Application of a Multiobjective Artificial Neural Network (ANN) in Industrial Reverse Osmosis Concentrate Treatment with a Fluidized Bed Fenton Process: Performance Prediction and Process Optimization

2021 ◽  
Author(s):  
Qinqing Cai ◽  
Brandon Chuan Yee Lee ◽  
Say Leong Ong ◽  
Jiangyong Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fluidized Bed ◽  
Reverse Osmosis ◽  
Process Optimization ◽  
Performance Prediction ◽  
Process Performance ◽  
Fenton Process ◽  
Concentrate Treatment ◽  
Artificial Neural Network Ann

Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor

2013 ◽  
Vol 69 (4) ◽  
pp. 768-774 ◽  
Author(s):  
André L. N. Mota ◽  
Osvaldo Chiavone-Filho ◽  
Syllos S. da Silva ◽  
Edson L. Foletto ◽  
José E. F. Moraes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Uv Light ◽  
Fenton Process ◽  
Ann Model ◽  
Ferrous Ions ◽  
Phenol Mineralization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe2+ and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.

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