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- W4310073256 abstract "This study aims to design both the batch time and the process variable (PV) profiles (x) to ensure that the endpoints (y), such as the product quality and the material properties, possess the target values following a batch process. To handle datasets based on different batch times, the time-series data of the PVs were either extrapolated for short batches or transformed by discrete Fourier transform. In addition, a Gaussian mixture regression model was constructed between x and y, allowing the direct prediction of x from y, enabling a direct inverse analysis. The effectiveness of the proposed method was verified via numerical simulations and a fed-batch bioreactor process. The results indicate that this method can accurately predict the end-point while appropriately designing the PV batch times and profiles. The proposed method successfully designed the batch profile by increasing the product concentration by 50% in the fed-batch bioreactor process." @default.
- W4310073256 created "2022-11-30" @default.
- W4310073256 creator A5082283686 @default.
- W4310073256 date "2023-01-01" @default.
- W4310073256 modified "2023-09-23" @default.
- W4310073256 title "Direct prediction of the batch time and process variable profiles using batch process data based on different batch times" @default.
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- W4310073256 doi "https://doi.org/10.1016/j.compchemeng.2022.108072" @default.
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