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J-Park Simulator: Roadmap to Smart Eco-Industrial Parks

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Kleinelanghorst, MJ 
Zhou, L 
Sikorski, J 
Shyh, EFY 
Aditya, K 

Abstract

This paper presents the J-Park Simulator (JPS), a virtualisation of an Eco-Industrial Park (EIP). The JPS combines concepts of machine-to-machine (M2M) communication inspired by the Semantic Web and Industry 4.0, and advanced mathematical modelling to create a modelling platform for designing, computer-aided process engineering (CAPE) and managing an EIP. The overall aim is to reduce carbon footprint and maximise resource efficiency by taking advantage of symbiotic inter-company exchanges of material and energy. The paper outlines system architecture, supporting infrastructure, and its components such as database, data processing, data editing and visualisation, and system modelling tools. A cross-domain ontology is used to represent the wealth and complexity of data at multiple levels and across domains united in a shared data and information hub that provides real-time situational awareness of industrial processes. Networks of fast-to-evaluate surrogate models are employed to conduct real time simulations that quantify CO2 emission reduction using, for example, waste heat recovery (WHR), and carry out cross-domain simulations both at steady-state and in transient operation. The cross-domain ontology is furthermore used to answer semantic queries. The approach and some of the benefits of this platform are demonstrated in several case studies. We find that there is significant scope to realise as yet unexploited potential for energy savings.

Description

Keywords

46 Information and Computing Sciences, 40 Engineering, 4010 Engineering Practice and Education, Generic health relevance, 12 Responsible Consumption and Production, 9 Industry, Innovation and Infrastructure

Journal Title

ACM International Conference Proceeding Series

Conference Name

ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing

Journal ISSN

Volume Title

2017-January

Publisher

ACM