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20EI501-Process Control

Department: Electronics and Instrumentation Engineering

Batch/Year: 2021-25/III

Created by:

Ms. K.R. Chairma Lakshmi AP/EIE

Table of Contents

1. COURSE OBJECTIVES

2. PRE REQUISITES

4. SYLLABUS

SUBJECT CODE : 20EI501

SUBJECT NAME: PROCESS CONTROL

L T P C :3 2 0 4

UNIT I PROCESS MODELLING AND DYNAMICS

Need for process control – Mathematical Modeling of Processes: Level, Flow, Pressure and Thermal processes – Continuous and batch processes – Interacting and Non-Interacting system - Self regulation – Servo and regulatory operations – Lumped and Distributed parameter models – Heat exchanger – CSTR

– Linearization of nonlinear systems.

​

UNIT II FINAL CONTROL ELEMENTS

Actuators: Pneumatic and electric actuators – Control Valve Terminology - Characteristic of Control Valves: Inherent and Installed characteristics - Valve Positioner – Modeling of a Pneumatically Actuated Control Valve – Control Valve Sizing: ISA S 75.01 standard flow equations for sizing Control Valves – Cavitation and flashing – Control Valve selection

​

UNIT III CONTROL ACTIONS

Characteristic of ON-OFF, Proportional, Single speed floating, Integral and Derivative controllers P+I, P+D and P+I+D control modes – Practical forms of PID Controller – PID Implementation Issues: Bumpless, Auto/manual Mode transfer, Anti-reset windup Techniques – Direct/reverse action.

​

UNIT IV PID CONTROLLER TUNING

PID Controller Design Specifications: Criteria based on Time Response and Criteria based Frequency Response - PID Controller Tuning: Z-N and Cohen-Coon methods, Continuous cycling method and Damped oscillation method, optimization methods, Auto tuning – Cascade control – Feed-forward control

SYLLABUS

UNIT V MODEL BASED CONTROL SCHEMES

​

Smith Predictor Control Scheme - Internal Model Controller – IMC PID controller –

- Three- element Boiler drum level control - Introduction to Multi-loop Control

Schemes – Control Schemes for CSTR, and Heat Exchanger - P&ID diagram.

4. Course Outcomes

5. CO-PO/CO-PSO MAPPING

6. LECTURE PLAN-UNIT- V

7. ACTIVITY BASED LEARNING

1. Topic: What is that device?/Where is it located?/Why is it there?

​

Activity: Interpreting Symbol

When we spot one of these on a P&ID, the students should be able to glean three things from it, including:

What is that device? Where is it located? Why is it there?

The 'what' and 'where' aspects can be determined from the symbol shape. The 'why' part comes from text placed inside the symbol that is made up of two parts that form the "tag number".

8. Lecture Notes-Unit-V

Smith Predictor control scheme

Consider the general feedback control system shown in fig. All the dynamic components of the loop may exhibit significant time delays in their response.

Fig:Closed loop system

The main process may involve transportation of fluids over long distances or include phenomena with long incubation periods.

The measuring device may require long periods of time for completing the sampling and the analysis of the measured output(ex: Gas chromatograph)

​

The final control element may need some time to develop the actuating signal.

A human controller may need sufficient time to think and take proper control action.

In all the above situations, a conventional feedback controller will not give satisfactory closed loop response due to the following reasons

A disturbance entering the process will not be detected until after a significant period of time.

The control action that will be taken on the basis of the last measurement will be inadequate because it attempts to regulate a situation (eliminate an error) that

originated while back in time

​

The control action will also take some time to make its effect felt by the process.

From the above factors, it is noted that significant dead time is a significant source of instability for closed loop response.

