The importance of exploiting degrees of freedom within a crude oil distillation process for improving energy performance has been a feature of process integration from the earliest days. Combining process changes with changes to the heat recovery system leads to far better results, compared with changes to the heat recovery system alone. However, in order to obtain the best results, the distillation process and heat exchanger network need to be optimised simultaneously. Whilst in principle this is straightforward, there are many difficulties. Methods for the optimisation of heat exchanger networks are well developed. In these methods, heat exchanger network models are based on network details, such as stream connections between heat exchangers, heat transfer area of individual units, etc. The consideration of these network details is important to design and optimise crude oil distillation systems. On the other hand, the distillation process model to be coupled with the heat exchanger network model needs to be simple and robust enough to be included in an optimisation framework. If distillation models and heat recovery models can be combined effectively, then there are not just opportunities for design and retrofit, but also for operational optimisation. One of the big challenges to progress the application of this approach is the effective generation of reduced distillation models. Short-cut distillation models can be used, but many other options are available, such as the use of artificial neural networks. This paper reviews various crude oil distillation modelling approaches and highlights the areas of application of these different approaches. An example illustrates the computational performance of reduced and rigorous crude oil distillation models. (C) 2014 Elsevier Ltd. All rights reserved.