2017
DOI: 10.2516/ogst/2017004
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Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Abstract: -The existence of hydrogen sulfide (H 2 S) in the gas effluents of oil, gas and petrochemical industries causes environmental pollution and equipment corrosion. These gas streams, called offgas, have high H 2 S concentration, which can be used to produce sodium sulfide (Na 2 S) by H 2 S reactive absorption. Na 2 S has a wide variety of applications in chemical industries. In this study, the reactive absorption process was performed using a spray column. Response Surface Methodology (RSM) was applied to design … Show more

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Cited by 13 publications
(8 citation statements)
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“…where y represents the response variable (COD removal efficiency), b 0 , b i , b ii , and b ij are regression coefficients for the constant term, the square term, and the interaction term, respectively, z i is a coded variable, and 1 is a residual factor associated with the experiments (Bashipour et al 2017).…”
Section: Applied Models and Experimental Designmentioning
confidence: 99%
“…where y represents the response variable (COD removal efficiency), b 0 , b i , b ii , and b ij are regression coefficients for the constant term, the square term, and the interaction term, respectively, z i is a coded variable, and 1 is a residual factor associated with the experiments (Bashipour et al 2017).…”
Section: Applied Models and Experimental Designmentioning
confidence: 99%
“…F k is the nonlinear activation transfer function, which is one of the main characteristic elements of an ANN with the most common type of sigmoidal transfer functions. According to equation (2), input and output values were employed as normalized values in the range of 0-1 to gain higher ANN performance and consistent results [29,30]:…”
Section: Artificial Neural Network (Ann) Modelmentioning
confidence: 99%
“…The latest strategy developed for density estimation is the application of artificial intelligence which produces reliable estimates heavy oil density [27]. This type of smart computations has been employed for describing intricate problems with considerably nonlinear trends in wide areas of petroleum and chemical engineering [28][29][30]. Recently, two rigorous versions of smart computations including Radial Basis Function Neural Network (RBFNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) have been proposed by Tavakoli et al [31] and Abbasi et al [27] to prognosticate the diluted density of Athabasca bitumen by n-tetradecane, respectively.…”
Section: Introductionmentioning
confidence: 99%