Bringing AI-Powered Polymer Identification to the PV Industry
ANYMO – a new way of analyzing Polymers in PV modules
The pre-seed startup ANYMO has developed an innovative non-invasive method to identify the composition of polymer components, like backsheets/encapsulants in photovoltaic modules, in the field.
The method relies on Near-Infrared Reflectance Absorbance (NIRA) and a handheld sensor, eliminating the need for destructive sampling. By leveraging a machine learning model trained on a dataset collected at HI ERN, ANYMO enables rapid, on-site material identification in just a few seconds.
In collaboration with Solar TAP, the team is further refining the technology with the Solar TAP partners LayTec and Spectral Engines.
ANYMO field system with a cable mounted NIRA-sensor and measurement software on a tablet PC.
Project details
Key Achievements
Within HI ERN ANYMO has developed a mobile NIRA-based identification system for PV backsheets and encapsulants, enabling fast, non-destructive material analysis. For this ANYMO, together with Spectral Engines created a proprietary AI-based model trained on an extensive dataset from HI ERN, significantly improving classification accuracy up to 95%. It is further collaborating with Spectral Engines to refine measurement precision and expand material coverage now.
Linking Science and Industry
The collaboration between ANYMO, Solar TAP researchers at HI ERN, and industry partners has been instrumental in bridging the gap between research and application. HI ERN’s expertise in data science and materials analysis allowed for the rapid development of an AI-based identification tool, while industry engagement ensures the technology meets real-world operational needs. With LayTec as the vendor and ongoing cooperation with Spectral Engines, the project exemplifies successful technology transfer from research to commercial use.
Future Outlook
ANYMO and Solar TAP will continue to refine the technology, expanding the AI model’s database to cover a wider range of backsheet materials. Additional research will explore applications in encapsulant analysis and expand to the next generation of PV components and technologies. With further industrial partnerships, the system has the potential to become a standard tool for PV module assessment, supporting quality control, maintenance, and recycling efforts in the solar industry.
ANYMO wireless NIRA sensor in use on a PV module together with the identification app on a cell phone.
Added Value
for the Industrial Partner
- Immediate access to a cutting-edge material identification tool
- Faster quality assessments and reduced need for lab testing
- Competitive advantage in PV module recycling and repurposing
for Research
- Access to field datasets for further model improvements
- Opportunity to integrate advanced AI and spectroscopy techniques into PV diagnostics
- Closer collaboration with industry players to accelerate market adoption
for Society
- Enabling better recycling and sustainability in the PV industry
- Supporting safer, more durable PV modules through better material assessment
- Reducing costs and environmental impact of quality control and maintenance
