Ontologies in Process Systems Engineering

Batres, Rafael

doi:10.1002/cite.201700037

Cited by 9 publications

(8 citation statements)

References 58 publications

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“…Examples of systems that have benefited from ontologies are infrastructure of information technology, 125,126,128 process control, 142 and supply chain. 128 Onto-CAPE, which has been in development since the early 2000s, is the most widely used ontology in PSE 141 and has been applied in several modeling and design applications. 143,144 Modeling tools in PSE research come in many forms.…”

Section: Model-based Process Systems Engineeringmentioning

confidence: 99%

Model‐based systems engineering approaches to chemicals and materials manufacturing

Le,

Feingold,

Glandorf

et al. 2023

AIChE Journal

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Model‐based systems engineering (MBSE) is part of a long‐term trend toward model‐centric approaches adopted by many engineering disciplines. We establish the need of an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. We discuss the origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) and discuss limitations of the Process Systems Engineering (PSE) framework. We make the case for MBSE as a more generalizable approach. We introduce systems modeling strategies for MBSE, and a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. We demonstrate a specific use case of this method by creating a systems model for the manufacturing of an active pharmaceutical ingredient, Atropine. We conclude with a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains.

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Section: Model-based Process Systems Engineeringmentioning

confidence: 99%

Model‐based systems engineering approaches to chemicals and materials manufacturing

Le,

Feingold,

Glandorf

et al. 2023

AIChE Journal

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“…This information should be shared and exchanged autonomously amongst the entities. Ontology technology has received great attention in the past decade as an advanced tool to tackle these challenges (Batres, 2017;Ekaputra et al, 2017).…”

Section: Ontology-based Multiagent System (Mas)mentioning

confidence: 99%

Big data and machine learning: A roadmap towards smart plants

et al. 2022

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Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

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“…Integration. Ontology is a formal model that uses mathematical logic to clarify and define things as well as their relations [137]. Since they can be described by mathematical models, it is possible to implement them into a computerized environment.…”

Section: Ontology-based Knowledge Representation Andmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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Ontologies in Process Systems Engineering

Cited by 9 publications

References 58 publications

Model‐based systems engineering approaches to chemicals and materials manufacturing

Model‐based systems engineering approaches to chemicals and materials manufacturing

Big data and machine learning: A roadmap towards smart plants

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

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