The technology readiness levels of cloud infrastructure and edge devices have increased significantly in recent years. This means that companies now have a growing number of computing environments at their disposal th...
详细信息
In the era of Industry 4.0 (I4.0), Cyber Physical Production systems (CPPS) and upcoming industrial transformations, there's a great impulse for smarter, more connected, and adaptable industries. To support this s...
详细信息
The increasing need for simulation-based analysis along the life-cycle of modern process plants introduces new requirements and limitations in the development of simulation models. In the context of modular plants, ac...
详细信息
Signed Directed Graphs (SDGs) are among the very beneficial, and well-known tools for fault diagnosis in chemical process plants. Based on the size of the process plant, their corresponding SDGs can be very large in s...
详细信息
The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintai...
详细信息
Large-scale water electrolysis systems are built from a multitude of stack units. These stack units have to be controlled during operation. The question arises how processcontrol of the system can be designed in that...
详细信息
Modular process plants require a new interaction of process engineering and automation engineering. During the engineering of process Equipment Assemblies (PEA) the automation functions are encapsulated in services wh...
详细信息
In the era of Industry 4.0 (I4.0), Cyber Physical Production systems (CPPS) and upcoming industrial transformations, there's a great impulse for smarter, more connected, and adaptable industries. To support this s...
详细信息
ISBN:
(数字)9798331527471
ISBN:
(纸本)9798331527488
In the era of Industry 4.0 (I4.0), Cyber Physical Production systems (CPPS) and upcoming industrial transformations, there's a great impulse for smarter, more connected, and adaptable industries. To support this shift, our industrial devices need to be upgraded. They should not only do their usual tasks reliably but also support new technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins (DT), etc. seamlessly. This is where cognition in devices becomes significant. This paper showcases an innovative cognitive system design for edge devices for control, drawing inspiration from the well-established concept of cognitive control. This approach highlights the symphonious existence of conscious (controlled) and unconscious (automatic) processing. The system architecture demonstrates the concurrent processing of real-time tasks, like control loops, field devices, etc., and non-real-time tasks such as AI, ML, Neural Network (NN) models, DT models, etc. within the edge device. This corresponds to the unconscious and conscious cognitive functions in human beings. This paper outlines the characteristics of such cognitive devices. The potential technologies that can help in achieving these characteristics like virtualization, multi-core edge devices, concurrency, etc. have also been explored. Additionally, a proof-of-concept demonstration use case has been presented that exhibits the implementation of the Open Cognitive controlsystems (OCCS) architecture in edge devices. This work sets a stepping stone towards making industrial devices smarter.
The technology readiness levels of cloud infrastructure and edge devices have increased significantly in recent years. This means that companies now have a growing number of computing environments at their disposal th...
详细信息
ISBN:
(数字)9798331527471
ISBN:
(纸本)9798331527488
The technology readiness levels of cloud infrastructure and edge devices have increased significantly in recent years. This means that companies now have a growing number of computing environments at their disposal that could be suitable for the computing and control tasks involved in their production processes. Consequently, the need arises to select the best computing environment based on objective criteria and metrics. In order to create the basis for such a decision-making process, first an overview of the definitions and a comparison between edge and cloud environments is provided. Then a decision-making framework is derived that aims to facilitate this selection process. This is accomplished by providing a set of criteria that can be detailed to analyze a given use case. This analysis is then represented in a custom radar chart, which offers decision-makers a compact but meaningful representation of the decision space. Finally, the limits and future potentials of such a decision-making framework are discussed.
BACKGROUND: Computational and data-intensive technologies such as model predictive control and artificial intelligence have the potential to increase process yields in the process industry. In modular plant designs, t...
详细信息
暂无评论