15_Pole_Mole_KL_Ch6_W12

15_Pole_Mole_KL_Ch6_W12 - Chapter 6 Dynamic Response...

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Unformatted text preview: Chapter 6 Dynamic Response Characteristics of More Complicated Systems G (s) = Inverse Responses -Differences caused by different zero in G(s) = Fig. 6.3 G (s) K ( a s 1) ( 1s 1)( 2 s 1) slope If a 0 response faster than if = 0 a 0 .... " inverse " response K ( a s 1) ( 1s 1)( 2 s 1) K ( a s 1) (4 s 1)(1s 1) (t =0) 0 (see Fig. 6.3) a : the “zero” of the transfer function is at –1/a Use nonlinear regression for fitting step response data (graphical method not available) – “step2g” program at OSU Fig. 6.3 Step response of an overdamped second-order system for different values of a G(s) has 4 poles “in the complex s plane” (denominator is 4th order polynomial) Fig. 6.4 Two first-order process elements acting in parallel. (can lead to a 1st/2nd order transfer function. Y ( s) K1 K2 K ( s 1) K 2 ( 1s 1) G( s) 12 1s 1 2 s 1 ( 1s 1)( 2 s 1) U ( s) K K s K1 K 2 = 12 221 1 2 s 1 2 s 1 For inverse response K1 2 K 2 1 0 K1 K 2 (6 21) G ( s) K s ( 1s 1)( s 2 s 1) 22 More General Transfer Function Models In the second, we easily see the values of the “zeros” and • Poles and Zeros: the “poles”. • The dynamic behavior of a transfer function model can be characterized by the numerical value of its poles and zeros. • General Representation of a TF: G s bm s z1 s z2 s zm an s p1 s p2 s pn (6-7) where {zi} are the “zeros” and {pi} are the “poles”. There are two common equivalent representations. In the first, the coefficient (an a or a b) on the lowest order of s = 1 in numerator and denominator. • We will assume that there are no “pole-zero” cancellations. That is, that no pole has the same numerical value as a zero. If there is such an equivalence, (6-7) shows they “cancel” algebraically. m G s K bi s i i 0 n (4-40) ai si i 0 • Review: in order to have a physically realizable system. n m Ususally there are more poles than zeros, but never fewer ! What are the “poles and zeroes” of a process? The poles and zeros of the transfer function model = One output / one input What physically is a “zero” ? A modification of simple dynamics = a change in the “shape”, but not the “speed” of response. Where do zeroes “come from” ? Process interactions. Why or How ? Let’s look at an example. Fig. 6.13 Two tanks in series whose liquid levels interact. H1 / Qi has “numerator” dynamics (a zero) (see 6-72) See CHE 361 Interacting Tanks Handout for more details on derivation of transfer functions with and without a zero. Time Delays Time delays occur due to: 1. Fluid flow in a pipe 2. Transport of solid material (e.g., conveyor belt) Fig. 6.6 The effect of a time delay is a translation of the function in time. 3. Chemical analysis - Sampling line delay - Time required to do the analysis (e.g., on-line gas chromatograph) Mathematical description: A time delay, θ, between an input u and an output y results in the following expression: 0 y t u t θ for t θ for t θ (6-37) pg.98 G( s) e s Y (s) X ( s) (6-27) Approximation of Higher-Order Transfer Functions In this section, we present a general approach for approximating high-order transfer function models with lower-order models that have similar dynamic and steady-state characteristics. Why? Because methods have been developed to “design” industrial PID controllers for lower-order models with time delay. In Eq. 6-4 we showed that the transfer function for a time delay can be expressed as a Taylor series expansion. For small values of s, eθs 1 θs a single RHP zero (6-57) 1 s 2s2 2 12 = 2nd -order Pade' approx. s 2s2 1 2 12 • An alternative first-order approximation consists of the transfer function, e θs 1 e θs 1 a single LHP pole 1 θs (6-58) where the time constant has a value of θ. • These simple expressions can be used to approximate time delay as a single pole or a single zero (and visa versa) in a transfer function. Pade’ (rational) approximations use equal number of poles and zeros, e.g. two poles and two zeros for a second-order Pade’ approximation, which can be much more accurate. Example 6.4 Skogestad’s “half rule” Consider a transfer function, 1 RHP zero, 3 LHP poles: • Skogestad (2002) has proposed an approximation method for higher-order models that contain multiple time constants. • He approximates the largest neglected time constant in the following manner. One half of its value is added to the existing time delay (if any) and the other half is added to the smallest retained time constant. Time constants that are smaller than the “largest neglected time constant” are approximated as time delays using (6-58), just add them to any pure time delay to get , the overall time delay of the approximate model. Solution (a) Taylor series: The dominant (largest) time constant (5) is retained. Applying the approximations in (6-57) and (6-58) gives: Approximate the RHP zero as a time delay 0.1s 1 e 0.1s Approximate the smaller time constants (3 and 0.5) as time delays. 1 1 (6-62) e 3s e 0.5 s 3s 1 0.5s 1 Substitution into(6-59) gives the Taylor Series (TS) G s: approximation, TS Ke0.1s e 3s e0.5 s Ke3.6 s GTS s 5s 1 5s 1 (6-63) G s K 0.1s 1 5s 1 3s 1 0.5s 1 (6-59) Derive an approximate first-order-plus-time-delay model, Keθs G s τs 1 (6-60) using two methods: (a) The Taylor series expansions of Eqs. 6-57 and 6-58. (b) Skogestad’s half rule Compare the normalized responses of G(s) and the approximate models for a unit step input. (b) To use Skogestad’s method, we note that the largest neglected time constant in (6-59) has a value of three. • According to his “half rule”, half of this neglected value is added to the next largest time constant to generate a new dominant time constant τ 5 0.5(3) 6.5. • The other half provides a new time delay of 0.5(3) = 1.5. • The approximation of the RHP zero in (6-61) provides an additional time delay of 0.1. • Approximating the smallest time constant of 0.5 in (6-59) by (6-58) produces an additional time delay of 0.5. • Thus the total time delay in (6-60) is, θ 1.5 0.1 0.5 2.1 Multivariable Processes = MIMO and G(s) can be approximated as: Ke2.1s GSk s 6.5s 1 (6-64) The normalized step responses for G(s) and the two approximate models are shown in Fig. 6.10. Skogestad’s method provides better agreement with the actual response. Figure 6.10 Comparison of the actual and approximate models for Example 6.4. 2 Outputs = T(t) and h(t) 4 Inputs = wh(t), wc(t), Th(t) and Tc(t) For constant density, G23 and G24 = 0 G12 G G ( s ) is a 2 x 4 matrix 11 G21 G22 12.8e X D ( s ) 16.7 s 1 X ( s) 7 s B 6.6e 10.9 s 1 s 18.9e 21s 1 R( s ) 3 s 19.4e S ( s ) 14.4 s 1 3 s G13 G14 G23 G24 SISO = single input / single output is the “common” method for process control, but interactions may present “tuning” problems (trial and error iterations for controller settings may or may not work). In multivariable control design, interactions are taken into consideration, but controller design equations are more complicated, from CHE 461 to CHE 550. ...
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