Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams. (2nd September 2016)

• Record Type: Journal Article
• Authors: Arroyo, Esteban; Hoernicke, Mario; Rodríguez, Pablo; Fay, Alexander
• Is Part Of: Computers & chemical engineering. Volume 92(2016)
• Page Start: 112
• Page End: 132
• Publication Date: 2016-09-02
• Subjects: Digital plant -- Legacy engineering documentation -- Piping and instrumentation diagram -- Optical recognition -- Automatic model generation -- Qualitative simulation model; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.04.040
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A method for converting process diagrams into simulation models is proposed. The method is based on optical recognition, semantic analysis and OO modelling. Resulting models can be used for validation of control solutions within FAT. A case study of a real process plant demonstrates the applicability of the method. Abstract: Confronted with the need of plant modernization, facility owners and contractors in the process industry invest significant efforts to create digital plant models allowing for simulation and thereby validation of new engineering solutions. Although an important part of the information required for this task already exists in form of legacy engineering documentation, current computer-aided methods for generating digital plant models cannot exploit this source of knowledge owing to the non-computer-interpretable nature of the available information sources. In an effort to bridge the existing gap, this contribution presents a method based on optical recognition and semantic analysis, which is capable of automatically converting legacy engineering documents, specifically piping and instrumentation diagrams, into object-oriented plant descriptions and ultimately into qualitative plant simulation models. Resulting simulation models can serve as a basis to support engineering tasks requiring low-fidelity simulation, such as the validation of base control functions during the factory acceptance test (FAT).