scholarly journals Kinetic study, optimization and comparison of sun drying and superheated steam drying of asam gelugor (Garcinia cambogia)

Food Research ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 396-406
Author(s):  
G.W. Lim ◽  
S. Jafarzadeh ◽  
Norazatul Hanim M.R.
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Superheated Steam ◽  
Acid Number ◽  
Drying Process ◽  
Drying Time ◽  
Sun Drying ◽  
Superheated Steam Drying ◽  
Steam Drying

The purposes of present study are to compare the kinetic drying of the G. cambogia through sun drying and superheated steam drying (SSD) method and optimizing the quality of SSD of it through response surface methodology. G. cambogia fruit rinds were dried at temperature of 150°C, 200°C and 250°C. The drying curves were fitted into the mathematical model of Page, Lewis and Henderson-Pabis models. Page model was found to be the best in describing the drying behavior of G. cambogia. Drying rate constant (k) increased as temperature increased and SSD method had overall higher drying rates ranged from 5.929 x 10-5 to 5.861 x 10-4 min-1 than sun drying method which was 4.980 x 10-6 min-1 . Total acid number showed a trend of increased followed by decreased over drying time. superheated steam drying process of G. cambogia fruit rinds was optimized by using response surface methodology employing a central composite design. Drying time and temperature were the factors in optimization while moisture content (wet basis), acid number and lightness (*L) were the response parameters. Experimental results were fitted to a second-order polynomial model and the model fitness and optimal drying condition were determined by regression analysis and analysis of variance. The optimal conditions for superheated steam drying of G. cambogia fruit rinds were identified as 46.60 mins and 150°C with the composite desirability of 0.913. Application of superheated steam drying under controlled conditions resulted in faster drying process and better quality of dried G. cambogia than conventional sun drying technique.

Drying of pineapple slices using combined low-pressure superheated steam and vacuum drying

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jingcheng Wang ◽  
Qing Xu ◽  
Jianbo Liu ◽  
Shuaishuai Zheng ◽  
Ruifang Wang ◽  
...  
Keyword(s):  
Superheated Steam ◽  
Retention Rate ◽  
Sugar Content ◽  
Low Pressure ◽  
Vacuum Drying ◽  
Drying Time ◽  
Superheated Steam Drying ◽  
Health Demand ◽  
Steam Drying ◽  
Material Temperature

Abstract A method of combining low-pressure superheated steam drying (LPSSD) and vacuum drying (VD) was proposed to improve the dried pineapple quality and increase the drying rate. It was found that the inversion temperature in low-pressure superheated steam drying of pineapple was 85.75 °C in terms of the first falling rate period. The combining drying (LPSSD–VD) reduced the maximum material temperature by 9.5 °C and 0.35 °C, and shortened the drying time by 50 min and 90 min compared with LPSSD and VD at the same drying temperature of 90 °C. The vitamin C retention rate of dried pineapple by LPSSD–VD was 29.33% and 15.94% higher than that of LPSSD and VD, respectively. The color of dried pineapple was also improved. Moreover, the sugar content of dried pineapple can be well controlled to meet the health demand of low sugar and ensure the taste of dried pineapple during LPSSD–VD process.

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

scholarly journals Integration of Safety Aspects in Modeling of Superheated Steam Flash Drying of Tobacco

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5927
Author(s):  
Robert Adamski ◽  
Dorota Siuta ◽  
Bożena Kukfisz ◽  
Michał Frydrysiak ◽  
Mirosława Prochoń
Keyword(s):  
Superheated Steam ◽  
Correct Selection ◽  
Drying Process ◽  
Explosion Pressure ◽  
Superheated Steam Drying ◽  
Proposed Model ◽  
Before And After ◽  
Fire Properties ◽  
Drying Properties ◽  
Steam Drying

Knowledge of the drying properties of tobacco in high temperatures above 100 °C and its dust are crucial in the design of dryers, both in the optimization of the superheated-steam-drying process and in the correct selection of innovative explosion protection and mitigation systems. In this study, tobacco properties were determined and incorporated into the proposed model of an expanding superheated steam flash dryer. The results obtained from the proposed model were validated by using experimental data yielded during test runs of an industrial scale of a closed-loop expansion dryer on lamina cut tobacco. Moreover, the explosion and fire properties of tobacco dust before and after the superheated steam-drying process at 160, 170, 180, and 190 °C were experimentally investigated, using a 20 L spherical explosion chamber, a hot plate apparatus, a Hartmann tube apparatus, and a Godbert–Greenwald furnace apparatus. The results indicate that the higher the drying temperature, the more likely the ignition of the dust tobacco cloud, the faster the explosion flame propagation, and the greater the explosion severity. Tobacco dust is of weak explosion class. Dust obtained by drying with superheated steam at 190 °C is characterized by the highest value of explosion index amounting to 109 ± 14 m·bar·s−1, the highest explosion pressure rate (405 ± 32 bar/s), and the maximum explosion pressure (6.7 ± 0.3 bar). The prevention of tobacco-dust accumulation and its removal from the outer surfaces of machinery and equipment used in the superheated steam-drying process are highly desirable.

scholarly journals Optimization of Foam-Mat Drying Process of Watermelon Pulp Using Response Surface Methodology

