A scalable IoT solution utilising machine-learning to discover actionable insights to reduce energy/resource consumption utilising manufacturing process data.

可扩展的物联网解决方案，利用机器学习来发现可行的见解，从而利用制造过程数据来减少能源/资源消耗。

• 批准号: 10029064
• 金额: $ 105.85万
• 依托单位: EDGEMETHODS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10029064
• 关键词: scalable; IoT; solution; utilising; machine

EdgeMethods(IoT platform developers/integrators) and Victrex(High-performance polymer manufacturers) have completed a successful _Proof of Science_ energy efficiency project on a single polymer powder manufacturing process. Work included defining the key energy and emissions reduction KPIs and integrating key technological requirements (e.g. sensors to read gas measurements on oil skids) for energy efficiency measurement. Utilising the EdgeMethods _SaaS_ IoT platform, machine-learning based energy efficiency models were developed enabling optmisation of the polymer manufacturing process and real-time energy-based event detection.This project will focus on further development of our novel sustainability models that have been evaluated in a Proof of Science environment and are ready to be further developed with real-world data in Victrex's polymer production factory.This project will enable EdgeMethods to finalise core platform development and develop/improve/validate sustainability ML algorithms within a polymer manufacturing process which has synergies enabling us to rapidly scale across the polymer/chemical and pharmaceutical and food/drink manufacturing sectors.

EdgeMethods（物联网平台开发商/集成商）和Victrex（高性能聚合物制造商）已经成功完成了一个关于单一聚合物粉末制造工艺的科学证明能源效率项目。工作包括确定关键的能源和减排关键绩效指标，并纳入能源效率计量的关键技术要求（例如，读取石油撬装板上气体测量值的传感器）。利用EdgeMethods _SaaS_ IoT平台，开发了基于机器学习的能源效率模型，使聚合物制造过程和基于能源的实时事件检测得以优化。该项目将专注于进一步开发我们的新型可持续发展模型，这些模型已经在科学证明环境中进行了评估，并准备进一步开发真实的-该项目将使EdgeMethods能够完成核心平台开发，并在聚合物制造过程中开发/改进/验证可持续性ML算法，这将使我们能够在聚合物/化学品和制药和食品/制药行业快速扩展。饮料制造业。