Intelligent Optimization of Resin Level in Industrial Silos Using Radar Sensors and Random Forest Algorithms

Sulca P.; Quispe R. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.1120

Resumen

The implementation of Siemens SITRANS LR560 radar sensors in industrial silos, combined with a predictive system based on machine learning, has optimized the monitoring of polypropylene resin levels. A 75% reduction in maintenance costs and an 87.5% decrease in downtime were achieved, significantly improving operational efficiency. A Random Forest model was employed to predict failures, validated using class balancing techniques and K-Fold cross-validation, achieving an accuracy of over 95%. The integration of the SCADA system via TIA Portal enables real-time monitoring and the generation of alerts for critical events. The results were compared with previous studies, demonstrating that the application of artificial intelligence and IoT in Industry 4.0 enhances the reliability of granular material storage and distribution. Future improvements are recommended, including the optimization of communication infrastructure in industrial environments, real-time data processing, and enhanced decision-making processes.

Instituciones: Universidad Privada del Norte