Abstract：
Achieving consistency of growth pattern for commercial yeast fermentation over batches through addition of water, molasses and other chemicals is often very complex in nature due to its bio-chemical reactions in operation. Regression models in statistical methods play a very important role in modeling the underlying mechanism, provided it is known. On the contrary, artificial neural networks provide a wide class of general-purpose, flexible non-linear architectures to explain any complex industrial processes. In this paper, an attempt has been made to find a robust control system for a time varying yeast fermentation process through statistical means, and in comparison to non-parametric neural network techniques. The data used in this context are obtained from an industry producing baker\'s yeast through a fed-batch fermentation process. The model accuracy for predicting the growth pattern of commercial yeast, when compared among the various techniques used, reveals the best performance capability with the backpropagation neural network. The statistical model used through projection pursuit regression also shows higher prediction accuracy. The models, thus developed, would also help to find an optimum combination of parameters for minimizing the variability of yeast production.
摘要：
通过添加水实现商业酵母发酵的生长模式的一致性，糖蜜和其他化学品由于其在操作中的生物化学反应而在性质上通常非常复杂。统计方法中的回归模型在对潜在机制进行建模中起着非常重要的作用，只要知道。相反,人工神经网络提供了一类广泛的通用，灵活的非线性架构来解释任何复杂的工业过程。在本文中,试图通过统计手段为时变酵母发酵过程找到一个鲁棒的控制系统，与非参数神经网络技术相比。在本文中使用的数据是从通过补料分批发酵工艺生产面包酵母的工业获得的。预测商业酵母生长模式的模型精度，当在使用的各种技术之间进行比较时，揭示了反向传播神经网络的最佳性能。通过投影寻踪回归使用的统计模型也显示出更高的预测精度。模特们，如此发展，还将有助于找到参数的最佳组合，以最大程度地减少酵母生产的变异性。
