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A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics
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Yeo, Zhiquan, Choong Low, Jonathan Sze, Loong Tan, Daren Zong, Chung, Si Ying, Tjandra, Tobias Bestari and Ignatius, Joshua (2019) A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics. In: 26th CIRP Conference on Life Cycle Engineering, Purdue University, West Lafayette, IN, May 7-9 2019. Published in: Procedia CIRP, 80 pp. 643-648. ISSN 2212-8271. doi:10.1016/j.procir.2019.01.015
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Official URL: https://doi.org/10.1016/j.procir.2019.01.015
Abstract
Industrial Symbiosis (IS) adopts a collaborative approach, which aims to re-channel resources – traditionally considered spent and non-productive – towards alternative value-adding pathways. Empirically, the concept of IS has been rapidly implemented in practice through a facilitated approach, whereby businesses are engaged and “match-made” via a facilitating body. While recommending alternative pathways for companies to establish IS-based transactions is a long-standing practice, recent technological advancement has shifted the nature of this task from one that is based purely on human intellect and reasoning, towards one which leverages intelligent recommendation algorithms to provide relevant suggestions. Traditionally, these recommendation engines rely on manually populated knowledge bases that are not only labor-intensive to build but also costly to maintain. This work presents the creation of a self-learning waste-to-resource database supporting an IS recommendation system by utilizing text analytics techniques. We further demonstrate its practical application to support IS facilitating bodies in their core activity.
| Item Type: | Conference Item (Paper) | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures |
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| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Industrial ecology , Materials management -- Databases, Machine learning, Industrial ecology -- Databases | ||||||||
| Journal or Publication Title: | Procedia CIRP | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 2212-8271 | ||||||||
| Official Date: | 2019 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 30 July 2020 | ||||||||
| Volume: | 80 | ||||||||
| Page Range: | pp. 643-648 | ||||||||
| DOI: | 10.1016/j.procir.2019.01.015 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Conference Paper Type: | Paper | ||||||||
| Title of Event: | 26th CIRP Conference on Life Cycle Engineering | ||||||||
| Type of Event: | Conference | ||||||||
| Location of Event: | Purdue University, West Lafayette, IN | ||||||||
| Date(s) of Event: | May 7-9 2019 |
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