Crude oil distillation is an energy intensive and environmentally challenging process. To decrease the large energy demand of crude oil distillation, heat integration is implemented. The system (i.e. distillation unit and heat exchanger network, HEN) needs to perform an energy-efficient separation in a broad range of scenarios (e.g. changes in product yields or product specifications), without compromising overall profit. Operational optimisation and revamp projects are frequently implemented to adapt an existing system to such diverse scenarios.The present work provides a new approach for optimising crude oil distillation systems. The scope of this methodology consists of: 1) finding the operating conditions for the distillation system that maximise net profit, while 2) proposing retrofit modifications for the HEN that allow a feasible operation. Artificial neural networks (ANN) are used to represent the distillation process. In the proposed modelling strategy, results of rigorous simulations provide the data used to train the ANN models. The resulting ANN models have the advantages of overcoming convergence problems presented by both rigorous and simplified models, of handling fewer variables and performing calculations in less time.The HEN models used in this work consist of a retrofit model and a simulation model. The HEN retrofit model employed by Chen (2008) is extended to include constraints on heat transfer areas, utility consumption; and to optimise stream split fractions. In addition, the segmented linear data used by Chen (2008) to calculate temperature-dependent heat capacities are replaced by models tailor-made for each stream. This allows a more flexible and accurate representation of these properties, compared to the approach of Chen (2008). The HEN simulation model of de Oliveira Filho et al. (2007) is modified and extended in this work to simulate simple unit operations and to consider heat exchangers specified in terms of heat loads.Distillation, HEN and economic models are implemented in a two-level optimisation framework. The first level consists of a simulated annealing algorithm that optimises the operating conditions of the distillation unit (e.g. flow rates of products and stripping steam, pump-around duties and temperature drops, furnace exit temperature) and HEN topology (i.e. number and location of heat exchangers and stream splitters). The second level solves a non-linear least squares problem that addresses the violation of HEN constraints. Different objective functions can be considered, such as maximising net profit or minimising total annualised costs.The case studies presented in this work show that ANN models are suitable for their implementation in optimisation methodologies for crude oil distillation systems. Results indicate that interactions between the distillation process and HEN are captured, and that significant economic improvements can be achieved with the proposed optimisation approach.