Real-time Optimization and Control of an Industrial Ethylbenzene Dehydrogenation Process

Abstract

Styrene is one of the most important monomers in the petrochemical industry. In this study, a novel methodology for tight integration of Real-Time Optimization (RTO) and Model Predictive Control (MPC) is applied to an industrial ethylbenzene dehydrogenation process for producing styrene. This optimization-control framework is able to improve the automation level of ethylbenzene dehydrogenation process under various unfavourable conditions, especially the plant-model mismatch and disturbances. In the RTO layer, a constraint-adaptation strategy is adopted, which makes it easier to update the steady-state model. In the MPC layer, an MPC with Nonlinear Successive Linearization (MPC-NSL) is employed to deal with the nonlinear dynamic characteristics of the process and to match the steady-state gain at the steady-state point. In addition, an economic Steady- State Target Optimization (SSTO) layer is inserted between RTO and MPC layers to update continuously the set-points for MPC. The two-layered integration framework is easy to maintain because the models adopted in the three layers originate from the same nonlinear dynamic model. The comprehensive dynamic model of the styrene process is developed based on two large core fixed-bed axial-radial flow reactors described by Partial Differential Algebraic Equations (PDAE). The model parameters are corrected, and accuracy is validated using actual industrial data before it is used for simulation. Case study shows that the proposed optimization-control framework significantly improves the operation level.