@ARTICLE{Kowalski_Włodzimierz_P._Application_2002,
 author={Kowalski, Włodzimierz P. and Kołodziejczyk, Krzysztof and Zacharz, Tomasz},
 volume={vol. 28},
 number={No 2},
 pages={59-70},
 journal={Archives of Environmental Protection},
 howpublished={online},
 year={2002},
 publisher={Polish Academy of Sciences},
 abstract={The sedimentation devices are commonly used in the clarifying of industrial suspensions and in the civil engineering. The sedimentation efficiency plays very important role in the environmental protection. The aim of the research was to investigate the possibilities of applying neural networks in computing the efficiency of sedimentation processes. Input data were the results of computer stimulation performed according to the mathematical model taking into account the overflow rate in the sedimentation facilities and physical parameters of the suspension, such as probability density function of solid particle size. Two probability density functions of solid particle size were compared: log-normal distribution and gamma distribution. Feed-forward neural networks (with no feedback and with one- stream flow of information) were applied in research work. Teacher-supervised teaching, according to back-propagation method with the use of Levenberg-Marquardt algorithm, was chosen. When neural networks were taught with the use of sets including less than 400 data elements, the errors were more than I%. Neural networks taught by means of series including more than 500 data sets would yield acceptable results and the error was less than I%. Accordingly, one can presume that the smallest teaching set is the one composed of 500 data elements. The best results were obtained when the number of data sets was about 5000- the differences in computed sedimentation efficiency were then less than 0.5%. A further increase in the number of data elements - above 5000 - would lead to lower accuracy of calculations.},
 type={Article},
 title={Application of Neural Networks to Mathematical Modeling of Sedimentation Processes},
 URL={http://ochroma.man.poznan.pl/Content/123856/PDF/8_AE_VOL_28_2_2002_Kowalski_Application_of_Neural.pdf},
 keywords={environmental science and technology, water purification, neural networks},
}