Optimization of operating parameters and performance evaluation of forced draft cooling tower using response surface methodology (RSM) and artificial neural network (ANN)

2012 ◽  
Vol 26 (5) ◽  
pp. 1643-1650 ◽  
Author(s):  
Ramkumar Ramakrishnan ◽  
Ragupathy Arumugam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Performance Evaluation ◽  
Response Surface ◽  
Cooling Tower ◽  
Operating Parameters ◽  
Forced Draft ◽  
And Performance ◽  
Artificial Neural Network Ann

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

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