APPLICATION OF ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY FOR MODELLING OF HYDROGEN PRODUCTION USING NICKEL LOADED ZEOLITE

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Fazureen Azaman ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Mahadhir Mohamed ◽  
Kamaruzzaman Yunus ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Hydrogen Gas ◽  
Hydrogen Yield ◽  
Glycerol Conversion ◽  
Production Of Hydrogen ◽  
Artificial Neural

Hydrogen gas production via glycerol steam reforming using nickel (Ni) loaded zeolite (HZSM-5) catalyst was focused on this research. 15 wt % Ni(HZSM-5) catalyst loading has been investigated based on the parameter of different range of catalyst weight (0.3-0.5g) and glycerol flow rate (0.2-0.4mL/min) at 600 ºC and atmospheric pressure. The products were analyzed by using gas-chromatography with thermal conductivity detector (GC-TCD), where it used to identify the yield of hydrogen. The data of the experiment were analyzed by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in order to predict the production of hydrogen. The results show that the condition for maximum hydrogen yield was obtained at 0.4 ml/min of glycerol flow rate and 0.3 g of catalyst weight resulting in 88.35 % hydrogen yield. 100 % glycerol conversion was achieved at 0.4 of glycerol flow rates and 0.3 g catalyst weight. After predicting the model using RSM and ANN, both models provided good quality predictions. The ANN showed a clear superiority with R2 was almost to 1 compared to the RSM model.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

Investigation on Fluidized Bed Bioreactor Treating Ice Cream Wastewater Using Response Surface Methodology and Artificial Neural Network

2014 ◽  
Vol 12 (1) ◽  
pp. 563-573 ◽  
Author(s):  
K. Thirugnanasambandham ◽  
V. Sivakumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Fluidized Bed ◽  
Flow Rate ◽  
Ice Cream ◽  
Agitation Speed ◽  
Fluidized Bed Bioreactor ◽  
Artificial Neural

Abstract In this study, a comparative approach was developed between response surface methodology (RSM) and artificial neural network (ANN) in the predictive capabilities for the removal of chemical oxygen demand (COD) from ice cream industry wastewater using fluidized bed bioreactor. The effects of process variables such as pH, temperature, flow rate and agitation speed investigated using a four-factor three-level Box–Behnken experimental design (BBD). Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of statistical parameters including coefficient of determination (R2). The results showed that properly trained ANN model is more accurate in prediction as compared to RSM model. Under the optimum conditions (pH of 7, temperature of 40°C, flow rate of 20 ml/min and agitation speed of 175 rpm), 91% of COD was removed.

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

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