J-park simulator: roadmap to smart eco-industrial parks

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.

[1]  Eric S. Fraga,et al.  A multi-agent system to facilitate component-based process modeling and design , 2008, Comput. Chem. Eng..

[2]  Edrisi Muñoz,et al.  Towards an ontological infrastructure for chemical batch process management , 2010, Comput. Chem. Eng..

[3]  Rainer Draht,et al.  Datenaustausch in der Anlagenplanung mit AutomationML , 2010 .

[4]  Leenard Baas,et al.  Critical success and limiting factors for eco-industrial parks: global trends and Egyptian context , 2011 .

[5]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[6]  Sebastian Mosbach,et al.  Design technologies for eco-industrial parks: From unit operations to processes, plants and industrial networks , 2016 .

[7]  I. Sobol.  On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[8]  Christian Bizer,et al.  DBpedia: A Multilingual Cross-domain Knowledge Base , 2012, LREC.

[9]  Asunción Gómez-Pérez,et al.  Ontological Engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web , 2004, Advanced Information and Knowledge Processing.

[10]  Selden B. Crary,et al.  Design of Computer Experiments for Metamodel Generation , 2002 .

[11]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[12]  M. Chertow.  “Uncovering” Industrial Symbiosis , 2007 .

[13]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[14]  Markus Kraft,et al.  Quantitative tools for cultivating symbiosis in industrial parks; a literature review , 2015 .

[15]  Sebastian Mosbach,et al.  The future of computational modelling in reaction engineering , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[17]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[18]  Wolfgang Marquardt,et al.  An ontology based approach for operational process modeling , 2011, Adv. Eng. Informatics.

[19]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[20]  Jan Morbach,et al.  OntoCAPE: A Re-Usable Ontology for Chemical Process Engineering , 2009 .

[21]  Jean Belanger,et al.  eMEGAsim: An Open High-Performance Distributed Real-Time Power Grid Simulator. Architecture and Specification , 2007 .

[22]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[23]  Dilek Küçük,et al.  A high-level electrical energy ontology with weighted attributes , 2015, Adv. Eng. Informatics.

[24]  Zushu Li,et al.  Petroleum Exploration Domain Ontology-Based Knowledge Integration and Sharing System Construction , 2011, 2011 International Conference on Network Computing and Information Security.

[25]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.