The effects of air velocity, temperature and particle size on low-temperature bed drying of wood chips

Fernando, Niranjan; Narayana, Mahinsasa; Wickramaarachchi, W. A. M. K. P.

doi:10.1007/s13399-017-0257-7

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…It can produce quantitative predictions of air circulation and temperature distribution inside the GHDs under different climatic conditions, considering the external incident radiation distributed in three wavelength bands and the spectral optical properties of the involved materials . It can also predict the mass transfer behavior in the food products during the drying process . CFD enables easier studies of scalar and vector variables, which determines greenhouse microclimate with respect to its structural specifications and used equipment …”

Section: Introductionmentioning

confidence: 99%

“…14 It can also predict the mass transfer behavior in the food products during the drying process. [20][21][22][23] CFD enables easier studies of scalar and vector variables, which determines greenhouse microclimate with respect to its structural specifications and used equipment. 14,15,[24][25][26] There are several studies conducted on the greenhouse solar in which programming software such as MATLAB, C/C ++, Fortran, and TRNSYS has been used to simulate the behavior of air flow inside GHDs, 16,[27][28][29][30][31][32][33] but very few studies have been done using the CFD software.…”

Section: Introductionmentioning

confidence: 99%

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Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Román-Roldán

López‐Ortiz

Ituna-Yudonago

et al. 2019

Energy Science & Engineering

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The distribution of turbulent kinetic energy (TKE), temperature, and velocity of humid air inside the greenhouse solar dryer (GHSD) was numerically investigated using 3D CFD ANSYS FLUENT code. The effect of solar radiation was coupled with the energy equations using the discrete ordinate model. Numerical simulations were based on two geometric models: Real model and model with reduced height, the solution was in good agreement with experimental data of temperature. The results of the real model showed that the TKE is ranged between 1.27 m2/s2 and 6 m2/s2 with an average of about 1.6 m2/s2 for the entire greenhouse dryer (GHD) volume. The greatest TKE magnitude is in the paths of the diffusers, which caused a temperature drop of about 2 K in the areas near the walls. Consequently, almost homogeneous temperature distribution was obtained in the entire volume of the GHD, although the average temperature was 315 K, and a gradient with respect to ambient temperature was of 14 K, that is, suitable for drying. Also, the average air velocity at 1 m height was 0.71 m/s, which is a value near the lowest limit (0.6 m/s) of forced convection drying. The improvement in the GHD by 36.5% volume reduction allowed an increase in the average TKE of 3.8 m2/s2 (2.4 times more than the previous one) located in the middle of the greenhouse; the average temperature reached 316.5 K with a gradient of 15.5 K, which represents an increase of 1.5 K (11%) compared to the real geometric model. The air velocity at 1 m height increased to 0.9 m/s in the improved geometric model (a growth of 35.7% compared with the previous geometry). More than 95% of the improved GHD volume has a uniform temperature, which is very suitable for a good quality drying process with higher speed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Román-Roldán

López‐Ortiz

Ituna-Yudonago

et al. 2019

Energy Science & Engineering

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“…Mathematical simulation can be used to dynamically describe the drying and self-heating of wood chip piles under different environmental conditions [8,[29][30][31]. More models have been developed for describing the chip drying and self-ignition in industrial drying process, where hot air flows through a bed of chips [32][33][34], but these models are not directly applicable for simulating moisture dynamics in a large-scale wood chip storage in field.…”

Section: Introductionmentioning

confidence: 99%

Forest chip drying in self-heating piles during storage as affected by temperature and relative humidity conditions

Ahmadinia

Palviainen

Kiuru

et al. 2022

Fuel

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“…Modeling the drying process of cellulose fibers is one technique to manage and identify the drying process. Several investigations have been carried out to simulate and examine the drying kinetic of wood and cellulosic fibers based on Fick's law of diffusion (Fernando et al 2018;Chanpet et al 2020;Autengruber et al 2020;Remond et al 2005;Winberg et al 2000;Salin 2008;Dincer 1998). Recently, several studies have shown that the theoretical models do not adequately describe the fundamental aspects of the experimental wood drying process.…”

Section: Introductionmentioning

confidence: 99%

A sutible model for drying kenitic of wood fiber using halogen moistuer analayzer

Arabi

2022

Preprint

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Drying wood fiber is a complex, dynamic, highly nonlinear, and energy-intensive process involving simultaneous heat and mass transfer. So, the theoretical modeling of wood fiber drying because wood fiber shrinks anisotropically during a drying process is very complicated. The semi-theoretical and empirical thin-layer drying models developed on a laboratory scale are a suitable option for examining the drying kinetics of lignocellulose materials. In this study, drying kinetics of wood fibers were evaluated using 18 semi-empirical models at three temperatures of 105°C, 120°C, and 135°C, utilizing a halogen moisture analyzer. The findings revealed that raising the temperature increased the drying constant while decreasing the drying time of wood fibers. According to drying curves, no constant drying rate was observed and all the drying process occurred in two falling drying rate periods. More specifically, despite the high initial moisture content of wood fiber about 170% (based on dry weight), the wood fiber drying process was mainly controlled by diffusion mechanism. The fitness of drying curves on semi-theoretical and empirical models based on statistical parameters, including root mean square error (RMSE), sum of square errors (SSE), and coefficient of determination (R2) showed that the Midilli et al. model had the highest coefficient of determination and the lowest error percentage. Also, the moisture content was more accurately predicted by ANN model than the Midilli et al. model.

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The effects of air velocity, temperature and particle size on low-temperature bed drying of wood chips

Cited by 8 publications

References 14 publications

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Forest chip drying in self-heating piles during storage as affected by temperature and relative humidity conditions

A sutible model for drying kenitic of wood fiber using halogen moistuer analayzer

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