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   Modeling and Optimization of Biosorptive Removal of Zn (Ii) Ions Process Using Hybrid Artificial Neural Network and Genetic Algorithm  
   
DOR 20.1001.2.9919199705.1399.11.1.17.2
نویسنده - - ,- - ,- -
منبع كنگره مهندسي شيمي - 1399 - دوره : 11 - یازدهمین کنگره بین المللی مهندسی شیمی - کد همایش: 99191-99705
چکیده    Zn (ii) is one of the common pollutants among heavy metals found in industrial effluents# removal of pollutant from industrial effluents like water can be accomplished by various techniques# the absorption process is an attractive method for wastewater treatment because it is economically feasible# in the last two decades, the use of artificial neural networks (ann) is welcome by many researchers due to their acceptable accuracy for modeling purposes# indeed, the ann modeling technique has many favorable features such as efficiency, generalization, and simplicity, which make it an attractive tool for modeling of complex systems, such as wastewater treatment processes# in the present study, ann-model has been effectively employed to predict the uptake rate of zn (ii) ions from industrial effluents# a multilayer perceptron (mlp) ann and genetic algorithm (ga) have been used to predict zn (ii) uptake rate and optimize operating conditions of the process# four independent variables including ph, initial concentration of zn (ii) ions, temperature and dosage of biosorbent were selected as the input variables of the model# the results indicated that the ann-model was successfully predicted the uptake rate of zn (ii) ions with acceptable accuracy, with the relative error as 12#12%# at the optimal conditions, the biosorption capacity was 75#172 (mg/g)#
کلیدواژه Artificial Neural Networks ,Modeling ,Optimization ,Zn (Ii) Ions ,Water Treatment
آدرس Tafresh University, Iran, Tafresh University, Iran, Tafresh University, Iran
 
     
   
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