Periodic Reporting for period 2 - HyperCOG (Hyperconnected Architecture for High Cognitive Production Plants)
Reporting period: 2020-09-01 to 2022-02-28
EU manufacturing companies are facing increasingly competitive and dynamic markets. 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.
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 CPS 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.
HyperCOG will show the potential of these technologies and will evaluate their replicability and transferability to different industrial sectors. The objective is to increase the production performance while reducing the environmental impact by reducing the energy consumption and the CO2 emissions thereof. Society will get profit of this project not only throughout the environmental impact, but through the lifelong learning of workers and vocational training for digitisation.
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 CPS 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.
HyperCOG will show the potential of these technologies and will evaluate their replicability and transferability to different industrial sectors. The objective is to increase the production performance while reducing the environmental impact by reducing the energy consumption and the CO2 emissions thereof. Society will get profit of this project not only throughout the environmental impact, but through the lifelong learning of workers and vocational training for digitisation.
The HyperCOG project has initially identified the existing state-of-the-art digital solutions in smart manufacturing as basis for developed HyperCOG elements, including frameworks for suitable use cases, assure ethical, legal, privacy and security requirements as well as Cyber-Physical System architecture concepts. Based on the requirements obtained, the design of an innovative Industrial Cyber-Physical System has already been performed, and currently the project is facing the development of a hyperconnected CPS network of digital nodes and its communication that will support industrial production.
HyperCOG is deeply grounded in the last advances in Artificial Intelligence such as modelling for twin factories, decision-support systems for human-machine interaction. As a first step, industrial data has been collected, in order to investigate data analysis methods that distill knowledge hidden in the data and make it usable for optimizing processes that occur in the three use cases (steel, cement and chemical). A preliminary model and optimization algorithms have been developed for the production planning optimization of the steel use case.
HyperCOG will provide a comprehensive and quantitative sustainability assessment of the industrial CPS designed. In this regard, the goal and scope definition of the LCA for the three case studies (steel, cement and chemical) have been documented. In this context, in order to give support for industrial production needs in the nowadays technological context of the Industry 4.0 an innovative CPS is been developed. This innovative architecture, this system, is based on advanced technologies that enable the development of a hyperconnected network composed of digital nodes. In the same way, this system is defined as a composition of nodes running in devices that communicate with each other without hierarchical layers or the requirement of neither gateways nor message brokers. So, this innovative approach, starting from the Reference Architecture Model for Industry 4.0 (RAMI 4.0) breaks down with the traditional hierarchical information systems, given the hyperconnecting capabilities. Through this system, the nodes can acquire outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. For this, a set of nodes have been developed, in order to be able to acquire information in real time from data provided by a physical sensor connected to a PLC or other acquisition device or system, collect historic data, record data, compute models or algorithms or check the status of the system, among others. For which a middleware has been use to abstract the communications in a distributed system, as a CPS, composed of several connected devices. Also, a standard Functional Mock up Interface has been implemented to be able the interoperability between the different software languages that can be part of the algorithm models to be developed.
On the other hand, a monitoring tool has been developed with the aim of monitoring the acquired data, analysing the correct communication between the different nodes or the status of these nodes, reporting logs with the indication of possible mistake, showing them in a colour scale in function of the level of the error. Also, with this tool it is possible to verify the value of data and to estimate the correct operation of the system.
HyperCOG is deeply grounded in the last advances in Artificial Intelligence such as modelling for twin factories, decision-support systems for human-machine interaction. As a first step, industrial data has been collected, in order to investigate data analysis methods that distill knowledge hidden in the data and make it usable for optimizing processes that occur in the three use cases (steel, cement and chemical). A preliminary model and optimization algorithms have been developed for the production planning optimization of the steel use case.
