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|Title:||First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit|
|Citation:||Bhutani, N., Rangaiah, G.P., Ray, A.K. (2006-11-08). First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit. Industrial and Engineering Chemistry Research 45 (23) : 7807-7816. ScholarBank@NUS Repository. https://doi.org/10.1021/ie060247q|
|Abstract:||The first-principles, data-based, and hybrid modeling strategies are employed to simulate an industrial hydrocracking unit, to make a comparative performance assessment of these strategies, and to do optimization. A first-principles model (FPM) based on the pseudocomponent approach (Bhutani, N.; Ray, A. K.; Rangaiah, G. P. Ind. Eng. Chem. Res. 2006, 45, 1354) is coupled with neural network(s) in different hybrid architectures. Data-based and hybrid models are promising for important predictions in the presence of variations in operating conditions, feed quality, and catalyst deactivation. Data-based models are purely empirical and are developed using neural networks, whereas the neural-network component of a hybrid model is used to obtain either updated model parameters in the FPM connected in series or to correct predictions of the FPM. This article presents data-based models and three hybrid models, their implementation and evaluation on an industrial hydrocracking unit for predicting steady-state performance, and finally the optimization of the hydrocracking unit using the data-based model and a genetic algorithm. © 2006 American Chemical Society.|
|Source Title:||Industrial and Engineering Chemistry Research|
|Appears in Collections:||Staff Publications|
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