Consider the open loop transfer function

d

If t = 0.01min(i.e very small), then

cross over frequency = 160rad/min

​

the ultimate gain = 80.01

If the dead time is increased to t_d = 0.1

cross over frequency = 17rad/min

​

the ultimate gain = 8.56

Fig: Effect of dead time over cross over frequency

0.5s +1

_{GoL =} c

_{K e}−t_ds

It is noticed that the increase in dead time has introduced significant additional phase lag, which reduces the cross over frequency and the maximum allowable gain. In other words, the increase in dead time has made the closed loop response more sensitive to periodic disturbances and has brought the system closer to the edge of instability.

​

Remarks:

As the dead time of an open loop transfer function increases, the following two undesirable effects takes place:

The cross over frequency decreases: This implies that the closed loop response will be sensitive even to lower frequency periodic disturbances entering the system.

The ultimate gain decreases: Therefore, to avoid the instabilities of the closed loop response, reduce the value of the proportional gain K_c, which leads to sluggish response.

Therefore a control system different from conventional feedback loop is needed to compensate for dead time effects.

​

Dead time compensation

A modification of classical feedback control system was proposed by O.J.M.Smith for the compensation of dead time effects. It is known as the smith predictor or the dead time compensator.

To understand the nature of dead time compensation proposed by Smith, consider the simple feedback loop with set point changes as shown in fig. Assumed all dead time is caused by the process.

​

G_p(s)= G(s) e^-t s

d

Fig: Feedback system with process dead time

For simplicity assume G_m(s) = G_f(s) = 1. The open loop response to a change in the set point is equal to

In order to eliminate the undesired effects, open loop feedback signal should be considered for current state and not the delayed information.

Above expression is calculated, if the below expression is added with the open loop response

Therefore

Fig(b): feedback with complete dead time compensation

c sp

d

−t s

y(s) = G (s)^{[G(s)e ]y (s)}

*

y (s) = G_c (s) G(s) y_sp (s)

y(s)

G (s) G(s) y (s)

c sp

'

d

−t s

y (s) = (1− e )

y (s)+ y(s)= y (s)

' *

Fig(c): Net result of dead time compensation

Adding to the signal is shown in the figure (b). In that fig(b) can be taken by a simple local loop around the controller, which is called as the dead time compensator or smith predictor. The simplified loop of fig( c) is completely equivalent to fig(b) and indicates the real effect of the dead time compensator.

​

It moves the effect of dead time outside the loop

​

Remarks:

In the block diagram of fig( c), it is not correct to think that we take a measurement signal after the block G(s) because such a signal is not measurable in a real process with dead time. The only measurable signals are the process output, , and the manipulated variable. Therefore, the block diagram of fig(

^{c) is mea}yⁿ(^ts)^{to give only a schematic representation of what is the effect of the} dead time compensator, not to depict the physical reality.

The dead time compensator predicts the delayed effect that the manipulated variable will have on the process output. This prediction led to the term smith predictor and it is possible only if we have a model for the dynamics of the process(transfer function, dead time).

In most process control problems the model of the process is not perfectly known; i.e., G(s) and t_d are known only approximately. Consider that G(s) and t_d

represent the ―true‖ characteristics of the process, while G^’(s) and t_d represent

’

their approximations, as these are given by some mathematical model for the process. Then, using G^’(s) and t_d to construct the smith predictor, we take the

’

System shown below. In this case the composite open loop feedback signal is

Or

The equation above indicates some important features of dead time compensators:

Only for perfectly known processes will have perfect compensation(i.e., for G=G^’ and t_d= t_d ).

’

The larger the modeling error[i.e., the larger the differences(G-G^’) and (t_d-t_d )_’ , the less

effective is the compensation.

​

The error in estimating the dead time is more detrimental for effective dead time

(G-G^’), because of the exponential

compensation [i.e., (t_d-t_{d ’}) is more crucial than

function.

The dead time in a chemical process is usually caused by material flows. Since the flow rate is not normally constant but shows variations during the operation of a plant, the value of the dead time changes. Therefore, if the dead time compensator is designed for a certain value of the dead time, then for new value of t_d the compensation will not be as effective.

