Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter

2015 ◽  
Vol 76 ◽  
pp. 408-417 ◽  
Author(s):  
Eriola Betiku ◽  
Samuel S. Okunsolawo ◽  
Sheriff O. Ajala ◽  
Olatunde S. Odedele
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Performance Evaluation ◽  
Production Process ◽  
Response Surface ◽  
Process Parameters ◽  
Biodiesel Production ◽  
Vitellaria Paradoxa ◽  
Shea Tree

Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network

2022 ◽  
Vol 184 ◽  
pp. 753-764
Author(s):  
Gul Muhammad ◽  
Ange Douglas Potchamyou Ngatcha ◽  
Yongkun Lv ◽  
Wenlong Xiong ◽  
Yaser A. El-Badry ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Biodiesel Production ◽  
Microalgae Biomass ◽  
Artificial Neural ◽  
Wet Microalgae

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

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.

scholarly journals Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network

2019 ◽  
Vol 22 ◽  
pp. 100238 ◽  
Author(s):  
A.A. Ayoola ◽  
F.K. Hymore ◽  
C.A. Omonhinmin ◽  
O.C. Olawole ◽  
O.S.I. Fayomi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Biodiesel Production ◽  
Groundnut Oil ◽  
Artificial Neural

scholarly journals DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

2020 ◽  
Vol 60 (5) ◽  
pp. 369-390
Author(s):  
Ilesanmi Daniyan ◽  
Isaac Tlhabadira ◽  
Khumbulani Mpofu ◽  
Adefemi Adeodu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Titanium Alloy ◽  
Response Surface ◽  
Process Parameters ◽  
Numerical Models ◽  
Physical Experiments ◽  
Artificial Neural Network Ann

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

Estimation of mechanical properties of friction stir processed Al 6061/Al2O3-Tib2 hybrid metal matrix composite layer via artificial neural network and response surface methodology

2021 ◽  
pp. 146442072110345
Author(s):  
Vahid M Khojastehnezhad ◽  
Hamed H Pourasl ◽  
Arian Bahrami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Friction Stir Processing ◽  
Wear Behavior ◽  
Friction Stir ◽  
Al 6061 ◽  
Artificial Neural

Friction stir processing is one of the solid-state processes which can be used to modify the structure and properties of alloys. In addition, it has become one of the most promising techniques for the preparation of the surface layer composites. To pursue cost savings and a time-efficient design, the mathematical model and optimization of the process can represent a valid choice for engineers. Friction stir processing was employed to generate an Al 6061/Al2O3-TiB2 hybrid composite layer, and mechanical properties such as the hardness and wear behavior were also measured. The relationship between the hardness and wear behavior, process parameters of friction stir processing were evaluated using an artificial neural network and response surface methodology. The rotational speed (1500–1800 rpm), traverse speeds (25, 50, 100 mm/min), and the number of passes (1–4) with constant axial force (2.61 kN) were used as the input, while the hardness and weight loss values were the output. Experimentally, the results showed that the process parameters have significant effect on hardness and wear behavior of Al 6061/Al2O3-TiB2. In addition, the developed artificial neural network and response surface methodology models can be employed as alternative methods to compute the hardness and weight loss for given process parameters. The results of both models showed that the estimated values for the hardness and wear behavior of the processed zone had an error less than 0.60%, which indicated reliability, and an evaluation of the estimated values of both models and the experimental values confirmed that the artificial neural network is a better model than response surface methodology.

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

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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