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.

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.

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.

scholarly journals Prediction of standard aeration efficiency of propeller diffused aeration system using response surface methodology and artificial neural network

2021 ◽  
Author(s):  
Subha M. Roy ◽  
Mohammad Tanveer ◽  
Debaditya Gupta ◽  
C. M. Pareek ◽  
B. C. Mal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Deviation ◽  
Operating Parameters ◽  
Aeration Efficiency ◽  
Artificial Neural

Abstract Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters include the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of the both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. The cross-validation results show that the best approximation of optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.

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