Abstract
Industrial Symbiosis (IS) is an innovative environmental practice which assists companies from all business sectors to commercially trade resources (material, energy, water and waste), building networks that are environmentally integrated and efficient. IS is a very promising and practical approach, however the existing practice presents a number of challenges, a manual procedure heavily reliant on practitioners' knowledge and interpretation of information which not only results in high costs but also limits the scope of opportunity identification and lacks responsiveness to the dynamicity that exists within the IS domain. In response to these challenges, this research introduces a semantic approach for building IS networks and identifying potential synergies between industries which are keen on exploring possible symbiotic opportunities with the aim of reducing consumption of natural resources, environmental strain and waste stream to landfills. Using ontology modeling the research proposes the semantic formalism of tacit and explicit knowledge within the IS domain including information about resources, processing technologies and their characteristics as well as practitioners' knowledge and expertise. The use of semantic web services is also proposed for annotating industries’ profiles. The semantic modeling and annotation allows for automated machine processing of IS domain knowledge. Furthermore the research introduces an algorithm which exploits semantic and quantitative models to allow automated and enhanced identification of potential synergies. The algorithm incorporates graph modeling for semantic distance measurement of resource types and vector space modeling for comparison of IS enabling metrics. The algorithm is further extended to incorporate dynamics handling that heavily influence various stages of synergy identification process. The approach moreover supports composition of complex IS chains based on semantic relevance between partners by recursive repetition of the process. The algorithm computes a matching degree or similarity measure between industries to depict the ratio of potentiality of forming an environmentally and economically sustainable synergy, cultivating the companies' planning and decision making for establishing a symbiotic link. The thesis provides an experimental study as part of the eSymbiosis platform, implemented using the proposed semantic approach verifying the practicality and benefits of it.