EconPapers    
Economics at your fingertips  
 

A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks

Anna Gatzioura (), Miquel Sànchez-Marrè () and Karina Gibert ()
Additional contact information
Anna Gatzioura: Knowledge Engineering & Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Centre (KEMLG-@-IDEAI), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain
Miquel Sànchez-Marrè: Knowledge Engineering & Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Centre (KEMLG-@-IDEAI), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain
Karina Gibert: Knowledge Engineering & Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Centre (KEMLG-@-IDEAI), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain

Energies, 2019, vol. 12, issue 18, 1-24

Abstract: Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms.

Keywords: hybrid recommender systems; industrial symbiotic networks; case-based reasoning; waste optimization; energy consumption optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/18/3546/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/18/3546/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:18:p:3546-:d:267704

Access Statistics for this article

Energies is currently edited by Prof. Dr. Enrico Sciubba

More articles in Energies from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().

 
Page updated 2019-11-24
Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3546-:d:267704