Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air

Bastistella, Luciane; Rousset, Patrick; Aviz, Antônio; Caldeira-Pires, Armando; Humbert, G.; Nogueira, Manoel

doi:10.3390/bioengineering5010013

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…273 The initial temperature of substantial oxidation reactions, indicated by oxygen consumption, was observed to be as low as close to 60 °C. 273 Additionally, the contributions from water adsorption and condensation and microbiological processes could not be excluded because biochar can adsorb moisture to release heat 274 and the moist porous biochar may potentially accommodate microbial activity particularly for the biochar from mild treatment.…”

Section: Low Temperature Chemical Oxidationmentioning

confidence: 99%

Review on Self-Heating of Biomass Materials: Understanding and Description

Sheng

Yao

2022

Energy Fuels

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Storage of bulk biomass materials is essential along the feedstock supply chain of bioenergy production. The self-heating accompanying biomass storage has hazardous consequences. With the increasing biomass utilization for bioenergy production, the risk and economic and environmental concerns about biomass storage have been attracting extensive research attention aimed at understanding the processes of heat generation, evaluating the propensity of a material to self-heating and spontaneous ignition, describing and predicting the self-heating process and consequences in biomass storage, and developing strategies and technologies for storage management. The advances in the understanding and description of heat generation processes including water-associated physical processes, microbiological degradation processes, and chemical oxidative processes during the self-heating in biomass storage are reviewed, with the focus on understanding the processes and their underlying mechanisms, reaction kinetics, and heats of reaction through experimental studies and description of heat generation and self-heating processes through kinetic analyses and modeling. This review highlights the need to improve the mechanistic model description of the self-heating processes and associated material changes and product formation, which demand a better understanding and description of microbial degradation and chemical oxidation through experimental study.

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Section: Low Temperature Chemical Oxidationmentioning

confidence: 99%

Review on Self-Heating of Biomass Materials: Understanding and Description

Sheng

Yao

2022

Energy Fuels

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“…For cocultures, different combinations of 2–9 isolates were mixed at the same concentrations to a total cell density of 1 × 10 6 CFU/mL. We obtained a total of 95 culture trials (Supplementary Table 2 ), including 9 monoculture and 86 coculture trails, which were randomly generated by Xlstat in Excel [ 30 ]. Monoculture and coculture experiments were performed in 1.5 mL of chemically defined medium in 96-well plates under static and anaerobic conditions at 30 °C for 8 days, and the final bacterial solution was collected for biomass determination.…”

Section: Methodsmentioning

confidence: 99%

Metabolic cooperation between conspecific genotypic groups contributes to bacterial fitness

Lin

et al. 2023

ISME Communications

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Microbial interactions are important for the survival of species and the stability of the microbial ecosystem. Although bacteria have diverse conspecific genotypes in the natural microbial ecosystem, little is known about whether wild-type strains within species would interact with each other and how the intraspecific interaction influences the growth of the species. In this work, using Lactobacillus acetotolerans, a dominant species with diverse conspecific genotypes in natural food fermentation ecosystems as a case, we studied the interactions between different genotypic groups of this species. In interspecific and intraspecific pairwise cocultures, the growth of L. acetotolerans decreased, but the increase of the phylogenetic similarity would reduce this negative effect, indicating a potential intraspecific interaction of this species. Meanwhile, the strain classification method affected the analysis of intraspecific interactions, which can be efficiently demonstrated using 99.5% average nucleotide identity (ANI) as the strain-level classification method. Using this ANI classification method, we revealed the population fitness significantly increased in cocultures of different genotypic groups. Facilitation involving 11 amino acids was identified between different ANI genotypic groups, which was beneficial for increasing population fitness. This work revealed that wild-type conspecific strains could interact with each other via cooperative metabolic changes and benefit each other to increase fitness. It shed new light on the survival and stability of species in natural microbial ecosystems.

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“…New experimental techniques, as well as modern variants on known methods, have recently been employed to investigate the fundamental reactions underlying the oxidation of biochar. The purpose of study executed by Bastistella and et.al [27] was to experimentally and statistically study how the relative humidity of air, mass, and particle size of four biochars influenced the absorption of water and increase in temperature. A random factorial design was employed using the intuitive statistical software Xlstat.…”

Section: Introductionmentioning

confidence: 99%

“…Five variables, including factors and their interactions, were found to be common to the two models. Sample mass and relative humidity influenced the two qualitative variables, while particle size and biochar type only influenced the temperature [27]. In this research, the relationship between Landsat 8 satellite imagery and terrestrial station data is investigated in estimating combined quantities using different machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning methods and satellite images to estimate combined climatic indices

Norouzi

Behzadi

2019

NMCE

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The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role in modeling and data mining of climatic phenomena as a result of their significant advantages, including their availability and analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms using remote sensing data is inevitable. In this study, 7 well-known machine learning algorithms are applied with different initial data to show that satellite images are able to estimate the combined indices more accurately. A new index (HT) is also defined by combining the quantities of relative humidity and temperature. Then, machine learning algorithms are trained for each of these three quantities. For each of the temperature and relative humidity quantities, four optimal bands were selected using the PCA method, then a combination of these optimal bands was determined for the HT index. Two criteria are used to validate the results: Root Mean Square Error (RMSE) statistic and comparing the map of the interpolation method with the result of this study. RMSE values show that satellite imagery could have a high ability to model and estimate composite indices. Classification-KNN-Coarse and Ensemble-Bagged Trees with accuracy of 79.8626 % and 84.9281% are identified as the best machine learning methods for temperature and relative humidity, while the best accuracy to estimate the HT index is 92.8792% for Matern 5/2 GPR. Therefore, it can be said that by changing the methods of database preparation, the modeling results can be changed effectively in order to train models.

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Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air

Cited by 5 publications

References 15 publications

Review on Self-Heating of Biomass Materials: Understanding and Description

Review on Self-Heating of Biomass Materials: Understanding and Description

Metabolic cooperation between conspecific genotypic groups contributes to bacterial fitness

Evaluating machine learning methods and satellite images to estimate combined climatic indices

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