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Initial Input-output Variable Selection

Hovd and Skogestad (1993) give a comprehensive treatment of the selection of a regulatory control structure for a catalytic cracking unit. This discussion includes the selection of controlled and manipulated variables. Based on this discussion and the discussion in Huq et al. (1995) and Khandelakar and Briggs (1993), the controlled and manipulated variables are selected, as shown in Table 35.1. [Pg.505]

Some local controls are assumed to be in place, such as anti-surge control for the compressors. In addition, some valves are fiilly open or fixed, as shown in Fig. 35.1. The selection of controlled variables is strongly related to the goals that are set for operation of the fluid catalytic cracker. There are numerous constraints that all have to be met. Moreover, two main objectives should be met maximization of the total feed rate and dismtbance rejection. [Pg.505]

On the horizontal axis in Table 35.1, the controlled variables are listed the manipulated variables are hsted vertically. The variables have the following meaning  [Pg.505]

There ean only be four manipulated variables Fjud, Vift, product and Vstact, since the oil flow is chosen as an independent variable. The model with these input and output variables is simulated in FCCUO.mdl. This program makes use of several subprograms each subprogram simulates a particular piece of equipment. Step disturbances can be given in the manipulated variables to get an impression of the dynamics of the uncontrolled process. First it will be determined how these manipulated variables can be used to control the four controlled variables, which still have to be selected. [Pg.506]

From Fig. 35.1 it is clear that the regenerator air supply can best be controlled by using Vi. There will be no other manipulated variable that would give a more favorable and faster response. The valve in the spill air tine is fixed to achieve a division of air between the spill [Pg.506]


The Matlab Simulink Model was designed to represent the model stmctuie and mass balance equations for SSF and is shown in Fig. 6. Shaded boxes represent the reaction rates, which have been lumped into subsystems. To solve the system of ordinary differential equations (ODEs) and to estimate unknown parameters in the reaction rate equations, the inter ce parameter estimation was used. This program allows the user to decide which parameters to estimate and which type of ODE solver and optimization technique to use. The user imports observed data as it relates to the input, output, or state data of the SimuUnk model. With the imported data as reference, the user can select options for the ODE solver (fixed step/variable step, stiff/non-stiff, tolerance, step size) as well options for the optimization technique (nonlinear least squares/simplex, maximum number of iterations, and tolerance). With the selected solver and optimization method, the unknown independent, dependent, and/or initial state parameters in the model are determined within set ranges. For this study, nonlinear least squares regression was used with Matlab ode45, which is a Rimge-Kutta [3, 4] formula for non-stiff systems. The steps of nonlinear least squares regression are as follows ... [Pg.385]

The value of deadtime is set by clicking on the icon, clicking the right mouse button, and selecting Forms and All Variables. The window shown at the top of Figure 3.75 opens, which shows some default values for input and output. Make an initialization run to get the correct values, as shown in at the bottom of Figure 3.75. The deadtime value has also been inserted. Figure 3.76 shows the flowsheet and faceplate with the temperature and level controllers installed. [Pg.173]

A summary screen which includes the input and output values for the basic parameters is presented to the user as one of the output selections. In this summary screen a graphical representation of the temperature and conversion profiles along the reactor for each bed is presented to give the user an initial look at these values before going to the detailed individual values for each variable (Figure 6.32). [Pg.436]


See other pages where Initial Input-output Variable Selection is mentioned: [Pg.505]    [Pg.506]    [Pg.508]    [Pg.505]    [Pg.506]    [Pg.508]    [Pg.500]    [Pg.114]    [Pg.331]    [Pg.338]    [Pg.330]    [Pg.388]    [Pg.271]    [Pg.177]    [Pg.199]    [Pg.164]    [Pg.123]   


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Initial Selection

Input Variable Selection

Input variability

Input variable

Input/output

OUTPUT SELECT

Output variability

Output variable

Variable selection

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