Information needs and challenges in future process safety

Lee, John; Hassall, Maureen

doi:10.1016/j.dche.2022.100017

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…Natural language processing (NLP) and text mining techniques can be used to automatically extract information from unstructured data sources, such as documents (Cascini et al, 2004) or technical reports (Feng et al, 2021). Lee et al (2022) suggested a framework to explain the sources of data, their nature and the information challenge particularly facing the considerations of process safety. Their P3model focuses on people, plant and procedures within the field of process safety.…”

Section: Approaches For Predefining and Automating Information Retrievalmentioning

confidence: 99%

Introducing a multipliable BOM-based automatic definition of information retrieval in plant engineering

Layer,

Neubert,

Stelzer

2024

Proc. Des. Soc.

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The complexity of process plants and the growing demand for digitalization require efficient and accurate information retrieval throughout the lifecycle phases of a process plant. This paper discusses the concept of instantiation and introduces a method for identifying and multiplying required information in plant engineering using scalable so-called Instantiation Blocks linked to the Bill of Material. Core functionality, an ontology graph and a user interface based on Python and React are developed to demonstrate the implementation of the framework and validate its effectiveness in practice.

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Section: Approaches For Predefining and Automating Information Retrievalmentioning

confidence: 99%

Introducing a multipliable BOM-based automatic definition of information retrieval in plant engineering

Layer,

Neubert,

Stelzer

2024

Proc. Des. Soc.

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“…Lee et al [1] show that the digital twin serves as an essential basis for optimized process control, and those process safety accidents can be reduced. The authors conclude that for process safety considerations, approaches must be developed that use data that unifies different aspects: frameworks for describing structures, plant descriptions, and employees' work processes [2].…”

Section: Improvement Of Plant Maintenance Service and Safetymentioning

confidence: 99%

“…A mathematical description of the idea of GNNs is represented by Eq. ( 1) and (2). The information that a node gets from its neighbors is aggregated with an aggregation function and processed with an update function to the new state of the node.…”

Section: Graph Neural Networkmentioning

confidence: 99%

Graph Learning in Machine‐Readable Plant Topology Data

Oeing

Brandt

Wiedau

et al. 2023

Chemie Ingenieur Technik

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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.

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“…This framework is needed to conquer both the volume and variety of data needed to implement digital twins. Lee et al (2022) give typical sizes for data generated in each phase of the development of an oil platform. Conceptual design generates 100000 documents per project, detailed design generates 100000 tags, each with around 40 attributes.…”

Section: Objectivementioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

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The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

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Information needs and challenges in future process safety

Cited by 14 publications

References 22 publications

Introducing a multipliable BOM-based automatic definition of information retrieval in plant engineering

Introducing a multipliable BOM-based automatic definition of information retrieval in plant engineering

Graph Learning in Machine‐Readable Plant Topology Data

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

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