人工神经网络结合粒子群算法优化两相体系催化合成β –苯乙醇

范文编号:SW087  范文字数:11753,页数:27

摘 要：本范文主要研究了马克斯克鲁维酵母（Kluyveromyces marxianus AS.2.1440）两相体系生物催化合成β-苯乙醇培养条件的优化。首先从7种有机溶剂中筛选到油酸作为两相体系的最佳原位提取剂。采用Plackett-Burman实验筛选出对发酵液中β-苯乙醇浓度有显著影响的三个因素：有显著负效应的两相比（油酸：水）、有显著正效应的接种量、培养温度。再用最陡爬坡路径逼近上述三个因素的最大产β-苯乙醇区域。由中心组合实验及人工神经网络结合粒子群算法确定培养基的最佳条件。最佳条件：两相比（油酸：水）为1.1，接种量7.7%，温度31.7℃。根据最佳条件进行实验，产β-苯乙醇的浓度达到1.28g/L，较水相发酵产β-苯乙醇0.93g/L，提高了38%，提高效果显著。

关键词：β-苯乙醇；Plackett-Burman；人工神经网络；粒子群算法

Two-phase fermentation for β-phenylethanol using Artificial Neural Network coupling with Particle Swarm Optimization Algorithm
 
                               
Abstract: This article describes bioconversion of L-phenylalanine to β-phenylethanol with Kluyveromyces marxianus AS.2.1440 was performed in an aqueous/organic solv- ent biphasic system. At first, the appropriate organic solvent used for the bioconversion was studied. The results showed that oleic acid was the best organic solvent for establishing two-phase system. The fermentation conditions were optimized by Plackett-Burman design, artificial neural network (ANN) and particle swarm optimization (PSO).  Firstly, the two-level Plackett–Burman design was applied to screen related factors from six variables that significantly influence β-PE production. The inoculum volume, temperature and the ratio of oleic acid phase to aqueous phase were identified as the most important significant factors. Subsequently, the steepest ascent experiment was used to approach the optimal region of the above three factors. Finally, ANN coupling PSO algorithm based on central composite design (CCD) and the steepest ascent experiment data was applied to predict these factors’ mutual interactions for β-PE production. The modeled maximum β-PE yield reached 1.32 g/L after a 48-h fermentation period under the optimized condition: inoculums (7.7%), temperature (31.7°C) and the ratio of oleic acid phase to aqueous phase (1.1). The optimized conditions allowed β-PE yield to reach 1.28 g/L after verification experiments test, which increased by 38 percent than 0.93 g/L in monophasic aqueous system.
Keywords：β-phenylethanol；Plackett-Burman；artificial neural network（ANN）；
particle swarm algorithm(PSO)

目  录
中文摘要 I
英文摘要 II
目录 III
1. 绪论 1
 1.1 产品概述 1
 1.2 β-苯乙醇的制备 1
 1.3 生物转化法合成β-苯乙醇的途径及影响因素 2
 1.4 原为产物提取技术在β-苯乙醇生产中的应用 5
 1.5 人工神经网络结合粒子群算法在培养基优化中的应用  5
 1.6 本课题的研究意义  6
 1.7 本课题的研究任务  7
2. 实验部分 8
 2.1 实验试剂及设备 8
 2.2 菌种 8
 2.3 培养基及培养条件 9
 2.4 有机相的选择 9
  2.5 发酵产物的分析方法 9
 2.6 实验设计 11
3. 结果与讨论 13
 3.1 有机溶剂的选择   13
 3.2 影响发酵液中β-苯乙醇浓度的重要因素 13
 3.3 最陡爬坡实验逼近最大产β-苯乙醇区域 14
 3.4 中心组合实验   15
 3.5 人工神经网络结合粒子群算法模拟培养基的最佳培养条件 17
 3.6 验证实验   18
4．总结与展望 19
致谢 20
参考文献 21

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