Modeling and Optimization of Subcritical CO<sub>2</sub> Extraction of Safflower Seed Oil Using Response Surface Methodology and Artificial Neural Networks

LIU Guoyi; GUO Jianzhang; CHEN Xing; WANG Weiqiang

doi:10.13386/j.issn1002-0306.2023070185

Volume 45 Issue 10

May 2024

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Science and Technology of Food Industry > 2024 > 45(10): 225-233. > DOI: 10.13386/j.issn1002-0306.2023070185

LIU Guoyi, GUO Jianzhang, CHEN Xing, et al. Modeling and Optimization of Subcritical CO₂ Extraction of Safflower Seed Oil Using Response Surface Methodology and Artificial Neural Networks[J]. Science and Technology of Food Industry, 2024, 45(10): 225−233. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023070185.

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Modeling and Optimization of Subcritical CO₂ Extraction of Safflower Seed Oil Using Response Surface Methodology and Artificial Neural Networks

1.
School of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
2.
School of Mechanical Engineering, Shandong University, Jinan 250061, China

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Received Date: July 20, 2023
Available Online: March 17, 2024

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Abstract

Abstract

This article aimed to find effective modeling methods to predict the extraction rate of safflower seed oil by subcritical CO₂ extraction, and optimize its extraction process conditions. Based on single-factor experiments, Box-Behnken experimental design was adopted to study the effects of extraction pressure, separation temperature, and extraction time on the extraction rate of safflower seed oil. Response surface methodology (RSM) and artificial neural network (ANN) were used to model and analyze the same experiment. The process conditions were optimized using RSM numerical optimization, ANN, and the combination of artificial neural network and genetic algorithm (ANN-GA). The results showed that both RSM and ANN models could accurately predict the extraction rate. However, by comparing the determination coefficient (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the two models, it was concluded that the ANN model (R²=0.9966) had a better predictive effect than the RSM model (R²=0.9950). The optimal extraction conditions and extraction rate determined by ANN-GA were as follows: Extraction pressure of 19.04 MPa, separation temperature of 55.50 °C, extraction time of 134.98 min, and extraction rate of 23.52%. The study showed that both RSM and ANN methods could be used for modeling and optimization of subcritical CO₂ extraction of safflower seed oil, but ANN had better prediction accuracy and fitting ability.
- subcritical CO₂ extraction,
- safflower seed oil,
- response surface method,
- artificial neural network,
- genetic algorithm

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