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Investigation of the Solubility of Elemental Sulfur (S) in Sulfur-Containing Natural Gas with Machine Learning Methods

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  • Yuchen Wang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Zhengshan Luo

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Jihao Luo

    (School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

  • Yiqiong Gao

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Yulei Kong

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Qingqing Wang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

Abstract

Some natural gases are toxic because they contain hydrogen sulfide (H 2 S). The solubility pattern of elemental sulfur (S) in toxic natural gas needs to be studied for environmental protection and life safety. Some methods (e.g., experiments) may pose safety risks. Measuring sulfur solubility using a machine learning (ML) method is fast and accurate. Considering the limited experimental data on sulfur solubility, this study used consensus nested cross-validation (cnCV) to obtain more information. The global search capability and learning efficiency of random forest (RF) and weighted least squares support vector machine (WLSSVM) models were enhanced via a whale optimization–genetic algorithm (WOA-GA). Hence, the WOA-GA-RF and WOA-GA-WLSSVM models were developed to accurately predict the solubility of sulfur and reveal its variation pattern. WOA-GA-RF outperformed six other similar models (e.g., RF model) and six other published studies (e.g., the model designed by Roberts et al.). Using the generic positional oligomer importance matrix (gPOIM), this study visualized the contribution of variables affecting sulfur solubility. The results show that temperature, pressure, and H 2 S content all have positive effects on sulfur solubility. Sulfur solubility significantly increases when the H 2 S content exceeds 10%, and other conditions (temperature, pressure) remain the same.

Suggested Citation

  • Yuchen Wang & Zhengshan Luo & Jihao Luo & Yiqiong Gao & Yulei Kong & Qingqing Wang, 2023. "Investigation of the Solubility of Elemental Sulfur (S) in Sulfur-Containing Natural Gas with Machine Learning Methods," IJERPH, MDPI, vol. 20(6), pages 1-21, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:6:p:5059-:d:1095890
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    References listed on IDEAS

    as
    1. Yue Wang & Shanjiang Liu & Wentao Xue & He Guo & Xinrong Li & Guoyuan Zou & Tongke Zhao & Hongmin Dong, 2019. "The Characteristics of Carbon, Nitrogen and Sulfur Transformation During Cattle Manure Composting—Based on Different Aeration Strategies," IJERPH, MDPI, vol. 16(20), pages 1-18, October.
    2. Noemí DeCastro-García & Ángel Luis Muñoz Castañeda & David Escudero García & Miguel V. Carriegos, 2019. "Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm," Complexity, Hindawi, vol. 2019, pages 1-16, February.
    3. Valentine A. Chanturiya & Eugenia A. Krasavtseva & Dmitriy V. Makarov, 2022. "Electrochemistry of Sulfides: Process and Environmental Aspects," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    4. Junqi Zhu & Li Yang & Xue Wang & Haotian Zheng & Mengdi Gu & Shanshan Li & Xin Fang, 2022. "Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM," IJERPH, MDPI, vol. 19(19), pages 1-22, October.
    5. Shiyuan Ding & Yingying Chen & Qinkai Li & Xiao-Dong Li, 2022. "Using Stable Sulfur Isotope to Trace Sulfur Oxidation Pathways during the Winter of 2017–2019 in Tianjin, North China," IJERPH, MDPI, vol. 19(17), pages 1-12, September.
    6. Odey Alshboul & Ali Shehadeh & Rabia Emhamed Al Mamlook & Ghassan Almasabha & Ali Saeed Almuflih & Saleh Y. Alghamdi, 2022. "Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
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