resinSENSE - real-time, on-line monitoring of resin mix-ratio monitoring system

This project is to accelerate the development of novel and scalable technology to address a significant problem in the renewable energy sector; the need to better control resin mix-ratios in the production of wind turbine blades.

Wind energy represents a key component of the UK's energy and decarbonisation mix, with UK wind production increasing by 715% from 2009 to 2020 (ONS, 2021). However, to continue increasing wind energy generation and enable "Net Zero" by 2050, the number of wind turbines manufactured per year needs to triple within the next decade (ORE Catapult). One barrier to this increased production is the need to make the manufacture of the turbine blades more efficient and less wasteful.

Turbine blades take multiple days and many dozens of employees to manufacture a single blade, with an associated manufacturing cost running into many hundreds of thousands of pounds (CompositesWorld). Half of all defective blades produced are as a direct result of errors and inconsistencies of the resin mixture. Our innovation is to monitor the resin mix-ratios non-invasively and in-process using ultrasonic sensing to dramatically improve production throughput and reduce wastage. This is particularly relevant from a sustainability perspective, with the turbine blade being one of the few components of a wind turbine that cannot be easily recycled (Bloomberg Green). The technology solution developed during this project will also lead to improved traceability during the manufacturing process, allowing for the use of two-part resins in new markets, where the regulatory and quality assurance regimes currently limit their usage.

This involves applying our sensing technology to characterise the resin in the mixing stage, before it is dispensed into the fibreglass mould, therefore avoiding the write-off of the blade. This project builds upon promising results obtained during a self-funded feasibility study, which demonstrated that it was possible to distinguish the mix-ratios of polyester resins down to levels of 0.1% using ultrasonic sensing. The project will be focused on designing and creating a measurement cell, improving the measurement algorithm, and embedding the algorithm onto a device for real-time measurement capabilities.

The prototype sensing system, as well as providing a vital developmental feedback loop, will lead to increased confidence in the technology, accelerating commercialisation to solve this progress-limiting problem.