An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework

In this study an expert system of a crude oil distillation column is designed to predict the unknown values of required product flow and temperature in required input feed characteristics. The system is also capable to optimize the distillation process with minimizing the model output error and maximizing the required oil production rate with respect to control parameter values. The designed expert system uses the practical data of an operating refinery located in Abadan. The input operating variables of the column were operational parameters of crude oil such as flow and temperature, while the system output variables were defined as oil product qualities. We can make the knowledge database of these input–output values of plant with the aid of a neural networks model (NNM) to organize and collect all data related to this process and also to predict the unknown output values of required inputs. In addition we have made the ability of system’s optimization with the use of genetic algorithm (GA) with the aim of error minimizing of expert system’s output and also maximizing the required product rate with respect to its industrial importance. The built expert system can be used by operators and engineers to calculate and get some unknown data for operational values of this distillation column.