A machine learning model to predict the pyrolytic kinetics of different types of feedstocks

Wang, Shule; Jin, Yanghao; Zaini, Ilman Nuran; Li, Yan; Tang, Chuchu; Mu, Wangzhong; Wen, Yuming; Jiang, Jianchun; Jönsson, Pär G.; Yang, Weihong

doi:10.1016/j.enconman.2022.115613

Cited by 34 publications

(8 citation statements)

References 61 publications

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“…Cellulose and hemicellulose exhibit O/C ratios of 0.83 and 0.80, respectively. These values are noticeably higher than lignin’s O/C ratio, which stands at 0.35 50 . Notably, bio-oil derived from the pyrolysis of cellulose and hemicellulose (xylan) demonstrated a superior H/C ratio compared to that from lignin pyrolysis 63 .…”

Section: Resultsmentioning

confidence: 73%

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Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Wang,

Shi

et al. 2023

Commun Chem

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Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique.

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Section: Resultsmentioning

confidence: 73%

“…Lignin has a lower H/C ratio (1.14) compared to cellulose (1.67) and hemicellulose (1.60) 50 . Therefore, a lower H/C ratio in lignocellulosic biodegradable waste indicates a relatively higher lignin content.…”

Section: Resultsmentioning

confidence: 98%

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Wang,

Shi

et al. 2023

Commun Chem

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“…For instance, Bong et al modeled weight loss in pyrolysis, 101 while Pathy et al examined the biochar production yield; 102 in another work, Wang et al evaluated the pyrolytic activation energy using ML. 103 Transesterification is an easy and established process to produce bio-oil from microalgae; however, it requires the energy intensive drying step to achieve high bio-oil yields. Some methods, such as hydrothermal liquefaction (HTL), on the other hand, do not need a drying step; 112 hence, they may have higher potential.…”

Section: Biofuel Productionmentioning

confidence: 99%

Section: Application Of Machine Learning On Microalgae-based Biofuelsmentioning

confidence: 99%

Machine learning for algal biofuels: a critical review and perspective for the future

2023

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“…60 Machine learning, a tool that can reduce experimental costs and improve research efficiency, has gradually been applied in various research fields in recent years. 61 In the field of sludge treatment and disposal research, machine learning has been used to quantify the relationship between bacterial 16S rRNA sequencing and their function in granular sludge within an anaerobic digestion reactor. This approach has enabled the identification of the core bacterial community with different functions.…”

Section: Analysis Of Publication Characteristicsmentioning

confidence: 99%

Worldwide Research Progress and Trend in Sludge Treatment and Disposal: A Bibliometric Analysis

Li-yin

Zhao

et al. 2023

ACS EST Eng.

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As a byproduct of sewage treatment in wastewater treatment plants, sludge has dual attributes with pollution and resource, and its research is important to ecological environment and energy security. Therefore, it is of great significance to analyze and summarize the development of research in the field of sludge treatment and disposal. Based on a bibliometric analysis, this paper analyzed the worldwide research process and trend of sludge treatment and disposal among 49,300 research articles and reviews from 1975 to 2021, which were divided into six time periods for a more detailed analysis. In this analysis, countries, institutions, authors, and keywords were discussed respectively through visual maps. The United States (before 2007−2011) and China (after 2007−2011) were the countries that published the most papers, and cooperation between China and other countries became more frequent after 2007−2011. Research institutions and authors from developed countries and China accounted for the largest proportion. As the main research objects, activated sludge and sewage sludge appeared most frequently in keywords analysis. In terms of the overall trend, sludge treatment and disposal gradually developed from harmless treatment and reduction of sludge to the control of emerging pollutants, recycling of sludge and multidisciplinary research. The analysis of cited authors (W. W. Eckenfelder, M. Henze, and Y. Liu) reflected the recognition of worldwide researchers. The top 10 cited journals were classified as Q1 and Q2 according to journal citation reports, and they were favored by researchers.

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A machine learning model to predict the pyrolytic kinetics of different types of feedstocks

Cited by 34 publications

References 61 publications

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Machine learning for algal biofuels: a critical review and perspective for the future

Worldwide Research Progress and Trend in Sludge Treatment and Disposal: A Bibliometric Analysis

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