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Immobilisierung von Manganabfallschlamm im Zement – Natrolith – Kalkmischung: Prozessoptimierung durch künstliche neuronale Netzwerke und multikriterielle Funktionen
Author(s) -
Ukić Š.,
Dimić P.,
Šiljeg M.,
Ujević Bošnjak M.,
Šipušić J.,
Bolanča T.
Publication year - 2013
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.201300050
Subject(s) - manganese , leaching (pedology) , zeolite , lime , flexural strength , materials science , cement , portland cement , artificial neural network , waste management , metallurgy , environmental science , composite material , chemistry , engineering , computer science , catalysis , biochemistry , machine learning , soil science , soil water
In this study, stabilization/solidification process of manganese contaminated mud using portland cement was optimized. For that purpose, immobilization process was modeled applying artificial neural networks with radial basis activation function. The optimal model presented satisfactory prediction characteristics ( R 2 value for manganese leaching was 0.9615 while and for concrete flexural strength 0.8748). Therefore, it was used in combination with seven in‐house developed multi‐criteria optimization functions, separately, in order to optimize concrete formulation. The used approach proved itself as efficient and cost effective alternative in ecological material formulation process. The best properties ( i. e. high flexural strength and lowest manganese leaching) manifested stabilization/solidification matrix consisted of 350 g of portland cement, 20 g of lime, 70 g of natural zeolite, 10 g of manganese waste mud and 180 g of water.