An experimental and simulating study of supercritical CO2 extraction for pepper oil

Li, Zhiyi; Liu, Xuewu; Chen, Shuhua; Zhang, Xiaodong; Yuanjing, Xia; Yong, Wei Peng; Xiao, Feng

doi:10.1016/j.cep.2005.08.005

Cited by 24 publications

(15 citation statements)

References 2 publications

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“…Tipsrisukond et al (1998), obtained similar yields of recovery as solvent-extracted (6.1 %) with the one obtained with SCCO 2 at pressures of 28 MPa and 55 C (6.13 %). These results are in agreement with the optimal process condition of the SFE for pepper oil at 22-26 MPa, 318 K, found by Zhiyi et al (2006).…”

Section: Black Peppersupporting

confidence: 93%

“…In SCCO 2 extraction of black pepper at 16-26 MPa, temperature of 308-323 K, solvent flow rate from 0.2-0.4 m 3 /h, and diameter of ground material of 20-50 meshes the same behavior was found (Zhiyi et al 2006). In fact, the extraction yield was found to increase with temperature at 26 MPa and to decrease at 16 MPa.…”

Section: Pressure and Temperaturesupporting

confidence: 69%

“…At lower pressure, the change of solvent density is more effective than that of the solute vapor pressure, as extraction rate increases with a decrease in temperature. However, the increase in solvent density with pressure overcomes the relatively small change of solute vapor pressure; for instance, at 25 MPa the extraction rate is dependent on the solute vapor pressure increasing with temperature (Zhiyi et al 2006). The retrograde condition was found at 15 MPa and also the increase of particle size reduced the extract yield and extraction rate (Kumoro et al 2010).…”

Section: Black Peppermentioning

confidence: 98%

“…Moreover, SCCO 2 extraction of black pepper at 16-26 MPa, temperature of 308-323 K, with a solvent flow rate from 0.2 to 0.4 m 3 /h, and diameter of ground material of 20-50 meshes was investigated, and the optimum process condition of the SFE for pepper oil was determined, suggesting that the extraction rate increases also with a rise in the flow rate of carbon dioxide from 0.2 to 0.4 m 3 /h (Zhiyi et al 2006).…”

Section: Supercritical Fluid Flow Ratementioning

confidence: 99%

See 3 more Smart Citations

Supercritical Fluid Extraction of Compounds from Spices and Herbs

Coelho

Palavra

2014

Food Engineering Series

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Section: Black Peppersupporting

confidence: 93%

Section: Pressure and Temperaturesupporting

confidence: 69%

Section: Black Peppermentioning

confidence: 98%

Section: Supercritical Fluid Flow Ratementioning

confidence: 99%

See 2 more Smart Citations

Supercritical Fluid Extraction of Compounds from Spices and Herbs

Coelho

Palavra

2014

Food Engineering Series

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“…However, its main disadvantage lies in operating at the boiling point of water, leading to the loss of many water-sensitive or temperature-sensitive combinations. Supercritical fluid extraction (SFE) in the second category has been recently used for the extraction of the natural materials to a great extent [6][7][8][9]. Working at lower temperatures, fast speed of the process, use of clean inexpensive fluid, and easy separation of the products are the main suitable features of SFE.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network in Simulation of Supercritical Extraction of Valerenic Acid from Valeriana officinalis L.

Kenaree

Fatemi

2012

ISRN Chemical Engineering

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Application of artificial neural network (ANN) has been studied for simulation of the extraction process by supercritical CO 2 . Supercritical extraction of valerenic acid from Valeriana officianalis L. has been studied and simulated according to the significant operational parameters such as pressure, temperature, and dynamic extraction time. ANN, using multilayer perceptron (MLP) model, is employed to predict the amount of extracted VA versus the studied variables. Three tests, validation, and training data sets in three various scenarios are selected to predict the amount of extracted VA at dynamic time of extraction, working pressure, and temperature values. Levenberg-Marquardt algorithm has been employed to train the MLP network. The model in first scenario has three neurons in one hidden layer, and the models associated with the second and the third scenarios have four neurons in one hidden layer. The determination coefficients are calculated as 0.971, 0.940, and 0.964 for the first, second, and the third scenarios, respectively, demonstrating the effectiveness of the MLP model in simulating this process using any of the scenarios, and accurate prediction of extraction yield has been revealed in different working conditions of pressure, temperature, and dynamic time of extraction.

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