Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm

Feed optimization in the fluidized catalytic cracking (FCC) process is a prominent chemical engineering problem, where the objective is to maximize the production of high-quality gasoline stocks at a low energy consumption level. However, the various feeds, based on the density and volumetric flow rate of its constituent stream, are conflicting in nature and subjected to many practical constraints. As such, this chapter presents the application of a multi-objective evolutionary algorithm (MOEA) which will simultaneously optimize the various flow streams in a FCC feed surge drum of a local refinery. An interactive Graphical User Interface (GUI) based MOEA toolbox developed by the authors is used as the platform for optimization. The various trade-off surfaces between the different objectives evolved by the MOEA provide further insights to this problem and allow more optimal choices during the decision making process. Lastly, a performance comparison based on several key performance indexes shows that the overall economic gain offered by MOEA optimization against the conventional approach like linear programming is significantly higher.