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A simple model for estimation of higher heating value of oily sludge

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  • Chen, Xiaoling
  • Zhang, Yongxing
  • Xu, Baoshen
  • Li, Yifan

Abstract

Oily sludge, as a waste containing a large amount of oil within the range of 10–90 wt%, has a potential to be utilized as a source of fuel. Heating value is of utmost importance in the assessment of thermal conversion of waste to energy and/or other valuable products. The aim of the current study is to develop correlations based on proximate and ultimate analysis to predict HHV of oily sludge accurately and easily. The analysis data were collected from previous studies conducted by global researchers. Four correlations were proposed by using the regression analysis method, one based on proximate analysis and three based on ultimate analysis. Among them, the correlations based on ultimate analysis show higher accuracy than the one based on proximate analysis and those established in the literature. Interestingly, the addition of variables has a negative contribution to the accuracy of correlations due to the collinearity between C and H. Thus, the simplest one (i.e. HHV = 0.431C + 1.234) only containing C presents the highest R2¯ and lowest bias error, especially for HHV higher than 20 MJ/kg (more preferable to be used as fuel source). The correlations were then validated with samples collected from local oilfield, showing a similar result with that obtained for the regression analysis.

Suggested Citation

  • Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021691
    DOI: 10.1016/j.energy.2021.121921
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    1. Hiago Oliveira & Isabela Pinheiro & Ana Ramos & Osvaldo Venturini & Adriano Mariano & York Santiago, 2024. "Influence of Physicochemical Properties of Oil Sludge on Syngas Production for Energy Applications," Resources, MDPI, vol. 14(1), pages 1-27, December.
    2. Leng, Lijian & Zhou, Junhui & Zhang, Weijin & Chen, Jiefeng & Wu, Zhibin & Xu, Donghai & Zhan, Hao & Yuan, Xingzhong & Xu, Zhengyong & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2024. "Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes," Energy, Elsevier, vol. 288(C).
    3. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    4. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    5. Mollanoori, Mohammad & Dehghan, Ali Akbar, 2024. "Estimating the higher heating value and chemical exergy of solid, liquid, and natural gas fossil fuels," Energy, Elsevier, vol. 302(C).

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