 4 will develop a modular one-stop computational platform for in silico modelling of vaccine bio-manufacturing and stability testing. In parallel to the scientific-technical work, the partners will develop strategies and roadmaps for positioning the newly developed models in the regulatory framework and integrating them into pharmaceutical vaccine development. The overall workplan is underpinned by horizontal activities on coordination/management and dissemination/communication, including data management and future sustainability. To achieve these ambitious objectives, Inno4Vacc has assembled a multidisciplinary consortium from academic and research institutions, industries, regulatory bodies, and vaccine R&D alliances. This unique partnership brings together clinical, immunological, microbiological, systems biology, mathematical models, and regulatory expertise and includes world-leaders in each respective field.