Sensor systems for high-speed intelligent sorting of waste paper in recycling

Sorting paper into compatible grades is a necessary step before recycling. Current methods of man¬ually sorting are tedious, slow, and expensive. High-speed automation of the sorting process will improve the cost efficiency and increase the amount of paper recycled significantly. Correct identification of the sample in real-time before the sample reaches the actuation station still remains a challenge because different types of paper and board samples are mixed in a waste stream, and have a lot of variation in terms of color, chemical composition, coating, and printing in black and white and color to different degrees. This primarily is because of a lack of satisfactory sen¬sors and sensor fusion algorithms for sample identification in real-time. In this paper, we identify key parameters that must be measured, discuss a sensor system design, and describe integration of the output from sensors to interpret the type of sample using a fuzzy inference algorithm. Results show that the sensor system proposed is capable of identifying the samples at 90% accuracy compared to manual identification. The sensor system can be integrated onto a conveyor and actuation system for automated sorting.