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Quality Prediction for a Fed-Batch Fermentation Process Using Multi-Block PLS

机译:使用多块PLS对FED批量发酵过程的质量预测

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Quality prediction is usually required for product quality monitoring and setting up control strategy can reduce operating cost and improve production efficiency. Partial least square (PLS) regression is a popular statistical method for predictive modelling. The amount of data measured and stored in a typical indus-trial process is dramatically increased due to the fast development of computer and measuring system. It is hard to analyse all measured data using one matrix for its complexity. Multi-Block PLS model allows the data to be separated into sub-blocks and the sub-blocks can be analysed independently. Data from the fed-batch fermentation process is used to build models. Data is divided by different modes and different phases and model parameters are used to select variables that can be used as good predictors. The new set of data after variable selections is used to build a new model again. In most cases, new models show improved prediction performances compared with results from the conventional method.
机译:产品质量监测和建立控制策略的质量预测通常需要降低运营成本并提高生产效率。部分最小二乘(PLS)回归是一种用于预测建模的流行统计方法。由于计算机和测量系统的快速发展,测量和储存在典型的indus-inst-insial过程中的数据量显着增加。难以使用一个矩阵分析所有测量数据,以实现其复杂性。多块PLS模型允许将要分成子块的数据,并且可以独立地分析子块。来自FED批量发酵过程的数据用于构建模型。数据被不同的模式除以不同的模式,并且使用不同的阶段和模型参数来选择可用作良好预测器的变量。变量选择后的新数据集用于再次构建新模型。在大多数情况下,与传统方法的结果相比,新模型显示出改善的预测性能。

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