1. Asphalt Innovations
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Asphalt Sensor: Cognitive sensor of bitumen content in recycled asphalt (CAS1)
This disruptive sensor performs an automated determination of the bitumen content in RAP, in-line and in almost real-time, eliminating the need for sampling. Contrary to the existing commercial equipment’s that only can measure a representative sample, this sensor measures the bitumen content of all RAP added to the mixture. Also, it completely eliminates the errors of measurement related to sampling issues as well as frees the operators’ time, while eliminating all health-related issues.
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Asphalt Sensor: Cognitive Sensor of Amount of Filler (CAS2)
In CAPRI, an online solution will be developed to allow measuring in real time the amount of the filler, the finest particles of the asphalt production, extracted through the aspiration pipe of the baghouse. The main result will be a cognitive sensor with digital technologies to extract the necessary information to provide detailed insights into process control and resource availability.
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Predictive maintenance of baghouse based on cognitive sensors and expert knowledge (CAO1)
A predictive maintenance system based on already available measurements like drop pressure and power electric consumption in connection with the Drum Cognitive control will be done to reduce maintenance costs and spare costs (different machine components). The integration of historical maintenance intervention and maintenance operator’s knowledge will include the robustness and reliability of the predictive maintenance system.
2. Steel Innovations
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Steel Sensor: Cognitive steel solidification sensor for continuous casting processes (CSS2)
A real time simulation model of the continuous casting process of steel (and potentially other materials), providing detailed information on the temperature field and solidification front in the casting strands as well as the temperature profile, casting history and relevant properties from the previous liquid steelmaking processes for the billets cut at the end of the casting process.
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Steel Sensor: Steel Product tracking (CSS1)
Allow the digital twins of products to identify their (approximate) position. This sensor utilises existing tracking possibilities but also introduces additional hardware sensors and QR code markings.
3. Pharma Innovations
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Pharma Sensor: Cognitive sensor for granule quality (CPS2)
Implementation (hardware and software) of a sensor for in-line measurement of granule size distribution. The innovation provides real-time data (captured inline) of granule size in continuous manufacturing plants. The information generated by the proposed sensor can be used for process optimization and quality control concepts.
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Pharma Sensor: Quality attributes sensor (CPS3)
CPS3 is a soft-sensor solution and therefore will not require extensive capital investment or engineering installation effort. This cognitive solution should prove to be easily integrated into almost all continuous drying processes. The aim of the soft-sensor is to predict granule moisture in a fluid bed dryer by means of a mathematical model from available process data like drying air temperatures, drying air humidity and air flow rate.
4. CAP Innovations
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Cognitive Automation Platform: Cognitive Automation Platform (CAP) for Process Industry
The Cognitive Automation Platform (CAP) is a modular and open-source framework to deploy cognition functions from the edge to the cloud. It is based of best of breeds European open-source community (i.e. FIWARE), complemented with worldwide OSS market leader such as APACHE. Reference implementations, validated in the three domains of CAPRI, will provide evidence on the value added of the CAP for the whole Process Industry including European SME’s, easing replication and fast adoption scenario.
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Cognitive Automation Platform: Open data
Open Data will be generated and made available by demo cases and by industrial use cases. These data are meant for researchers, practitioners and young talents in the SPIRE sector, to be used even with the Open-Source software, to shape training courses and consultancy webinars to establish a mind-set of Cognitive and smart technologies that can change the industry, during and after the project ends.
5. Educational Innovations
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PI HUB
PI HUB network initiative, which aims to create a platform for industrial DIHs to collaborate and share solutions and services related to digital transformation in the process industry. The initiative is based on the successful DIH4INDUSTRY platform and will use a service taxonomy called DBEST to facilitate information exchange.
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6/7Ps Digital Transformation Model
6Ps Digital Transformation Assessment Model, which is designed to help companies generate strategies for Industry 4.0. The model assesses the current level of digital maturity of manufacturing companies and quantifies the desired level of digital maturity they wish to achieve. It is based on 6 pillars (Products, Process, Platform, People, Partnership, and Performance), each containing six dimensions of Industry 4.0 analysis with 5 possible maturity levels.
