iFuelActive - A smart, cloud-connected, diesel fuel solution that will capture operational economies for end-users and support the transition to a low-carbon industry
iFuelActive - A smart, cloud-connected, diesel fuel solution that will capture operational economies for end-users and support the transition to a low-carbon industry
批准号:
10000364
负责人:
金额:
$44.51万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Fuel contamination is endemic, a global issue and results in incremental costs from engine damage and the voiding of warranties as exhibited in Tesco's 2018 Fuelgate scandal, where one contaminated tank cost Tesco £500,000 in damages \[Evans,2018\].Fuel becomes contaminated over time and contamination is mainly a result of either water, bacteria and/or particulates settling to the bottom of the tank and then being extracted by standard fuel pipes. Fuel contamination can lead to engine damage, machine downtime and reduced productivity, particularly in sectors such as construction and agriculture. Research in the industry and from existing clients (Volvo, Komatsu) indicates that there is an unmet need for monitoring fuel within fuel tanks and the identification of contamination levels.Fuel Active Ltd. has achieved demonstrable success in the fuel solutions market through development of innovative fuel delivery and monitoring systems that ensures the cleanest fuel is delivered to engines, preventing breakdowns from contaminated fuel.Fuel Active's development to date has been twofold:* Floating fuel extraction pipe to ensure that the cleanest fuel is delivered to the engine, mitigating the risk of injector damage and engine failure* iFuelActive, a smart fuel extraction unit which uses in-tank sensors and telemetry to remotely monitor fuel quality (levels; water content; temperature; particulates) and report on any issues to equipment users.This 12-month project will further develop iFuelActive by applying machine-learning algorithms to deliver two solutions, low-cost and high-capability models, that will provide intelligent data analytics, supporting in predicting maintenance timelines and equipment life, and will highlight potential cost-saving practices for operators.Applicable to multiple sectors, project outputs will support reduced machine downtime, improved fuel efficiency and preventative maintenance, generating significant financial returns for customers.
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