Semantic approach for pre-assessment of environmental indicators in Industrial Symbiosis

Trokanas, Nikolaos; Cecelja, Franjo; Raafat, Tara

doi:10.1016/j.jclepro.2013.12.046

Cited by 41 publications

(38 citation statements)

References 16 publications

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“…A latest incarnation of such online materials exchange attempts to advance the free‐market mechanism through ontology engineering and semantic matching. The eSymbiosis platform demonstrated the use of an ontology to structure listing data, enabling automatic generation of suggestions of possible matches based on semantic similarity among its listings (Cecelja, Raafat, et al., ; Cecelja, Trokanas, Raafat, & Yu, ; Raafat, Trokanas, Cecelja, & Bimi, ; Trokanas, Cecelja, & Raafat, ; Trokanas, Cecelja, & Raafat, ).…”

Section: Thematic Analysismentioning

confidence: 99%

“…Similarly, process input–output based matching tools are observed to be applied at the scale of industrial park (Hein et al., ; Sterr & Ott, ), city/region (Brown et al., ; Cecelja, Raafat, et al., ; Cecelja, Trokanas, et al., ; Low et al., ; Raafat et al., ; Song et al., ; Trokanas et al., ), and the national level (Grant et al., ; Jensen, Basson, Hellawell, Bailey, & Leach, ; Paquin & Howard‐Grenville, ; Wang, ). This tool has also been applied in the self‐driven (Cecelja, Raafat, et al., ; Cecelja, Trokanas, et al., ; Raafat et al., ; Trokanas et al., ), top‐down driven (Sterr & Ott, ), and intermediary‐driven context (Grant et al., ; Jensen et al., ; Paquin & Howard‐Grenville, ; Wang, ).…”

Section: Thematic Analysismentioning

confidence: 99%

See 1 more Smart Citation

Tools for promoting industrial symbiosis: A systematic review

Yeo

Masi

Low

et al. 2019

J of Industrial Ecology

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Industrial symbiosis (IS) employs a cross‐organizational perspective to seek synergistic pairings of one company's waste output to another company's input, enabled by interfirm cooperation through resource and information sharing. Orchestrating IS relationships among companies, however, remains a complex process. In the literature, a wide range of qualitative and quantitative tools have emerged, tackling issues ranging from identifying IS creation opportunities to performance evaluation. Thus far, the available literature has focused on separate aspects and perspective of IS creation. Each individual work contributes, in part, to the overall process of IS creation. The disparate perspectives provided by the literature reflect the fragmented nature of available tools supporting IS, which operate in isolation of each other. An encompassing view of tools supporting the process of IS creation is missing to date. Therefore, to fill this gap, this study aims to develop a more comprehensive description of the landscape of IS tools by analyzing the associated approaches, roles, and contribution of existing tools. Through this understanding, the insights gained can be used to aid future development and advancement of tools for IS practitioners.

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Section: Thematic Analysismentioning

confidence: 99%

Section: Thematic Analysismentioning

confidence: 99%

Tools for promoting industrial symbiosis: A systematic review

Yeo

Masi

Low

et al. 2019

J of Industrial Ecology

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“…its functionality in terms of the process and/or unit it represents. In addition, each model is (semantically) described by requirements and other characteristics that form a comprehensive knowledge model (Koo et al (2016)) which includes model input(s), output(s), precondition(s), and the environment in which each process model operates (Trokanas et al 2014;Trokanas, Cecelja, et al 2015). The inputs and outputs are not limited to physical properties and can be extended to additional data or other properties.…”

Section: Model and Data Representationmentioning

confidence: 99%

A semantic framework for enabling model integration for biorefining

Koo

Trokanas

Cecelja

2017

Computers & Chemical Engineering

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Please cite this article as: Koo, Linsey., Trokanas, Nikolaos., & Cecelja, Franjo., A semantic framework for enabling model integration for biorefining. Computers and Chemical Engineering http://dx.doi.org/10.1016/j.compchemeng.2017.02.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A semantic framework for enabling model integration for biorefining Highlights We propose a semantic approach for establishing the interoperability between models and data.  Introduction of input/output matching as a mean of integration based on the structure of domain ontology.  The algorithm is implemented as a service.  Demonstration of performance is achieved using a real-life biorefining modelling scenario. AbstractThis paper introduces a new paradigm for establishing a framework that enables interoperability between process models and datasets using ontology engineering. Semantics are used to model the knowledge in the domain of biorefining including both tacit and explicit knowledge, which supports registration and instantiation of the models and datasets. Semantic algorithms allow the formation of model integration through input/output matching based on semantic relevance between the models and datasets. In addition, partial matching is employed to facilitate flexibility to broaden the horizon to find opportunities in identifying an appropriate model and/or dataset. The proposed algorithm is implemented as a web service and demonstrated using a case study.

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“…The environmental performance of IS networks is evaluated by five metrics calculated from environmental properties in Table 1 and they include: i) landfill diversion savings , ii) embodied carbon impact , iii) transportation impact TI, and iv) virgin materials saved VMS. Calculation of all five metrics is in detail explained in reference (Trokanas et al 2015) and summarised in Table 2.…”

Section: Modelling Of Environmental Effectsmentioning

confidence: 99%