Artificial intelligence (AI) with its efficiency for complex systems is growing in popularity in many engineering fields. The ability of an AI method to be successfully applied is highly dependent on the previous research, which makes knowledge sharing within and across fields extremely valuable. This work focuses on crude distillations units (CDU), whose energy optimization has been a tremendous challenge because of its complexity. The presented overview shows that soft sensors are the most common application of artificial intelligence for a CDU, although a number of recent publications focus on optimization problems. The approaches for optimization are very diverse, which makes them hardly applicable in the current engineering practice. This work provides a guideline for selecting the right method, but also addresses the fact that different methods excel at different problems and with different data set sizes. For neural networks (NN), this further depends on their architecture and hyperparameter adjustment. This urges future research, whose goal could be a workflow that would automatically adapt methods and perform parameter tuning with minimum user input.