Results
Overview
Watch our short documentary film about HyperCOG’s key innovations, filmed at the SIDENOR pilot site
Over the past four years, the HyperCOG project has been working to demonstrate the potential of cyber-physical systems (CPS) and data analytics to transform the process industry and its business models in an innovative way. A CPS architecture has been developed, implemented and analysed to showcase the potential of cognitive manufacturing and the potential of the individual technologies that enable cyber-physical connectivity.
HyperCOG has demonstrated the potential of these technologies in the value chain of three industrial centres processing steel, chemicals and cement. At each of the three demonstration sites, advanced data analytics tools have provided insights and knowledge from the data streams, in real-time, providing learning and predictive capabilities. Machine learning has also been employed for data-driven model building and hidden insights discovery.
The project has ensured these results are replicable and transferable to different industrial sectors, thus promoting widespread industrial adoption. Bespoke life cycle analysis (LCA) methodologies have been developed, and these models can be applied across other industries, while specific research has taken place on the replicability of specific aspects of HyperCOG in the glass processing industry. The implementation, testing and LCA modelling of HyperCOG technology that has taken place in the three use cases has demonstrated how data technologies embedded in a CPS platform applied in the process industry can streamline processes, achieve a step gain in efficiency, sustainability and resource utilisation, and act as a basis for the provision of new services.
Another key aspect of the project has been the development of bespoke training modules to ensure maximum engagement of existing workforces with the new technology being adopted and to encourage new workers to enter the process industry.
Key results
The three main results from the HyperCOG project
Further innovations
Other key innovations made during the HyperCOG project
Software suite for implementing industrial cyber-physical systems
The current position
The full automation of the process industry, where distributed control is applied involving integration between different subsystems and substations, is a complex challenge. So far, this challenge has been approached through a classical automated communication hierarchy where layers of different levels of devices are used, ie: field devices, control devices, SCADA, MES, etc.
Innovation
HyperCOG has delivered an innovative Industrial Cyber-Physical System (CPS) marking a departure from traditional hierarchical information systems used in the process industry.
The project, which began by identifying and benchmarking state-of-the-art digital solutions, has successfully developed a network of interconnected digital nodes that operate without hierarchical layers or the need for gateways or message brokers.
This innovative approach, which deviates from the Reference Architecture Model for Industry 4.0 (RAMI 4.0), empowers nodes to acquire real-time data streams. With high computing capabilities, these nodes provide sensing, knowledge, and cognitive reasoning, ensuring companies remain robust in the face of ever-changing scenarios that occur in real time on the production line.
The HyperCOG system’s nodes are designed to acquire real-time information from physical sensors connected to Programmable Logic Controllers (PLCs) or other acquisition devices. This includes collecting historic data, recording data, computing models, algorithms, and monitoring the system’s status. A middleware has been implemented to abstract communications in this distributed CPS, ensuring seamless connectivity among various devices, while a Functional Mock-up Interface ensures interoperability between different software languages used in algorithm models.
HyperCOG incorporates the latest advances in Artificial Intelligence (AI), including twin factories modelling and decision-support systems that enhance human-machine interaction.
For more information on this innovation, contact Peter Craamer of MSI or Fran Huertos of Lortek
Blockchain tool for supply chain management and traceability
The current position
The 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 not well characterised, and they can easily get the raw materials they need from other sources.
There is a lack of confidence in the supply chain (in how the products/raw materials/components are treated or managed in the logistic chain) and a lack of digital evidence which can be used to provide additional value to the asset and to solve conflicts between providers and consumers. This forces steelmaking companies to either sell this product cheaply or to dispose it in landfills. While HyperCOG only analysed this issue at the SIDENOR use case, this situation often appears in other sectors as well.
Innovation
The blockchain tool is a digitally accessible solution in which all parties involved can see every step in a process in real time, providing confidence for suppliers and customers. In its most mature stage, this technology can enable the automatic validation of transactions of goods and automatic payments based on the goods delivered, with no intervention by the personnel receiving those goods.
The blockchain tool for supply chain management and traceability enables a unique and agreed consensus on the shared data by all participants.
It increases transparency and traceability of all the assets processed, resulting in a more optimised supply chain and improved efficiency, which translates into savings for all the companies involved.
At the same time, adopting a blockchain-based traceability solution creates a fully auditable ecosystem in which weak points can be identified, targeted and solved, and courses of action can be planned using the evidence tracking system that is also fully auditable.
