In the present paper, the degree of precalcination in the precalciner of cement plants is increased via a proportional-integral (PI) model predictive abgasses temperature controller. The controller is based on polynomial neural network identification of the plant at a particular operating point as well as on a polynomial non-linear predictor depending on autoregressive identification predictors of temperature disturbances. According to simulation results, the degree of precalcination becomes significantly increased while energy consumption is reduced.
GriparisM. K.KoumboulisF. N.VlachosN. S.MarinosJ.Precalcination in cement plants (system descriptions and control trends). In IFAC Symposium onManufacturing, Modeling, Management and Control, University of Patras, Rio, Greece, 12–14 July 2000, pp. 262–267.
4.
KouvakasN. D.Identification and adaptive control of a precalciner for cement production. Post-graduate thesis, Department of Mechanical and Industrial Engineering, University of Thessaly, Greece, 2001.
5.
KoumboulisF. N.KouvakasN. D.Indirect adaptive neural control for precalcination in cement plants. In Proceedings of the IFDICON and SERVICEROB Conference, Santorini, Greece, 25–27 June 2001; also in Mathematics and Computers in Simulation, 2002, Vol. 60, pp. 325–334.
6.
ClarkeD. W.MohtadiC.TuffsP. S.Generalized predictive control—Part I. The basic algorithm. Automatica, 1987, 23 (2), 137–148.
7.
ClarkeD. W.MohtadiC.TuffsP. S.Generalized predictive control—Part II. Extensions and interpretations. Automtica, 1987, 23 (2), 149–160.
8.
RawlingsJ. B.Tutorial overview of model predictive control. IEEE Control Systems Mag., June 2000, 38–52.
