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中国精品科技期刊2020
谢春平,赵良忠,李明,等. 基于遗传算法-神经网络的豆渣酶解工艺优化及其动力学研究[J]. 食品工业科技,2021,42(16):213−220. doi: 10.13386/j.issn1002-0306.2020120261.
引用本文: 谢春平,赵良忠,李明,等. 基于遗传算法-神经网络的豆渣酶解工艺优化及其动力学研究[J]. 食品工业科技,2021,42(16):213−220. doi: 10.13386/j.issn1002-0306.2020120261.
XIE Chunping, ZHAO Liangzhong, LI Ming, et al. Optimization of Enzymatic Hydrolysis of Soybean Dregs Based on Genetic Algorithm-Neural Network and Its Kinetics[J]. Science and Technology of Food Industry, 2021, 42(16): 213−220. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020120261.
Citation: XIE Chunping, ZHAO Liangzhong, LI Ming, et al. Optimization of Enzymatic Hydrolysis of Soybean Dregs Based on Genetic Algorithm-Neural Network and Its Kinetics[J]. Science and Technology of Food Industry, 2021, 42(16): 213−220. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020120261.

基于遗传算法-神经网络的豆渣酶解工艺优化及其动力学研究

Optimization of Enzymatic Hydrolysis of Soybean Dregs Based on Genetic Algorithm-Neural Network and Its Kinetics

  • 摘要: 目的:获得纤维素酶酶解豆渣的最佳工艺条件,建立豆渣酶解动力学模型。方法:在单因素实验的基础上,采用响应面法(Response Surface Methodology,RSM)和遗传算法-神经网络模型(Genetic Algorithm- Neural Network, GA-ANN)两种方法研究pH、纤维素酶添加量、酶解温度和酶解时间对还原糖生成量的影响。结果:遗传算法-神经网络优化的豆渣酶解工艺条件为:pH5.2、纤维素酶添加量4.4%、酶解温度52 ℃、酶解时间3.2 h,在此条件下还原糖生成量为2.65 g/kg,高于响应面优化的结果(还原糖生成量为2.54 g/kg)。利用类分形动力学得到豆渣酶解动力学参数k0为0.5372,分形维数h为0.1530,根据实验数据与动力学模型进行拟合,拟合度达到0.9827,拟合效果良好。结论:本文采用响应面法和遗传算法-神经网络模型对比优化了豆渣酶解工艺,并建立了豆渣酶解动力学模型,可为豆渣酶解工艺提供理论参考。

     

    Abstract: Objective: The optimal process conditions for the solution of legumes by cellulase enzymes were obtained, and a model of the electrodynamics of bean slag enzymes was established. Methods: Based on the single-factor experiment, the effects of pH, cellulase addition, enzymatic temperature and enzyme solution time on reduced sugar production were studied by using the response surface method (RSM) and the genetic algorithm-neural network model (GA-ANN). Results: Genetic algorithm-neural network optimization of the bean slag enzyme solution process conditions were: pH5.2, cellulase addition 4.4%, enzymatic temperature 52 ℃, enzymatic time 3.2 h, under these conditions reduced sugar production was 2.65 g/kg, higher than the results of response surface optimization (reduced sugar production was 2.54 g/kg). The kinetics parameter k0 was 0.537 and the scropular dimensionality h was 0.153, according to the experimental data and the dynamic model, the fit was 0.9827, and the fitting effect was good. Conclusion: In this paper, the soybean slag enzyme solution process was optimized by using the response surface method and the genetic algorithm-neural network model, and the soybean slag enzyme solution dynamics model was established, which could provide theoretical reference for the soybean slag enzyme solution process.

     

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