Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying

2011 ◽  
Vol 75 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Tayyeb Nazghelichi ◽  
Mortaza Aghbashlo ◽  
Mohammad Hossein Kianmehr
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Fluidized Bed ◽  
Network Topology ◽  
Fluidized Bed Drying ◽  
Artificial Neural ◽  
Coupled Response

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network—Genetic algorithm approach

2016 ◽  
Vol 109 ◽  
pp. 305-311 ◽  
Author(s):  
Fábio Coelho Sampaio ◽  
Tamara Lorena da Conceição Saraiva ◽  
Gabriel Dumont de Lima e Silva ◽  
Janaína Teles de Faria ◽  
Cristiano Grijó Pitangui ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Kluyveromyces Lactis ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Batch Growth

Investigation on Fluidized Bed Bioreactor Treating Ice Cream Wastewater Using Response Surface Methodology and Artificial Neural Network

2014 ◽  
Vol 12 (1) ◽  
pp. 563-573 ◽  
Author(s):  
K. Thirugnanasambandham ◽  
V. Sivakumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Fluidized Bed ◽  
Flow Rate ◽  
Ice Cream ◽  
Agitation Speed ◽  
Fluidized Bed Bioreactor ◽  
Artificial Neural

Abstract In this study, a comparative approach was developed between response surface methodology (RSM) and artificial neural network (ANN) in the predictive capabilities for the removal of chemical oxygen demand (COD) from ice cream industry wastewater using fluidized bed bioreactor. The effects of process variables such as pH, temperature, flow rate and agitation speed investigated using a four-factor three-level Box–Behnken experimental design (BBD). Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of statistical parameters including coefficient of determination (R2). The results showed that properly trained ANN model is more accurate in prediction as compared to RSM model. Under the optimum conditions (pH of 7, temperature of 40°C, flow rate of 20 ml/min and agitation speed of 175 rpm), 91% of COD was removed.

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