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

燃料污染是地方性的，是一个全球性的问题，并会导致因发动机损坏和保修无效而增加的成本，就像乐购2018年的燃油门丑闻所展示的那样，一个受污染的油箱给特易购GB造成了500,000英镑的损失[埃文斯，2018\]。随着时间的推移，燃料会受到污染，污染主要是水、细菌和/或微粒沉淀到油箱底部，然后被标准燃油管道提取的结果。燃料污染可能导致发动机损坏、机器停机和生产率下降，特别是在建筑和农业等行业。业内和现有客户(沃尔沃、小松)的研究表明，对油箱内燃料的监控和污染级别的识别存在尚未得到满足的需求。Fuel Active Ltd.通过开发创新的燃料输送和监控系统，确保将最清洁的燃料输送到发动机，防止受污染的燃料故障，在燃料解决方案市场上取得了明显的成功。Fuel Active迄今的发展有两个方面：*浮动燃料提取管，以确保将最清洁的燃料输送到发动机，降低喷油器损坏和发动机故障的风险*iFuelActive，一种使用油箱内传感器和遥测来远程监控燃油质量(水平；这个为期12个月的项目将通过应用机器学习算法来进一步开发iFuelActive，以提供两种解决方案，即低成本和高性能模型，这两种解决方案将提供智能数据分析，支持预测维护时间表和设备寿命，并将突出运营商潜在的成本节约做法。项目产出适用于多个部门，将支持减少机器停机时间、提高燃料效率和预防性维护，为客户带来显著的财务回报。

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.