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

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
负责人:
金额:
$105.85万
依托单位:
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
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
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英文摘要
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.
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