人工神经网络建模结合遗传算法优化岗松油环糊精包合物制备工艺参数
Optimization of the process parameters of Baeckeae oil-β-cyclodextrin inclusion complex by artificial neural network and genetic algorithm
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摘要: 采用均匀设计法制定岗松油β-环糊精包合物制备的实验方案，应用人工神经网络对影响包合物制备的因素与考察指标之间的关系进行模型拟合，并结合遗传算法优化包合物的制备工艺参数。优化结果为：环糊精与岗松油的用量配比7.1、包合温度46.6 ℃、时间149.9 min、搅拌速度417.8 r/min。参照优化后的工艺条件所制备的包合物，含油率和包合率分别为11.89%和89.55%。结果可见，人工神经网络建模与遗传算法寻优相结合，为药物制剂工艺的多维非线性系统的优化提供了有效途径。Abstract: The preparation process was optimized by U11(1110) uniform design,and a mathematical model of relationship between the independent and dependent variables was established by using back-propagation（BP）artificial neural networks（ANN）,the process parameters were optimized with genetic algorithm(GA).The optimum process was established as follows:7.1 as the ratio of β-cyclodextrin to Baeckeae oil,46.6 °C as temperature for inclusion,149.9 min as duration of stirring and 417.8 r/min as stirring rate.The average drug-loading rate 11.89% and inclusion yield 89.55%(n=3) were the results of the verification test of the inclusion complex according to the optimized process parameters,and they were better than any orther results among the uniform design.Combining BP ANN modeling with GA provided an effective way to solve the multi-dimensional optimization problem of nonlinear systems in pharmaceutical technology.
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