Comparative assessment of response surface methodology quadratic models and artificial neural network method for dry reforming of natural gas

Gendy, Tahani S.; El‐Temtamy, Seham A.; El‐Salamony, Radwa A.; Ghoneim, Salwa A.

doi:10.1080/15567036.2018.1486476

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…In a nutshell, the results show the supremacy of the ANN over the other modeling techniques applied in terms of minimum RMSE, and R 2 , R 2 adj values near one. This result agrees with that obtained by many researchers confirming that the ANN model has the best prediction [17,18,20,36].…”

Section: Resultssupporting

confidence: 93%

“…A Radial Basis Function (RBF) is a real-valued function that depends only on the distance from the origin, Any function ϕ that satisfies the property ϕ (x) = ϕ (ǁ x ǁ) is a radial function. Even though the norm is usually Euclidean distance, other distance functions can also be possible [36]. RBF uses a series of basic functions that are symmetric and cantered at each sampling point, and it was originally developed for scattered multivariate data interpolation [25].…”

Section: Methodsmentioning

confidence: 99%

“…In order to evaluate the goodness of the model fitting and prediction accuracy of the constructed models, R 2 and error analyses were performed between the experimental and predicted data in the LRBF, QRBF, RSM, and ANN models. Many approaches for validation stated in the literature are used for error analyses, with some listed in a previous study [36].…”

Section: Methodsmentioning

confidence: 99%

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Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Osman

Shigidi

Arabi

2019

Foods

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This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents—hexane, chloroform, and acetone—with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10−3 to 3.70 × 10−7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.

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Section: Resultssupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Osman

Shigidi

Arabi

2019

Foods

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“…In order to evaluate the goodness of the model fitting and prediction accuracy of the constructed models, R 2 , F_ratio and error analyses were performed between the experimental and predicted data in the RSM, and ANN models. Many approaches for error analyses are stated in the literature, with some listed in a previous study [11] [45]. The formulas employed in this study for performance evaluation and error analyses are listed in Tables 1(a)-(c).…”

Section: Models Validation and Evaluationmentioning

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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“…R 2 and error analyses were performed between the experimental and predicted data in the four models to evaluate the goodness of the model fitting and prediction accuracy of the constructed models. Many approaches for validation are stated in the literature are used for error analyses, some are listed in [40].…”

Section: Model Validation and Evaluationmentioning

confidence: 99%

Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay

Osman

2019

Processes

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In this work, used lube oil was treated using natural acid-free clay. Clay was added at different amounts (5, 10, and 20 g) to 100 mL of waste engine oil at various temperatures (250, 350, 400, and 450 °C) and mixed at a speed of 800 rpm for 30 min. After settling and separation, the treated oil was diluted with kerosene before being examined using a Ultraviolet–visible (UV) spectrophotometer. In order to achieve cost-effective recycling, this process is modeled using the response surface method (RSM). Five regression models (linear, quadratic, Two Factor Interactions (2FI), cubic, and reduced-order quadratic model) were developed, then tested, and examined by calculating the statistical performance indicators (R2, R2adj, Akaike’s Information Criterion corrected (AICc), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE)). The results obtained reveal that the modified quadratic model outperforms the rest of the models in terms of the low value of RMSE, the lowest AICc, lowest BIC, and the highest R2 and R2adj. The developed modified quadratic model is optimized successfully to predict optimum operation conditions. Results show that optimum operation conditions are at the minimum area under the curve for UV absorption at 223.358; this can be achieved with a process temperature of 266.246 °C and clay quantity of 5.331 g. This model agreed with experimental data regardless of the effectiveness of red clay in the therapy of lube oil.

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Comparative assessment of response surface methodology quadratic models and artificial neural network method for dry reforming of natural gas

Cited by 8 publications

References 18 publications

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

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

Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay

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