Machine learning prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass

Oraléou Sangué Djandja, Shimin Kang, Zizhi Huang, Junqiao Li, Jiaqi Feng, Zaiming Tan, Adekunlé Akim Salami and Bachirou Guene Lougou

Energy, 2023, vol. 271, issue C

Abstract: Machine learning approaches are emerging as a promising method for assisting in the control of thermochemical processes. eXtreme Gradient Boosting (XGB) and Random Forest (RF) were applied, for the first time, for prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge (SS) and biomass. XGB outperformed RF in the prediction of carbon content, O/C, higher heating value, and mass and energy yields, while RF surpassed XGB in the prediction of H/C, N/C, and fuel ratio. The R2 between the predicted and experimental values for the best models was in [0.94–1] and [0.83–0.95], respectively for training and test. The feature importance and partial dependence analyses were used to interpret models and provide comprehensive understanding of the input features’ impact. Based on the best models, a graphical user interface was created to make prediction easier for other researchers. By only knowing the properties of SS and lignocellulosic biomass, the authors could prior to experiments explore various co-HTC conditions and SS ratios to find the most appropriate conditions to obtain some given properties of hydrochar. This will save time and resources that are usually spent on several trial experiments that may sometimes not yield positive results.

Keywords: Sewage sludge; Lignocellulosic biomass; Hydrothermal carbonization; Fuel; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223003626
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:271:y:2023:i:c:s0360544223003626

DOI: 10.1016/j.energy.2023.126968

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().