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.

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.

scholarly journals Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

2021 ◽  
Author(s):  
Jorge Marcos Rosa ◽  
Flavio Guerhardt ◽  
Silvestre Eduardo Rocha Rocha Ribeiro Júnior ◽  
Peterson Adriano Belan ◽  
Gustavo Araujo Lima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Coefficient Of Determination ◽  
Main Process ◽  
Modeling And Optimization ◽  
Dyeing Process ◽  
Artificial Neural ◽  
Cotton Dyeing

Abstract This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

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