About Plant Engineering and Ontology
Plant engineering refers to all technical work in the design and construction of plants (factories), such as chemical plants and power plants. Plant engineering involves a wide range of technical issues, including the selection of equipment and facilities necessary for plant operation, process flow studies, process control design, and handling of environmental measures, etc. To solve these issues, mechanical engineering, electrical engineering, chemical engineering, civil engineering, computer science, control engineering, etc. To solve these issues, expertise in mechanical engineering, electrical engineering, chemical engineering, civil engineering, computer science, and control engineering is required.
Plant engineering also has important elements such as safety and environmental protection. Plant construction and operation involve hazards such as explosions, fires, and leaks. Therefore, it is necessary to select equipment, layout, and install sensors to ensure safety. In addition, environmental protection must be considered in the design of waste and pollutants discharged from the plant.
An ontology is a formal definition of concepts and relationships in a particular domain, which is useful for knowledge sharing and information integration in that domain. In plant engineering, the use of ontology enables the sharing of design and technical information, as well as operation monitoring and control through plant automation, etc. Ontologies are used to define objects that represent equipment and devices in a plant, properties that represent their functions and parameters, and relationships that represent relations among equipment and devices. It is possible to define objects that represent equipment and devices in a plant, properties that represent their functions and parameters, and relationships that represent the relationships among equipment and devices in an ontology.
This facilitates organization and retrieval of design information, data sharing among systems, and operation monitoring and control through automation, leading to improved productivity and safety. The ontology is also useful for modifying existing ontologies as equipment and processes are changed, and for modifying and retrofitting equipment and processes when adapting to new conditions.
This section describes this plant engineering and ontology based on the first chapter “ISO 15926” of “Ontology Modeling in Physical Asset Management“.
Plant Engineering Ontology ISO15926
ISO 15926 is a platform for data integration, exchange and sharing, originally developed for the purpose of “Integration with industrial automation systems – Integration of life cycle data for process plants, including oil and gas production facilities”. The content of the standard is specified from Part 1 to Part 13, with descriptions related to ontology modeling from Part 4 to Part 8.
The main users are plant engineering companies in the U.S. and Europe, FIATECH (Fully Integrated & Automated Technologies) in the U.S., and PSOC Caesar Association (PCA) in Europe. In Europe, PSOC Caesar Association (PCA) provides not only ontologies but also systems that utilize them. In Europe, the PSOC Caesar Association (PCA) provides not only ontologies but also systems that utilize them. A large scale of data has been constructed, and together with XML PLM of siemence, it will be a reference material when constructing ontologies for physical assets in the manufacturing industry.
The table of contents is as follows.
1.1 Introduction 1.2 The ISO15926 1.2.1 Why ISO15926 1.2.2 The History 1.3 Part2: The Data Model 1.4 Part4: The Initial Reference Data 1.5 Part7: The Template Methodology 1.6 Reference Data 1.7 Object Information Model(OIM) 1.8 Part8:The Implementation 1.8.1 RDF and OWL 1.8.2 Data Flow 1.8.3 Storage 1.8.4 Part9:The Facade and SPARQL 1.8.5 Peer-to-Peer Inferencing Appendix A:Life-Cycle Activities
How ISO 15926 can be used
ISO 15926, as noted above, has become an international standard to support information integration and data interoperability, providing a framework for the consistent representation of information used in different industries and domains. The following describes how ISO 15926 can be used.
- Improving data integration and interoperability: ISO 15926 provides a standard for improving data interoperability between different systems and applications. This will facilitate the sharing and integration of information between systems with different data formats and data models.
- Project Life Cycle Efficiency: ISO 15926 supports consistency and integration of information throughout the project life cycle, including plant design, construction, operation, and maintenance. This facilitates coordination and sharing of information between different phases and stakeholders.
- Improved knowledge management: ISO 15926 can improve knowledge management within an organization through consistency and interoperability of information. This facilitates information sharing and knowledge reuse between different departments and teams within an organization.
- Facilitate system integration: ISO 15926 provides a common data model to ensure consistency of information across different systems and databases. This allows for integration of different systems and seamless information flow.
- Improved data quality: ISO 15926 provides criteria for improving data quality; ISO 15926 enables data to be validated and quality assured in terms of consistency, accuracy, and completeness of information.
Specific uses of ISO 15926 will vary depending on industry and project requirements, but common uses include standardization and data modeling efforts within organizations and the application of data formats for information exchange between industries.
About the case study of combining AI technology with ISO 15926
The following are possible examples of how ISO 15926 and AI technology can be combined
- Data quality control: ISO 15926 supports the improvement of data quality, but by leveraging AI technology, a system can be created to automatically assess, monitor, and improve data quality. This can be accomplished by using AI algorithms to detect outliers and inconsistencies in the data and implementing automated processes for correction and completion.
- Automated data mapping: data mapping between different data sources and data models is a key challenge in the application of ISO 15926. The application of AI techniques to this is expected to develop tools and systems for automated data mapping and transformation. Specifically, machine learning algorithms could be used to analyze the structure and meaning of data and automatically map it to the corresponding ISO 15926 concepts.
- Knowledge Enrichment: While ISO 15926 provides a framework for achieving information consistency, AI techniques can be combined to improve information richness and insight. This can be achieved by leveraging natural language processing (NLP) and knowledge graph techniques to connect ISO 15926 data with relevant information and extract information from relevant documents and knowledge bases.
- System Integration and Automation: Project and system integration using ISO 15926 can be a lot of work. The application of AI technology to this is expected to enable automatic generation and updating of ISO 15926-compliant data and automation of data flow between systems. Specifically, AI algorithms for data enrichment and automated data validation tools could be developed that would enable quality control of ISO 15926 data and efficient system integration.
Example implementation of SO15926 in python
Implementing ISO 15926 is complex and different approaches may be taken based on specific requirements and objectives. Below is an example of an ISO 15926 implementation in Python, but this example is simplistic and should be customized according to actual requirements.
# Import required libraries
import rdflib
# Creating RDF Graphs
graph = rdflib.Graph()
# Loading RDF Data
graph.parse('iso15926.ttl', format='turtle')
# Creating Queries
query = """
SELECT ?s ?p ?o
WHERE {
?s ?p ?o.
}
"""
# Query Execution
results = graph.query(query)
# Display Results
for row in results:
subject = row.s
predicate = row.p
object = row.o
print(f"Subject: {subject}, Predicate: {predicate}, Object: {object}")In this example, the rdflib library is used to manipulate ISO 15926 RDF data. Here, a Graph object is first created, the RDF data is read in using the parse method, and then a query is created. In this example case, we have created a simple query that retrieves all triples, but the query can be customized as needed.
Finally, the created query is passed to the query method and executed to retrieve the results. The results are retrieved using iteration, showing the subject, predicate, and object of each triple.
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