Asset Lifecycle Information Orchestration in Process Systems Engineering

Produktbeschreibung

This thesis explores the application of a holistic data integration approach with a focus on common planning objects in the process industry. The hypothesis posits that this approach can improve the quantity and quality of mapping relations between data models for data engineers along the asset lifecycle. To evaluate this hypothesis, a comprehensive set of evaluation paradigms was defined and applied to various data standards, with assessments conducted with different levels. The research provides valuable insights into the challenges and potential benefits of data integration in the process industry. Findings demonstrate that the adoption of a holistic approach, centered around common planning objects, indeed enhances mapping relations between data models. Benefits include improved data quality, increased data quantity, and enhanced efficiency in managing complex data structures

Contents
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . III
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX
Abstract (English) . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIV
Kurzfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV
1. Introduction 1
1.1. Does a holistic data model help? . . . . . . . . . . . . . . . . . 3
1.1.1. Why former approaches failed . . . . . . . . . . . . . . . 4
1.1.2. From theoretical approaches to industrial practice . . . . 4
1.1.3. The small focus of practically used data models . . . . . 5
1.1.4. Semantical incorrectness of export information . . . . . . 6
1.2. Towards a holistic hypothesis . . . . . . . . . . . . . . . . . . . 7
1.2.1. The enabler . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.2. Focus of the problem . . . . . . . . . . . . . . . . . . . 9
1.2.3. Topics that are not explicitly covered . . . . . . . . . . . 9
1.3. Summary and Overview . . . . . . . . . . . . . . . . . . . . . . 10
2. State of the art in data integration methods and standards 13
2.1. Digitization requires data handling . . . . . . . . . . . . . . . . 13
2.2. Scientific approaches . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1. Semantic and syntactic heterogeneity . . . . . . . . . . . 16
2.2.2. Data integration principles . . . . . . . . . . . . . . . . . 17
2.2.3. Introduction to ontologies . . . . . . . . . . . . . . . . . 19
2.2.4. Collaborative and distributed chemical engineering . . . . 20
2.2.5. The OntoCAPE ontology . . . . . . . . . . . . . . . . . 22
2.3. Industry 4.0 – Cyber Physical Systems . . . . . . . . . . . . . . . 28
2.3.1. Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.3.2. Technologies . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3.3. The Asset Administration Shell: Enabling Digital Transformation in Industry 4.0 . . . . . . . . . . . . . . . . . . . 32
2.4. Industry approaches and international standards . . . . . . . . . 34
2.4.1. International standardization initiatives . . . . . . . . . . 35
2.4.2. Industry In-house initiatives . . . . . . . . . . . . . . . . 39
2.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3. A new, holistic and expandable integration approach 43
3.1. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1.1. Different data perspectives . . . . . . . . . . . . . . . . 44
3.1.2. Document driven information exchange . . . . . . . . . . 45
3.1.3. The interoperability challenge . . . . . . . . . . . . . . . 46
3.2. The holistic asset lifecycle modelling approach . . . . . . . . . . 47
3.2.1. The Functional Requirements aspect . . . . . . . . . . . 49
3.2.2. The Functional-Design and Asset-Specification aspects . . 52
3.2.3. Assets in operation . . . . . . . . . . . . . . . . . . . . . 60
3.3. Discussion and remarks on the integration approach . . . . . . . 61
3.3.1. Examples for aspects of process and plant elements . . . 61
3.3.2. The asset lifecycle phases and conventional approaches . . 62
3.3.3. Asset Model Differentiation . . . . . . . . . . . . . . . . 64
3.4. New paradigms based on the asset lifecycle integration approach . 66
3.4.1. Categorization disciplines of norms and standards . . . . . 66
3.4.2. Evaluation criteria according to the literature . . . . . . . 68
3.4.3. Evaluation criteria according to industry requirements . . 68
3.4.4. A comprehensive evaluation criteria list as paradigms . . . 70
3.5. Summary on methodology . . . . . . . . . . . . . . . . . . . . . 72
4. Paradigm application on industry standards along the asset
lifecycle integration concept 73
4.1. Investigation of approaches and standards . . . . . . . . . . . . . 73
4.1.1. General standards for data exchange, data integration and
interoperability . . . . . . . . . . . . . . . . . . . . . . . 74
4.1.2. Classification, identification and plant breakdown structures 78
4.1.3. Standardization of processes structures and process design
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.1.4. Taxonomies of apparatuses and machines . . . . . . . . . 83
4.1.5. Process control and instrumentation . . . . . . . . . . . 84
4.1.6. Standardization of schematic diagrams . . . . . . . . . . 88
4.2. Quantification of paradigms with respect to standards . . . . . . 89
4.2.1. Paradigm scoring . . . . . . . . . . . . . . . . . . . . . . 90
4.2.2. Evaluation of standards according to paradigms . . . . . . 91
4.3. Conclusion on standards evaluation . . . . . . . . . . . . . . . . 98
5. Review, Conclusion and Outlook 101
5.1. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.2. Approaches and publication that have not been covered yet . . . 102
5.3. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.3.1. Relevance to Artificial Intelligence in the Process Industry 104
5.3.2. Application of Research in the Process Industry . . . . . . 105
A. Appendix: Industrial application 109
A.1. Use Cases in Data Exchange . . . . . . . . . . . . . . . . . . . . 109
A.1.1. The missing specification problem . . . . . . . . . . . . . 109
A.1.2. The 1:n integration problem . . . . . . . . . . . . . . . . 110
A.2. Consistent configuration for seamless tool integration . . . . . . . 112
B. Appendix: Towards better decision making along the asset
lifecycle 113
B.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
B.2. Introduction to decision extraction . . . . . . . . . . . . . . . . 115
B.2.1. Structured decision handling . . . . . . . . . . . . . . . . 117
B.2.2. Requirements for a data structure of design rationales . . 117
B.2.3. Foundations of design rationales . . . . . . . . . . . . . . 118
B.2.4. Decision capturing . . . . . . . . . . . . . . . . . . . . . 121
B.2.5. Design rationals in process engineering . . . . . . . . . . 123
B.2.6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 125
B.3. The engineering decision extraction method . . . . . . . . . . . . 125
B.3.1. Generel Methodology . . . . . . . . . . . . . . . . . . . 126
B.3.2. Three Tier Approach for decision extraction . . . . . . . 127
B.3.3. The decision extraction software framework . . . . . . . . 129
B.4. Applications of the decision extraction method . . . . . . . . . . 138
B.4.1. Decision extraction from simulations and piping diagrams 138
B.4.2. Decision extraction from P&IDs . . . . . . . . . . . . . . 140
B.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Bibliography 145

Keywords: Anlagenlebenszyklus, Datenintegration, Prozessleittechnik, Paradigmenbewertung, CAESysteme, Interoperabilität, Prozesssystemtechnik, Ontologiemodellierung, Datenkonsistenz, Asset Lifecycle, Data Integration, Process Systems Engineering, Paradigm Evaluation, CAE Systems, Interoperability, Process