An Optimized Fed-Batch Culture Strategy Based on Multidimensional Time Series Aggregation

An optimized feeding strategy for the fed-batch fermentation of 2-keto-L-gulonic acid (2-KGA) is proposed to enhance total profit a workshop. Instead assigning same quantity substrate each batch in empirical scheduling, online offers practical approach that allows precise management maximize economic performance. The forecasting function has been achieved through similarity measurement and multidimensional time series aggregation method. This method solves problem predicting benefit at end fermentation, absence data. Pseudo-online simulation conducted, utilizing data derived from ninety industrial batches. A 6% increase observed, which indicates utilizes other related resources more efficiently leads higher overall profit.