J-Park Simulator Research Articles

Semantic 3D city interfaces—Intelligent interactions on dynamic geospatial knowledge graphs

Abstract This article presents a system architecture and a set of interfaces that can build scalable information systems capable of large city modeling based on dynamic geospatial knowledge graphs to avoid pitfalls of Web 2.0 applications while blending artificial and human intelligence during the knowledge enhancement processes. We designed and developed a GeoSpatial Processor, an SQL2SPARQL Transformer, and a geospatial tiles ordering tasks and integrated them into a City Export Agent to visualize and interact with city models on an augmented 3D web client. We designed a Thematic Surface Discovery Agent to automatically upgrade the model’s level of detail to interact with thematic parts of city objects by other agents. We developed a City Information Agent to help retrieve contextual information, provide data concerning city regulations, and work with a City Energy Analyst Agent that automatically estimates the energy demands for city model members. We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. The logical foundations and CityGML-based conceptual schema used to describe cities in terms of the OntoCityGML ontology, together with the system of intelligent autonomous agents based on the J-Park Simulator Agent Framework, make such systems capable of assessing and maintaining ground truths with certainty. This new era of GeoWeb 2.5 systems lowers the risk of deliberate misinformation within geography web systems used for modeling critical infrastructures.

ElChemo: A cross-domain interoperability between chemical and electrical systems in a plant

The paper proposes a novel framework capable of establishing machine-to-machine (M2M) interactions between chemical and electrical systems in the industry. The framework termed as ElChemo addresses the challenges in M2M interaction of entities from different silos, such as differences in the domains’ behaviour, the heterogeneities arising from different vocabularies and software. The OntoTwin ontology has been developed based on OntoPowSys and OntoEIP ontologies, which are parts of an intelligent platform called the “J-Park Simulator (JPS)”. The ElChemo framework uses Description Logic (DL) and SPIN reasoning techniques to establish the interaction between the chemical and electrical systems in a plant. This paper presents a depropaniser section of a chemical plant and its corresponding electrical system as a use case scenario to demonstrate the interoperability between the two silos within the ElChemo framework. The results from the use case demonstrate, as a proof of concept, the potential of the proposed framework and can be considered as the first step towards the development of a knowledge graph based framework capable of increasing interoperability between cross-domain interactions.

Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology.

In this paper, we develop a knowledge graph-based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene)dimethyl ether (PODEn, where n = 3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park simulator (JPS, theworldavatar.com), an existing cross-domain knowledge graph. OntoChemExp is fully capable of supporting experimental results in the Process Informatics Model (PrIMe) database. Following this, a set of software agents are developed to perform experimental result retrieval, sensitivity analysis, and calibration tasks. The sensitivity analysis agent is used for both generic sensitivity analyses and reaction selection for subsequent calibration. The calibration process is performed as a sampling task, followed by an optimization task. The agents are designed for use with generic models but are demonstrated with ignition delay time and laminar flame speed simulations. We find that calibration times are reduced, while accuracy is increased compared to manual calibration, achieving a 79% decrease in the objective function value, as defined in this study. Further, we demonstrate how this workflow is implemented as an extension of the JPS.

Multiscale Cross-Domain Thermochemical Knowledge-Graph.

In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.

OntoPowSys: A power system ontology for cross domain interactions in an eco industrial park

Knowledge management in multi-domain, heterogeneous industrial networks like an Eco-Industrial Park (EIP) is a challenging task. In this paper, an ontology-based management system has been proposed for addressing this challenge. It focuses on the power systems domain and provides a framework for integrating this knowledge with the other domains of an EIP. The proposed ontology, OntoPowSys is expressed using a Description Logics (DL) syntax and the OWL2 language was used to make it alive. It is then used as a part of the Knowledge Management System (KMS) in a virtual EIP called the J-Park Simulator (JPS). The advantages of the proposed approach are demonstrated by conducting two case studies on the JPS. The first case study illustrates the application of optimal power flow (OPF) in the electrical network of the JPS. The second case study plays an important role in understanding the cross-domain interactions between the chemical and electrical engineering domains in a bio-diesel plant of the JPS. These case studies are available as web services on the JPS website. The results showcase the advantages of using ontologies in the development of decision support tools. These tools are capable of taking into account contextual information on top of data during their decision-making processes. They are also able to exchange knowledge across different domains without the need for a communication interface.

