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Application of machine learning in optimizing thermochemical conversion processes with pre-treatment to get higher bio-oil yield from biomass waste

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Title Application of machine learning in optimizing thermochemical conversion processes with pre-treatment to get higher bio-oil yield from biomass waste
 
Creator Kamarajan, Murugan
Srinivasan, Kandasamy Sundaresan
Ravichandran, Cingaram
 
Subject Biomass waste
Bio-oil
Machine learning
Pearson matrix
Pre-treatment
Thermochemical liquefaction
 
Description 11-19
Improving the bio-oil yield is a challenging part in the thermochemical conversion processes of biomass. Implementing
suitable pre-treatment technology to improve the biomass characteristics is an effective technique to increase the yield. In
this study, a multi-variate random forest algorithm has been used to optimize the pre-treatment method in order to improve
the biomass characteristics. The data collected from many previous studies are analysed to identify the importance of
biomass characteristics in bio-oil yield. The correlation between biomass characteristics and bio-oil yield, is analysed using
Pearson method and the important influencing parameters %C and %H have a very good positive correlation with a
coefficient value range 0.455 to 0.818. Among the six pre-treatment methods analysed, thermochemical pre-treatment
method was found effective with more than 95% improvement of many biomass characteristics. The range of voting given
to the parameters identify %H be the important characteristic to be optimized first. The suggested method is validated by
laboratory experiments and % accuracy between predicted and calculated biomass characteristic values showed more than
90% accuracy for all the biomass characteristic parameters tested in this study.
 
Date 2024-01-12T12:52:10Z
2024-01-12T12:52:10Z
2024-01
 
Type Article
 
Identifier 0975-0991 (Online); 0971-457X (Print)
http://nopr.niscpr.res.in/handle/123456789/63210
https://doi.org/10.56042/ijct.v31i1.6357
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJCT Vol.31(1) [January 2024]