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.

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.

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.

Modular Automation offers promising technologies for the process and chemical industry. It meets the requirements for greater flexibility and a shorter development cycle for production facilities by dividing the process into smaller standardized units. Key element of Modular Automation is the Module Type Package (MTP). It represents a standardized and manufacturer-independent description of the automation interface for self-contained production units, or Process Equipment Assemblies (PEA), and endows the plug-and-produce capability of PEAs. Software solutions to program MTP compatible PEAs are mostly provided by the Programmable Logic Controller (PLC) manufacturers. They are proprietary and bound to certain hardware and even programming languages. Therefore, their suitability for implementation of soft sensors based on data-driven advanced analytics is strongly limited. In this article we present an opensource Python package, MTPPy, designed for rapid prototyping of MTP-capable soft sensors with a focus on AI-based research applications. MTPPy is aimed at the accelerated deployment of soft sensors in the modular plants; thus, closing the gap between the data-driven model development and integration into the production.

This paper proposes a first step into a common solution, where combined and extended interests will hopefully allow us to surpass this threshold. While there are still some open issues, we hope to not only propose a basic working mechanism but also provide fresh ideas to start thinking off the beaten path. Our main contribution is to create a lightweight, end-to-end binding between path and service, which is then used as a basis to associate further attributes and mechanisms to this binding.