Performance evaluation of an ANN-GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor
Performance evaluation of an ANN-GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor
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DOI：
10.1002/jctb.3900
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发表时间：
2013-05-01
影响因子：
3.4
通讯作者：
Sen, Ramkrishna
中科院分区：
文献类型：
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作者：
Sivapathasekaran, Chandrasekaran;Sen, Ramkrishna
背景：采用改进的弹性反向传播神经网络模型，结合遗传算法辅助优化技术，对海洋环状芽孢杆菌生产脂肽生物表面活性物质的工艺参数进行优化。结果：用人工神经网络(ANN)建立了以生物表面活性剂浓度为输出的4个独立参数(搅拌、曝气、温度和pH)的24因素中心组合设计的非线性模型。利用遗传算法对多项式模型进行了优化，使脂肽生物表面活性剂的浓度达到最大。通过一次单因素实验策略确定了这些关键工艺参数的范围和水平。使用MatLab v.7.6进行了改进的ANN-GA建模和优化，并使用Design Expert v.7.0进行了实验设计。神经网络模型是使用一种称为弹性反向传播算法的高级神经网络结构开发的。结论：利用人工神经网络-遗传算法对海洋微生物表面活性物质的最大产量进行了工艺优化，并通过实际发酵实验对预测的最优工艺条件进行了验证。使用上述优化策略，生物表面活性剂浓度提高了约52%。(C)2012年化学工业学会
BACKGROUND: An improved resilient back-propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non-linear model based on a 24 full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single-factor-at-a-time experimental strategy. Improved ANN-GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back-propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN-GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above-mentioned optimization strategy. (c) 2012 Society of Chemical Industry