| Summary: | The importance of exploiting degrees of freedom within the 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 process models and heat exchanger network models need to be optimized simultaneously. Whilst in principle this is straightforward, there are many difficulties. Methods for the optimization of heat exchanger networks are well developed. It is important that heat exchanger network models are based on the network details for all but grassroot design applications. The process model to be coupled with the heat exchanger network model needs to be simple and robust enough to be included in an optimization model. If process models and heat recovery models can be combined effectively, then there are not just opportunities for design and retrofit, but also operational optimization. One of the big challenges to progress the application of this approach is the effective generation of reduced process models for use in such applications. Shortcut process models can be used, but many other options are available, such as the use of artificial neural networks. This paper reviews the different approaches and highlights the areas of application of the different approaches to producing reduced models. A case study will be presented to demonstrate operational optimization and retrofit for energy and for optimization of heat integrated systems to maximise process profit through manipulation of feeds and products, as well as energy integration.
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