Effects of Chemical Composition and Pyrolysis Process Variables on Biochar Yields: Correlation and Principal Component Analysis

Batista, Renato Rocha; Marques, Márcia

doi:10.1590/2179-8087-floram-2021-0007

Cited by 6 publications

(6 citation statements)

References 78 publications

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“…% in volatiles. The lignin-rich TDs have a higher fixed carbon content (around 63% after pyrolysis) and can lead to higher biochar yields, as lignin has a complex structure and poses more resistance to thermal degradation than holocellulose (cellulose with hemicellulose), also due to its high level of aromaticity, size, and structural arrangement, which affects the proportion of the solid product generated 24 . Moreover, the decrease in biochar yield is due to increased pyrolysis temperature, probably from the thermal cracking of volatile components into lower molecular weight liquids and gases rather than biochar 25 .…”

Section: Resultsmentioning

confidence: 99%

Influence of lignin and cellulose from termite-processed biomass on biochar production and evaluation of chromium VI adsorption

Kopp Alves,

Hauschild,

Basegio

et al. 2024

Sci Rep

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The increasing water contamination by toxic heavy metals, particularly hexavalent chromium, has become a significant environmental concern. This study explores the pyrolysis of termite-processed biomass, specifically Pinuselliottii particleboard and its termite droppings (TDs), to produce biochar and its application for chromium (VI) adsorption. Termite droppings, rich in lignin, and particleboard, rich in cellulose, were pyrolyzed at various temperatures to assess the effect of biomass composition on biochar properties. The study found that lignin-rich termite droppings produced biochar with higher fixed carbon content and specific surface area than cellulose-rich particleboard biochar. FTIR and Raman spectroscopy revealed significant molecular structure changes during pyrolysis, which influenced the adsorption capabilities of the biochar. Adsorption experiments demonstrated that TD biochar exhibited significantly higher chromium (VI) adsorption capacity, attributed to its distinct chemical composition and enhanced surface properties due to higher lignin content. These findings underscore the crucial role of lignin in producing efficient biochar for heavy metal adsorption, highlighting the practical applicability of termite-processed biomass in water purification technologies.

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Section: Resultsmentioning

confidence: 99%

Influence of lignin and cellulose from termite-processed biomass on biochar production and evaluation of chromium VI adsorption

Kopp Alves,

Hauschild,

Basegio

et al. 2024

Sci Rep

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“…These correlation values express the strength and direction of the correlations between the predictor columns and the replies. They assist us in understanding how changes in one variable affect changes in another. − …”

Section: Resultsmentioning

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

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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.

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“…In a research focusing on the production of biochar through pyrolysis of five lignocellulosic biomasses (olive husk, beech wood, corncob, spruce wood, and hazelnut shells), under constant pressure (0.1 MPa) and temperature from 650.2 to 973.0 K. Interactions between process variables (temperature, residence time of the vapor phase and heating rate), biomass chemical composition variables (lignin, holocellulose, ash, carbon, nitrogen, oxygen, and hydrogen content), and biochar yield‐CY were evaluated by Pearson's correlation matrix and PCA. At the end of the research, the biomass chemical composition with PC1 and process variables such as heating rate and the vapor residence time were determined as PC2 44 . Couto et al 45 focused on eucalyptus biomass from different clones as feedstock in which the first two PCs accounted for 72% of the total variance of the original data.…”

Section: Resultsmentioning

confidence: 99%

“…At the end of the research, the biomass chemical composition with PC1 and process variables such as heating rate and the vapor residence time were determined as PC2. 44 Couto et al 45 focused on eucalyptus biomass from different clones as feedstock in which the first two PCs accounted for 72% of the total variance of the original data. The input parameters (pyrolysis temperature, holding time, and gas flow rates) used in this study were defined with a single PC1 with 96.7% accuracy.…”

Section: The Results Of Principal Component Analysesmentioning

confidence: 99%

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Altikat

Alma

2021

Intl J of Energy Research

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Summary Biochar, bio‐oil, and synthesis gas are products obtained from the pyrolysis process, which have alternative usage areas. Biochar is used for adsorption of pollutants, soil conditioner, and as an alternative heating source. Bio‐oil is used as a fuel for conventional engines after various upgrading methods, and the synthesis gas is a heat source in energy production. Biochar, bio‐oil, and synthesis gas yields obtained from the Atriplex nitens S. plant at the end of the pyrolysis process were modeled using artificial neural networks (ANNs) and hybrid models in this study. Multiple linear regression, ANNs, principal component analysis + multiple linear regression, and principal component analysis + ANN models were used. In addition, 48 different network architectures in ANNs were tested. At the end of the study, the best prediction results of biochar, bio‐oil, and synthesis gas were obtained from the ANN 35 (R2 = 0.977), ANN 17 (R2 = 0.985), and ANN 44 (R2 = 0.969) ANN architecture, respectively. The sensitivity analyses were performed using these best models for biochar, bio‐oil, and synthesis gas. As a result of the sensitivity analysis, it was determined that the most effective factor in the production of biochar and bio‐oil was the gas flow rate, and this was followed by the holding time and pyrolysis temperature. However, the most effective factor in synthesis gas yield was determined as the holding time, and this was followed by gas flow rate and pyrolysis temperature parameters.

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Effects of Chemical Composition and Pyrolysis Process Variables on Biochar Yields: Correlation and Principal Component Analysis

Cited by 6 publications

References 78 publications

Influence of lignin and cellulose from termite-processed biomass on biochar production and evaluation of chromium VI adsorption

Influence of lignin and cellulose from termite-processed biomass on biochar production and evaluation of chromium VI adsorption

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

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

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