381715 Ontology Engineering: Support to Industrial Symbiosis Networks
Industrial Symbiosis (IS) is an innovative approach that aims in creating sustainable industrial networks set to process waste into materials, energy and water. Economic benefits are generated by normally low costs of waste or by-products, by using alternative energy sources and by environmental savings. Environmental benefits are inherent in IS and measured by landfill diversion but also by reduction in emissions and by water savings. Operating within confined geographic and administrative boundaries, IS also generates tangible social benefits to local communities, including job generation and retention, as well as new investments.
The key to formation of IS networks is the mediation between participants, the process which requires expertise, hence knowledge in different areas, i.e. waste composition, capability of processing technologies and environmental effect, among the others. The whole process is currently managed by trained practitioners supported by proprietary databases. Limited to the level of expertise and intuition of practitioners and lacking readily available repository of tacit knowledge, the all operation is backward looking and focusing on past successful examples with innovative networks being incidental.
This paper presents design and implementation of a semantic web platform which supports creation and operation of IS networks i) by screening the opportunities based on technological capability and resource availability of registered companies, and ii) by monitoring the IS operation and assessing sustainability using economic, environmental and social parameters. The platform employs ontologies to embed tacit knowledge in the domain of IS, knowledge gained from past experience but also from the latest research and otherwise advances in IS. More specifically, a set of integrated ontologies address off-spec nature of waste, i.e. variability in composition, dynamics in availability and pricing, as well as economic and environmental properties including hazardousness. Similarly, processing technologies are modeled in terms of processing capabilities, which include range of type of inputs, conversion rates, water and energy requirements, range of capacities, emissions as well as fixed and operational costs and environmental effects. Explicit knowledge is collected in the process of ontology instantiation with actual data collected from the IS participants during the registration. The ontologies are designed using ontology web language and hence prepared to grow and to share. In the current implementation more than 1500 different waste types and over 200 different technologies have been included.
Purpose designed matchmaker identify synergies between participants on their semantic and explicit relevance, the process crucial to formation of IS networks. Semantic relevance defines suitability from the type of waste/by-product and range of technology inputs, including complex composites of waste, i.e. biodegradable waste, and it is calculated from distance between the two instances in the respective ontology. Semantic relevance also includes participant general suitability for particular type of IS. Explicit relevance is calculated using vector similarity algorithm for respective properties, such as quantity, availability, geographical location and hazardousness. More intuitive and complex IS networks are proposed by reclusively repeating matches between two participants which in turn gives an opportunity for even better economic and environmental savings and/or targeted production. Both semantic and explicit matching relevance are aggregated in into a numerical value used for match ranking.
The eSymbiosis platform has been implemented as a web service with performance validated verified in the industrial region in Viotia, Greece and with several hundred participating company.
The effort has been funded by the LIFE+ initiative (LIFE 09 ENV/GR/000300), which authors acknowledge.
See more of this Group/Topical: Computing and Systems Technology Division