Large language models (LLMs) have gained significant attention for their success in natural language generation and their widespread adoption across various business sectors. In process automation (PA) engineering, LLMs can be utilized to assist with tasks such as PLC programming, software development, and data processing. Despite their advantages, LLMs face challenges like biased responses, non-deterministic outputs and model hallucinations. Therefore, it is essential to validate the responses generated by LLMs before relying on them. This work presents a novel workflow to automatically validate unstructured outputs generated by LLMs used in question answering (Q&A) applications based on the user-specified documents in the PA domain. The Domain of Validity (DoV) of these LLMs is first estimated based on the user-specified documents. The proposed workflow uses the estimated DoV to evaluate the relevance of the outputs generated by the LLM with respect to the user documents. A regular expression approach is included in the workflow to check whether the mentioned tag names in the LLM output, if any, exist in the user-specified documents. Furthermore, an LLM-as-a-judge approach is used then to cross-check the LLM generated outputs with the ground truth information. The proposed workflow enables users to automatically detect invalid LLM outputs. This allows the user to focus on cases that truly require human judgment and act accordingly by re-prompting the LLM, adding documents, or switching models. A fictitious PA example is employed to demonstrate the versatility and benefits of the developed workflow.

Asset engineering process in energy industry domain is a complex multi-disciplinary and multi-organizational process including an iterative exchange of a large number of artifacts. Legacy document-centric exchange process of those artifacts is an error-prone semi-manual process negatively impacting overall project duration and quality. We present results of a collaborative study between ABB and Equinor exploring the potential of the digitalization of this process based on Industry 4.0 technologies, especially the Asset Administration Shell. An exemplary engineering workflow including OPC UA and AutomationML models embedded in Asset Administration Shells was implemented along with novel Asset Administration Shell infrastructure. Despite minor flaws in maturing Industry 4.0 technologies, no major roadblocks were identified.

Automation Engineering of a process automation system is still a very manual effort due to limited support for the interpretation and processing of process design specification documents. Even though standards for digital data exchange between process and automation engineering do exist, those formats are rarely used and consequently the immense automation potential in automation engineering cannot be lifted. This contribution presents an AI -based approach and prototype - using an ontology-enhanced LLM -based agent and a mixture-of-experts system - to structure and formalize multimodal unstructured process design information as in PDF, Excel, and Word formats and make it available for state-of-the-art engineering tools for the long-known “Automation of Automation”.

Looking at Process and Automation Engineering (P&AE) today, there are many different tools available to support the engineer in his work from translating engineering intentions, via describing modules, to defining entire process plant setups, for export to a control system. As of today, the tools to support these steps are rarely linked to each other and hence do not allow to communicate with each other or exchange knowledge or data. Therefore, we came up with the idea of the Semantic Facilitation & Integration Layer, SemFIL, an ontology-based software system and architecture, to overcome tool, knowledge, and data silos, and to facilitate integration of and interoperability between applications, especially in P&AE. In this work, we present and evaluate the concept of SemFIL, and how it allows to connect and integrate all P&AE tools into a powerful, comprehensive, integrated workflow. We conclude with a discussion of the proposed SemFIL, and list required efforts for as well as advantages from its implementation. Lastly, we give an outlook on the most relevant future research questions and directions.

Process plant operators use graphical user interfaces to supervise complex processes with tens of thousands of instruments. Such systems may require more than 1000 operator screens, but an operator can only view 3-4 screens simultaneously ("keyhole-effect"), thus prolonging root cause analysis and emergency reactions. This effect contributes to the billions of USD lost in the process industry every year due to unscheduled downtimes. Researchers have proposed alternative, zoomable 2D user interfaces (“HawkEye HMIs”) similar to Google Maps, to address the keyhole-effect, but manual efforts to create these HMIs proved to be cost-prohibitive. This paper proposes a novel method for HawkEye-HMI-Generation based on semi-automated page layouting and automated level-of-detail zooming, which can significantly lower graphics engineering costs. We tested a prototypical implementation on two cases from large production plants with up to 90 process graphics and more than 5000 graphical nodes, demonstrating high scalability, good usability, and a more than 70 percent cost savings potential.

The modularization of process plants and their modular automation are innovative concepts to meet increasing demands for flexibility in plant operation. However, previous approaches to support the engineering of such modular plants do not focus on the early phases of requirements elicitation and concept design, although these early phases are crucial for the economic efficiency of the later plant. This paper presents a method for the formalized description of intentions in the early engineering phases of modular process plants. According to this method, plant operators create a formalized model of their intentions and provide it to the module manufacturers in a modularized form. This allows a reduction of effort in the planning and development of the plant. The approach is demonstrated using an oil, gas and water separation process performed by a modular process plant.

Modular plants build a production system by integrating a set of pre-designed modules. Integration of these modules, supplied by different vendors, is performed using various design tools during the engineering phase. The integration process must perform a rigorous validation of the correctness of a module specification (MTP). Otherwise, the integration process can fail without providing enough failure details. Consequently, Such a failure at the later stage can significantly impact implementation, testing, integration, and SAT. In this paper, we describe a validator tool, that allows a plant designer to define a set of invariants that must be satisfied so that an MTP can be deemed fit for integration. We have tested the validator on a set of MTPs and reported our findings. We expect that the use of such a validator can significantly reduce the possibility of introducing errors during the engineering phase.

The modularization of process plants represents an innovative approach to meet the increasing requirements regarding flexibility. The current research focuses on 100%modular process plants, which are characterized by high flexibility of the plant, but unlike monolithic process plants, they are not designed for a specific and optimized operating point. In addition, the flexibility provided by the modularity is not needed everywhere in the plant. Therefore, hybrid process plants, i.e. a combination of a monolithic plant and several modules, are expected to overcome this issue. This paper presents the key terms in the context of hybrid process plants as well as the requirements for the engineering and automation of such plants. Furthermore, a generic workflow model for the engineering of hybrid process plants is derived, with focus on the conceptual design.

There is growing interest in building and operating industrial plants from standardized modules described by MTP's (Module Type Package). But this new approach brings with it its own set of new challenges. While in the traditional approach of building an industrial plant, one would deal with individual sensors and actuators, this new approach requires, amongst other things, the management of large pools of modules in the engineering and operation phases of a plant. In this contribution, we analyze these new requirements and report on our approach on building a demonstrator to deal with these new issues.