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Direct prediction of the batch time and process variable profiles using batch process data based on different batch times
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DOI：10.1016/j.compchemeng.2022.108072.png)
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摘要
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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.
Keyword:
Batch design
Batch optimization
Batch profile
Batch trajectory
Direct inverse analysis
Gaussian mixture regression
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3.9
论文数:
8.1K
被引数:
1.7W
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Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor.png)
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