Data driven digitalization of industrial cleaning processes
| Reference number | |
| Coordinator | RISE Research Institutes of Sweden AB |
| Funding from Vinnova | SEK 3 748 299 |
| Project duration | October 2021 - May 2024 |
| Status | Completed |
| Venture | Advanced digitalization - Enabling technologies |
| Call | Advanced and innovative digitalization - spring 2021 |
Important results from the project
The data-driven digitalization of industrial cleaning processes project aimed to revolutionize industrial cleaning through data analytics and automation. Objectives included extending cleaning bath life, improving sustainability, and enhancing process efficiency. The project developed a real-time data analytics system, doubling bath life and reducing chemical use and lowering environmental impact. Collaboration ensured practical implementation, enhancing Swedish manufacturing´s competitiveness, setting new sustainability and innovation standards.
Expected long term effects
The Digiclean project developed a digial data-driven platform with advanced sensor technology, significantly reducing chemical use and waste. Customers report high satisfaction with the adapted chemistry and service. The focus has shifted from selling chemicals to providing service solutions. Key outcomes include the application of machine learning models for predictive maintenance, operational efficiency, and expansion into other sectors. The project emphasizes scalability, sustainability, and enhanced efficiency, supporting broader environmental and quality goals
Approach and implementation
** Denna text är maskinöversatt ** The developed system is a combination of different techniques for real-time monitoring of cleaning fluids. Machine learning models predict maintenance needs, optimizing efficiency. Durable hardware and customized software ensure reliable data collection and user-friendly interfaces. Pilot tests led to system improvements. The project shifts its focus from selling chemicals to providing services, with an emphasis on sustainability and scalability in different sectors. This improves both operational efficiency and environmental impact.