Describe how you would be able to check for stability for this closed loop system.

Process_5_Modelling Assignment_2021 Page 1 of 4

SHC4032 Process Control Coursework: Modelling and Systems Analysis Assignment

Ensure no spelling, punctuation or grammatical errors when writing your answers.

Present all equations, substitutions and place a box around the final answer, where

applicable.

A model for a batch reactor has been derived as follows:

𝑑𝑦

𝑑𝑑 = (𝐼𝐷5)𝑦 βˆ’ (𝐼𝐷6)𝑦2

𝑑π‘₯

𝑑𝑑 = (𝐼𝐷7)𝑦

where the initial values of x and y are 0 and 0.03, respectively.

Using MS Excel, determine y(1) using the following:

a) Euler’s method

b) Fourth Order Runge Kutta method

The following chemical reaction takes place in a CSTR:

𝐴

π‘˜1

β‡Œ

π‘˜2

𝐡

π‘˜3

β‡Œ

π‘˜4

𝐢

where the rate constants are as follows:

k1 = (ID4) min–1 k2 = (ID5) min–1

k3 = (ID6) min–1 k4 = (ID7) min–1

Determine the following:

a) Rate expressions for components A, B and C.

b) Final steady state concentrations of Component B and C using the Euler

method.

Process_5_Modelling Assignment_2021 Page 2 of 4

Determine 𝑦(2) from the following 2nd order differential equation:

𝑑2𝑦

𝑑𝑑2 + 𝑑𝑦

𝑑𝑑 + 𝑦 = (𝐼𝐷7)

where 𝑦(0) = 𝑦′(0) = 0. Use:

a) Euler method

b) Fourth Order Runge Kutta method

(Bequette, 2003) Use the initial and final value theorems to determine the initial and

final values of the process output for a unit step input change for the following

transfer functions:

a) 5𝑠+12

7𝑠+4

b) (7𝑠2+4𝑠+2)(6𝑠+4)

(4𝑠2+4𝑠+1)(16𝑠2+4𝑠+1)

c) 4𝑠2+2𝑠+1

8𝑠2+4𝑠+0.5

(Bequette, 2003) Derive the closed loop transfer function between L(s)and Y(s) for

the following control block diagram (this is known as a feed forward / feedback

controller).

Figure 0-1 Feedforward / Feedback Control Loop

a) Describe how you would be able to check for stability for this closed loop system.

b) Will the stability of this system be any different than that of the standard feedback

system? Why?

Process_5_Modelling Assignment_2021 Page 3 of 4

(Bequette, 2003) A process has the following transfer function:

𝐺𝑃(𝑠) = 2(βˆ’3𝑠 + 1)

(5𝑠 + 1)

a) Using a P-controller, find the range of the controller gain that will yield a stable

closed loop system.

b) Simulate the process with the P controller to confirm the range of stability as

determined in part (a).

Consider the open-loop unstable process transfer function:

𝐺𝑃(𝑠) = 1

(𝑠 + 2)(𝑠 βˆ’ 1)

a) Find the range of KC for a P-only controller that will stabilize this process.

b) As it turns out, 𝐾𝐢 = 4 will yield a stable closed-loop (does this match with your

answer in part (a)?). Typically, there is a measurement lag in the feedback loop.

Assuming a first-order lag on the measurement, find the maximum measurement

time constant which is allowed before the system (with 𝐾𝐢 = 4) is destabilized.

c) Confirm all of your calculations with the system reproduced using

MATLAB/Simulink. Print out your results.

A PI controller is used on the following second order process:

𝐺𝑃(𝑠) = 𝐾𝑃

𝜏2𝑠2 + 2πœπœπ‘  + 1

The process parameters are:

𝐾𝑃 = 1, 𝜏 = 2, 𝜁 = 0.7

The tuning parameters are:

𝐾𝐢 = 5, 𝜏𝐼 = 0.2

a) Determine if the process is closed-loop stable.

b) Reproduce your results using MATLAB/Simulink and print out your results.

Process_5_Modelling Assignment_2021 Page 4 of 4

(Marlin, 2000) The process shown below consists of a mixing tank, mixing pipe, and

continuously stirred tank reactor. The following assumptions are applied to the

system:

Both tanks are well mixed and have constant volume and temperature.

All pipes are short with negligible transportation delay.

III. All flows and densities are constant.

The first tank is a mixing tank.

The mixing pipe has no accumulation, and the concentration, CA3, is

constant.

The second tank, CSTR, with 𝐴 β†’ π‘ƒπ‘Ÿπ‘œπ‘‘π‘’π‘π‘‘π‘ , and π‘Ÿπ΄ = βˆ’π‘˜π΄πΆπ΄

3 2⁄ .Mixing

Point

q1

CA0

q2

CA2

q3

CA3

q4

CA4

q5

CA5

V1

V2

Mixing

Tank

CSTR

Figure 0-2 Mixing tank, mixing pipe, and CSTR Process

Derive a linearized model relating 𝐢𝐴2

β€² (𝑑) to 𝐢𝐴0

β€² (𝑑).

Derive a linearized model relating 𝐢𝐴4

β€² (𝑑) to 𝐢𝐴2

β€² (𝑑).

Derive a linearized model relating 𝐢𝐴5

β€² (𝑑) to 𝐢𝐴4

β€² (𝑑).

Combine the models in (a) to (c) into one equation 𝐢𝐴5

β€² (𝑑) to 𝐢𝐴0

β€² (𝑑) using

Laplace transforms.