Prediction and optimization of stability parameters for titanium dioxide nanofluid using response surface methodology and artificial neural networks

AbstractThe effect of various process parameters on the stability of TiO2 nanofluid, which can mostly be defined as zeta potential and particle size, was studied using response surface methodology (RSM) by the design of experiments and was predicted through a trained artificial neural network (ANN). The process parameters studied were weight percentage of surfactant (sodium lauryl sulfate) (0.01–0.2 wt%) and the value of pH (10–12). Central composite design and the RSM were employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the stability parameters. From the analysis of variance, the significant factors that affected the experimental design responses were also identified. The predicted stability parameters using the RSM and ANNs were compared using figures and tables. It is shown that the trained ANN outperformed the RSM in terms of accuracy and prediction of obtained results.

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

Processes ◽

10.3390/pr8111430 ◽

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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OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY

Journal of Engineering Studies and Research ◽

10.29081/jesr.v26i2.171 ◽

2020 ◽

Vol 26 (2) ◽

pp. 77-84

Author(s):

SYLVESTER UWADIAE ◽

FAITH OVIESU ◽

BAMIDELE AYODELE

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Process Parameters ◽

Box Behnken Design ◽

Garcinia Kola ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Solvent Volume

The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. The optimized process values were 397.86 mL and 399.99 mL for solvent volume; 109.32 min and 107.55 min for extraction time; 72.64 g and 70 g for sample mass and maximum yields of 20.839 wt% and 20.488 wt% for RSM and ANN respectively. The highly positively correlated experimental and anticipated values validated the models.

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OPTIMIZATION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK IN GEARED DRILLING

International Journal of Modern Manufacturing Technologies ◽

10.54684/ijmmt.2021.13.2.116 ◽

2021 ◽

Vol 13 (2) ◽

pp. 116-123

Author(s):

Yegireddi Shireesha ◽

◽

Nandipati Govind ◽

◽

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Process Parameters ◽

Filler Material ◽

Drill Bit ◽

Ann Model ◽

Drilling Operation ◽

Artificial Neural

Drill on different layered composite causes to force the layers ahead of it, creating unacceptable delamination on the leaving side. The current work describes the influence of different process parameters like spindle speed, feed, diameter of drill bit and addition of filler material on delamination damage on carbon and jute hybrid fiber composites during drilling operation. The experimental output for delamination is optimized by RSM-Response Surface Methodology and ANN-Artificial Neural Network Model. The experimental prediction model was established by considering DOE (design of experiments) of three levels performed with drilling operation by varying above said process parameters The optimum values for minimized delamination damage conditions found to be at (J-C-C-J) +5g of filler (fly-ash) composite. This is drilled with 8.5mm diameter drill bit with a feed of 0.08mm/rev at 875rpm speed. From the theoretical results it is recognized that cutting-speed and filler-material have much influencing factors on responses (delamination), and their individual contribution in an order of 47.25% and 47.32% respectively. By using Box-Behnken design RSM model is developed, with a feed-forward back-propagation method to develop the predictive ANN model which consists of 15 neurons in its hidden layer along with ANN Model. Here ANN Results (R2=0.99and RMSE=1.99) showed that the developed model is performing better to predict content of delamination when compare to RSM results (R2=0.97and RMSE=2.24).

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