Development of a Nonlinear Soft-Sensor Using a GMDH Network for a Refinery Crude Distillation Tower

Abstract

SUMMARY In atmospheric distillation processes, stabilization of processes is required in order to optimize the crude‐oil composition that corresponds to product market conditions. However, the process control systems sometimes fall into unstable states in the case where unexpected disturbances are introduced, and these unusual phenomena have had an undesirable effect on certain products. Furthermore, a useful chemical engineering model has not yet been established for these phenomena. This remains a serious problem in atmospheric distillation processes. This paper describes a new modeling scheme to predict unusual phenomena in the atmospheric distillation process using a GMDH (Group Method of Data Handling) network, which is a type of network model. According to the GMDH network, the model structure can be determined systematically. However, the method of least squares has been commonly utilized in determining weight coefficients (model parameters). Estimation accuracy is not entirely essential, because the sum of the squared errors between the measured values and estimates is evaluated. Therefore, instead of evaluating the sum of the squared errors, the sum of the absolute values of the errors is introduced and the Levenberg–Marquardt method is employed in order to determine the model parameters. The effectiveness of the proposed method is evaluated by foaming prediction in the crude oil switching operation in the atmospheric distillation process.