Automation of reformer process in petroleum plant using fuzzy supervisory model predictive multivariable control system

Describes a hierarchical control system with fuzzy supervisory control system and model predictive multivariable control system (PMC system) in a petroleum plant. The PMC system is effective in the area of time-delay and interference. However, due to their poor response to large time-delay and non-linearity, it sometimes fails to control actual plants in a refinery. On the other hand, a fuzzy logic controller is effective for plants with large time-delay and non-linearity. The proposed hierarchical control system therefore combines their advantages. The fuzzy supervisory control system which determines set points for the PMC system consists of two blocks, an estimation block and a compensation block. We use a statistical model using multi-regression analysis for the estimation block in order to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct the output of the statistical model. The hierarchical control system has been applied to the actual plant in an oil refinery, where the PMC system controls the average temperature of three reactors within the range that is given from the fuzzy supervisory control system, and showed a satisfactory performance. The advantages are (1) reduction of operator's interaction and (2) stabilization in the quality of products.