In a major leap forward for smart manufacturing processes, the EU-funded HyperCOG project has come to a successful end by delivering an innovative industrial cyber-physical system that does away with traditional hierarchical information systems.
The HyperCOG project was conceived four years ago with the goal of demonstrating how cyber-physical systems and advanced data analytics techniques based on artificial intelligence can be used to drive transformation within the European process industry, improving efficiency and competitiveness by harnessing the power of data. With the project now at an end, the system it has created has demonstrated this potential to great effect.
The HyperCOG system can be understood as a network of interconnected digital nodes that operates without hierarchical layers. Each node, regardless of its role in the system, can communicate with any other node. This approach breaks down the traditional concept of information systems for industrial process environments, replacing hierarchies with a networked cyber-physical system.
HyperCOG’s ground-breaking approach deviates from the Reference Architecture Model for Industry 4.0 (RAMI 4.0), empowering nodes to acquire real-time data streams. With high computing capabilities, the nodes provide sensing, knowledge, and cognitive reasoning, enabling the system to quickly react to the ever-changing scenarios that occur in real time on the production line.
With different types of nodes performing different operations – listening to data, retrieving, or storing information, calculating – the possibilities are endless. By combining various types of nodes, complex connections can be created that meet the intercommunication needs of industrial plants and enable step-change improvements in efficiency, cost and carbon output.
Tested in three industrial settings
Over the past four years, the project has focused on demonstrating and validating its technology in three key industrial use cases: steel, cement, and chemical manufacturing. The researchers collected industrial data for each use case and developed models and optimisation algorithms to enhance production planning. A monitoring tool was created to analyse data communication between nodes, identify potential issues, and ensure the correct operation of the system.
The project's success was reflected in substantial reductions in waste, energy consumption, and solvent use across the three use cases. On top of this, two market-ready results have been created: the software suite for implementing industrial cyber-physical systems and a blockchain tool for supply chain management and traceability.
Blockchain technology for asset tracing
Notably, aside from the main cyber-physical system, a blockchain tool for supply chain management and traceability was developed during the project. Slag generated as a by-product in the steelmaking process can be used to manufacture cement, reducing waste being generated and contributing to the circular economy. However, cement companies often disregard this product because it is difficult to ascertain its quality, and they can easily get the raw materials they need from other sources.
The new blockchain tool enables all parties involved to see every step in the process in real time, providing confidence for suppliers and customers and enabling a unique and agreed consensus on shared data about products such as slag.
This blockchain innovation can be used to increase the transparency and traceability of all the assets processed in a manufacturing process, resulting in a more optimised supply chain and improved efficiency, which translates into savings for all the companies involved.
Life-cycle assessment in real time
The project integrated life cycle assessment (LCA) concepts into its work, with a key innovation being the integration of cutting-edge LCA models directly into a node in the cyber-physical system. This will help to improve decision making at the factory level by introducing real-time environmental, social and economic perspectives, with the consequences of decisions being made at factory level shown straight away to operators. This represents a step forward in the direction of “digital product passports”, and is a big step ahead of current practices which use very generic data in footprint calculations.
Training materials for industrial workers
Importantly, human perspectives were not overlooked, with the project identifying competence gaps in workers and providing specialised training content for 20 workers across the three sites. Additionally, training material on cyber-physical systems was prepared for students that is available to be used now. In this way, HyperCOG has not only provided society with environmental benefits but has also contributed to the lifelong learning of workers and vocational training for digitisation.
With business plans in place to exploit the exciting innovations that have arisen from this project, HyperCOG stands poised to revolutionise the manufacturing landscape, offering flexible and sustainable solutions for the future.
For more information on the results of the HyperCOG project, visit the website or email harry@insightm.co.uk
