Industrial modernisation is essential for economic dynamism and the creation of growth and jobs in the EU. Digitalisation will play an essential role in modernising Europe’s production capabilities, allowing companies to be well positioned in the race for global competitiveness and sustainability.
EU manufacturing companies are facing increasingly competitive and dynamic markets. Continuous process industries have so far largely failed to adopt digital solutions to improve their performance. To compete in the modern world, companies in the process industry need highly flexible manufacturing environments, capable of continuously adapting to changing conditions by means of advanced technologies and decision-making processes that take advantage of big data in real-time. Enterprises need to harness the knowledge held within their data streams to become more energy and resource efficient while improving safety and lowering their environmental impact.
Cognitive manufacturing refers to a new manufacturing paradigm where machines are fully connected through wireless networks, monitored by sensors, and controlled by advanced computational intelligence to fine-tune product quality, optimise performance and sustainability, and reduce costs. Such cognitive features have not been realised yet in process industry. In the past decades, advanced data analytics for reasoning, planning, decision making, and learning have been successfully implemented in information-based systems, but have had limited introduction to systems that interact with the physical world.
The HyperCOG project aims to show that cyber-physical systems and data analytics can be used to drive transformation within the European process industry, improving efficiency and competitiveness by harnessing the power of data..
The main objectiveof HyperCOG is to demonstrate the potential of cyber-physical systems and data analytics to transform the process industry and associated business models.
Cyber-physical systems control and monitor physical processes using computer-based algorithms to provide useful information to operators. The cyber-physical system architecture being developed by HyperCOG will attempt to realise the concept of cognitive manufacturing, combining cognitive computing techniques (such as artificial intelligence), the Industrial Internet of Things, and advanced data analytics to optimise manufacturing processes in ways that were not previously possible.
HyperCOG will show the potential of these technologies and will evaluate their replicability and transferability to different industrial sectors. The project will demonstrate how data technologies embedded in a cyber-physical platform can streamline processes, achieve a step gain in efficiency, sustainability and resource utilisation, and act as a basis for the provision of new services.
1. Development of a platform that converts manufacturing industries into more flexible environments, capable of continuously adapting themselves to changing conditions. Current industrial systems are normally implemented following a hierarchical structure making them rigid and less efficient at making good and fast decisions. HyperCOG will build on existing robust core functionalities with modern digital technologies to improve this decision-making process.
2. Implementation of advanced data analytics for extracting knowledge from production databases to optimise operations and support engineering management activities. This will be achieved using analytical concepts, dedicated software and IT systems which evaluate the data of the company, production data and the market development. HyperCOG aims to detect and isolate behaviour patterns inherent in the data which are related to production irregularities in order to suggest connections with specific causes and propose possible corrective or preventive actions.
3. Development of a decision support system to make the best possible decision in a specific situation. Operators will receive information about errors in manufacturing and be provided with steps to solve the error and recover the operation.
4. Leverage cybersecurity concerns about cyber-physical systems and Internet of Things devices as a business enabler. HyperCOG will deliver cybersecurity as a layer of protection, improving business efficiency and productivity through security and privacy by design, raising organisational resilience through situational knowledge by threat intelligence based preventive methods and leveraging on data analytics.
5. Development of strategies for training and re-skilling human resources. The project will identify and implement professional competencies triggered by new scientific know-how and technological changes, and enable these competencies in an industrial context to address new professional needs.
The HyperCOG project is developing an innovative cyber-physical system that supports industrial production. Cyber-physical systems control and monitor physical processes using computer-based algorithms to provide useful information to operators. The system being developed by HyperCOG will have the ability to interact with humans and expand the capabilities of industrial processes through computation, communication and control.
Three aspects of the system will be explored in detail:
1. Interconnection and interoperability among heterogeneous devices which ensures that real-time data acquisition from the production environment commands feedback from the computer system
2. Management and analysis of multi-source and heterogeneous big data
3. Knowledge acquisition and learning methodologies that support intelligent decision-making to move towards the smart factory concept.
As a result of the implementation of its technical objectives, HyperCOG will achieve clear and measurable impacts. These benefits will be demonstrated in three different use-cases – steel, cement, and chemical plants. KPIs of current processes will be compared with the KPIs obtained in the new digitalised lines, while the demonstration activities will address scalability and replicability of the proposed concepts. The impacts include: