Optimization for vacuum belt drying process of Qibai Pingfei granule through response surface methodology

2015 ◽  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Drying Process ◽  
Vacuum Belt

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.

Modeling and optimization of red currants vacuum drying process by response surface methodology (RSM)

2016 ◽  
Vol 203 ◽  
pp. 465-475 ◽  
Author(s):  
Zdravko Šumić ◽  
Anita Vakula ◽  
Aleksandra Tepić ◽  
Jelena Čakarević ◽  
Jasmina Vitas ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Drying Process ◽  
Modeling And Optimization

Optimization of Fluidized Bed Drying Process of Green Peas Using Response Surface Methodology

2008 ◽  
Vol 26 (7) ◽  
pp. 920-930 ◽  
Author(s):  
Rajesh R. Burande ◽  
Baburao K. Kumbhar ◽  
Prabal K. Ghosh ◽  
Digvir S. Jayas
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Fluidized Bed ◽  
Drying Process ◽  
Fluidized Bed Drying ◽  
Green Peas

scholarly journals Optimizing the tray dryer temperature and time of white corn flour culture

Food Research ◽  
2021 ◽  
Vol 5 (5) ◽  
pp. 95-104
Author(s):  
D. Hunaefi ◽  
Rahmawati R. ◽  
D. Saputra ◽  
R.R. Maulani ◽  
T. Muhandri
Keyword(s):  
Response Surface Methodology ◽  
Aspergillus Niger ◽  
Water Content ◽  
Response Surface ◽  
Water Activity ◽  
Penicillium Citrinum ◽  
Drying Process ◽  
Corn Flour ◽  
Acremonium Strictum ◽  
Candida Famata

This research aimed to optimize the tray dryer temperature and time of white corn flour culture by Response Surface Methodology (RSM). There were two cultures used in this research, namely Amylolytic Culture (AC) and Complete Culture (CC). AC consisted of Penicillium citrinum, Aspergillus niger, Acremonium strictum, and Candida famata, while CC consisted of Penicillium chrysogenum, Penicillium citrinum, Aspergillus niger, Rhizopus stolonifer, Rhizopus oryzae, Fusarium oxysporum, Acremonium strictum, Candida famata, Kodamaea ohmeri and Candida krusei/incospicua. The independent variables in this study were drying temperature and time, where the quality indicators used were total viability of mold and yeast, water content, water activity, and pH. This research used a factor response surface methodology. Data were analyzed by ANOVA with an α level of 95%. The result of this research showed that the optimum drying process for AC starter was 40°C for 10 hrs, with characteristic response viability 8.8×107 CFU/g, water activity 0.43, water content 8.90%, and pH 4.05. CC starter showed an optimum drying process at 49°C for 4.5 hrs, with characteristic response viability 4.9×107 CFU/g, water activity 0.49, water content 7.02%, and pH 3.95. The optimum tray dryer temperatures and times were achieved for AC and CC starters.

Optimization of the cold drying process of mango (Mangifera indica L.) Tu Quy variety by response surface methodology

Food Research ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 386-393
Author(s):  
T.Y.N. Tran ◽  
T.T. Dang ◽  
T.N. Do ◽  
V.T. Pham ◽  
V.L. Do ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ascorbic Acid ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Mangifera Indica ◽  
Slice Thickness ◽  
Ultrasonic Power ◽  
Drying Process ◽  
Total Polyphenol ◽  
Wide Range

Mango (Mangifera india L.) is a common fruit that has been found in a wide range of applications. However, the diversification of mango products has been limited. In this study, the manufacture of dried mango was attempted to process by adopting the cold drying technique. The response surface methodology was used to evaluate the simultaneous effects of cold drying temperature - CT (℃), ultrasonic power - UP (W) and slice thickness-ST (cm) on total polyphenol (TP) and total ascorbic acid (TA) content by using Box Behnken experimental design. The established model was considered suitable to describe the experimental data. The optimized drying parameters were determined at the temperature of 45.02℃, ultrasonic power of 312.22 W, and slice thickness of 0.96 cm. These conditions effectively resulted in the highest retention of TP values of 338.994 mg GAE/100 g and TA of 36.783 mg AA/100 g.

scholarly journals Optimization of solar drying process parameters of cooking banana using Response Surface Methodology

2021 ◽  
pp. e00964
Author(s):  
Promise Joseph Etim ◽  
Akachukwu Ben Eke ◽  
Kayode Joshua Simonyan ◽  
Kingsley Charles Umani ◽  
Samuel Udo
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Solar Drying ◽  
Drying Process ◽  
Cooking Banana
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