Multiobjective Optimization of Industrial Petroleum Processing Units Using Genetic Algorithms

For many years most of refining processes were optimized using single objective approach, but practically such complex processes must be optimized with several objectives. Multiobjective optimization allows taking all of desired objectives directly and provide search of optimal solution with respect to all of them. Genetic algorithms proved themselves as a powerful and robust tool for multi-objective optimization. In this article, the review for a last decade of multi-objective optimization cases is provided. Most popular genetic algorithms and techniques are mentioned. From a practical point it is shown which objectives are usually chosen for optimization, what constraint and limitations might impose multi-objective optimization problem formulation. Different types of petroleum refining processes are considered such as catalytic and thermal.