Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil

Alardhi, Saja Mohsen; Jabbar, Noor Mohsen; Al-Jadir, Thaer; Ibrahim, Neran K.; Dakhil, Ali M.; Al-Saedi, Noor Dh.; Al-Saedi, Haneen Dh.; Adnan, Mustafa

doi:10.1063/5.0091975

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…As a result, (ANNs) have been applied in a wide range of fields, including physics, finance, neurophysiology, robotics, image processing, and computer and electrical engineering [ 34 ]. Numerous chemical engineering applications have made use of ANN [ 35 ].…”

Section: Introduction Of Artificial Neural Networkmentioning

confidence: 99%

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Fiyadha¹,

Alardhi²,

Omar³

et al. 2023

Heliyon

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Section: Introduction Of Artificial Neural Networkmentioning

confidence: 99%

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Fiyadha¹,

Alardhi²,

Omar³

et al. 2023

Heliyon

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“…When compared with alternative factorial design methods, the BBD approach was able to reduce the necessary number of experiments while maintaining a high level of accuracy in the optimization. Following BBD, the minimum number of experiments needed to account for the variables in the study was: 𝑁 = 2𝑘(𝑘 − 1) + 𝑟 (1) Where K is the number of studied variables, N is the number of experiments, and r is the number of central points which usually (3)(4)(5)(6). According to BBD, only three values (-1,0,1) were allowed for the variables in the analysis, with the distance between each level having equal values.…”

Section: Methodsmentioning

confidence: 99%

“…Crude oil is either considered sweet (with a sulfur concentration of less than 0.5%) or sour (with a sulfur content of greater than 0.5%). The Location of a crude oil reservoir is a key factor in determining its sulfur concentration [1]. Market prices for crude oil are based mostly on its sulfur content and API, with a preference for crude oil with a low sulfur content [2].…”

Section: -Introductionmentioning

confidence: 99%

Adsorptive Desulfurization of Iraqi Light Naphtha Using Calcite and Modified Calcite

Qasim,

Alwan

2024

IJCPE

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This study used the adsorption method to remove sulfur compounds from light naphtha fuel by using calcite and modified calcite as adsorbents. The calcite was prepared from chicken eggshells by heating and activation methods. It was modified by mixing it with commercial activated carbon as a new adsorbent. XRD and FTIR were used to characterize the adsorbents. Light naphtha fuel from the Al-Diwaniyah refinery, with a sulfur concentration of 776 ppm, was used in batch adsorption studies. Various operation conditions that affect the adsorption process were studied such as temperature (20–40 °C), weight of the adsorbent (1-3 g), and contact time (15–45 min) at constant mixing speed (300 rpm). In this study, the Minitab Program-Box-Behnken design was used to design experiments in batch adsorption studies of light naphtha, which is considered more straightforward and accurate because it shows the effect of each dependent factor on the adsorption efficiency and removal ratio. Results and analysis showed that the increase in temperature, the amount of adsorbent, and contact time would increase the removal efficiency. The analysis of adsorption equilibrium isotherms shows that the experimental data follows the Freundlich isotherm model for adsorbents. According to the results of the study, the highest removal percentages of sulfur content of light naphtha using calcite and modified calcite were 61% and 79%, respectively.

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“…Various gas-sweetening techniques must be used depending on the type and amount of acid gas impurities that need to be eliminated (Alardhi et al 2022). The currently used processes are as follows: chemical absorption (Zhu et al 2021), physical absorption (Burr and Lyddon 2008), membrane (Liu et al 2020), direct conversion to sulphur (Mokhatab and Poe 2012), and physical adsorption (Webley 2014).…”

Section: Introductionmentioning

confidence: 99%

Design of Artificial Neural Network for Prediction of Hydrogen Sulfide and Carbon Dioxide Concentrations in a Natural Gas Sweetening Plant

Alardhi¹,

Al-Jadir²,

Hasan³

et al. 2023

Ecol. Eng. Environ. Technol.

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Gas sweetening is a fundamental step in gas treatment processes for environmental and safety concerns. One of the most extensively used and largely recognized solvents for gas sweetening is methyl diethanolamine (MDEA). One of the most crucial metrics for measuring the effectiveness of gas treatment units is the amount of acid gas that has been treated with MDEA solution. As a result, it should be regularly monitored to avoid operational issues in downstream processes and excessive energy consumption. In this study, the artificial neural network (ANN) approach was followed to predict the H 2 S and CO 2 sour gases concentrations of sweetening process. The model was built using dataset gathered from a real operation plant in Iraq, collected from February 2019 to February 2020, and used as input to the neural network. The data include H 2 S and CO 2 concentrations of the feed gas, temperature, pressure, and flow rate of the unit. The designed ANN model showed good accuracy in modeling the process under investigation, even for a wide range of parameter variability. The testing outcomes demonstrated a high coefficient of determination (R 2 ) of greater than 0.99, while the overall training performance showed a low mean squared error (MSE) of less than 0.0003.

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Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil

Cited by 9 publications

References 21 publications

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Adsorptive Desulfurization of Iraqi Light Naphtha Using Calcite and Modified Calcite

Design of Artificial Neural Network for Prediction of Hydrogen Sulfide and Carbon Dioxide Concentrations in a Natural Gas Sweetening Plant

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