A Smart Contract-based agent marketplace for the J-Park Simulator - a knowledge graph for the process industry

The chemical industry is increasingly relying on agents for data acquisition, optimization, and simulation. In order to enable efficient management of agents, Knowledge Graphs (KG) together with agent composition frameworks are therefore applied. However, a method to assess the reliability of agents for such systems is absent. Therefore, this paper proposes a Smart Contract-based agent marketplace for composition frameworks to estimate the reliability of agents. In this agent marketplace, we improved the feedback-based reputation system by leveraging Smart Contracts to eliminate fraudulent ratings and to enable automation. The marketplace incorporates a rating-dependent payment mechanism as well, to further enhance trust. The paper also illustrates how this marketplace is integrated into the J-Park Simulator (JPS) agent composition framework for the automated agent selection and transaction.

Linking reaction mechanisms and quantum chemistry: An ontological approach

In this paper, a linked-data framework for connecting species in chemical kinetic reaction mechanisms with quantum calculations is presented. A mechanism can be constructed from thermodynamic, reaction rate, and transport data that has been obtained either experimentally, computationally, or by a combination of both. This process in practice requires multiple sources of data, which raises, inter alia, species naming and data inconsistency issues. A linked data-centric knowledge-graph approach is taken in this work to address these challenges. In order to implement this approach, two existing ontologies, namely OntoKin, for representing chemical kinetic reaction mechanisms, and OntoCompChem, for representing quantum chemistry calculations, are extended. In addition, a new ontology, which we call OntoSpecies, is developed for uniquely representing chemical species. The framework also includes agents to populate and link knowledge-bases created through the instantiation of these ontologies. In addition, the developed knowledge-graph and agents naturally form a part of the J-Park Simulator (JPS) – an Industry 4.0 platform which combines linked data and an eco-system of autonomous agents for cross-domain applications. The functionality of the framework is demonstrated via a use-case based on a hydrogen combustion mechanism.

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

The J-Park Simulator (JPS) acts as a continuously growing platform for integrating real-time data, knowledge, models, and tools related to process industry. It aims at simulation and optimization in cross-domain and multi-level scenarios and relies heavily on ontologies and semantic technologies. In this paper, we demonstrate the interoperability between different applications in JPS, introduce new domain ontologies into the JPS, and integrate live data. For this, we utilize a knowledge graph to store and link semantically described data and models and create agents wrapping the applications and updating the data in the knowledge graph dynamically. We present a comprehensive industrial air pollution scenario, which has been implemented as part of the JPS, to show how knowledge graphs and modular domain ontologies support the interoperability between agents. We show that the architecture of JPS increases the interoperability and flexibility in cross-domain scenarios and conclude that the potential of ontologies outweighs additional wrapping efforts.

An agent composition framework for the J-Park Simulator - A knowledge graph for the process industry

Digital twins, Industry 4.0 and Industrial Internet of Things are becoming ever more important in the process industry. The Semantic Web, linked data, knowledge graphs and web services/agents are key technologies for implementing the above concepts. In this paper, we present a comprehensive semantic agent composition framework. It enables automatic agent discovery and composition to generate cross-domain applications. This framework is based on a light-weight agent ontology, OntoAgent, which is an adaptation of the Minimal Service Model (MSM) ontology. The MSM ontology was extended with grounding components to support the execution of an agent while keeping the compatibility with other existing web service description standards and extensibility. We illustrate how the comprehensive agent composition framework can be integrated into the J-Park Simulator (JPS) knowledge graph, for the automatic creation of a composite agent that simulates the dispersion of the emissions of a power plant within a selected spatial area.

An Ontology and Semantic Web Service for Quantum Chemistry Calculations.

