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    Leaching kinetics of stibnite in sodium hydroxide solution

    , Article International Journal of Engineering, Transactions B: Applications ; Volume 27, Issue 2 , February , 2014 , Pages 325-332 ; SSN: 10252495 Dodangeh, A ; Halali, M ; Hakim, M ; Bakhshandeh, M. R ; Sharif University of Technology
    Abstract
    The leaching kinetics of stibnite in basic solution has been investigated. Spherical pellets of antimony sulphide were dissolved in 1 molar sodium hydroxide solutions at different temperatures. It was found that the shrinking core with ash layer model could satisfactorily explain the dissolution process. Using this model, it was found that initially the rate controlling step was a chemical reaction with activation energy of 10.2 kJ/mol. As the ash layer built up, diffusion through the ash layer became the rate controlling step. The activation energy for this step was found to be 33.4 kJ/mol. It was also observed that smaller particle size, larger solid to liquid ratio, and higher NaOH... 

    Investigating and modeling the cleaning-in-place process for retrieving the membrane permeate flux: Case study of hydrophilic polyethersulfone (PES)

    , Article Journal of the Taiwan Institute of Chemical Engineers ; Volume 62 , May , 2016 , Pages 150–157 ; 18761070 (ISSN) Hedayati Moghaddam, A ; Shayegan, J ; Sargolzaei, J ; Sharif University of Technology
    Taiwan Institute of Chemical Engineers  2016
    Abstract
    In this work the effects of backwash pressure, duration of acid and sodium hydroxide backwashing, sodium hydroxide concentration, and the duration of forward washing on performance of permeate flux recovery (PFR) were investigated. A two-level fractional factorial design (FFD) was used to design the experiments. The ability of back propagation neural network (BPNN) and radial basis function neural network (RBFNN) in predicting the performance of cleaning-in-place (CIP) of hydrophilic polyethersulfone (PES) membrane were investigated. It is found that BPNN has better ability in predicting the PFR performance than RBFNN. The best architecture of BPNN was a network consisting of 1 hidden layer...