Role of Metals in Biochar Production and Utilization in Catalytic Applications: A Review

Shrestha, Priya; Chun, Dave D.; Kang, Kang; Simson, Amanda; Klinghoffer, Naomi

doi:10.1007/s12649-021-01519-6

Cited by 23 publications

(6 citation statements)

References 143 publications

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“…Despite their seemingly minor importance, alkaline-earth metals play an important role in defining the properties of the biochar. 188 It is therefore important to recognize and incorporate these aspects when carrying out a biochar analysis, as their impact can have a disproportionate impact on the attributes of the final product. A proper evaluation and comprehension of these seemingly small variables can offer comprehensive insights into the composition and functionality of the biochar.…”

Section: Sensitivity Of Machine Learning Models To Input Variablesmentioning

confidence: 99%

“…It has also been observed that although some components, such as alkaline‐earth metals, may have a modest content and sensitivity to biochar, their impact on the characteristics of the biochar can be significant. Despite their seemingly minor importance, alkaline‐earth metals play an important role in defining the properties of the biochar 188 . It is therefore important to recognize and incorporate these aspects when carrying out a biochar analysis, as their impact can have a disproportionate impact on the attributes of the final product.…”

Section: Feature Importance and Sensitivity Analysismentioning

confidence: 99%

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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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Section: Sensitivity Of Machine Learning Models To Input Variablesmentioning

confidence: 99%

Section: Feature Importance and Sensitivity Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…The results indicated a significant increase of 30% in the overall gas output. The application of catalytic gasification resulted in a reduction in both the biochar and ash contents, leading to an enhanced usage rate of biomass (Shrestha et al, 2022;Raihan et al, 2023e). There exists a contention that catalytic cracking exhibits more economic viability when compared to conventional techniques of biofuel production, namely pyrolysis and fermentation (Chia et al, 2022).…”

Section: Forest Biomass To Gaseous Biofuelsmentioning

confidence: 99%

A concise review of technologies for converting forest biomass to bioenergy

Raihan

2023

JTIE

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The use of biomass is vital in reducing the negative effects of rising fossil fuel consumption. Given its quantity and diversity, forest biomass has garnered a lot of interest among the many kinds of biomass. This study evaluates the various strategies for transforming woody waste into usable biofuels. Carbon dioxide emissions from traditional energy generation systems could be mitigated through the direct utilization of forest biomass. Low energy conversion rates, as well as soot emissions and residues, are some of the problems that come up when directly using forest biomass. The sustainability of direct energy generation from forest biomass is also seriously threatened by the lack of constant access to biomass. Co-combustion with coal and pelletizing biomass is two solutions proposed for this issue. Co-combustion of forest biomass with coal has the potential to lower the process's emissions of carbon monoxide, nitrogen oxides, and sulfides. This article reviews and discusses the biochemical and thermochemical mechanisms that can transform forest biomass into a variety of liquid and gaseous biofuels. Future research using cutting-edge sustainability assessment tools like life cycle assessment, exergy, etc. should investigate the sustainability of forest biomass conversion processes to bioenergy further.

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“…S5, ESI †). The presence of alkali and alkaline earth metals (AAEM), P and S is also not responsible for this increase 76 since the concentrations of these elements have not varied signicantly in the derived biochar as seen in Fig. S6, ESI.…”

Section: Transformation Of the Biochar Carbon With Scalingmentioning

confidence: 99%

The influence of sample mass (scaling effect) on the synthesis and structure of non-graphitizing carbon (biochar) during the analytical pyrolysis of biomass

et al. 2023

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Role of Metals in Biochar Production and Utilization in Catalytic Applications: A Review

Cited by 23 publications

References 143 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

A concise review of technologies for converting forest biomass to bioenergy

The influence of sample mass (scaling effect) on the synthesis and structure of non-graphitizing carbon (biochar) during the analytical pyrolysis of biomass

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