The traceability tool is based on blockchain technology. Its mission is to simplify the traceability process, helping to improve cybersecurity in businesses while complying with industry standards. It allows for the creation and transfer of traceable digital assets, the customising of properties, the managing of related elements and compositions, and the tracing of ownership changes.
For more information on this innovation, contact Achutha Prabhu of Tecnalia
Hyperspectral camera system for white slag analysis
The current position
During the secondary metallurgy of the steel-making process (SIDENOR use case), the slag is extracted from the ladle furnace in order to evaluate its characteristics. The operator visually checks its fluidness, colour, etc. and makes decisions about the components to add that would improve the steel or the next steps in the process. The operator also takes a sample to evaluate its composition, but the assay is not easy nor is it fast and the results are not usually used by the operator in making decisions. When the steel-making process finishes, slags are thrown out to a slag landfill where all slags are mixed.
The operator makes decisions based on his experience and waits until the laboratory results come back with the composition. Improved sample preparation for the assay will reduce time spent while samples from the landfill can be taken for analysis.
Innovation
A hyperspectral camera system for fast slag composition analysis is the innovation. It is a system composed of a hyperspectral camera and a computer running artificial intelligence algorithms that provide estimations of the slag composition. A training period is needed in order to obtain a model that adapts to the slags produced in any particular steel workshop. Once the model is created, and after the sample is positioned in the system, the slag estimation is shown in just a few seconds.
The unique selling point of this innovation is that it provides a fast estimation of the slag composition, which will support operators in their decision making. With this system, it will be possible to have several estimations during each heating and checks on how the slag evolves. The system would be used with the slags from the landfill.
For more information on this innovation, contact Jose Antonio Arteche of Tecnalia
Data analytics system
An advanced data analytics system for extracting knowledge from production databases to optimise operative activities and support engineering management activities to achieve corporate goals. This knowledge is gained by means of analytical concepts, dedicated software and IT systems which evaluate the data of the company, production data and the market development.
Decision support system
HyperCOG has developed a decision support system (DSS) to help workers make the best possible decisions in a specific situation in any given manufacturing process. An operator can receive information about an error and options for all the available steps to solve it and recover the operation, considering aspects of safety as well as efficiency and quality. This allows the complexity of the technical resolution of any problem to lay in the system. The DSS includes an accessible Human Machine Interface (HMI) that allows the operator to interact with the machines using intelligence derived from the platform in an intuitive way that optimises the process they are working on.
For more information on this innovation, contact Peter Craamer of MSI or Fran Huertos of Lortek
Cybersecurity
Cybersecurity concerns surrounding cyber-physical systems and Internet of Things devices have been addressed. HyperCOG delivers cybersecurity as a layer of protection, building privacy and security into the HyperCOG CPS by design, and raising organisational resilience through intelligence-based preventive methods that leverage data analytics.
For more information on this innovation, contact Anett Madi-Nator of Cyber Services
Intelligent ladle monitoring system
An innovative ladle monitoring system based on infrared thermal cameras to control the status of ladles containing molten steel has been developed at the steel use-case at Sidenor – a pivotal achievement with far-reaching consequences.
By accurately identifying and locating ladles within thermal images, the steel factory can benefit from increased precision in managing these essential components. This innovation improves the tracking of individual ladles and could also facilitate data collection for process optimisation, contributing to enhanced quality control and resource management within the factory. It also has the potential to enable predictive maintenance.
For more information on this innovation, contact Fran Huertos of Lortek
Re-skilling and training strategies
Strategies for the training and re-skilling of human resources were developed. These include modules that identify skills gaps and provide professional training for workers to increase their scientific know-how and give them a working understanding of HyperCOG technologies on the ground. This training will enable industries to implement digital changes on an industrial scale, providing more and better jobs for their workers. The HyperCOG training strategies are replicable across all process industry sectors.
For more information on this innovation, contact Cynthia Lamothe of ESTIA
Life cycle analysis tool
A key innovation for HyperCOG’s LCA work has been the integration of cutting-edge LCA models directly into the cyber-physical system. This will help to improve decision making at the factory level by introducing environmental, social and economic perspectives into the implementation of CPS. Because the LCA model is integrated into the cyber-physical system, access to the data is better and the quality of the assessment this data provides improves substantially. Feedback is also much quicker, with the consequences of decisions being made at factory level available quickly which leads to improved outcomes.
For more information on this innovation, contact Miguel Fernandez Astudillo of 2.-0 LCA consultants
