scholarly journals Determination of Optimal Surface Area to Volume Raio for Thin-Layer Drying of Breadfruit (Artocarpus altilis)

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
C. George ◽  
R. McGruder ◽  
K. Torgerson
Keyword(s):  
Surface Area ◽  
Volume Ratio ◽  
Optimal Size ◽  
Drying Characteristics ◽  
Artocarpus Altilis ◽  
Sun Drying ◽  
Thin Layer Drying ◽  
Optimal Surface ◽  
The Caribbean

Experiments to determine the optimal size shred of breadfruit for sun drying in the Caribbean were conducted and verified. To determine optimal shred size, ease of shredding and handling as well as the drying characteristics were considered. Additional experiments compared the drying characteristics of breadfruit to several types of produce more readily available for use in the laboratory and examined the effect of alternative bases or backgrounds for sun drying. An optimal surface area to volume ratio is recommended and found to dry breadfruit under average Caribbean conditions (27-30 ˚C, 60-65% RH, ~800 W/m2 solar radiation and 1.5-2.0 m/s prevailing winds) in about three hours.

scholarly journals Drying of green bean and okra under solar energy

2011 ◽  
Vol 17 (2) ◽  
pp. 199-205 ◽  
Author(s):  
İbrahim Doymaz
Keyword(s):  
Green Bean ◽  
Chi Square ◽  
Sun Drying ◽  
Constant Rate ◽  
Thin Layer Drying ◽  
Rate Period ◽  
Drying Models ◽  
Drying Curves ◽  
The Times

In this study, sun drying behaviours of green bean and okra were investigated. Drying experiments were conducted in Iskenderun-Hatay, Turkey. The drying study showed that the times taken for drying of green bean and okra from the initial moisture contents of 89.5% and 88.7% (w.b.) to final moisture content of around 15?0.5% (w.b.) were 60 and 100 h in open sun drying, respectively. The constant rate period is absent in drying curves. The drying process took place in the falling rate period. The drying data were fitted to thirteen thin-layer drying models. The performance of these models was investigated by comparing the determination of coefficient (R2), reduced chi-square (c2) and root mean square error (RMSE) between the observed and predicted moisture ratios. Estimations by Approximation of diffusion (for green bean) and Midilli et al. models (for okra) were in good agreement with the experimental data obtained.

scholarly journals Mathematical modeling of thin layer drying characteristics and proximate analysis of turkey berry (Solanum torvum)

2021 ◽  
pp. 75-75
Author(s):  
Sekar Sivakumar Dana ◽  
Sekar Subramani ◽  
Valarmathi Thirumalai Natesan ◽  
Mudhu Marimuthu ◽  
Godwin Arockiaraj
Keyword(s):  
Thin Layer ◽  
Proximate Analysis ◽  
Coefficient Of Determination ◽  
Positive Sign ◽  
Passive Mode ◽  
Solanum Torvum ◽  
Drying Characteristics ◽  
Active Mode ◽  
Sun Drying ◽  
Thin Layer Drying

In the present work the drying characteristics and proximate analysis of turkey berry (Solanum torvum) were analyzed under open sun drying and greenhouse drying with two different glazing materials (UV Polyethylene sheet and Drip lock sheet) under passive and active modes. The drying rate under different modes of drying are 18.73g/h in drip lock greenhouse active mode,12.50 g/h in UV polyethylene sheet greenhouse active mode,15.22 g/hin drip lock sheet greenhouse passive mode, 11.84 g/h in UV polyethylene sheet greenhouse passive mode and 10.65 g/h in open sun drying. Twelve mathematical models were chosen to determine the drying characteristics of Turkey berry. From the statistical analysis it is found that Modified Henderson and Pabis model is the best drying model describing thin layer drying characteristics of turkey berry in both open sun drying and green house drying. The goodness of the fit achieved is based on the values of coefficient of determination(R2), sum square error(SSE), root mean square error(RMSE) and reduced chi square (?2).From the proximate analysis of dried turkey berry it is found that more amount of carbohydrate is retained in UV polyethylene greenhouse dryer under passive mode. In drip lock greenhouse dryer under passive mode the retention of vitamins such as protein, vitamin C and ash content showed a positive sign. In drip lock greenhouse dryer under active mode the retention of calcium, iron and dietary fibre is found to be high. Finally it is observed that more amounts of nutrients are retained in greenhouse drying than in open sun drying.

scholarly journals A New Method to Estimate Fuel Surface Area-to-Volume Ratio Using Water Immersion.

