Cost Optimized Scheduling in Modular Electrolysis Plants

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.

Automated Process Planning Based on a Semantic Capability Model and SMT

In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. Approaches in this research area develop information models that capture all information relevant to interpret the requirements, effects and behavior of functions. These approaches are intended to overcome the heterogeneity resulting from the various types of processes and from the large number of different vendors. However, these models and associated methods do not offer solutions for automated process planning, i.e. finding a sequence of individual capabilities required to manufacture a certain product or to accomplish a mission using autonomous robots. Instead, this is a typical task for AI planning approaches, which unfortunately require a high effort to create the respective planning problem descriptions. In this paper, we present an approach that combines these two topics: Starting from a semantic capability model, an AI planning problem is automatically generated. The planning problem is encoded using Satisfiability Modulo Theories and uses an existing solver to find valid capability sequences including required parameter values. The approach also offers possibilities to integrate existing human expertise and to provide explanations for human operators in order to help understand planning decisions.

Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.

Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems

In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast amounts of potentially valuable data are being generated. Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected. The knowledge obtained can in turn be used to improve tasks like diagnostics or maintenance planning. However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISP-DM), often fail due to the disproportionate amount of time needed for understanding and preparing the data. The application of domain-specific ontologies has demonstrated its advantageousness in a wide variety of Industry 4.0 application scenarios regarding the aforementioned challenges. However, workflows and artifacts from ontology design for CPPSs have not yet been systematically integrated into the CRISP-DM. Accordingly, this contribution intends to present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS. The result is exemplarily applied to an anomaly detection use case.

Systematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production

To achieve a highly agile and flexible production, it is envisioned that industrial production systems gradually become more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, knowledge about individual production assets is readily available throughout their entire life-cycles. To realize this vision, adequate use of information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs typically play a more passive role in monitoring production resources and processing information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.

A Graphical Modeling Language for Artificial Intelligence Applications in Automation Systems

Artificial Intelligence (AI) applications in automation systems are usually distributed systems whose development and integration involve several experts. Each expert uses its own domain-specific modeling language and tools to model the system elements. An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist. As a result, there is often a lack of interdisciplinary system understanding, leading to increased development, integration, and maintenance efforts. This paper therefore presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level. This makes it possible to subdivide individual subareas into domain specific subsystems and thus reduce the existing efforts.

A Capability and Skill Model for Heterogeneous Autonomous Robots

Teams of heterogeneous autonomous robots become increasingly important due to their facilitation of various complex tasks. For such heterogeneous robots, there is currently no consistent way of describing the functions that each robot provides. In the field of manufacturing, capability modeling is considered a promising approach to semantically model functions provided by different machines. This contribution investigates how to apply and extend capability models from manufacturing to the field of autonomous robots and presents an approach for such a capability model.

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.

Toward a Generic Mapping Language for Transformations between RDF and Data Interchange Formats

While there exist approaches to integrate heterogeneous data using semantic models, such semantic models can typically not be used by existing software tools. Many software tools - especially in engineering - only have options to import and export data in more established data interchange formats such as XML or JSON. Thus, if an information which is included in a semantic model needs to be used in a such a software tool, automatic approaches for mapping semantic information into an interchange format are needed. We aim to develop a generic mapping approach that allows users to create transformations of semantic information into a data interchange format with an arbitrary structure which can be defined by a user. This mapping approach is currently being elaborated. In this contribution, we report our initial steps targeted to transformations from RDF into XML. At first, a mapping language is introduced which allows to define automated mappings from ontologies to XML. Furthermore, a mapping algorithm capable of executing mappings defined in this language is presented. An evaluation is done with a use case in which engineering information needs to be used in a 3D modeling tool.

Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes

Individualized products and shorter product life cycles have driven companies to rethink traditional mass production. New concepts like Industry 4.0 foster the advent of decentralized production control and distribution of information. A promising technology for realizing such scenarios are Multi-agent systems. This contribution analyses the requirements for an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture, as the communication between the agents is a major driver of the scheduling execution time. The approach schedules production, transportation, buffering and shared resource operations such as tools in an integrated manner to account for interdependencies between them. Part of the logistics requirements reflect constraints for large workpieces such as buffer scarcity. The approach aims at providing a general solution that is also applicable to large system sizes that, for example, can be found in production networks with multiple companies. Further, it is applicable for different kinds of factory organization (flow shop, job shop etc.). The approach is explained using an example based on industrial requirements. Experiments have been conducted to evaluate the scheduling execution time. The results show the approach's linear scaling behavior. Also, analyses of the concurrent negotiation ability are conducted.