The process industry is continuously looking for new ways to improve resource efficiency due to high dependence on resources (energy, raw materials and utilities). In large scale production even small changes in using raw materials and in energy can significantly improve process efficiency. The MORSE approach is to adopt new software tools for model-based predictive control, multi-criterial through process optimisation and quality management with overall process coordination. The application of these new software tools will lead to process improvements - reducing the use of raw material and energy while increasing the high quality and production rates. The Morse project aims to further develop and to integrate a set of software tools that have partly already been validated in different process steps in steel industries. These software prototype tools and models were developed and evaluated by six R&D partners of the consortium in collaboration with three process industry partners. With the enhanced Morse tools companies of the process industry will be enabled to optimise the use of raw materials and energy by coordinated prediction and control of resource input and product quality along the entire process route from raw material and energy intake to customer delivery. The mission of the Morse project is to develop model-based, predictive raw material and energy optimisation tools for the whole process route. This approach will be demonstrated in steel industry, to increase yield and product quality in production of high-strength carbon steels, stainless steels and cast steels.

In many aspects batch processes are superior to continuous. Therefore it is worthwhile to take advantage of recent progress in sensor technologies, modelling and automation to develop a new paradigm for the design and conduction of batch processes: a) operation at maximum efficiency, b) dynamic, quality driven process trajectories rather than fixed schedules c) detailed analysis and tracking of all relevant process and product parameter. The main objective of the proposed project is the maximization of efficiency (reg. quality, energy, raw materials, and costs) of batch processes. Integrated process control is essential for an efficient operation of industrial batch processes: it tracks the evolution of product properties, detects deviations from the target values for product quality and derives corrective actions at a stage when an automatic compensation of deviations from an optimal trajectory is still possible. This contributes to optimal energy and raw material utilisation, shortens production time and enhanced the product quality. With the ambition to deliver solutions with relevance to all sectors of the process industries, the RECOBA consortium represents a selection of batch processes operating industries and partners across the value chain of batch process control, among them 3 global players from the polymer industry (BASF), the steel industry (TKSE), and the silicon metal industry (ELKEM). Within RECOBA there will be developed and validated: (1) new & innovative solutions for the measurement of different types of quality aspects, (2) new models to realise integrated process control of batch processes & suitable online parameter adaptation technologies to keep these models valid, (3) control modules to realise concepts for real-time, model based & closed loop process control, which are easily adaptable to existing batch processes in various industrial sectors, (4) business models to approach relevant industrial sectors for a future market entry.

Rapid up-scaling and deployment of more cost-efficient and sustainable carbon capture solutions is needed to reduce the emissions of CO2-intensive industries. Solvent-based carbon capture is an important technology that can be readily adopted to many emission sources. Such technology can achieve high capture rates and deliver CO2 at high purity with a relatively low energy demand. In AURORA the open and non-proprietary CESAR1 solvent technology will be optimised and qualified for commercial deployment. The technology will be demonstrated at TRL7-8 for three CO2 intensive industries: refining, cement, and materials recycling, for which there are few other options to achieve climate neutrality. The partners will demonstrate negligible environmental impact (emissions being a potential issue for solvent technology), capture rates at 98%, and capture costs reduced by at least 47% compared to a benchmark process with the MEA solvent. This will be achieved due to the following innovations: 1) Holistic optimisation of solvent composition, process design, emission monitoring and control, and solvent management, 2) Validated models for use in commercial process simulators 3) enhanced waste heat integration with carbon capture for reduced external heat demand and operational costs 4) Improved and integrated advanced control system for reduced OPEX and optimised performances. These innovations will be integrated in four optimised capture processes and various aspects will be demonstrated in pilots of various size and complexity. The partners will ensure transferability of results to other CO2 intensive industries thanks to the large variations in CO2 source and developed clusters addressed in the project and a strong stakeholder participation. The project will also do full CCUS chain assessments for its end-users. It is noteworthy that the end-users are situated in two different regions of Europe offering different conditions for the implementation of CCUS value chains.

While the concept of digitalisation and Industry 4.0 is making rapid inroads into the European manufacturing sector, there are several aspects that can be still incorporated into the system which can strengthen the goal of optimal process operations. One such aspect to the digitalisation vision is the "cognitive element", where the process plants can learn from historical data and adapt to changes in the process while also being able to predict unwanted events in the operation before they happen. Through this project, COGNITWIN (Cognitive digital Twin), we aim to add the cognitive element to the existing process control systems and thus enabling their capability to self-organise and offer solutions to unpredicted behaviours. To achieve the objectives of the project, we have partnered with six industries and seven research groups from seven European nations, each of whom will bring their expertise in data analytics and pattern recognition which are going to be at the heart of the COGNITWIN solution platform. The set-up of the platform includes a sensor network that will continuously monitor and collect data from various plant processes and assets which will be stored at a database. This data will be used to develop a digital twin of the process and will also be used to develop models with cognitive capability for self-learning and predictive maintenance which will lead towards optimal plant operations. The project builds on ideas and technologies that have been validated in controlled environments (TRL 5) to arrive at prototype demonstrations in operational environments (TRL 7). The COGNITWIN project results will be implemented to our industrial partner's processes to demonstrate the transition from TRL 5 to TRL 7. TRL – Technology Readiness Level