Detalle del congreso

Autores: A. Marchetti; G. François; D. Bonvin.

Resumen: This contribution proposes a framework for using MA during the transient phase toward steady state, thereby attempting to reach optimality in a single iteration to steady state. With this approach, a modified optimization problem is solved repeatedly at each optimization instant during transient, with the input-affine correction terms, which theoretically depend on steady-state plant quantities, being estimated on the basis of transient measurements. Note that such an attempt has already been documented in the literature but, as for the aforementioned implicit methods, the ?steady-state? gradients were estimated using transient information in the framework of both multiple units and neighboring extremals. In contrast, this work estimates the steady-state outputs from transient outputs and then uses these estimates ?correctly? in the expressions for computing the steady-state gradients. For this, we propose to use the best available dynamic model and perform state estimation using an Extended Kalman Filter (EKF) framework. Since the model is typically not perfect, one key parameter related to the static gain is made adjustable for each input-output pair. This way, the EKF feeds on the transient plant outputs and estimates, at the current time t, the corresponding steady-state outputs, which leads to the computation of the static gradient. The dynamic model at hand can be seen as a surrogate model that, although not sufficiently accurate globally for process optimization, can process measurements to generate an estimate of the local gradients. The approach will be illustrated on various numerical examples and then applied to the optimization of a continuous stirred-tank reactor.

Tipo de reunión: Encuentro.

Producción: On the Use of Transient Information for Static Real-Time Optimization.

Reunión científica: AIChE Annual Meeting 2015.

Lugar: Salt Lake City.

Institución organizadora: AIChE.

Publicado: No

Mes de reunión: 11

Año: 2015.