Artificial Intelligence (AI) assisted Industrial Symbiosis (IS)

人工智能（AI）助力产业共生（IS）

• 批准号: 2851677
• 金额: --
• 依托单位: University of Huddersfield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2851677
• 关键词: Artificial; Intelligence; AI; assisted; Industrial

"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."

工业共生（Industrial Symbiosis，IS）是指两个或两个以上产业之间通过交换/共享材料、能源、服务和/或知识而发展的互利关系。在过去几年里，由于一些障碍，主要是财政和社会障碍，这种计划的发展已经停止。基础设施服务从业人员一致认为，需要有能够帮助克服这些障碍的促进者。计算算法及其通过信息和通信技术（ICT）工具的应用可以发挥这一作用。与此同时，工业生态系统（工业4.0）的日益数字化和物联网（IoT）网络的广泛部署，导致了大量数据的生成和捕获。人工智能可以提供广泛的强大技术，通过处理获取的数据和支持IS生态系统复杂和动态方面的管理来提供智能。然而，只有少数开发的工具利用人工智能工具来解决与IS开发相关的问题，主要集中在与工业设施，废物生产和供应链经济学相关的数据分析上。本项目的目标是为动态IS优化奠定理论基础并开发人工智能框架，通过机会识别，效率评估和控制，并协助有关利益攸关方的决策进程，以此作为加强基础设施服务生态系统基础的基础。将利用人工智能领域的最新进展，改进和扩大以前开发的工具，该工具是为促进基础设施建设计划（基于固体、液体和气体废物流）而开发的。现有的工作表明，一套机器学习技术，包括推荐算法，如关联规则挖掘，基于案例的推理，协同过滤，基于知识的推荐，基于规则的推荐适用于识别IS机会。开发的算法将能够：（i）检测有利的地理区域和工业部门，以建立IS方案; ㈡发现需要处理的材料、废物和能源流动中的异常情况，以优化流程; ㈢分析和预测可能影响资源供求的重大事件，为预测性供求平衡和物流优化提供依据;（iv）支持根据用户定义的偏好优化基础设施服务匹配;（v）自动确定基础设施服务计划要考虑的其他合适的废物产品，并通过考虑与计划中已包括的利益攸关方的共生关系，提出优化的价值链。"