1998 ◽  
Vol 8 (3) ◽  
pp. 121 ◽  
Author(s):  
PM Fernandes ◽  
FC Rego
Keyword(s):  
Surface Area ◽  
Particle Density ◽  
Low Cost ◽  
Water Immersion ◽  
Volume Ratio ◽  
Adsorbed Water ◽  
General Technique ◽  
Fuel Surface ◽  
Before And After

A fast, simple, low cost and general technique for estimating fuel surface area-to-volume ratio is presented. The technique requires particle density, the determination of fuel weight before and after immersion in water and the theoretical thickness of the adsorbed water pellicle that is assumed constant. Estimates by the technique were consistent and in good agreement with published surface area-to-volume ratios for the same fuels obtained through other methods, and its performance was judged appropriate for current fire behaviour modelling needs. This water immersion technique was applied to various shrubs and trees grown in Portugal. Limitations and possibilities for improvement of the newly developed technique are discussed.

Thin-Layer Drying Characteristics of Fresh and Sun-Dried Buckwheat Hay

2021 ◽  
Vol 37 (4) ◽  
pp. 713-724
Author(s):  
Fuji Jian ◽  
Mehul Patil ◽  
Digvir S. Jayas ◽  
Jitendra Paliwal
Keyword(s):  
Activation Energy ◽  
Moisture Content ◽  
Thin Layer ◽  
Growth Time ◽  
List Type ◽  
Experimental Conditions ◽  
Drying Time ◽  
Drying Characteristics ◽  
Sun Drying ◽  
Thin Layer Drying

Highlights Thin-layer drying of fresh and sun-dried buckwheat hay was studied at 30°C to 180°C and 12.5% to 60% relative humidities. Buckwheat hay drying occurred in the falling-rate period. Partial sun-drying of the hay could reduce drying time by 50%. The D eff values of fresh flowers, leaves, and stems ranged from 1.4×10 -10 to 60×10 -10 m 2 /s. Abstract. Thin-layer drying characteristics of fresh and sun-dried buckwheat hay were studied at 30°C to 180°C, 12.5 to 60% relative humidities, and 0.2 m/s constant air velocity. The hay was harvested on three different times with a 10 to 12 d interval between the harvesting times. Half of the harvested hay was sun-dried on the field for 4 d (referred to as sun-dried hay). The drying behavior of flowers, leaves, and stems of the fresh and sun-dried hay was characterized. Moisture content of the fresh buckwheat flower was 0.777 to 1.633 (decimal dry basis), and fresh stems had a maximum moisture content of 5.64. Moisture content of the fresh hay decreased with the increase of growth time. Sun-drying on field could decrease more than half of the moisture content of the harvested fresh hay. Flowers, leaves, and stems needed varying drying times to reach their equilibrium moisture contents. The order of the drying time from the fastest to the slowest was flowers, leaves, then stems. Sun-dried and later harvested hay needed less drying time. The logarithmic model was the best fit for all drying processes of the flowers, leaves, and stems at different harvesting times and drying conditions. The effective moisture diffusivity of both fresh and sun-dried hay ranged from 1.4×10-10 to 60× 10-10 m2/s depending on different experimental conditions. The activation energy of the hay was from 21.08 to 33.85 kJ/mol. A power equation was the best equation to describe the drying constant of hay with their drying temperature. Keywords: Activation energy, Combination drying, Hay drying, Thin-layer drying, Water diffusivity.

scholarly journals Measuring the specific surface area of wet snow using 1310 nm reflectance