HyperCOG will provide a comprehensive and quantitative sustainability assessment of the industrial CPS designed. In this regard, the goal and scope definition of the LCA for the three case studies (steel, cement and chemical) have been documented. In this context, in order to give support for industrial production needs in the nowadays technological context of the Industry 4.0 an innovative CPS is been developed. This innovative architecture, this system, is based on advanced technologies that enable the development of a hyperconnected network composed of digital nodes. In the same way, this system is defined as a composition of nodes running in devices that communicate with each other without hierarchical layers or the requirement of neither gateways nor message brokers. So, this innovative approach, starting from the Reference Architecture Model for Industry 4.0 (RAMI 4.0) breaks down with the traditional hierarchical information systems, given the hyperconnecting capabilities. Through this system, the nodes can acquire outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. For this, a set of nodes have been developed, in order to be able to acquire information in real time from data provided by a physical sensor connected to a PLC or other acquisition device or system, collect historic data, record data, compute models or algorithms or check the status of the system, among others. For which a middleware has been use to abstract the communications in a distributed system, as a CPS, composed of several connected devices. Also, a standard Functional Mock up Interface has been implemented to be able the interoperability between the different software languages that can be part of the algorithm models to be developed.
On the other hand, a monitoring tool has been developed with the aim of monitoring the acquired data, analysing the correct communication between the different nodes or the status of these nodes, reporting logs with the indication of possible mistake, showing them in a colour scale in function of the level of the error. Also, with this tool it is possible to verify the value of data and to estimate the correct operation of the system.
Expected results until the end of the project:
1. Development of a platform that converts manufacturing industries into more flexible environments
2. Implementation of advanced data analytics for extracting knowledge from production databases
3. Development of a decision support system to make the best possible decision in a specific situation.
4. Leverage cybersecurity concerns about cyber-physical systems and Internet of Things devices as a business enabler
5. Development of strategies for training and re-skilling human resources.
Potential impacts:
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:
• A reduction in CO2 emissions by 2.5% (steel plants), 5% (cement plants), 6% (chemical plants)
• A reduction in NOx emissions by 6% in cement plants
• A reduction in waste spills by 5%
• A reduction in energy consumption by 2.5% (steel plants), 7% (cement plants), 5% (chemical plants);
• A reduction in the consumption of raw material by 3.38% (steel plants), 5% (cement plants), 5% (chemical plants);
• A reduction in waste production by 20% (steel plants), 5% (chemical plants);
• An increase by-products valorization by 12% (from steel plant to cement plant), helping to determine the sustainability potential of ICPS;
• An increase in productivity by 4% (steel plants), 5% (cement plants), 10% (chemical plants);
• An increase in profitability by 14% (steel plants), 27% (cement plants), 20% (chemical plants)
• An increase of 20% in young people being attracted to working in aged industrial sectors of SPIRE
• An increase of 30% of woman being employed in traditionally gender unbalanced SPIRE sectors
• The effective dissemination of digital innovations to the employees of SPIRE sectors
• The capture of the deep expertise and knowledge of those already in the industry and the translation of this into knowledge of the “cognitive factory”.
1. Development of a platform that converts manufacturing industries into more flexible environments
2. Implementation of advanced data analytics for extracting knowledge from production databases
3. Development of a decision support system to make the best possible decision in a specific situation.
4. Leverage cybersecurity concerns about cyber-physical systems and Internet of Things devices as a business enabler
5. Development of strategies for training and re-skilling human resources.
Potential impacts:
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:
• A reduction in CO2 emissions by 2.5% (steel plants), 5% (cement plants), 6% (chemical plants)
• A reduction in NOx emissions by 6% in cement plants
• A reduction in waste spills by 5%
• A reduction in energy consumption by 2.5% (steel plants), 7% (cement plants), 5% (chemical plants);
• A reduction in the consumption of raw material by 3.38% (steel plants), 5% (cement plants), 5% (chemical plants);
• A reduction in waste production by 20% (steel plants), 5% (chemical plants);
• An increase by-products valorization by 12% (from steel plant to cement plant), helping to determine the sustainability potential of ICPS;
• An increase in productivity by 4% (steel plants), 5% (cement plants), 10% (chemical plants);
• An increase in profitability by 14% (steel plants), 27% (cement plants), 20% (chemical plants)
• An increase of 20% in young people being attracted to working in aged industrial sectors of SPIRE
• An increase of 30% of woman being employed in traditionally gender unbalanced SPIRE sectors
• The effective dissemination of digital innovations to the employees of SPIRE sectors
• The capture of the deep expertise and knowledge of those already in the industry and the translation of this into knowledge of the “cognitive factory”.