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Valorization of lignite wastes into humic acids: Process optimization, energy efficiency and structural features analysis

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  • Sarlaki, Ehsan
  • Sharif Paghaleh, Ali
  • Kianmehr, Mohammad Hossein
  • Asefpour Vakilian, Keyvan

Abstract

The present work investigates the optimization of humic acids (HAs) production from lignite processing wastes using a developed mechanical agitated (MA) tank reactor based on the results of response surface methodology (RSM). The structures of the obtained HAs were characterized using elemental analysis, spectroscopy, scanning electron microscopy (SEM), particle size distribution (PSD), acidic functional groups, and ash content. The highest extraction efficiency of HA was 54.2%, which was obtained from the MA tank reactor under optimal conditions of using 0.5 M NaOH, the reaction time of 4 h, stirring speed of 850 rpm, and temperature of 70 °C. The developed MA tank reactor was capable of improving the HA extraction and energy efficiency of the process up to 26.9% and 24.8%, respectively, compared to the conventional magnetic stirred (MS) glass reactor. The extracted HA from the MA tank reactor exerted better quality indices, including higher oxygen content (37.02%), higher C/N and O/C ratios (61.26 and 0.47, respectively), higher total acidity and phenolic-OH contents (8.87 and 5.6 meq.g−1, respectively), higher E4/E6 ratio (3.765), more homogeneously porous structure, higher specific surface area, lower H/C ratio (0.64), and lower ash content (2.2%), than the MS method, which indicate its suitability in agricultural applications.

Suggested Citation

  • Sarlaki, Ehsan & Sharif Paghaleh, Ali & Kianmehr, Mohammad Hossein & Asefpour Vakilian, Keyvan, 2021. "Valorization of lignite wastes into humic acids: Process optimization, energy efficiency and structural features analysis," Renewable Energy, Elsevier, vol. 163(C), pages 105-122.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:105-122
    DOI: 10.1016/j.renene.2020.08.096
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    Cited by:

    1. Chen, Qi & Qu, Zhaoming & Ma, Guohua & Wang, Wenjing & Dai, Jiaying & Zhang, Min & Wei, Zhanbo & Liu, Zhiguang, 2022. "Humic acid modulates growth, photosynthesis, hormone and osmolytes system of maize under drought conditions," Agricultural Water Management, Elsevier, vol. 263(C).
    2. Nassef, Ahmed M. & Sayed, Enas T. & Rezk, Hegazy & Inayat, Abrar & Yousef, Bashria A.A. & Abdelkareem, Mohammad A. & Olabi, A.G., 2020. "Developing a fuzzy-model with particle swarm optimization-based for improving the conversion and gasification rate of palm kernel shell," Renewable Energy, Elsevier, vol. 166(C), pages 125-135.
    3. Esmaili, Maryam & Aliniaeifard, Sasan & Mashal, Mahmoud & Vakilian, Keyvan Asefpour & Ghorbanzadeh, Parisa & Azadegan, Behzad & Seif, Mehdi & Didaran, Fardad, 2021. "Assessment of adaptive neuro-fuzzy inference system (ANFIS) to predict production and water productivity of lettuce in response to different light intensities and CO2 concentrations," Agricultural Water Management, Elsevier, vol. 258(C).

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