Classification of single particles by neural networks based on the computer-controlled scanning electron microscopy data

The identification of sources of particles found in chemical process equipment such as photographic printer cabinets and their quantitative apportionment to those sources could lead to effective control strategies that would improve productivity and customer satisfaction with the service. Computer-controlled scanning electron microscopy (CCSEM) has proven to be a powerful tool in the characterization of individual particles. Thus, in this paper the samples of particles taken from experiments examining the formation of particles when cutting photographic paper and particles collected in a printer cabinet have been characterized by using CCSEM analysis. The obtained data have been analyzed by using two different neural networks, namely, the adaptive resonance theory based neural network (ART-2a) and Kohonen neural network. Both neural networks can be used to perform an unsupervised pattern recognition examination of which particles should be grouped together. The results show that they are generally able to extract the main particle groups present in the data set. The produced particle groups are almost homogeneous based on the major chemical elements. From the general size, shape and density parameters provided by the CCSEM analysis, the volume and mass of each particle were estimated. Then the mass fractions of each particle class produced by the neural networks were calculated. Based on the mass conservation principle and the resulting mass balance, the particle class balance model has been used to discern particles from different sources and apportion the corresponding source contributions.