The purpose of this article is to present an ontology, termed OntoCompChem, for quantum chemistry calculations as performed by the Gaussian quantum chemistry software, as well as a semantic web service named MolHub. The OntoCompChem ontology has been developed based on the semantics of concepts specified in the CompChem convention of Chemical Markup Language (CML) and by extending the Gainesville Core (GNVC) ontology. MolHub is developed in order to establish semantic interoperability between different tools used in quantum chemistry and thermochemistry calculations, and as such is integrated into the J-Park Simulator (JPS)-a multidomain interactive simulation platform and expert system. It uses the OntoCompChem ontology and implements a formal language based on propositional logic as a part of its query engine, which verifies satisfiability through reasoning. This paper also presents a NASA polynomial use-case scenario to demonstrate semantic interoperability between Gaussian and a tool for thermodynamic data calculations within MolHub.

J-Park Simulator, an intelligent system for information management of eco-industrial parks

This paper presents new insights into constructing a cyber-infrastructure system, called J-Park Simulator, for the resource and energy management of eco-industrial parks. The concept of Industry 4.0 is applied to develop the system. Advanced information modeling and managing technology – ontology technology is introduced to build the knowledge base for J-Park Simulator, which delivers a decentralized information managing system. The system allows information of the individual entities been managed locally by the entity themselves, in the meanwhile facilitates information sharing via the Internet and/or intranet. A bottom-up object-oriented approach is adopted to partition and organize information of the complex eco-industrial park. The proposed work addresses key issues in computer-aided design and operation of eco-industrial parks in the context of realizing Industry 4.0.

Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks

In this paper, we introduce the concept of constructing an ontology-based decision support system called J-Park Simulator for the design and operation of eco-industrial parks. It is inspired by Jurong industrial park in Singapore. A biodiesel plant is implemented into the system as a first step. OntoCAPE is adapted for the purposes of the biodiesel plant to establish a knowledge base, which is employed to carry out a number of applications via J-Park Simulator. Firstly, information query can be performed. Information of the biodiesel plant can be extracted through natural language query. Secondly, J-Park Simulator can be used to carry out process simulation. New process equilibrium can be evaluated after certain operation parameters change. Thirdly, process optimization can be realized through J-Park Simulator. Optimal operation condition under different market scenarios can be obtained for the biodiesel producing process in order to reduce the energy consumption and achieve maximal plant profit.

Knowledge management of eco-industrial park for efficient energy utilization through ontology-based approach

An ontology-based approach for Eco-Industrial Park (EIP) knowledge management is proposed in this paper. The designed ontology in this study is formalized conceptualization of EIP. Based on such an ontological representation, a Knowledge-Based System (KBS) for EIP energy management named J-Park Simulator (JPS) is developed. By applying JPS to the solution of EIP waste heat utilization problem, the results of this study show that ontology is a powerful tool for knowledge management of complex systems such as EIP. The ontology-based approach can increase knowledge interoperability between different companies in EIP. The ontology-based approach can also allow intelligent decision making by using disparate data from remote databases, which implies the possibility of self-optimization without human intervention scenario of Internet of Things (IoT). It is shown through this study that KBS can bridge the communication gaps between different companies in EIP, sequentially more potential Industrial Symbiosis (IS) links can be established to improve the overall energy efficiency of the whole EIP.

Effect of solvents on kinetics of oxidation of butyraldehyde with molecular oxygen

In this paper, a linked-data framework for connecting species in chemical kinetic reaction mechanisms with quantum calculations is presented. A mechanism can be constructed from thermodynamic, reaction rate, and transport data that has been obtained either experimentally, computationally, or by a combination of both. This process in practice requires multiple sources of data, which raises, inter alia, species naming and data inconsistency issues. A linked data-centric knowledge-graph approach is taken in this work to address these challenges. In order to implement this approach, two existing ontologies, namely OntoKin, for representing chemical kinetic reaction mechanisms, and OntoCompChem, for representing quantum chemistry calculations, are extended. In addition, a new ontology, which we call OntoSpecies, is developed for uniquely representing chemical species. The framework also includes agents to populate and link knowledge-bases created through the instantiation of these ontologies. In addition, the developed knowledge-graph and agents naturally form a part of the J-Park Simulator (JPS) – an Industry 4.0 platform which combines linked data and an eco-system of autonomous agents for cross-domain applications. The functionality of the framework is demonstrated via a use-case based on a hydrogen combustion mechanism.