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Optimization of arsenic sludge immobilization process in cement – natural zeolite – lime blends using artificial neural networks and multi objective criteria functions

This work focuses on optimization of arsenic sludge immobilization process in cement – natural zeolite – lime blends using artificial neural networks and multi-objective criteria functions. Developed artificial neural network model describes relations between solidified/stabilized cement formulation and its mechanical (compressive strength) and ecological properties (arsenic and iron release). It is proven that developed artificial neural network solidified/stabilized model has satisfactory performance characteristics (R2> 0.9031 without presence of systematic error ; based on external validation experimental data set). Four multi-objective optimization criteria functions, different in terms of mathematical formulation and ecological interpretation, were developed. The developed criteria functions were used in combination with artificial neural network solidified/stabilized model, providing optimal cement formulation. Finally, this study describes efficient and cost effective alternative in ecological material formulation process.

solidification/stabilization ; cement ; natural zeolite ; arsenic sludge ; artificial neural network

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