AUTHOR=Liu Li , Yu Zhenwei , Chen Zheqi , Wang Kai , Xiao Qian , Chen Jingjing 

TITLE=Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

JOURNAL=Frontiers in Energy Research

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.967856

DOI=10.3389/fenrg.2022.967856

ISSN=2296-598X

ABSTRACT=<p>Since the beginning of 2016, China’s annual emissions of herbal residues (HR) have exceeded 30 million tons. As a kind of solid waste, HR still contains a large amount of organic matter, which requires further industrial extraction procedure. Most of the existing studies are concerned with the feasibility of utilizing traditional Chinese medicine residues, meanwhile there are very few studies regarding the kinetics of pyrolysis in the process of resource utilization of traditional Chinese medicine residues. In this study, we comprehensively studied the kinetics characteristics of raw materials with various heating rates (10, 20, 30, and 40°C/min) using a synchronous thermogravimetric analysis, and we adopted Coats-Redfern model to study the thermal kinetics and thermal analysis of GBR. A novel method combining Genetic algorithm and Adaboost algorithm (GA-Adaboost) is proposed to predict the thermogravimetric curve of the raw plant material with respect to the heating rate and temperature. The experimental result shows that the activation energy of the raw material was determined by the Kissinger-Akahira-Sunose (KAS) (<inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mn>148.71</mml:mn><mml:mi>k</mml:mi><mml:mi>J</mml:mi><mml:mo>/</mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:math></inline-formula>), and the correlation coefficient was greater than 0.99. The optimal reaction mechanism determined by the Coats - Redfern method was random nucleation and subsequent growth. The GA-Adaboost model achieved good performance (with a fitting degree of 99.88% on training data, 99.80% on verification data, and MSE of3.4173) while predicting the pyrolysis process of ginkgo biloba residue. This study will provide theoretical basis and technical support for the efficient resource utilization of pharmaceutical residues and reduce environmental pressure.</p>