Please use this identifier to cite or link to this item: https://doi.org/10.1021/ie060247q
Title: First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit
Authors: Bhutani, N.
Rangaiah, G.P. 
Ray, A.K. 
Issue Date: 8-Nov-2006
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
URI: http://scholarbank.nus.edu.sg/handle/10635/63926
ISSN: 08885885
DOI: 10.1021/ie060247q
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