2019 ◽  
pp. 1-11
Author(s):  
J. Isa ◽  
A. P. Olalusi
Keyword(s):  
Response Surface Methodology ◽  
Moisture Content ◽  
Response Surface ◽  
Methyl Cellulose ◽  
Drying Process ◽  
Air Velocity ◽  
Drying Time ◽  
Drying Characteristics ◽  
Moisture Content Level ◽  
Egg Albumen

Introduction: Foam mat drying involves the change of agricultural material from a high moisture content level to a stable foam which is achieved by moisture reduction mechanism. Aim: In this study, foam-mat drying process of watermelon was optimized using response surface methodology. Foaming conditions (carboxyl methyl cellulose and egg albumen) and the drying system parameters (air velocity and air temperature) were optimized using response surface methodology. Methodology: To evaluate the drying behaviour, the drying experiment was designed using design expert software using a central composite design setting variable of drying temperature (60°C – 80°C), air velocity (0.5 m/s – 2 m/s), carboxyl methyl cellulose (0.5% - 2.5%), egg albumen (5% - 15%). Twenty-two runs of the experiment were performed using different levels of variables combinations. Based on the statistical tests performed, the best model that described each response was selected using a polynomial analysis. Results: The optimum values for the drying conditions were: 77.42OC, 0.5m/s, 0.5% and 5% for temperature, air velocity, carboxyl methylcellulose and egg albumen respectively and the optimum values for the drying characteristics were: 25.07 KJ/mol, 1.7345E-10 m2/s, 29.019% (wet-basis). 0.742 g/cm3 and 540 minutes (approximately 9hrs) for activation energy, effective diffusivity, moisture content, foam density and the drying time respectively. Conclusion: The study of the foam-mat drying of watermelon pulp revealed that the inlet temperature, air velocity, CMC and egg albumen has a significant effect on its drying characteristics.

scholarly journals Optimization of onion Juice inhibiting white-blush in fresh-cutcarrots by response surface methodology

2021 ◽  
Vol 292 ◽  
pp. 03064
Author(s):  
Jiani Lu ◽  
Xiaoning Jiang
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Raw Materials ◽  
Process Conditions ◽  
Drying Time ◽  
Soaking Time ◽  
Fresh Cut ◽  
Reference Index ◽  
Physiological And Biochemical

The effect of onion juice on the whitening of fresh-cut carrots during storage was explored to optimize the processing technology of fresh-cut carrots. Using fresh carrot and onion as raw materials and whiteness value as reference index, the process parameters were optimized by response surface methodology (RSM) based on single factor experiment, and the physiological and biochemical indexes were determined. Results showed that the process conditions optimized by response surface processing of fresh-cut carrot onion were as follows: the concentration of the onion juice for 75%, soaking time for 15 min, spin-drying time for 60s. Under this condition, for the fresh-cut carrot, whiteness value theoretical value was 30.51, and the actual whiteness value was 31.26±0.5. After verification, the model was established and the fit worked well. The quality of fresh-cut carrots treated with onion juice was better. This study laid a scientific foundation for the inhibition of whitening of fresh-cut carrot.

scholarly journals Optimasi Kondisi Pengeringan Mie Singkong dengan Response Surface Methodology Terhadap Karakteristik Produknya

2019 ◽  
Vol 39 (2) ◽  
pp. 153
Author(s):  
Henny Krissetiana Hendrasty ◽  
Sundari Setyaningsih ◽  
Raden Sugiarto
Keyword(s):  
Response Surface Methodology ◽  
Water Absorption ◽  
Water Content ◽  
Response Surface ◽  
Cassava Starch ◽  
Quality Parameters ◽  
Cooking Loss ◽  
Drying Time ◽  
Drying Conditions

Wet cassava starch noodles have been developed in Srihardono Village, Pundong-Bantul District. The disadvantage of this noodle is the short shelf life. The objective of this study was to determine the optimum drying conditions to obtain the best quality of dried cassava noodles. The factors, such as temperature and drying time, and noodle layer were evaluated. Dried cassava noodles contain a combination of traditionally processed cassava starch and manufactured cassava starch. The ratio of traditional starch to manufactured starch was 3:2 (w/w). Drying was conducted using a cabinet drier at various temperatures (70, 80 and 90 °C) for 2 h, 2.5 h, 3 h, 3.5 h and 4 h. Noodle layers were 1, 2, 3 and 4. The observed quality parameters were elongation, water content, cooking loss and water absorption. The obtained data were analyzed using a Response Surface Methodology. The range of optimum drying condition was between 76 and 84 °C and drying time between 3.2 and 3.5 h. Noodles were arranged in 2 layers. Dried cassava noodles had an elongation value of 60 to 70%, water content of 8 to 9%, cooking loss of 10 to 12% and water absorption of 140 to 150%. 

Hybrid response surface methodology–artificial neural network optimization of drying process of banana slices in a forced convective dryer

2017 ◽  
Vol 24 (4) ◽  
pp. 277-291 ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Fatemeh Karimi ◽  
Mahmoud Karimi ◽  
Valiullah Lotfi ◽  
Golmohammad Khoobbakht
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Drying Rate ◽  
Drying Process ◽  
Air Velocity ◽  
Drying Time ◽  
Artificial Neural

The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to −0.42 (74.2 ℃), 1.00 (1.50 m/s), and −0.17 (2.50 h) in the coded units, respectively.

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