"Industrial Symbiosis (IS) is defined as the development of mutually beneficial relationships between two or more industries, by exchanging/sharing material, energy, services and/or knowledge. Over the last years, the development of such schemes has been halted due to several barriers, mostly financial and social. IS practitioners have agreed that there is need for facilitators that can help overcome those barriers. Computational algorithms, and their application through Information and Communication Technology (ICT) tools, can play this role. At the same time, the increasing digitalization of industrial ecosystems (Industry 4.0) and the widespread deployment of Internet of Things (IoT) networks, leads to the generation and capture of huge amounts of data. Artificial intelligence can provide a wide range of robust technologies that can deliver intelligence through processing the acquired data and supporting the management of complex and dynamic aspects of IS ecosystems. However, only a limited number of the developed tools have exploited AI tools to address problems related to IS development, mostly focusing on data analysis related to industrial facilities, waste production and supply chain economics. The objective of this project will be to lay the theoretical foundations and develop an AI framework for the dynamic IS optimization, through opportunity identification, efficiency assessment and control, and assisting in the decision process of the involved stakeholders as the basis towards enhancing the foundation of IS ecosystems. A previously developed tool, which has been developed for the facilitation of IS schemes (based on solid, liquid and gaseous waste streams), will be improved and extended by leveraging on recent advances in the artificial intelligence field. Existing work demonstrated the suitability of a set of machine learning techniques to identify IS opportunities, including recommender algorithms such as association rule mining, case-based reasoning, collaborative filtering, knowledge-based recommendation, and rule-based recommendation. The developed algorithm will be able to: (i) Detect favourable geographic areas and industrial sectors to establish IS schemes; (ii) Detect anomalies in the flow of materials, waste and energy that need to be treated to optimise the process; (iii) Analyse and predict significant events that may affect the demand and supply of resources providing the basis for predictive demand-supply balancing and logistics optimisation; (iv) Support the optimisation of IS matches based on user-defined preferences; (v) Automatically identify additional suitable waste products to be considered by IS schemes, and suggest an optimized value chain, by considering symbiosis with stakeholders already included in the schemes."