H<sub>2</sub>S Reactive Absorption from Off‐Gas in a Spray Column: Insights from Experiments and Modeling

Bashipour, Fatemeh; Khorasani, Saied Nouri; Rahimi, Amir

doi:10.1002/ceat.201500233

Cited by 10 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 6 shows that the ANN model with larger R 2 and smaller MSE and AAD values is superior to the RSM model in predicting Y. Additionally, the ANN model is not limited to number of experiments and it is applicable to complicated non-linear processes [21,28].…”

Section: Comparison Between Results Of Rsm and Ann Modelsmentioning

confidence: 99%

“…It consists of an input layer, one or more hidden layers and an output layer. Each layer involves processing units called neurons which operate independently of others [27,28]. In the current study, a MLFF network was applied with the architecture as shown in Figure 2: an input layer included three operating conditions as input neurons, an output layer with one neuron, and one hidden layer which its neurons can change to gain the best ANN performance.…”

Section: Ann Model Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Bashipour

Rahimi

Khorasani

et al. 2017

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

Self Cite

View full text Add to dashboard Cite

-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 and optimize experiments based on Central Composite Design (CCD). The individual and interactive effects of three independent operating conditions on the weight percent of the produced Na 2 S (Y) were investigated by RSM: initial NaOH concentration (10-20% w/w), scrubbing solution temperature (40-60°C) and liquid-to-gas volumetric ratio (15 9 10 À3 to 25 9 10 À3 ). Furthermore, an Artificial Neural Network (ANN) model was used to predict Y. The results from RSM and ANN models were compared with experimental data by the regression analysis method. The optimum operating conditions specified by RSM resulted in Y of 15.5% at initial NaOH concentration of 19.3% w/w, scrubbing solution temperature of 40°C and liquid-to-gas volumetric ratio of 24.6 9 10 À3 v/v.Résumé -Optimisation expérimentale et modélisation de la production de sulfure de sodium à partir d'H 2 S riche en gaz provenant de la surface d'intervention méthodologique et de réseau de neurones artificiels -La présence de sulfure d'hydrogène (H 2 S) dans les effluents gazeux rejetés par l'industrie pétrolière, gazière et pétrochimique entraîne une pollution de l'environnement ainsi que la corrosion des équipements. Ces flux gazeux, appelés effluents gazeux, présentent une forte concentration de H 2 S qui peut être employée pour produire du sulfure de sodium (Na 2 S) par absorption réactive de H 2 S. Le Na 2 S trouve une multitude d'applications dans les industries chimiques. Dans cette étude, le processus d'absorption réactive a été réalisé avec une colonne à pulvérisation. La méthode des surfaces de réponses (Response Surface Methodology, RSM) a été appliquée pour concevoir et optimiser des expériences basées sur les plans composites centrés (Central Composite Design, CCD). Les effets individuels et interactifs de trois conditions d'exploitation indépendantes sur le pourcentage en poids de Na 2 S produit (Y) ont été analysés par RSM, à savoir : la concentration de NaOH initiale (10 à 20 % p/p), la température de la solution de lavage (40 à 60°C) et le rapport du volume liquide/gaz (15 9 10

show abstract

Section: Comparison Between Results Of Rsm and Ann Modelsmentioning

confidence: 99%

Section: Ann Model Descriptionmentioning

confidence: 99%

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Bashipour

Rahimi

Khorasani

et al. 2017

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

Self Cite

View full text Add to dashboard Cite

show abstract

“…ANNs are inspired by biological nervous systems such as brain and consist of elements that receive inputs and generate a single output, where the output is a function of the inputs. ANN has large numbers of computational units connected in a massively parallel structure and do not require a mathematical formulation or physical relationships of the handled problem [20,21]. This property is a significant advantage of the flexible ANN model to predict complicated systems.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Predictive models for density correction factor of natural gas and comparison with standard methods

Bashipour

Hojjati

2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

Self Cite

View full text Add to dashboard Cite

Two intelligent-based models which do not require complete gas compositions are presented to estimate natural gas density correction factor using comprehensive datasets (nearly 60 000 instances) originating from the AGA8-DCM (Detail Characterization Method) standard: (1) NGDC-ANN model (Natural Gas Density Calculator based on Artificial Neural Network) and (2) AGA8-GCMD model (Gross Characterization Method Developed by applying genetic algorithm technique). In the suggested models, only five input variables (specific gravity at base condition, operating temperature and pressure and molar composition of CO2 and N2) are employed. The experimental datasets obtained from this work (68 instances) and literature (505 instances) are applied to validate the developed model showing a very good agreement between experimental and estimated data. Simplicity, improving accuracy and satisfactory results of the suggested models over a wide range of operational conditions show that these models would be excellent alternatives for the traditional standard methods, so that, the NGDC-ANN model prediction besides of its simplicity to use show the highest accuracy over a wide of operational range in comparison to similar models.

show abstract

“…For the latter purpose, the application of reliable and adequate process models is widespread 11. Useful models can be found in the literature for many chemical air scrubber applications, such as the desulfurization process 12–14, the absorption of odorous compounds 15, 16, and the absorption of ammonia in water using different scrubbing systems 17–22. However, none of these models consider the pollutant removal efficiency simultaneously with temperature and water evaporation, which is essential to assess the interaction between the scrubber performance, the temperature dynamics, and the water consumption.…”

Section: Introductionmentioning

confidence: 99%

Mechanistic Modeling of Pollutant Removal, Temperature, and Evaporation in Chemical Air Scrubbers

et al. 2016

View full text Add to dashboard Cite

Chemical air scrubbers reduce the concentration of water-soluble components such as ammonia from the outgoing ventilation air through absorption in water, followed by chemical conversions and removal of the end products. A mechanistic model for a countercurrent air scrubber was set up. Mass balances for ammonia, hydrogen sulfide, nitrous oxide, and methane were implemented, as well as the water mass balance and heat balances. The model was validated against experimental data from a conventional fattening pig housing facility. The effect of influent characteristics, design parameters, and control handles on the removal efficiency, the temperature profile, and the water evaporation rate were investigated through simulation. The model was able to describe the behavior of a countercurrent chemical air scrubber.

show abstract

H₂S Reactive Absorption from Off‐Gas in a Spray Column: Insights from Experiments and Modeling

Cited by 10 publications

References 30 publications

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Predictive models for density correction factor of natural gas and comparison with standard methods

Mechanistic Modeling of Pollutant Removal, Temperature, and Evaporation in Chemical Air Scrubbers

Contact Info

Product

Resources

About

H2S Reactive Absorption from Off‐Gas in a Spray Column: Insights from Experiments and Modeling

Cited by 10 publications

References 30 publications

Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Predictive models for density correction factor of natural gas and comparison with standard methods

Mechanistic Modeling of Pollutant Removal, Temperature, and Evaporation in Chemical Air Scrubbers

Contact Info

Product

Resources

About

H₂S Reactive Absorption from Off‐Gas in a Spray Column: Insights from Experiments and Modeling

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network