Optimization of free gossypol removal from cottonseed meal by the extrusion process based on artificial neural network with genetic algorithm
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摘要: 本文应用人工神经网络模拟了棉粕的挤压膨化脱酚工艺,建立了一个3层网络结构的BP神经网络模型用以预测游离棉酚的降解规律,采用十折交叉验证表明:选择隐藏层神经元数为8、网络训练函数为"traingdx",此网络参数条件下,网络预测准确度高,网络预测输出与实验结果的相关系数(R2)为0.9941、均方根误差为0.4971。基于神经网络模型利用遗传算法进行全局寻优的结果表明,棉粕挤压膨化脱酚的最佳工艺条件为膨化温度131℃、物料水分51%、螺杆转速158r/min、喂料速度136kg/h,在此条件下,游离棉酚的实际降解率为90.50%,与遗传算法优化预测结果的平均相对误差为1.38%,平均相对误差较小。本研究表明,神经网络模拟结合遗传算法对棉粕挤压膨化脱酚工艺具有较好的优化效果。Abstract: The artificial neural network ( ANN) was used for the simulation of the degradation of free gossypol in cottonseed meal by the extrusion process.A three- layer back propagation neural network was optimized to predict the degradation of free gossypol.The result of 10- fold cross validation showed that the model of back propagation neural network giving the smallest mean square error ( MSE) was the ANN with the training function as traingdx at hidden layer with 8 neurons.And ANN predicted results were very close to the experimental results with correlation coefficient ( R2) of 0.9941 and RMSE of 0.4971.A genetic algorithm ( GA) based on an established neural network model was also used to optimizing de- gossypol process.The results of GA obtained showed that the optimal condition of de- gossypol by the extrusion process was temperature 131℃, water ratio 51%, rotational speed158 r / min, and feeding speed 136 kg / h, and in this condition the degradation rate of free gossypol was 90.50%, which was close to the result of GA predicted with the small average relative error of 1.38%. These results suggested that the GA based on a neural network model might be an excellent tool for optimizing cottonseed meal de- gossypol process.
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Keywords:
- cottonseed meal /
- extrusion /
- de-gossypol /
- artificial neural network /
- genetic algorithm
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[1] 周磊.蛋白饲料棉籽饼粕的脱毒方法[J].农业技术与装 [2] Khodwe M S, Bhowmick D N.Separation of gossypol from cottonseed and preparation of gossypol-free cottonseed cake[J].International Journal of Recent Scientific Research, 2013, 4 (8) :1290-1295.
[3] Jalees M M, Khan M Z, Saleemi M K, et al.Effects of Cottonseed Meal on Hematological, Biochemical and Behavioral Alterations in Male Japanese Quail (Coturnix japonica) [J].Pakistan Veterinary Journal, 2011, 31 (3) :211-214.
[4] 陈琪, 石剑华.棉籽饼使用脱毒剂和生物发酵脱毒的营养分析[J].当代畜禽养殖业, 2013 (10) :6-8. [5] 周建国, 王洪武.棉籽粕双螺杆挤压脱毒中几个工艺参数的研究[J].农业工程学报, 2000, 16 (6) :110-113. [6] 周建国, 吴冬莉.棉籽饼粕双螺杆挤压脱毒中的参数研究[J].中国油脂, 2001, 26 (1) :46-49. [7] 王洪武, 周建国.加工参数对棉籽饼粕双螺杆挤压脱毒的影响[J].中国粮油学报, 2005, 20 (3) :70-72. [8] Morris A J, Montague G A, Willis M J.Artificial neural networks:studies in process modeling and control[J].Trans I Chem Eng, 1994, 72A:3-19.
[9] Pareek V K, Brungs A A, Sharma R.Artificial neural network modeling of a multiphase photodegradation system[J].Photochem Photobiol A:Chem, 2002, 149:139-146.
[10] He L, Xu Y Q, Zhang X H.Medium factors optimization and fermentation kinetics for phenazine-1-carboxylic acid production by Pseudomonas sp.M18G[J].Bioteehnology and Bioengineering, 2008, 100 (2) :250-259.
[11] Elmolla E S, Chaudhuri M, Eltoukhy M M.The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process[J].Journal of hazardous materials, 2010, 179 (1) :127-134.
[12] Witten I H, Frank E.Data Mining:Practical machine learning tools and techniques[M].北京:机械工业出版社, 2006:286. [13] Nagata Y, Chu K H.Optimization of a fermentation medium using neural networks and genetic algorithms[J].Biotechnology Letters, 2003, 25 (21) :1837-42.
[14] GB 13086-1991, 饲料中游离棉酚的测定方法[S]. [15] Hagan M T, Demuth H B, Beale M H.神经网络设计[M].北京:机械工业出版社, 2002:127-128.
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