Application of artificial neural networks and response surface methodology approaches for the prediction of oil agglomeration process

Yadav, Anand Mohan; Chaurasia, Ram Chandra; Suresh, N.; Gajbhiye, Pratima

doi:10.1016/j.fuel.2018.02.040

Cited by 37 publications

(17 citation statements)

References 53 publications

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“…Artificial neural Network (ANN) is a system that mimics the naturally inspired computational model. Thus, it emulates the workings of the human brain to take in certain connections among information inputs and yield outputs through trained data [27][28][29][30].…”

Section: Evaluation Performance Between Artificial Neural Network (Anmentioning

confidence: 99%

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

et al. 2019

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Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route.

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Section: Evaluation Performance Between Artificial Neural Network (Anmentioning

confidence: 99%

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

et al. 2019

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“…This test shows that the model can accurately deduce the data behavior in the investigated experimental area [23]. The low value (12.68 %) of coefficient of variation (C.V.) for RR 120 removal (%) reconfirmed the precision, higher reliability, and excellent reproducibility of the given quadratic model equation [32]. The full quadratic regression model for the RR120 removal (%) using BBD based on Eq.…”

Section: Anova and Empirical Model Fit Equationsmentioning

confidence: 85%

Box–Behnken Design for Optimizing the Synthesis Condition and Adsorption Capability of Covalently Crosslinked Chitosan/coal Fly Ash Composite

Mohammed

Malek

Jawad

et al. 2021

Preprint

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The growing concern regarding the environmental protection has encouraged researchers to focus their efforts on developing better and more effective possibilities of removing pollutants. In this research, a biocomposite adsorbent of covalently crosslinked chitosan-epichlorohydrin/coal fly ash (CHT-ECH/CFA) was synthesized and applied for reactive red 120 dye (RR120) removal. The CHT-ECH/CFA was characterized by BET, pH potentiometric, pHpzc, XRD, FTIR and SEM-EDX. Box–Behnken Design (BBD) was employed to assess the effects of the adsorption key parameters such as CFA loading into CTH-ECH matrix, adsorbent dose, solution pH, working temperature and contact time. The optimized CFA loading, adsorbent dose, temperature, time, and pH were observed to be 50 %, 0.07 g, 45 °C, 60 min, and pH 4 respectively. From BBD, the highest removal of RR120 removal at optimum conditions was found to be 90.2%. The results showed that adsorption performance can be modelled perfectly by both Langmuir and Freundlich isotherm models with maximum adsorption capacity of 237.7 mg/g at 45 °C. Moreover, the adsorption kinetics were well fitted to the pseudo-second order model. Based on the findings from the experiments conducted, the hybrid biocomposite adsorbent offers adequate potential for the treatment of anionic dye-polluted water.

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“…RSM can be divided into the following steps: 1) selection of the independent variables and responses, 2) selection of the experimental design, 3) execution of experiments and collection of results, 4) mathematical modeling of experimental data by polynomial equations, with the best fitting response, 5) checking of models through analysis of variance, 6) drawing of response surfaces, 7) evaluating main and interactional effect of variables using 2D or 3D plots, and, finally, 8) identification of optimal conditions [16] [26] [27].…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…Mathematical equation relating the input/output variables is given by using the following equation [27].…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

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The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

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Application of artificial neural networks and response surface methodology approaches for the prediction of oil agglomeration process

Cited by 37 publications

References 53 publications

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

Box–Behnken Design for Optimizing the Synthesis Condition and Adsorption Capability of Covalently Crosslinked Chitosan/coal Fly Ash Composite

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

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