1384584

11485

This paper describes the application of an industrial real-time optimization package (ROMeo 1) to the nonlinear model predictive control (NLMPC) of a simulated polymer grade transition. The NLMPC algorithm is formulated using orthogonal collocation to integrate the model equations, and sequential quadratic programming to solve the resulting nonlinear programming problem. A receding horizon estimation scheme and, separately, a Luenberger observer are designed to reconstruct unmeasured states. The resulting algorithm is demonstrated on two simulated polymerization case studies: (i) continuous methyl methacrylate and (ii) gas-phase polyethylene.

349

Receding horizon estimation , Nonlinear model predictive control , polymerization , Real-time optimization , Gradetransition , ROMeo , orthogonal collocation

Studia Iranica

364

364