Highly selective hydrodeoxygenation of wood pallet sawdust pyrolysis oil to methyl phenol derivatives using cobalt and iron on activated carbon supported catalysts

Tran, Quoc Khanh; Ly, Hoang Vu; Vo, Thuan Anh; Hwang, Hyun Tae; Kim, Seung-Soo

doi:10.1016/j.ecmx.2022.100184

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…This resulted in initial numbers of 172 and 2,392 documents, respectively, that were then screened to make sure they reported as many of the process variables noted in Table . After the screening process, a total of 15 and 29 papers were selected that fit the criteria, resulting in a total of 175 and 211 data points, respectively. − Studies that were not selected were deemed unfit due to noncompatible methodologies or underreporting of results and process conditions. This search for data and processing centers exclusively on changes to HHV or resulting HHV in BO from experiments but not directly on the chemical species found in the BO, which is beyond the scope of this work.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Higher Heating Values in Bio-Oil from Solvothermal Biomass Conversion and Bio-Oil Upgrading Given Discontinuous Experimental Conditions

Castro Garcia,

Ching,

et al. 2023

ACS Omega

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Both the conversion of lignocellulosic biomass to bio-oil (BO) and the upgrading of BO have been the targets of many studies. Due to the large diversity and discontinuity seen in terms of reaction conditions, catalysts, solvents, and feedstock properties that have been used, a comparison across different publications is difficult. In this study, machine learning modeling is used for the prediction of final higher heating value (HHV) and ΔHHV for the conversion of lignocellulosic feedstocks to BO, and BO upgrading. The models achieved coefficient of determination (R 2) scores ranging from 0.77 to 0.86, and the SHapley Additive exPlanations (SHAP) values were used to obtain model explainability, revealing that only a few experimental parameters are largely responsible for the outcome of the experiments. In particular, process temperature and reaction time were overwhelmingly responsible for the majority of the predictions, for both final HHV and ΔHHV. Elemental composition of the starting feedstock or BO dictated the upper possible HHV value obtained after the experiment, which is in line with what is known from previous methodologies for calculating HHV for fuels. Solvent used, initial moisture concentration in BO, and catalyst active phase showed low predicting power, within the context of the data set used. The results of this study highlight experimental conditions and variables that could be candidates for the creation of minimum reporting guidelines for future studies in such a way that machine learning can be fully harnessed.

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