Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system-Reference-Cited by-同舟云学术

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Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system

Published:2023-09-18 Issue:1 Volume:45 Page:84-100
ISSN:1741-5993
Container-title:Journal of Sulfur Chemistry
language:en
Short-container-title:Journal of Sulfur Chemistry

Author:

Pouladi Borj Babak¹²,Ali Fanaei Mohammad¹,Esfandyari Morteza³,Naddaf Atiyeh¹²,Jafari Dariush⁴,Baghmisheh Gholamreza⁵

Affiliation:

1. Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

2. South Pars Gas Complex (SPGC), Asaluyeh, Bushehr, Iran

3. Department of Chemical Engineering, University of Bojnord, Bojnord, Iran

4. Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

5. Sharif Engineering Process Development Company, Tehran, Iran

Publisher

Informa UK Limited

Subject

General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/17415993.2023.2257827

Reference29 articles.

1. Fossil Energy Demand and Economic Development in BRICS Countries

2. The energy–climate challenge: Recent trends in CO2 emissions from fuel combustion

3. Hydrodesulfurization unit for natural gas condensate

4. API Gravity, Sulfur Content, and Desulfurization of Crude Oil

5. First multi-tool exploration of a gas-condensate-pyrolysate system from the environment of burning coal mine heaps: An in situ FTIR and laboratory GC and PXRD study based on Upper Silesian materials

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1. Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey;Environmental Science and Pollution Research;2024-02-21

2. Integration of advanced technologies to improve the efficiency of gas condensate field development;Naukovij žurnal «Tehnìka ta energetika»;2024-01-19

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