Fig: Dead time compensation with inaccurate knowledge of Process transfer function and

dead time

y (s)= y(s)+ y (s )

* '

y (s)

sp

'

c

c

'

d

d

−t s

−t s

= [G Ge G G ]

+ (1 − e )

'

c

sp

'

d d

−t s ' −t s

y^* (s)= G [G + (Ge − G e )]

y (s)

Internal Model control

The main advantage of IMC is that it provides a transparent framework for control system design and tuning. The IMC control structure can be formulated in the standard feedback controkl structure. For many processes, this standard feedback control structure will result in a PID controller(sometimes cascaded with afirst order lag). The IMC design procedure is exactly that of the open loop control design procedure. A factorization of the process model was performed so that the resulting controller would be stable. If the controller is stable and the process is stable, then the overall controlled system is stable .

Although the IMC design procedure is identical to the open loop design procedure, the implementation of IMC results in a feedback system. Thus IMC is able to compensate for disturbances and model uncertainity, while open loop control is not. Note that the internal model controller must be detuned to assure stability if there is model uncertainty.

​

The IMC structure

The IMC structure is shown in fig below. The distingusing characteristic of this structure is the process model, which is in parallel with the actual process(plant)

Fig . The internal model control structure

Note that (῀) is generally used to rpresent signals associated with the model. The below fig. illustrates that both controller and model exist as computer computations. It is convenient to treat them separately for design and analysis.

Fig. The IMC strategy.

​

A list of transfer function variables shown in IMC block diagram are given below

The dotted line indicates the calculations performed by the model-based controller.

​

The signal to the controller is

Perfect Model, No Disturbances

If the model is perfect (𝑔_𝑝⁽𝑠⁾ = 𝑔˜_𝑝⁽𝑠⁾)and there are no disturbances (d(s) = 0), then the feedback

signal is zero. The relationship between r(s) and y(s) is then

​

𝑦⁽𝑠⁾ = 𝑔_𝑝⁽𝑠⁾𝑞⁽𝑠⁾𝑟(𝑠)

This is the same relationship that we get for an open-loop control system design.

​

A standard feedback controller could actually destabilize a process if we did not

Correctly choose the tuning parameters. An analysis of the poles of the closed-loop transfer function must be performed to determine the stability of standard feedback controllers.

​

Perfect Model, Disturbance Effect

If the model is perfect (𝑔_𝑝 ⁽𝑠⁾ = 𝑔˜_𝑝⁽𝑠⁾)and there is a disturbance, then the feedback signal is

(𝑑⁽𝑠⁾ = 𝑑^˜(𝑠⁾)

​

This illustrates that feedback is needed because of unmeasured disturbances entering a process.

​

Model Uncertainty, No Disturbances

If there are no disturbances [d(s) = 0] but there is model uncertainty(𝑔_𝑝 ⁽𝑠⁾ ≠ ˜𝑔_𝑝⁽𝑠⁾), which is

always the case in the real world, then the feedback signal is

​

𝑑^˜(𝑠⁾ = (𝑔_𝑝 ⁽𝑠⁾ − 𝑔˜ _𝑝 ⁽𝑠⁾) 𝑢⁽𝑠⁾

​

This illustrates that feedback is needed because of model uncertainty. The closed-loop relationship is

Recapitulating, the reasons for feedback control include the following:

​

• Unmeasured disturbances
• Model uncertainty
• Faster response than the open-loop system (with a static controller)
• Closed-loop stability of open-loop unstable system

​

The primary disadvantage of IMC is that it does not guarantee stability of open-loop unstable systems.

​

IMC PID CONTROL

In the IMC formulation, the controller, q(s), is based directly on the ―good‖ part of the process transfer function. The IMC formulation generally results in only one tuning parameter, the closed loop time constant ( , the IMC filter factor). The PID tuning parameters are then a function of this closed-loop time constant.

