Combined imaging and neural-network-based approach in solid waste sorting

Solid waste recycling is more and more increasing according to the need to realize dismantled material recovery and to reduce overall environmental pollution. When a recycling strategy is applied sorting strategies have to be developed and implemented. Such an approach ca be considered as the second logical step of the process that is, after that the attributes (physical, chemical, morphological, morphometrical, textural, etc.) of the materials resulting from classical processing (comminution, classification, separation, etc.) are detected and numerically modeled. The resulting feature vector need to be “handled” by a software architecture performing the required recognition/classification procedure and defining the quality of the investigated products. From the results further “feed-back” or “feed-forward” control strategies can be applied in order to improve equipment or processing architectures performances. In this paper are analyzed and described neural network based sorting strategies applied with reference to fluff (light fraction of the materials resulting from car dismantling) recognition.