Effect of mass on convective heat transfer coefficient during open sun drying of groundnut

Sahdev, Ravinder Kumar; Kumar, Mahesh; Dhingra, Ashwani Kumar

doi:10.1007/s13197-017-2858-3

Cited by 19 publications

(9 citation statements)

References 22 publications

(21 reference statements)

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“…They were also found comparable to the results of groundnuts drying under FCGD (Sahdev et al, 2018). Average values of h e were found to be 113.304 W/ m 2 °C, 41.483 W/m 2 °C, and 14.266 W/m 2 °C under MMGGD, MMMGGD, and OSGD, respectively, which were found comparable to the results reported for groundnut drying under OSD (Sahdev et al, 2017a) and NCGD (Kumar et al, 2019). The range of Gr and Pr numbers product was found to lie within the laminar region, that is, GrPr ≤10 7 .…”

Section: Thermal Analysissupporting

confidence: 83%

“…The quantity of the agricultural produce to be dried significantly affects the performance of the drying system. Sahdev et al (2017a) analyzed the influence of the mass of the groundnuts samples on the values of h c and h e under OSD mode. The average values of h c and h e were varied from 0.71 to 1.03 W/m 2 °C and 106.24 to 63.82 W/m 2 °C, respectively.…”

Section: Practical Applicationsmentioning

confidence: 99%

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Experimental investigations on latent heat storage based modified mixed‐mode greenhouse groundnuts drying

Shimpy

Kumar

Sahdev

et al. 2022

Food Processing Preservation

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This work explores the effect of latent heat storage on the performance of a passive‐type mixed‐mode greenhouse dryer for groundnuts drying. Experiments were conducted for mixed‐mode greenhouse groundnuts drying (MMGGD), modified mixed‐mode greenhouse groundnuts drying (MMMGGD), and open sun groundnuts drying (OSGD) under the climate of Hisar (India). Average convective heat transfer coefficients for MMGGD, MMMGGD, and OSGD were 0.738, 0.607, and 0.555 W/m2 °C, respectively, and the respective average evaporative heat transfer coefficients were 113.304, 41.483, and 14.266 W/m2 °C. Energy and exergy efficiencies were found higher for MMMGGD, whereas the specific energy consumption was found lower as compared to MMGGD. Statistical analysis has shown the appropriateness of the page model for predicting the thin‐layer drying behavior of groundnuts for MMGGD, MMMGGD, and OSGD. The lower value of energy payback time (4.47 years) and the higher value of CO2 mitigation (953.299 tons) for MMMGGD show the environmental suitability as compared to MMGGD. Practical applications The present study focuses on the development of drying systems to prevent post‐harvest losses in groundnuts due to moisture and uncontrolled weather conditions. The temperature range of the proposed modified mixed‐mode greenhouse dryer is suitable for drying for most of the agricultural commodities. This drying system can be used for a range of food crops in different small and medium industries. Due to electrical independence, the proposed system seems best suited for small farms and farmers in remote and rural areas. The modified mixed‐mode greenhouse may also be used as a field‐based storage house for grains and freshly harvested crops.

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Section: Thermal Analysissupporting

confidence: 83%

Section: Practical Applicationsmentioning

confidence: 99%

Experimental investigations on latent heat storage based modified mixed‐mode greenhouse groundnuts drying

Shimpy

Kumar

Sahdev

et al. 2022

Food Processing Preservation

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“…As per literature available, drying of corn kernels is not addressed properly with all the operating parameters. Due to simultaneous heat and mass transfer it is important to focus certain parameters to minimize energy cost of operation (Sahdev et al, 2017). The present study includes the most important parameter of corn kernel drying in fluidized bed dryer like air velocity, amount of feed, temperature of drying and time of drying.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Optimization of Convective Drying of Corn Kernels in a Fluidized Bed Dryer

Gautam

Gautam²,

Mahant³

2021

JER is an international, peer-reviewed journal that publishes f

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Food storage is an essential process for food security and it needs to be free from any biological contamination. For the same, agriculture produce needs to be completely dried before sending for storage. The present work discusses a systematic approach to model drying parameters of corn kernels in a fluidized bed dryer. Experiments were designed according to a higher level Box-Behnken design combined with response surface methodology. Four parameters were chosen to vary namely: amount of corn kernels (50 -100 gm), temperature of drying (50 – 80⁰C), air velocity (6.01 – 8.08 m/s) and drying time (30 – 60 min) for experiments as well as for the model. The reduction of moisture content was determined after each experiment for understanding the behaviour of drying process. The model equations were obtained and surface response plots were generated in MATLAB to investigate the drying behaviour of corn kernels with all four parameters. Ultimately, this work represents the dependence of moisture removal on all four parameters chosen with efficient use of response surface methodology and Box-Behnken design. Analysis of variance confirmed that velocity of air and amount of corn are the most significant parameters along with temperature and time of drying. Optimum condition with the model were obtained as 50 gm of corn kernels, 80 ⁰C drying temperature, 8 m/sec velocity of air, and 60 min time of drying for 73.3 % of moisture from corn kernels.

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“…Currently, sun dried in the open and convection by hot air, were two major drying methods used for peanut drying (Sahdev, Kumar, & Dhingra, 2017). However, sun dried method was usually influenced by rain, dust, insects, rodents and other factors (Diamante & Munro, 1993).…”

Section: Introductionmentioning

confidence: 99%

A moisture content prediction model for deep bed peanut drying using support vector regression

Wang

Jin

et al. 2020

J Food Process Engineering

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In order to make the moisture content monitoring more convenient and rapid during peanut drying process, the drying characteristics of peanut were investigated and a real‐time SVR moisture content monitoring model was established in this paper. The results showed that hot air temperature, initial moisture content, airflow rate and the layer height were the key factors on peanut drying, and the peanut variety showed little effect on drying. The SVR model exhibited a good performance with R2: 0.91, RMSE: 4.38, and bias: −7.5e‐3. Compared with the results of linear regression models and multilayer perceptron model, SVR model showed a better performance. In addition, the SVR model was validated by the drying data of other three varieties of peanuts. And the relative errors between the predicted values by SVR model and the measured values were within 20%, which suggested that SVR was a promising modeling algorithm for peanut drying. Practical application Peanut drying is essential for peanut production due to its high moisture content at harvest (about 30–50% on the wet basis), which makes it susceptible to mildew, or even produces aflatoxins. Peanut has a special physiological structure. In the drying process, the peanut kernels are gradually shrunk, which makes the air layer volume between the shell and the kernel gradually increases, thus hindering mass and heat transfer and leading to its different drying characteristics from other grain and oil crops. Furthermore, it is necessary to monitor the moisture content changes of peanut during deep bed drying to ensure the drying uniformity and prevent energy waste. Therefore, the drying characteristics of peanuts were studied and a moisture content prediction model for deep bed drying was established to assist the actual drying process.

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Effect of mass on convective heat transfer coefficient during open sun drying of groundnut

Cited by 19 publications

References 22 publications

Experimental investigations on latent heat storage based modified mixed‐mode greenhouse groundnuts drying

Experimental investigations on latent heat storage based modified mixed‐mode greenhouse groundnuts drying

A Statistical Optimization of Convective Drying of Corn Kernels in a Fluidized Bed Dryer

A moisture content prediction model for deep bed peanut drying using support vector regression

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