Research Project Title: "Testing a new nutrient analyser in various sea regions"
Objective:
To test the functionality of a new nutrient analyser in different environmental conditions and to get feedback on the feasibility of its integration into various platforms.
Challenge Addressed:
- Pollution Reduction and Circular Solutions: Developing new cost-efficient methods to determine the levels of marine eutrophication and to follow the success of remediation actions.
Integrated Research Infrastructure:
- Role: Easily accessible autonomous platform for integrating new sensors and supplying auxiliary measurements of core marine variables.
- Task: To conduct long term demonstrations in collecting autonomous nutrient data with a new sensor, using selected Ferrybox lines in contrasting environments (A and B). Discrete bottle samples for reference measurements are collected during transects and analysed by the facility owner.
Research Workflow:
User and facility owner (A) will have an onsite practical workshop to solve issues in the physical and electronical sensor integration and in the data recovery. This includes test runs in simulation mode and initial onboard tests, prior operational phase starts.
User follows the data flow in remote access mode, and facility owner (A) performs the required weekly sensor maintenance and adjustments and collects discrete reference samples to be analysed in their laboratory.
Once enough measurement results have been obtained from one region (A) and found to be reliable, planning for measurements in the region (B) will begin, with another facility owner.
Data collected in a range of environmental conditions and nutrient conditions will be analysed. The analysis will guide the next development phases of the sensor, leading towards a commercial product with highly improved capacity for online nutrient monitoring.
Outcome:
Demonstration of the new observation technology in various environmental conditions. Based on the comprehensive field data, university researchers can finetune the machine learning algorithm of the sensor to adapt in various conditions. The practical experiences gained will help the manufacturer to improve the durability and reliability the sensor. Project contributes greatly to the improvements of the ‘Technology Readiness Level’ of the sensor, towards commercialisation.