Heat capacity measurements of various biomass types and pyrolysis residues

Dupont, Capucine; Chiriac, Rodica; Gauthier, Guillaume; Toche, François

doi:10.1016/j.fuel.2013.07.086

Cited by 155 publications

(73 citation statements)

References 22 publications

(35 reference statements)

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“…For the CDP grassy meadow, the surface heat capacity is equal to the vegetation heat capacity c V . The default value of c V has been set to 5 × 10 4 J m −2 K −1 since the 1990s for numerical stability reasons in the coupling with NWP models but this is unrealistic when compared with values from the literature (Dupont et al, 2014). In the v8.0 SURFEX release (http://www.umr-cnrm.fr/surfex//spip.php?rubrique148), the current default value has been set to 10 4 J m −2 K −1 for lowlying vegetation.…”

Section: Soil Schemementioning

confidence: 99%

A multiphysical ensemble system of numerical snow modelling

Lafaysse¹,

Cluzet²,

Dumont³

et al. 2017

The Cryosphere

135

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Abstract. Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ES-CROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SUR-FEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

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Section: Soil Schemementioning

confidence: 99%

A multiphysical ensemble system of numerical snow modelling

Lafaysse¹,

Cluzet²,

Dumont³

et al. 2017

The Cryosphere

135

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show abstract

“…This value is the average of the char heat capacities of twenty woody biomasses determined experimentally by Dupont et al [31]. The gas heat capacities have been calculated from a polynomial relation from JANAF data [32].…”

Section: Conversion Stepmentioning

confidence: 99%

Energetic and economic evaluation of Chlamydomonas reinhardtii hydrothermal liquefaction and pyrolysis through thermochemical models

Hognon

Delrue

Boissonnet

2015

Energy

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“…A pyrolytic process converts biomass feedstock into three classes of products: a solid (or char), a liquid (also known as bio-oil from pyrolysis or simply bio-oil), and a gas. [1][2][3][4][5][6][7][8][9][10]. Bio-oils and gases may be a suitable source for chemicals.…”

Section: Introductionmentioning

confidence: 99%