Utveckling av beröringsfri sensorteknik för turbiditets- och vattennivåmätning: En innovativ lösning för hållbar vattenhantering
2025 (Swedish)Report (Other academic)
Abstract [en]
This report describes the development and implementation of an innovative contact-free sensor technology, Turbinator, for measuring turbidity and water levels in utility networks, particularly in stormwater wells. The development began in 2017 and underwent testing and improvements during the 2020–2024 period to meet the needs of Swedish water utility organizations. The project's objective was to create a cost-effective, maintenance-free solution for monitoring utility networks.
The technology is based on AI and computer vision to analyze laser reflections in water. Data from a development well and field tests in Stockholm, Gothenburg, and Malmö were utilized to develop and evaluate the technology. The results showed that the technology has the potential to emulate reference sensors with an average deviation of 13.6%, while tests confirmed a battery lifespan of up to three years under optimal conditions. Challenges were identified, however, particularly related to moisture and camera quality in the field. Suggestions for improvement include more robust hardware and optimized algorithms. The technology’s applications can reduce manual inspections, enhance maintenance planning, and detect pollutants, supporting a more sustainable and digitalized water management system. Further research is recommended to address technical limitations and explore new market opportunities, such as monitoring at construction sites and small-scale utility facilities.
Place, publisher, year, edition, pages
Göteborg: IVL Svenska Miljöinstitutet, 2025.
Series
B report ; B11012
Keywords [en]
turbidity, contact-free measurement, artificial intelligence, AI, water level, digitalization, utility systems, maintenance, environmental innovation, turbiditet, beröringsfri mätning, artificiell intelligens, AI, vattennivå, digitalisering
National Category
Artificial Intelligence Infrastructure Engineering Water Engineering Computer Vision and Learning Systems Environmental Management
Identifiers
URN: urn:nbn:se:ivl:diva-4795ISBN: 978-91-7883-757-1 (electronic)OAI: oai:DiVA.org:ivl-4795DiVA, id: diva2:2015526
Funder
IVL Swedish Environmental Research Institute2025-11-212025-11-212025-11-21