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.