Large-scale parameter estimation in low-density polyethylene tubular reactors

We propose a simultaneous approach for the solution of the associated DAE-constrained parameter estimation problem. Parameter estimation is an essential task in the development and on-line update of first-principles models for low-density polyethylene tubular reactors, consisting of nonlinear and stiff differential-algebraic equations (DAE). Our approach discretizes the reactor model equations in space, leading to a large-scale nonlinear program (NLP) that can be solved efficiently with state-of-the-art general-purpose NLP solvers. In doing so, more efficient estimation strategies can be considered, enabling the solution of challenging estimation problems including multiple data and large parameters sets. This approach is efficient in handling advanced regression problems such as the errors-in-variables-measured (EVM) formulation. The methodology is fast, robust, and reliable and can be used both for off-line and on-line purposes. Moreover, substantial improvements on the reactor model predictions have been obtained over previous approaches, making the model amenable for real-time optimization and control tasks.