Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions

Dong, Zixun; Bai, Xiaopeng; Xu, Daochun; Li, Wenbin

doi:10.1016/j.biortech.2022.128182

Cited by 54 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… ,, Although variations of these methods are also used for classification, this topic was not within the scope of our review. Some examples of classification in RRCC include the use of random forest and k -nearest neighbors to classify low, medium, and high ranges of biogas production in anaerobic digestion based on the combination of operational data and microbial community, , and the use of tree-based ML methods to classify gaseous, liquid, and solid phases of pyrolysis output utilizing operational data . Similarly, although unsupervised machine learning techniques such as k -means clustering have been applied to some feedstock supply related problems in RRCC (e.g., anaerobic digestion , and pyrolysis), such techniques are not included in this review.…”

Section: Methodsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

View full text Add to dashboard Cite

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

show abstract

Section: Methodsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

View full text Add to dashboard Cite

show abstract

“…AdaBoost’s ability to deal with both binary and multiclass classification issues well is a fundamental benefit. AdaBoost can capture complex decision boundaries and discover intricate patterns in data by merging numerous weak classifiers. , …”

Section: Methodsmentioning

confidence: 99%

“…AdaBoost can capture complex decision boundaries and discover intricate patterns in data by merging numerous weak classifiers. 48,51 The coefficients allocated to each input variable describe the variable's impact or weight on the target variable, demonstrating the direction and degree of the relationship. It is important to remember, however, that linear regression relies on specific assumptions to give accurate results.…”

Section: Experimental Datamentioning

confidence: 99%

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Le,

Pandey,

Sirohi

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

Biochar is found to possess a large number of applications in energy and environmental areas. However, biochar could be produced from a variety of sources, showing that biochar yield and proximate analysis outcomes could change over a wide range. Thus, developing a high-accuracy machine learning-based tool is very necessary to predict biochar characteristics. In this study, a hybrid technique was developed by blending modern machine learning (ML) algorithms with cooperative game theory-based Shapley Additive exPlanations (SHAP). SHAP analysis was employed to help improve interpretability while offering insights into the decision-making process. In the ML models, linear regression was employed as the baseline regression method, and more advanced methodologies like AdaBoost and boosted regression tree (BRT) were employed. The developed prediction models were evaluated on a battery of statistical metrics, and all ML models were observed as robust enough. Among all three models, the BRT-based model delivered the best prediction performance with R 2 in the range of 0.982 to 0.999 during the model training phase and 0.968 to 0.988 during the model test. The value of the mean squared error was also quite low (0.89 to 9.168) for BRT-based models. SHAP analysis quantified the value of each input element to the expected results and provided a more in-depth understanding of the underlying dynamics. The SHAP analysis helped to reveal that temperature was the main factor affecting the response predictions. The hybrid technique proposed here provides substantial insights into the biochar manufacturing process, allowing for improved control of biochar properties and increasing the use of this sustainable and flexible material in numerous applications.

show abstract

“…Evaluating the yield of pyrolysis products by RF algorithm is beneficial to optimize pyrolysis process. Dong et al employed RF algorithm to predict the yield of biochar, bio-oil and biogas under co-pyrolysis conditions (Dong et al 2022). It was found that the most critical factors to predict the yield of pyrolysis products were moisture content, carbon content and final heating temperature, and the characteristics of biomass were more central than pyrolysis conditions.…”

Section: Optimization Of Process Parametersmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

View full text Add to dashboard Cite

Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

show abstract

Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions

Cited by 54 publications

References 40 publications

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Contact Info

Product

Resources

About