Feedback-Feedforward Control System Design and Optimizing the Performance of Crude Oil Fired Heater Furnace Using Genetic Algorithm for Abnormal Conditions Management

In this study, feedback-feedforward control system design and optimizing the performance of crude oil furnace process was investigated in order to be recovered from possible abnormal conditions. First, by developing an accurate nonlinear analytical model, the effects of changes in input parameters and operating conditions on the system’s outputs were determined. Then, in order to eliminate the effects of disturbances on furnace, a feedback- feedforward control system for combustion management was suggested, where its performances were optimized genetic algorithm (GA). In addition, to enhance the thermal stability and to maintain product quality, output difference temperature control system was considered for load distribution between furnace’s streams. Also, in order to recover the furnace from abnormal conditions due to burners’ failures, a supervisory system was designed to change the firing rate setpoints. With respect to different failure scenarios, the optimal burners’ firing rate were captured by applying genetic algorithms to the system model. A multilayer perceptron neural network was employed as the core of the controller to interpolate between different conditions. The obtained results indicate the superior performances of the designed control systems.