响应面法和人工神经网络优化亚临界CO_(2)等压萃取葡萄籽油工艺及动力学研究

Response Surface Methodology and Artificial Neural Network Optimization of Subcritical CO_(2)Isobaric Extraction Process and Kinetics of Grape Seed Oil

以葡萄籽油萃取率为指标,通过响应面法(Response surface methodology,RSM)和人工神经网络(Artificial neural network,ANN)对亚临界CO_(2)等压萃取葡萄籽油工艺进行建模和优化,研究主要工艺参数(萃取压力、分离温度、萃取时间)对葡萄籽油萃取率的影响,且通过RSM和人工神经网络耦合遗传算法(Artificial neural network coupled genetic algorithm,ANN-GA)2种方法优化其最佳工艺条件并验证。在萃取过程中,通过不同萃取压力和不同萃取时间条件下葡萄籽油萃取率的变化,拟合出最佳的葡萄籽油萃取动力学模型并验证。结果表明:RSM与ANN这2种方法均能较为精准地预测,得出RSM模型(R^(2)=0.9940)的预测效果优于ANN(R^(2)=0.9879)模型。且RSM和ANN-GA优化最佳萃取条件及萃取率分别为:萃取压力16.13 MPa、分离温度59.55℃、萃取时间100.6 min,萃取率11.36%;萃取压力16.5 MPa、分离温度60.95℃、萃取时间87.3 min,萃取率11.32%。经实验验证,2种方法预测值与实验值基本一致,另外Logistic模型能够很好地拟合亚临界CO_(2)等压萃取葡萄籽油动力学过程(R2≥0.9990),模型验证值与预测值拟合度较高(R2≥0.9443),该研究结果可为葡萄籽油的资源开发利用提供理论和技术参考。

This article studies the modeling and optimization of subcritical CO_(2)isobaric extraction of grape seed oil using response surface methodology(RSM)and artificial neural network(ANN),with the extraction rate of grape seed oil as the indicator.The main process parameters(extraction pressure,separation temperature,extraction time)are usd to investigate the impact on the extraction rate of grape seed oil,And the optimal process conditions were optimized and validated through RSM and artificial neural network coupled genetic algorithm(ANN-GA).At the same time,during the extraction process,the optimal kinetic model for grape seed oil extraction was fitted and validated by analyzing the changes in grape seed oil extraction rate under different extraction pressure and time conditions.The results show that both RSM and ANN methods can accurately predict,and it is concluded that the RSM model(R^(2)=0.9940)has better prediction performance than the ANN model(R^(2)=0.9879).And RSM and ANN-GA optimized the optimal extraction conditions and extraction rate as follows:extraction pressure 16.13 MPa,separation temperature 59.55℃,extraction time 100.6 minutes,extraction rate 11.36%;The extraction pressure is 16.5 MPa,the separation temperature is 60.95℃,the extraction time is 87.3 minutes,and the extraction rate is 11.32%.After experimental verification,the predicted values of the two methods are basically consistent with the experimental values.In addition,the Logistic model can well fit the kinetic process of subcritical CO_(2)isobaric extraction of grape seed oil(R^(2)≥0.9990),and the model validation values and predicted values have a high degree of fit(R^(2)≥0.9443).The research results provide theoretical and technical reference for the development and utilization of grape seed oil resources.