J-Park Simulator: An ontology-based platform for cross-domain scenarios in process industry

Eibeck, Andreas; Lim, Mei Qi; Kraft, Markus

doi:10.1016/j.compchemeng.2019.106586

Cited by 55 publications

(62 citation statements)

References 31 publications

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“…Further opportunities arise regarding databases, frameworks, methodologies, and platforms for chemistry and reaction engineering [465] , [466] , [467] , [468] , [469] , [470] . Systematic and consistent approaches to numerical simulation of reacting systems [465] , preserving fundamental model details while saving computational time [466 , 467] , facile intercomparison of mechanisms and kinetic information [468 , 469] , recommendation of reaction routes to optimize synthesis strategies [470] , environmental information considering real-time combustion emissions and their local dispersion [469 , 472] are only a few examples that may spark off further ideas and applications for chemical processes and energy conversion systems [470] , [471] , [472] , [473] . The need for high-quality experimental data should, however, be stressed again in this context to ensure that simulations and models remain physically trustworthy.…”

Section: Final Thoughtsmentioning

confidence: 99%

Combustion in the future: The importance of chemistry

Kohse‐Höinghaus

2021

Proceedings of the Combustion Institute

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Combustion involves chemical reactions that are often highly exothermic. Combustion systems utilize the energy of chemical compounds released during this reactive process for transportation, to generate electric power, or to provide heat for various applications. Chemistry and combustion are interlinked in several ways. The outcome of a combustion process in terms of its energy and material balance, regarding the delivery of useful work as well as the generation of harmful emissions, depends sensitively on the molecular nature of the respective fuel. The design of efficient, low-emission combustion processes in compliance with air quality and climate goals suggests a closer inspection of the molecular properties and reactions of conventional, bio-derived, and synthetic fuels. Information about flammability, reaction intensity, and potentially hazardous combustion by-products is important also for safety considerations. Moreover, some of the compounds that serve as fuels can assume important roles in chemical energy storage and conversion. Combustion processes can furthermore be used to synthesize materials with attractive properties. A systematic understanding of the combustion behavior thus demands chemical knowledge. Desirable information includes properties of the thermodynamic states before and after the combustion reactions and relevant details about the dynamic processes that occur during the reactive transformations from the fuel and oxidizer to the products under the given boundary conditions. Combustion systems can be described, tailored, and improved by taking chemical knowledge into account. Combining theory, experiment, model development, simulation, and a systematic analysis of uncertainties enables qualitative or even quantitative predictions for many combustion situations of practical relevance. This article can highlight only a few of the numerous investigations on chemical processes for combustion and combustion-related science and applications, with a main focus on gas-phase reaction systems. It attempts to provide a snapshot of recent progress and a guide to exciting opportunities that drive such research beyond fossil combustion.

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Section: Final Thoughtsmentioning

confidence: 99%

Combustion in the future: The importance of chemistry

Kohse‐Höinghaus

2021

Proceedings of the Combustion Institute

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“…The retrieval or update query is sent to a SPARQL endpoint, a service that performs the query or update on a triple store. As described by Eibeck et al (2019), agents in JPS also communicate with each other directly. HTTP 5 is used as a communication protocol for this purpose by the agents, which use the GET 6 method to exchange data, as well as the POST 7 and PUT 8 methods to update each other about any changes in their states.…”

Section: Jps and Knowledge Graphmentioning

confidence: 99%

“…JPS and the Parallel World Framework rely heavily on the Semantic Web Stack. Its main architectural building blocks are detailed by Eibeck et al (2019). In this paper, Section 2 provides an overview of the JPS system as well as its knowledge graph technology.…”

Section: Introductionmentioning

confidence: 99%

A Parallel World Framework for scenario analysis in knowledge graphs

Eibeck

Chadzynski

Lim

et al. 2020

DCE

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This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture.

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“…This allows the representation of data encompassing both empirically observed results and calculated output to record the state of a system and involved models (both physics- and data-based) to characterize the system as a function of its state and other model parameters. JPS facilitates automation of tasks via an ecosystem of computational and representational agents (of various types, 14 featuring behaviors 15 including simple, composite, sequential, and parallel) that operate on the knowledge graph. The OntoAgent ontology 16 has the logical infrastructure and coverage in terms of concepts and properties for the codification of agents.…”

Section: Knowledge-graph Approachmentioning

confidence: 99%

Knowledge Graph Approach to Combustion Chemistry and Interoperability

et al. 2020

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In this paper, we demonstrate through examples how the concept of a Semantic Web based knowledge graph can be used to integrate combustion modeling into cross-disciplinary applications and in particular how inconsistency issues in chemical mechanisms can be addressed. We discuss the advantages of linked data that form the essence of a knowledge graph and how we implement this in a number of interconnected ontologies, specifically in the context of combustion chemistry. Central to this is OntoKin, an ontology we have developed for capturing both the content and the semantics of chemical kinetic reaction mechanisms. OntoKin is used to represent the example mechanisms from the literature in a knowledge graph, which itself is part of the existing, more general knowledge graph and ecosystem of autonomous software agents that are acting on it. We describe a web interface, which allows users to interact with the system, upload and compare the existing mechanisms, and query species and reactions across the knowledge graph. The utility of the knowledge-graph approach is demonstrated for two use-cases: querying across multiple mechanisms from the literature and modeling the atmospheric dispersion of pollutants emitted by ships. As part of the query use-case, our ontological tools are applied to identify variations in the rate of a hydrogen abstraction reaction from methane as represented by 10 different mechanisms.

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J-Park Simulator: An ontology-based platform for cross-domain scenarios in process industry

Abstract: The J-Park Simulator (JPS) acts as a continuously growing platform for integrating real-time data, knowledge, models, and tools related to process industry.

Cited by 55 publications

References 31 publications

Combustion in the future: The importance of chemistry

Combustion in the future: The importance of chemistry

A Parallel World Framework for scenario analysis in knowledge graphs

Knowledge Graph Approach to Combustion Chemistry and Interoperability

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