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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

A. Wurzinger, H. Leibinger, S. Jakubek, M. Kozek:
"Data driven modeling and nonlinear model predictive control design for a rotary cement kiln";
Vortrag: Joint Mechatronics 2019 & NolCoS 2019, Wien (eingeladen); 04.09.2019 - 06.09.2019; in: "Joint Mechatronics 2019 & NolCoS 2019", (2019), Paper-Nr. 176, 6 S.



Kurzfassung englisch:
Cement production is an energy intensive process, on the other hand the product
quality directly in
uences the economic bene t. In this work a nonlinear model predictive control
is designed to achieve an optimal compromise between energy consumption, production volume,
and product quality. Based on measurements from a 100 t/h rotary cement kiln a non-linear
autoregressive NARMAX-model is identi ed, and cross validation of this model shows good
accuracy for control design. A prediction of the most in
uential disturbance (quality of the feed
material) is utilized, product quality can be de ned as a set-point, and the optimization criterion
is de ned using time-varying performance weights. This design achieves good transient response
while still guaranteeing that the desired production volume is met. Validation results of the
model with measured data and simulation results for the closed-loop operation demonstrate the
functionality of the proposed methodology.

Schlagworte:
rotary cement kiln, NARMAX model, neural network model, nonlinear model predictive control, time-varying weights

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.