Karim M Nazmul, Hodge David, Simon Laurent
Department of Chemical Engineering, Colorado State University, Fort Collins, Colorado 80523, USA.
Biotechnol Prog. 2003 Sep-Oct;19(5):1591-605. doi: 10.1021/bp015514w.
Data-generated models find numerous applications in areas where the speed of collection and logging of data surpasses the ability to analyze it. This work is meant to addresses some of the challenges and difficulties encountered in the practical application of these methods in an industrial setting and, more specifically, in the bioprocess industry. Neural network and principal component models are the two topics that are covered in detail in this paper. A review of these modeling technologies as applied to bioprocessing is provided, and four original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.
数据生成模型在数据收集和记录速度超过分析能力的领域有众多应用。这项工作旨在解决在工业环境中，特别是在生物加工行业实际应用这些方法时遇到的一些挑战和困难。神经网络和主成分模型是本文详细讨论的两个主题。本文对应用于生物加工的这些建模技术进行了综述，并给出了四个使用工业发酵数据的原始案例研究，这些研究在生物过程性能的预测和监测背景下利用了这些模型。
