Modular process plants, built from Process Equipment Assembly (PEA), promise a higher flexibility compared to monolithic plants. Digital documentation is a key requirement for a seamless orchestration of a modular plant using PEAs from different vendors. From a process engineering functional view the Module Type Package (MTP) documents the capabilities realized by a PEA. From a automation perspective the documentation and specification can be optimized by utilizing a standard called System Control Diagrams (SCDs). There is up to now no connection of the individual standards. This connection could enable an integrated engineering approach to raise consistency and reduce engineering effort. This work presents an approach to map SCD based automation planning to the MTP. Therefore, both standards are introduced and a mapping from SCD to MTP is realized. It is shown, that most of the functions from SCD have a representation in the MTP, enabling the linking of basic automation functions to capabilities. This enables engineers which are using SCD as a planning tool to efficiently provide an MTP for PEAs or modular plants.

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 Control Systems (OCCS) architecture in edge devices. This work sets a stepping stone towards making industrial devices smarter.

In this paper we compare different operation strategies for alkaline electrolyzis (AEL) systems using numeric simulation. We derived general models for mass and energy balances from literature and composed them into a model for an AEL system. Here we considered a numbered-up plant topology, containing multiple AEL stacks with single electrolyte distribution and gas separation systems. The gas impurities and achievable minimal load distribution were compared for the different operating strategies. The results were discussed and further conclusions for the operation of a homogeneous numbered-up AEL system were derived.

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 size; and therefore, significantly difficult to navigate. Besides that, the recent advancement in semantic description of process structures and intelligent Process and Instrumentation Diagrams (P&IDs), has led to the development of several computer tools for navigating in a process plant structure and examining the connectivity of different process equipment to each other. In this study a prototype for an intelligent P&ID, in which the SDG is considered to help the user understand the influence of process parameters on each other is developed. Requirements were derived and implemented as a click prototype, using Axure RP. The evaluation results indicate that despite the high understandability of the prototype, the cognitive efforts for linking the information derived from the SDG with the plant’s P&ID scheme are still too high for achieving a good overall usability.

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 which represent process operations. In this work an approach is presented, where System Control Diagrams (SCD) are utilized to create a transition from process operations to automation functions to services all on the basis of the Piping and Instrumentation Diagram (P&ID). Using this method, a comprehensible graphical and machine-readable link between both sides is created. In combination with existing tools, it is shown that part of the automation could already be prepared during the design of the P&ID. The proposed concept is evaluated using a water deionization PEA.

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 maintaining flexibility. In this paper, existing control strategies are analyzed and compared for large scale electrolysis systems. It is argued, how modularization can enable the optimal distribution of set-points and enables the systems for capacity extension. An initial implementation in MATLAB is presented, showing the feasibility of the proposed power distribution concept.

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, the flexible and difficult-to-predict use of a particular module presents the challenge of providing the right amount of data, computing power, and storage capacity to use these technologies in each module.OBJECTIVE: Simplifying the deployment and reconfiguration of compute-intensive applications for modular plants.METHODS: Reviewing the current state of the art and combining the modular plant concept with a cloud-based Control-as-a-Service (CaaS) approach.RESULTS: A cloud-based, containerized CaaS concept that supports the deployment compute- and data-intensive methods for modular plants. The complementing communication stack consisting of standards such as APL, TSN and OPC UA FX.CONCLUSION: The proposed architecture simplifies IT/OT integration, eases the deployment of compute-intensive technologies, and allows for GMP compliant pre-qualification of software modules. However, the limiting factors like plant safety, conformity to regulations and widespread architecture adoption are not covered in detail and will be subject of future research.

For various applications, information models described in standards are available to enable a digital data exchange. For modular process plants, where different domains are interacting in different phases of the plant lifecycle, single, existing standards are not sufficient.In this work, we analyze the most relevant standards for process engineering, namely OntoCAPE, DEXPI, and IEC 61512, in regards to their application to support the lifecycle of modular process plants, which are described in the VDI 2776 and VDI/VDE/NAMUR 2658 standards. By challenging the standards with competency questions derived from a specific use case, we discuss which information models are most suitable for all individual domains in each lifecycle phase. The result is the specification of parts of these standards which are needed to create a fully digital support for modular process plants on the example of the introduced use case.

Modularization and the Module Type Package (MTP) technology are proven concepts to ensure flexible plants and shorter time-to-market. Since the concepts are generic, they can potentially be applied to other domains. While the MTP approach has so far been used mainly in the specialty chemicals and pharmaceuticals industries, other sectors such as shipbuilding, logistics and hydrogen production show potential for adoption of this approach. In this article, such domains will be briefly examined, and the characteristics of the specific application are assigned to the requirements from modularization. The resulting analysis is transferred to the electrolyzer domain in hydrogen production in order to explain how the MTP technology can be applied there and how the hydrogen industry can benefit from the MTP approach. Furthermore, it motivates to transfer this approach to other industrial sectors that might not be considered in this contribution.

Hazard & Operability (HAZOP) studies are a common expert-driven brainstorming methodology to analyze the safety of systems. In this paper, we propose an approach to integrate the content of HAZOP studies of (apparatus and process) by extracting and disclosing the causal structures implied in the HAZOPs. Thus, a methodology for data compression and integration of several HAZOP studies is supposed, resulting in a comprehensible summary of the underlying causal relationships withing the current system set-up. The resulting diagrams constitute a summary of the essential HAZOP studies by disclosing and visualizing the internal causal structures and are hence designed to facilitate interpretations and deductions performed by human operators and support machine-readability. The application of the methodology is implemented in the import of HAZOP-tables to Resource Description Framework (RDF). Using this formalized structure, the export to their original format as table is supported as well, so that the transformation can be conducted without the loss of information in both directions.

Discrete manufacturing and process industries move towards flexible production to cope with individualization. Safety systems often counteract these developments. In this paper, a generalized methodology to draft an overall safety concept using different technologies is presented. Motivated by use-cases from both domains, requirements for future safety systems are described and summarized in a structured way. These requirements are then generalized and classified in building blocks to fulfill the needs of flexible functional safety. A methodology to structure safety systems and their general parts is presented. The used building blocks refer to aspects as interfaces, communication, and certification. Within the building blocks different technologies can be applied, to fulfill the part of the safety system accordingly.

Modular process plants enable the process industry to adapt to volatile and fluctuating markets. The preferred process technology concept for these market scenarios is realized through combining pre-engineered process equipment assemblies (PEA) from different manufacturers with standardized interfaces. While the flexibility gain for this type of plants is high, the design of the safety systems of these modular plants in terms of exchangeability and compatibility is challenging. One solution could be the development of a safety-interface for PEAs to create flexible and scalable safety systems. The principles of modular basic process control are analyzed and requirements with respect to safety design are discussed. The authors provide a differentiation of the intramodular safety concept of a single PEA from the intermodular safety concept of a complete modular plant. A concept for safe and modular interaction of PEAs in a modular plant is developed and additionally validated on a basic demonstrator. Finally, from these findings, standardization requirements are provided and aspects for further research are pointed out.