Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard

Oeing, Jonas; Welscher, Wolfgang; Krink, Niclas; Jansen, Lars; Henke, Fabian; Kockmann, Norbert

doi:10.1016/j.dche.2022.100038

Cited by 12 publications

(22 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data collection takes place using a DEXPI2graphML converter, which was already shown in a previous publication [27]. The P&ID stored in the DEXPI format is described by three information levels, which are used to extract the topology.…”

Section: Pandid Datamentioning

confidence: 99%

“…The Piping-NetworkSystem describing the piping and piping components, as well as the InstrumentationFunction describing the signal connections and process control. All relevant plant information is extracted according to [27] and serves as a baseline for the pre-HAZOP presented in this publication. The P&ID graphs generated by the DEXPI2graphML converter [27] are subjected to further processing to obtain the most efficient generalization adapted to safety assessments.…”

Section: Pandid Datamentioning

confidence: 99%

“…All relevant plant information is extracted according to [27] and serves as a baseline for the pre-HAZOP presented in this publication. The P&ID graphs generated by the DEXPI2graphML converter [27] are subjected to further processing to obtain the most efficient generalization adapted to safety assessments. In particular, the processing of equipment groups, the detection of safetyrelevant functional assemblies and the processing of measuring points are important.…”

Section: Pandid Datamentioning

confidence: 99%

See 2 more Smart Citations

preHAZOP: Graph‐Based Safety Analysis for Early Integration into Automated Engineering Workflows

Oeing

Holtermann

Welscher

et al. 2023

Chemie Ingenieur Technik

Self Cite

View full text Add to dashboard Cite

The increasing digitalization and standardization within the process industry lead to a high availability of digital, machine-readable processes and plant descriptions. In particular, the publication of the DEXPI standard provides a digital representation of plant topologies including a complete description of all specifications. In early planning phases, this can be used as the basis for an automated safety assessment since digital availability significantly simplifies accessibility for smart search algorithms. This paper presents the preHAZOP search algorithm, which was developed to analyze P&IDs in DEXPI format and to detect safety-critical deviations regarding their risk according to a classical HAZOP analysis. The preHAZOP is of particular interest in early process development stages and can be easily integrated into modern, digital engineering workflows.

show abstract

Section: Pandid Datamentioning

confidence: 99%

Section: Pandid Datamentioning

confidence: 99%

Section: Pandid Datamentioning

confidence: 99%

See 1 more Smart Citation

preHAZOP: Graph‐Based Safety Analysis for Early Integration into Automated Engineering Workflows

Oeing

Holtermann

Welscher

et al. 2023

Chemie Ingenieur Technik

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other approaches already use artificial intelligence algorithms to predict the topology of plants. However, these mostly follow a sequential approach [17][18][19]. Thus, Vogel et al and Hirtreiter et al use strings in the SFILES 2.0 [20] notation to describe the topology of PFD, including control structure [17,18] as strings.…”

Section: Graph Learning On Machine-readable Pandid Datamentioning

confidence: 99%

“…These allow either the prediction of follow-up process units or additional control structures. As a preliminary work, Oeing et al [19] show, in addition to the sequential processing of plant topologies, especially the modeling of plant topologies as graphs. In this work, standardized DEXPI P&IDs were also already used as a data basis.…”

Section: Graph Learning On Machine-readable Pandid Datamentioning

confidence: 99%

Graph Learning in Machine‐Readable Plant Topology Data

Oeing

Brandt

Wiedau

et al. 2023

Chemie Ingenieur Technik

Self Cite

View full text Add to dashboard Cite

Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document‐oriented to a data‐oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well‐developed, machine‐readable data standard for describing piping and instrumentation diagrams (P&ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine‐readable P&IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine‐readable P&ID data, which results in the efficient engineering and development of new P&IDs.

show abstract

Toward automatic generation of control structures for process flow diagrams with large language models

Hirtreiter,

Schulze Balhorn,

Schweidtmann

2023

AIChE Journal

View full text Add to dashboard Cite

Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data‐driven method for the prediction of control structures. Our methodology is inspired by end‐to‐end transformer‐based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) without control structures are translated to PFDs with control structures. We represent the topology of PFDs as strings using the SFILES 2.0 notation. We pretrain our model using generated PFDs to learn the grammatical structure. Thereafter, the model is fine‐tuned leveraging transfer learning on real PFDs. The model achieved a top‐5 accuracy of 74.8% on 10,000 generated PFDs and 89.2% on 100,000 generated PFDs. These promising results show great potential for AI‐assisted process engineering. The tests on a dataset of 312 real PFDs indicate the need for a larger PFD dataset for industry applications and hybrid artificial intelligence solutions.

show abstract

Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard

Cited by 12 publications

References 10 publications

preHAZOP: Graph‐Based Safety Analysis for Early Integration into Automated Engineering Workflows

preHAZOP: Graph‐Based Safety Analysis for Early Integration into Automated Engineering Workflows

Graph Learning in Machine‐Readable Plant Topology Data

Toward automatic generation of control structures for process flow diagrams with large language models

Contact Info

Product

Resources

About