Machine learning for hydrothermal treatment of biomass: A review

Zhang, Weijin; Chen, Qingyue; Chen, Jiefeng; Xu, Donghai; Zhan, Hao; Peng, Haoyi; Pan, Jian; Vlaskin, Mikhail S.; Leng, Lijian; Li, Hailong

doi:10.1016/j.biortech.2022.128547

Cited by 47 publications

(13 citation statements)

References 119 publications

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“…These have included investigations of the effects of precisely projecting biochar yield on sustainable agriculture, climate change mitigation, and waste management. 63,130…”

Section: Types Of ML Algorithmsmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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“…These have included investigations of the effects of precisely projecting biochar yield on sustainable agriculture, climate change mitigation, and waste management. 63,130…”

Section: Types Of ML Algorithmsmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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“…88−91 The explosive increase in publications about biochar over the past 5 years (Figure S1) provides the possibility of predicting the stability of biochar with high precision in soil environments based on the massive amount of data, which can be achieved by machine learning or artificial intelligence. 92 However, most current studies fail to adhere to the FAIR data principles (as reflected in Table S1), and thus large amounts of reliable data required for stability prediction are lacking. Quantitative information on the contributions of these factors and processes to the stability of biochar from well-designed long-term field trials is still urgently needed to bridge these knowledge gaps.…”

Section: Stability Of Biocharmentioning

confidence: 99%

“…Further, most of the studies concerning the stability of biochar are conducted using single-factor and short-term experiments under simulated conditions, which is particularly true for hydrochar (Table S1). These assessments usually fail to reflect the impacts of interactive variables and cumulative effects on biochar stability in fields, such as biochar–mineral interactions and aggregation and microbial community succession induced by biochar application, which are highly time-dependent. − The explosive increase in publications about biochar over the past 5 years (Figure S1) provides the possibility of predicting the stability of biochar with high precision in soil environments based on the massive amount of data, which can be achieved by machine learning or artificial intelligence . However, most current studies fail to adhere to the FAIR data principles (as reflected in Table S1), and thus large amounts of reliable data required for stability prediction are lacking.…”

Section: Stability Of Biocharmentioning

confidence: 99%

Carbon Sequestration Strategies in Soil Using Biochar: Advances, Challenges, and Opportunities

Luo

Wang

et al. 2023

Environ. Sci. Technol.

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Biochar, a carbon (C)-rich material obtained from the thermochemical conversion of biomass under oxygen-limited environments, has been proposed as one of the most promising materials for C sequestration and climate mitigation in soil. The C sequestration contribution of biochar hinges not only on its fused aromatic structure but also on its abiotic and biotic reactions with soil components across its entire life cycle in the environment. For instance, minerals and microorganisms can deeply participate in the mineralization or complexation of the labile (soluble and easily decomposable) and even recalcitrant fractions of biochar, thereby profoundly affecting C cycling and sequestration in soil. Here we identify five key issues closely related to the application of biochar for C sequestration in soil and review its outstanding advances. Specifically, the terms use of biochar, pyrochar, and hydrochar, the stability of biochar in soil, the effect of biochar on the flux and speciation changes of C in soil, the emission of nitrogen-containing greenhouse gases induced by biochar production and soil application, and the application barriers of biochar in soil are expounded. By elaborating on these critical issues, we discuss the challenges and knowledge gaps that hinder our understanding and application of biochar for C sequestration in soil and provide outlooks for future research directions. We suggest that combining the mechanistic understanding of biochar-to-soil interactions and long-term field studies, while considering the influence of multiple factors and processes, is essential to bridge these knowledge gaps. Further, the standards for biochar production and soil application should be widely implemented, and the threshold values of biochar application in soil should be urgently developed. Also needed are comprehensive and prospective life cycle assessments that are not restricted to soil C sequestration and account for the contributions of contamination remediation, soil quality improvement, and vegetation C sequestration to accurately reflect the total benefits of biochar on C sequestration in soil.

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“…Inspired by the recent developments in the usage of ML in the study of thermochemical and hydrothermal conversion of biomass, , in this study, base-catalyzed and non-catalyzed hydrothermal depolymerization of lignin was investigated, for the first time, by combining ML modeling to predict the yield of bio-oil and solid residue and experimental work to test the validity of the models. Explainable variable importance for the models was obtained through two different methodologies in order to obtain insight into how the process variables in lignin depolymerization impact the results of the experiments.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization

Castro Garcia,

Cheng,

McGlynn

et al. 2023

ACS Omega

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Lignin, an abundant component of plant matter, can be depolymerized into renewable aromatic chemicals and biofuels but remains underutilized. Homogeneously catalyzed depolymerization in water has gained attention due to its economic feasibility and performance but suffers from inconsistently reported yields of bio-oil and solid residues. In this study, machine learning methods were used to develop predictive models for bio-oil and solid residue yields by using a few reaction variables and were subsequently validated by doing experimental work and comparing the predictions to the results. The models achieved a coefficient of determination (R 2 ) score of 0.83 and 0.76, respectively, for bio-oil yield and solid residue. Variable importance for each model was calculated by two different methodologies and was tied to existing studies to explain the model predictive behavior. Based on the outcome of the study, the creation of concrete guidelines for reporting in lignin depolymerization studies was recommended. Shapley additive explanation value analysis reveals that temperature and reaction time are generally the strongest predictors of experimental outcomes compared to the rest.

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Machine learning for hydrothermal treatment of biomass: A review

Cited by 47 publications

References 119 publications

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Carbon Sequestration Strategies in Soil Using Biochar: Advances, Challenges, and Opportunities

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization

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