食品科学技术学报2018，Vol.36Issue(3)：78-82,5.DOI:10.3969/j.issn.2095-6002.2018.03.011
基于均匀设计和BP神经网络的花生油SFE-CO2萃取预测
Prediction of Peanut Oil SFE- CO2Extraction Based on Uniform Design and BP Neural Network
摘要
Abstract
Extraction of peanut oil with SFE-CO2was studied by combining methods of uniform design and BP neural network. Taking peanut after being semi-baked and crushed as raw materials,four factors, including extraction pressure, temperature, time, and CO2flow rate, were tested at ten levels of each factor. Using the experimental data of uniform design as training samples, a neural network prediction model for SFE-CO2extraction of peanut oil was established. The extraction process was predicted, and the relationship between the experimental factors and the oil yield rate was analyzed. Therefore,the bet-ter technological condiitions were determined. A 4 -9 -1 neural network model was established. The prediction value of the oil yield was close to the experimental value,and the relative error(absolute val-ue) was less than 2%. The neural network model could predict the trend of peanut oil yield under the in-fluence of various parameters. Under the conditions of the extraction pressure 30 MPa,temperature 40.5℃,time 125 min, and the CO2flow rate 187 L/(h·kg), the expected value of peanut oil yield was 47.5%. The method provides a reliable theoretical basis for the prediction and control of SFE-CO2ex-traction of peanut oil.关键词
花生油/萃取/BP神经网络/SFE-CO2/均匀设计Key words
peanut oil/extraction/BP neural network/SFE-CO2/uniform design分类
轻工纺织引用本文复制引用
郭建章,陈博文,王威强..基于均匀设计和BP神经网络的花生油SFE-CO2萃取预测[J].食品科学技术学报,2018,36(3):78-82,5.基金项目
国家自然科学基金资助项目(2167060371). （2167060371）
