1 / 49

Chapter 2 Mathematical Background

Chapter 2 Mathematical Background. Professor Shi-Shang Jang Chemical Engineering Department National Tsing-Hua University Taiwan March, 2013. 2-1 The Essence of Process Dynamics. As the plant is not operated at its steady state as designed , the process is in a dynamic state .

worm
Download Presentation

Chapter 2 Mathematical Background

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 2 Mathematical Background Professor Shi-Shang Jang Chemical Engineering Department National Tsing-Hua University Taiwan March, 2013

  2. 2-1 The Essence of Process Dynamics • As the plant is not operated at its steady state as designed, the process is in a dynamic state. • In many cases, small deviations of process steady state (noise) is allowed, but keep monitoring on the system is very important. • In case of strong deviations (trend), process control becomes a must.

  3. Example: Thermal Process Ts Inputs: f(t), Ti(t),Ts(t) Output: T(t) CV: T(t) MV: f(t) DV: Ti(t), Ts(t)

  4. Noise v.s. Trend T Trend Noise time

  5. Conception of deviation variable Wild stream Fw control stream tank Disturbances Valve liquid level h Fc Plant H= hdeviation = h - hsteady-state Manipulative variables Controlled variables

  6. The feedback/feedforward process control needs to understand the relationships between CVs and MVs DVs and CVs the relationships are called process models. For the ease of mathematical analyses, the process modeling implements in Laplace transform instead of direct use of time domain process model. Implementation of deviation variables is needed. The Essence of Process Dynamics - Continued

  7. Conception of deviation variable ( =T-70)  Trend Noise time

  8. 2-2 Linear Systems

  9. 2-2 Linear Systems • Consider a process system with a CV, say y(t), and a MV, say m(t). In process industries, it is traditional to assume the system is lumped and linear. A lumped process system is such that: • A lumped system is linear if

  10. Linear Systems-Continued • A lumped process system is autonomous if • An autonomous lumped system is said to be stable at the origin if

  11. Linear Systems-Continued • An autonomous lumped system is called asymptotic stable if • Theory 2-1: A linear system is stable if the roots of the characteristic equation all have negative real part. • Corollary 2-1: A linear system is asymptotic stable if it is stable.

  12. Linear Systems-Examples Note that no matter what the initial conditions is, the solution indicates : The system is hence asymptotic stable.

  13. Linear Systems-Examples Note that the above system is unstable since: Note that the above system is also unstable since:

  14. Linear Systems-Continued • Theory 2-2: A forced linear system is stable if the inputs of the system is bounded. • A process control system is basically assuming that the controlled variable (CV) is to be controlled to a certain set point (zero), thus it is assumed that the input (MV) is the forcing function to a linear system with a dependent variable of CV.

  15. Linear Systems-Continued • A forced system is of the following form, for example: y”+a1y’+a0y=bm(t) • In case of m(t) is in special functions such sint the above equation can be solved by implementing particular solutions i,e., y(t)=yh+yp • However, in case of feedback process control, m(t) is most likely as a function of y(t), i.e.

  16. Linear Systems-Continued • In most analysis of a feedback control systems, it is our tradition to assume that the system performance is in its steady state, i.e. y”+a1y’+a0y=bm(t), y(0)=0; y’(0)=0 • For the convenience of the analysis, a Laplace transform expression of the above linear system becomes necessary.

  17. 2-3 Laplace Transform: Definitions and Properties Definition 2-1: Consider a function of time f(t), the Laplace transform of f(t) is denoted by Property 2-1: If f(t)=1, t 0, f(t)=0, t<0, this is called a step function as shown below (to describe an abrupt event to cause the change of operation), then: 0 time

  18. Scenario 1: Step function (shift) The process endures an abrupt long term change, such a PM on a tool of manufacturing plant.

  19. Definitions and Properties- Continued Corollary 2-1: Given the following step function: Then: 0 0 Property 2-2: Given a ramp function: Then: time time

  20. Scenario 2: Ramp function (drift) The process endures slow change on the process such as tool aging of a manufacturing plant.

  21. Definitions and Properties - Continued Definition 2-2: A pulse function is denoted by: 1/c Definition 2-3: A delta function (t)=limc0f(x) 0 c Property 2-3: delta function (t) time Then: Note that, a pulse function can also represent an abrupt event in plant operation, but the event can be vanished very soon unlike step function.

