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（IS）被定义为通过交换/共享材料，能源，服务和/或知识之间的两个或多个行业之间互惠关系的发展。在过去的几年中，此类计划的发展已停止，由于多个障碍，主要是财务上的财务和社会。 （ICT）工具可以扮演这一角色。在同一时间，工业生态系统的数字化（行业4.0）的数字化以及广泛的物联网（IoT）网络的部署，导致生成和捕获大量的人工智能可以提供大量的稳健技术有限的开发工具利用AI工具来解决与开发有关的问题，主要关注与工业设施，废物生产和供应链经济学有关的数据分析。该项目的目标是奠定理论基础，并通过对涉及的IS依据的构建来构建基础，以优化动态的AI框架是优化的，这是通过机会识别，效率评估，效率评估，并协助构建的依据。先前开发的工具是为了促进IS方案（基于固体，液体和气态废物流）开发的，它将通过利用人工智能领域的最新进展来改进和扩展。现有的工作证明了一组机器学习技术来识别机会是机会，包括推荐算法，例如关联规则挖掘，基于案例的推理，协作过滤，基于知识的建议和基于规则的建议。开发的算法将能够：（i）检测有利的地理领域和工业领域，以建立各个方案； （ii）检测需要处理以优化过程的材料，废物和能量流动的异常； （iii）分析和预测可能影响资源需求和供应的重要事件，从而为预测需求平衡和物流优化提供基础； （iv）支持基于用户定义的首选项的优化IS匹配； （v）通过考虑与计划中已经包含的利益相关者的共生，自动确定要由IS方案考虑的其他合适的废物产品，并提出优化的价值链。”