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Modelling of a clinker rotary kiln using operating functions concept
Author(s) -
Shahriari Kyarash,
Tarasiewicz Stanislaw
Publication year - 2011
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20398
Subject(s) - kiln , clinker (cement) , rotary kiln , representation (politics) , control theory (sociology) , process (computing) , state space representation , control engineering , computer science , state space , state variable , engineering , mechanical engineering , control (management) , mathematics , algorithm , cement , artificial intelligence , law , history , waste management , portland cement , archaeology , operating system , political science , thermodynamics , statistics , physics , politics
Modelling and parameter identification of complex dynamic systems/processes is one of the main challenging problems in control engineering. An example of such a process is clinker rotary kiln (CRK) in cement industry. In the prevailing models independently of which structure is used to describe the kiln's dynamics and the identification algorithm, parameters are assumed to be centralised and constant while the CRK is well known as a distributed parameter system with a strongly varying dynamic through time. In this work, the kiln's dynamic is described in the form of a state‐space representation with three state variables using a system of partial differential equations (PDE). The structure is chosen so that it can easily be embedded in classical state‐space control algorithms. The parameters of the PDE system are called operating functions since their numerical values vary with respect to different operating conditions of the kiln, to their position in the kiln, and through time. A phenomenological approach is also proposed in this paper to identify the operating functions for a given steady‐state operation of the kiln. The model is then used to perform a semi‐dynamic simulation of the process through manipulating main process variables.