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Title

Prediction of trans-anethole extraction yield from Pimpinella anisum seeds using ANN

Year: 1397
COI: CBGCONF05_122
Language: EnglishView: 182
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Authors

M Khajenoori - Department of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran
A Haghighi Asl - Department of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran

Abstract:

In this study, the extraction of trans-anethole (t-anethole) using subcritical water solvent was employed as a case-study. A feed-forward multilayer back propagation artificial neural network (ANN) with various train algorithms and number of neurons was considered for the prediction of t-anethole extraction yield (mg/g dry sample). The input variables were temperature (100-175oC), flow rate (0.5-4ml/min), mean particle size (0.25-1mm) and output was t-anethole extraction yield. The optimized structure of neural network is manufactured based on minimum mean square error (MSE) of training and testing data. The optimal ANN model consisted of one hidden layer andfive neurons. The Prediction of t-anethole extraction yield using the ANN model was proven to be an accurate, appropriate, and simple method

Keywords:

trans-anethole, extraction, subcritical water, ANN model.

Paper COI Code

This Paper COI Code is CBGCONF05_122. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/837041/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Khajenoori, M and Haghighi Asl, A,1397,Prediction of trans-anethole extraction yield from Pimpinella anisum seeds using ANN,The 5th International Conference on Applied Research in Chemistry and Chemical Engineering with an emphasis on indigenous technology in Iran,Tehran,https://civilica.com/doc/837041

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Type of center: دانشگاه دولتی
Paper count: 8,334
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