2014 ◽  
Vol 8 (4) ◽  
pp. 1139-1148 ◽  
Author(s):  
J.-C. Gallet ◽  
F. Domine ◽  
M. Dumont
Keyword(s):  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Volume Ratio ◽  
Liquid Water Content ◽  
Optical Methods ◽  
Integrating Sphere ◽  
Wet Snow ◽  
The Difference

Abstract. The specific surface area (SSA) of snow can be used as an objective measurement of grain size and is therefore a central variable to describe snow physical properties such as albedo. Snow SSA can now be easily measured in the field using optical methods based on infrared reflectance. However, existing optical methods have only been validated for dry snow. Here we test the possibility to use the DUFISSS instrument, based on the measurement of the 1310 nm reflectance of snow with an integrating sphere, to measure the SSA of wet snow. We perform cold room experiments where we measure the SSA of a wet snow sample, freeze it and measure it again, to quantify the difference in reflectance between frozen and wet snow. We study snow samples in the SSA range 12–37 m2 kg−1 and in the mass liquid water content (LWC) range 5–32%. We conclude that the SSA of wet snow can be obtained from the measurement of its 1310 nm reflectance using three simple steps. In most cases, the SSA thus obtained is less than 10 {%} different from the value that would have been obtained if the sample had been considered dry, so that the three simple steps constitute a minor correction. We also run two optical models to interpret the results, but no model reproduces correctly the water–ice distribution in wet snow, so that their predictions of wet snow reflectance are imperfect. The correction on the determination of wet snow SSA using the DUFISSS instrument gives an overall uncertainty better than 11%, even if the LWC is unknown. If SSA is expressed as a surface to volume ratio (e.g., in mm−1), the uncertainty is then 13% because of additional uncertainties in the determination of the volume of ice and water when the LWC is unknown.

scholarly journals A New Method to Estimate Fuel Surface Area-to-Volume Ratio Using Water Immersion

1998 ◽  
Vol 8 (2) ◽  
pp. 59 ◽  
Author(s):  
PM Fernandes ◽  
FC Rego
Keyword(s):  
Surface Area ◽  
Particle Density ◽  
Low Cost ◽  
Water Immersion ◽  
Volume Ratio ◽  
Adsorbed Water ◽  
General Technique ◽  
Fuel Surface ◽  
Before And After

A fast, simple, low cost and general technique for estimating fuel surface area-to-volume ratio was developed. It requires the knowledge of particle density, the determination of fuel weight before and after immersion in water, and theoretical thickness of the adsorbed water pellicle that is assumed constant. Estimates by the technique were consistent and in good agreement with published surface area-to-volume ratios for the same fuels obtained through commonly used methods, and its performance is judged appropriate for the current fire behaviour modeling needs. The water immersion technique was applied to five common shrub species in Portugal. Limitations and possibilities for improvement of the newly developed technique are discussed.

scholarly journals Comparison of the observed size-dependent melting point of CdSe nanocrystals to theoretical predictions

2018 ◽  
Vol 9 (1) ◽  
pp. 39-43
Author(s):  
Albert Demaine Dukes III ◽  
Christopher Dylan Pitts ◽  
Anyway Brenda Kapingidza ◽  
David Eric Gardner ◽  
Ralph Charles Layland
Keyword(s):  
Melting Point ◽  
Surface Area ◽  
Melting Temperature ◽  
Volume Ratio ◽  
Melting Point Depression ◽  
Cdse Nanocrystals ◽  
Size Dependent ◽  
Theoretical Predictions

Cadmium selenide nanocrystals were observed to have a size-dependent melting point which was depressed relative to the bulk melting temperature. The observed size-dependent melting point ranged from 500-1478 K, while a model based on the surface area to volume ratio predicted that is should range between 774-1250 K. The nanocrystals were heated in situ in the electron microscope, and the melting point was almost immediately followed by the vaporization of the CdSe nanocrystals, allowing for straightforward determination of the melting temperature. The differences between the observed melting point of CdSe nanocrystals and the values predicted by the surface area to volume ratio model indicates that additional factors are involved in the melting point depression of nanocrystals.

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