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- W2753243911 abstract "There are two approaches to develop a dynamic model for a given process:Theoretical approach: This approach involves the use of first principles based on the conservation of mass, energy, and momentum, together with the necessary auxiliary equations representing mass transfer, heat transfer, momentum transfer, phase equilibrium, kinetics, etc. The resulting models are nonlinear, relating the input and output process variables. Such models are useful for process simulation, process optimization, and design but are seldom used in the design of control systems.The detailed nonlinear dynamic models can be linearized to develop simple input-output or cause-and-effect dynamic models in the time domain (linear state-space models) or in the Laplace domain (transfer function models). These linear models are approximation of the detailed nonlinear models but are useful for the design of controllers.Empirical approach: The second approach that is often used in the design of control systems for complex processes is based on the use of measured input and output data of an existing process. Statistical methods such as the regression and time series analyses are used to fit the input-output data to linear models between the input and output variables. This approach is referred to as Process Identification technique.In this chapter, we shall cover the detailed dynamic modeling using a theoretical approach. In this chapter we shall deal with the linearization of the detailed models and the development of linear state-space models and transfer functions. At the end of this chapter, we shall study the process identification technique." @default.
- W2753243911 created "2017-09-15" @default.
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- W2753243911 date "2017-01-01" @default.
- W2753243911 modified "2023-09-23" @default.
- W2753243911 title "Theoretical Process Dynamic Modeling" @default.
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- W2753243911 doi "https://doi.org/10.1016/b978-0-08-101095-2.00003-5" @default.
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