Capturing and Exploiting Plant Topology and Process Information as a Basis to Support Engineering and Operational Activities in Process Plants Von der Fakultät für Maschinenbau der Helmut-Schmidt-Universität/Universität der Bundeswehr Hamburg zur Erlangung des akademischen Grades eines Doktor-Ingenieurs (Dr. Ing.) genehmigte DISSERTATION von M.Sc. Esteban Arroyo Esquivel aus Alajuela, Costa Rica Hamburg, 2017 Gutachter: Univ. Prof. Dr.-Ing. Alexander Fay Vorsitzender: Prof. Nina F. Thornhill Tag der mündlichen Prüfung: 9. Juni 2017 Information forms the basis for the analysis of any problem as well as for the creation of every model, method, or algorithm conceived to solve that problem. A Dio. Acknowledgments The completion of any important project in life involves the collaboration of many helping hands at different stages. This doctoral dissertation –final outcome of over three years of research at the Institute of Automation Technology, Helmut Schmidt University– was certainly not the exception. During this time, I was lucky to count on several wonderful people, who supported me academically and emotionally. I would like to thank every one of them for their contribution. My first thanks goes to Univ. Prof. Dr.-Ing. Alexander Fay for the opportunity to join his chair as a research assistant and thereby for the chance to get to know a new country, language, and culture, as well as to acquire cutting-edge knowledge on the field of automation technology. His guidance, scientific advice, and support along these years were fundamental for the completion of this work. With the same gratitude, I thank Prof. Nina F. Thornhill from the Center for Process Systems Engineering at Imperial College London for her interest in my research, as well as for the honor of serving as the second reviewer of this thesis. Another special thanks goes to Mario Hoernicke, research partner at ABB Corporate Research, for his support within the experimental stage of this work, especially for his input on topics related to automatic derivation of plant simulation models. Thanks as well to Johanna Meisner, Frank Schumacher, Zen-Zen Yen, Lorenzo Stroppa, and Sebas- tian Schroeck for their exhaustive review of the manuscript and their valuable recommendations. Likewise, I thank my colleagues, research assistants, and students for their feedback and interesting discussions on topics related to the content of this thesis. Last but not least, I express my sincere gratitude to my beloved ones –family, friends and (amazing) girlfriend– who supported me unconditionally during this journey. Many thanks to all! Cologne, June 2017 Esteban Arroyo Contents 1 Introduction 1 1.1 Motivation . .1 1.2 Context . .2 1.2.1 Automation of Automation (AoA) . .2 1.2.2 Overview of solutions for automatic engineering information reuse . .3 1.3 Scope . .3 1.3.1 Use cases . .3 1.3.2 Information . .5 1.3.3 Systems and processes . .7 1.4 Case study . .7 1.4.1 Overview . .7 1.4.2 Plant topology . .7 1.4.3 Process description . .9 1.5 Thesis layout . .9 2 Automatic information extraction from engineering documents 13 2.1 Documentation in process industries . 13 2.2 Theoretical framework . 14 2.2.1 Engineering diagrams . 14 2.2.2 Graphic types and data formats . 15 2.2.3 Graphic recognition techniques . 17 2.3 State of the art . 21 2.3.1 Raster-based approaches . 21 2.3.2 Vector-based approaches . 23 2.4 Drawbacks of the state of the art . 24 2.5 Proposed methods for engineering document analysis . 25 2.5.1 Problem overview and practical considerations . 25 2.5.2 Method 1: Raster-based document recognition . 26 2.5.3 Method 2: Vector-based document recognition . 32 2.6 Validation . 40 2.6.1 Recognition of a piping and instrumentation diagram . 40 i Contents ii 2.6.2 Recognition of a control logic diagram . 42 2.7 Assessment . 44 2.7.1 Advantages . 44 2.7.2 Limitations . 45 2.7.3 Industrial applicability . 46 2.8 Chapter summary . 47 3 Derivation of OO plant models 49 3.1 Object-orientation in process automation . 49 3.2 Theoretical framework . 50 3.2.1 Object-oriented markup and data exchange languages . 50 3.3 State of the art . 51 3.4 Proposed method for OO modeling of plant data . 52 3.4.1 OO model generation . 52 3.4.2 Visual representation and inspection . 57 3.4.3 Inter-model referencing . 58 3.5 Validation . 59 3.5.1 OO modeling of a piping and instrumentation diagram . 59 3.5.2 OO modeling of a control logic diagram . 62 3.6 Assessment . 64 3.7 Chapter summary . 64 4 Integration of engineering information sources 65 4.1 Data integration in the process industry . 65 4.2 Theoretical framework . 66 4.2.1 Document Object Model (DOM) . 66 4.2.2 Data access concepts in AutomationML . 67 4.3 State of the art . 68 4.4 Proposed method for plant data integration . 69 4.4.1 Integration of P&IDs and CLDs . 69 4.4.2 Identification and aggregation of additional plant and process data . 71 4.4.3 Data access and management within the integrated model . 74 4.5 Validation . 77 4.5.1 Integration of P&IDs and CLDs . 77 4.5.2 Aggregation of AUC AFDD data . 79 iii Contents 4.5.3 Data access and management in the integrated topology model . 80 4.6 Assessment . 81 4.7 Chapter summary . 81 5 Automatic derivation of low-fidelity simulation models 83 5.1 Simulation models within plant modernization projects . 83 5.2 Theoretical framework . 84 5.2.1 Types of simulation . 84 5.2.2 Simulation languages and environments . 85 5.2.3 Communication standards . 85 5.3 State of the art . 86 5.4 Proposed method for automatic creation of simulation models . 87 5.4.1 General simulation aspects . 87 5.4.2 Assumptions and prerequisites for simulation . 88 5.4.3 Algorithm for model generation . 89 5.4.4 Control execution . 91 5.5 Validation . 93 5.5.1 Generated low-fidelity model . 93 5.5.2 Application examples . 93 5.6 Assessment . 98 5.6.1 Considerations on the approach scope and limitations . 98 5.6.2 Levels of detail and additional use cases . 99 5.6.3 Applicability in industrial practice . 100 5.7 Chapter summary . 100 6 Plant abnormal behavior analysis 101 6.1 The complexity of plant monitoring in process plants . 101 6.2 Theoretical framework . 103 6.2.1 Classification of plant diagnosis methods . 103 6.2.2 Connectivity and causality . 104 6.2.3 Signed directed graphs . 106 6.3 State of the art . 107 6.3.1 Model-based methods . 107 6.3.2 Process history-based methods . 109 6.4 Drawbacks of the state of the art . 110 Contents iv 6.5 Proposed method for disturbance propagation analysis . 112 6.5.1 General diagnosis concept . 112 6.5.2 Conceptual artifacts . ..