  22. Scenario 3: Pulse function (Excursion) The process endures a sudden short change but returns to its original state.

  23. Definitions and Properties- Continued Property 2-4: Exponential function Then: Corollary 2-2 Then:

  24. Scenario 4: Exponential function (Excursion) The process endures a sudden abrupt change, but return to its original state gradually..

  25. Definitions and Properties- Continued Property 2-5: Sinusoildal Functions Then: Property 2-6: Sinusoildal Functions Then:

  26. Scenario 5: Sinusoidal function The process endures a sinusoidal wave input to force it response another periodical wave.

  27. Theory Theory 2-1: Linearity Consider two functions f(t) and g(t) with their Laplace transform F(s) and G(s) exists, and let a and b be two constants, then Theory 2-2: Derivative Let f(t) be differentiable, and its Laplace transform F(s) exist, then

  28. Theory - Continued Remark 2-1: Deviation variable (perturbation variable) Let y(t) has a steady state ys, then yd is called a deviation variable if yd(t)=y(t)-ys Remark 2-2: In the study of control theory, we all assume that the process is originally at its steady state, and then a change of the system starts. Therefore, y(0)= ys, or yd(0)=0. Corollary 2-3: Derivative of a deviation variable

  29. Theory - Continued Theory 2-3: Final Value Theorem Consider a function f(t) with its Laplace Transform F(s), then: Theory 2-4: Dead time (Time Delay, Translation in Time) Consider a function f(t) with its Laplace Transform F(s), then:

  30. Step 2 Solve for General Procedure Time domain Laplace domain ODE Step 1 Take Laplace Transform Initial conditions Step 3 Factor D(s) perform partial fraction expansion Solution y(t) Step 4 Take inverse Laplace transform

  31. Sol: Take the Laplace transform on both sides of the equation Solution of a Linear System By Laplace Transform Example: Solve the differentiation equation or,

  32. Solution of a Linear System By Laplace Transform – Partial Fraction Expansion

  33. 2-4 Transfer Functions – Example: Thermal Process Ts Inputs: f(t), Ti(t),Ts(t) Output: T(t)

  34. 2-4 Transfer Functions – Cont. Let f be a constant V= constant, Cv=Cp

  35. Gp(s) Y(s) M(s) Transfer Functions – Cont. where, Gp(s) is call the transfer function of the process, in block diagram:

  36. Scenario Simulation (Step Change)

  37. Homework – Due 3/19 Text p57 2-1,2-2, 2-6 (a), (c) Reading Assignments： Laplace Transform(p11-26) Homework and Reading Assignments

  38. F(X) aX+b X X0 -△ X0 X0+△ -△ 0 △ 2-5Linearization of a Function

  39. Linearization of a Function (single variable) • Example: Linearize the Arrhenius equation, at T=300C, k(300)=100 , E=22,000kcal/kmol.

  40. Linearization of a Function (two variables)

  41. Linearization of Differential Equations • Definition 2-4: A linear function L(x) is such that for xRn, for all scalars a and b, L(ax1+bx2)= aL(x1)+bL(x2). • Definition 2-5:A Linear system is denoted as the following: Provided that L1,…,Ln are linear functions

  42. Linearization - Continued • Consider a function f(x1,…,xn), the linearization of such a function to a point is defined by the first order Tylor expansion at that point, i.e.

  43. Linearization of a single input single output system

  44. f0 h V f Cross-sectional=A Example – Level Process A=5m2 f0=1m3/min Abrupt change of f0 from 1 to 1.2m3/min

  45. Example – Level Process - Continued Taking Laplace Transform on both sides: Now, F0(s)=0.2/s

  46. example_model [A B C D]=linmod('example_model'); [b,a]=ss2tf(A,B,C,D); Linearization

  47. Level Time(min.)

  48. Conclusive Remarks for Laplace Transform • Laplace transform is convenient tool to represent the dynamics of a linear system. • Laplace transform is very easy to use to special inputs, especially in case of abrupt change of system inputs and time delays. • Linearization is a frequent approach for a nonlinear system, and it is a good approximation in small change of the inputs.

  49. Homework – Due 3/19 • Text p57 • 2-17,2-23 Linearization of Function(p50-56)

More Related