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. (c) 2021 Elsevier Ltd. All rights reserved.

In this paper we propose a novel framework capable of establishing machine-tomachine (M2M) interactions between chemical and electrical systems in the industry. The semantic 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. As use-case we study 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 indicate that the proposed approach can achieve significant economic benefits. Highlights • OntoTwin ontology for cross-domain coupling of chemical and electrical domains in a chemical plant. • Implement ElChemo framework as a component of J-Park Simulator. • Ensure product quality and power quality in ElChemo framework by using the SPARQL Inference Notation (SPIN). • Discuss the potential of integrating ElChemo framework into agent composition framework.

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.

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.

Small Modular Reactor (SMR) is a small, compact version of a conventional Nuclear Power Plant (NPP) and holds much promise for the future. Installing SMRs in a region requires a series of carefully planned steps out of which site selection is a critical one. This paper proposes a novel mathematical model for evaluating potential land sites for their suitability of hosting a modular NPP. Most existing decision making tools for NPP site selection rely on qualitative information from the experts. These tools require significant resources and therefore can only be applied to a limited number of selected sites. The proposed model is a Mixed Integer Non-linear Programming (MINLP) formulation which considers a variety of factors like cost, cooling water availability, earthquake risk, etc. to identify best locations for the SMRs in a distributed power system. A case study based on a virtual EIP simulator is conducted to demonstrate the capabilities of the model by finding the optimal locations of modular NPPs. The model offers a preliminary platform for carrying out further extensive studies. (C) 2019 Elsevier Ltd. All rights reserved.

This paper provides insight into the methodology employed for the development of a smart system called the J-Park simulator (JPS). JPS provides a virtual representation of an Eco-Industrial Park (EIP) and contains information pertaining to various aspects of an EIP. An ontology based approach is used to store this information in a structured and machine-readable form. Ontology represents data in a structured form and can be used for the creation of knowledge bases. These knowledge bases can then be utilised to conduct case studies on JPS. In one such case study the most suitable locations for modular nuclear plants in an EIP is identified. The architecture for the case study and how it can be implemented in JPS is demonstrated in the paper.

The contact time in two batch adsorbers were optimized using unreacted shrinking core model for the adsorption of biopolymeric pigments in distillery spentwash by fly ash adsorbent. The total optimal contact time obtained was 40 min for 3 m3 adsorbate volume having initial biopolymer concentration of 1.5 mg/dm3. The film pore diffusion model was used to determine the external mass transfer coefficient, effective diffusion coefficient, and bed capacity for continuous adsorption process. The adsorbent bed capacities at different initial biopolymeric concentrations (2,000, 5,500, 8,700, and 12,500 mg/ml) are 69.26, 114.41, 143.64, and 172 mg/g, respectively. The effective diffusion coefficients at various bed heights were determined for continuous adsorption process.