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• W4308523218 abstract "Integrated operational decision-making in chemical plants is important for improving profitability. Integrated scheduling and control frameworks have been developed to enhance coordination between tactical and operational decisions. As such frameworks start to incorporate more features (e.g., uncertainty, detailed nonlinear process models) the computation time and resources that they require may increase significantly and lead to solutions that cannot be implemented online. Leveraging deep learning models that can capture process nonlinearities without a significant computational burden is a potential solution to this problem. Motivated by the above, we propose a two-stage stochastic integrated deep learning–scheduling–optimal control framework that can be used to model any batch process and can be solved efficiently. The benefits are demonstrated using a prototype case study." @default.
• W4308523218 created "2022-11-12" @default.
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• W4308523218 date "2022-11-08" @default.
• W4308523218 modified "2023-10-16" @default.
• W4308523218 title "An Integrated Stochastic Deep Learning–Short-Term Production Scheduling–Optimal Control Framework for General Batch Processes" @default.
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• W4308523218 doi "https://doi.org/10.1021/acs.iecr.2c02638" @default.
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