The IMC-based PID controller will not give the same results as the IMC strategy when there is process time delays, because the IMC-based PID procedure uses a Padé approximation for dead time, while the IMC strategy uses the exact representation for dead time.

​

THE EQUIVALENT FEEDBACK FORM TO IMC

In this section we derive the feedback equivalence to IMC by using block diagram manipulation. The IMC structure is shown in figure below.

The point of comparison between the model and process output can be moved as shown in Figure below.

The above figure can be rearranged to the form of figure below.

The arrangement shown inside the box of above Figure is shown below

The above figure can be rearranged to the form of Figure below.

Notice that r(s) y(s) is simply the error term used by a standard feedback controller. Therefore, we have found that the IMC structure can be rearranged to the feedback control (FBC) structure, as shown in Figure below. This reformulation is advantageous because we will find that a PID controller often results when the IMC design procedure is used. Also, the standard IMC block diagram cannot be used for unstable systems, so this feedback form must be used for those cases.

𝑐

𝑔⁽𝑠⁾=

Standard feedback diagram illustrating the Equivalence with IMC^{. The feedback controller, g}_c ^s, contains both the internal model, g^˜_p s,and internal model controller, qs.

Now, we can use the IMC design procedure to help us design a standard feedback controller. The ^{standard feedback controller is a function of the internal model, 𝑔˜}𝑝 ^{(𝑠), and internal model controller,} q(s), as shown in equation below.

The standard feedback controller which is equivalent to IMC is

𝑞(𝑠) 1−𝑔˜_𝑝⁽𝑠⁾𝑞(𝑠)

^{The above equation can be referred as the IMC-based PID relationship because the form of} 𝑔_𝑐⁽𝑠^{) is} often that of a PID controller.

​

The IMC-Based PID Control Design Procedure

​

The following steps are used in the IMC-based PID control system design.

1. Find the IMC controller transfer function, q(s), which includes a filter, f(s), to make q(s) semi- proper

or to give it derivative action (order of the numerator of q(s) is one order greater that the denominator of q(s)). Notice that this is a major difference from the IMC procedure. Here, in the IMC-based procedure,

we may allow q(s) to be improper, in order to find an equivalent PID controller.

𝑓⁽𝑠⁾ =

𝛾𝑠 + 1 (𝜆𝑠 + 1)^𝑛

2. Find the equivalent standard feedback controller using the transformation

𝑐

𝑔 ⁽𝑠⁾ =

𝑞(𝑠)

1 − 𝑔˜_𝑝⁽𝑠⁾𝑞(𝑠)

Write this in the form of a ratio between two polynomials.

3. Show this in PID form and find 𝑘_𝑐,𝑟_𝐼 , 𝑟_𝐷 .Sometimes this procedure results in an ideal PID controller cascaded with a lag term ^(𝑟𝐹⁾

𝑔_𝑐 ⁽𝑠⁾ = 𝑘_𝑐 [

𝑟_𝐼𝑆

] [

2

𝐼 𝐷 𝐼

𝑟 𝑟 𝑆 + 𝑟 𝑆 + 1

1

𝑟_𝐹 𝑆 + 1

]

4. Perform closed-loop simulations for both the perfect model case and cases with model mismatch. Choose the desired value for 𝜆 as a trade-off between performance and robustness.

Three Element Boiler Drum Level Control

A single element control system is one with just one control input, a two element control system is one with two control inputs, etc.

A Three Element Boiler Drum Level Control system is one which typically uses the measured water level, the steam flow rate from the boiler, and the water flow rate into the boiler to regulate the flow of water into the boiler.

Although you might think that measuring water level alone is sufficient, you have to bear in mind that the boiler water contains lots of steam bubbles. Bubble size is affected by pressure, so if a boiler experiences a sudden extra demand for steam, its pressure drops. The drop in pressure causes the steam bubbles in the boiler water to expand, and the level measurement can show an increase in level. The false high reading makes the water level control system reduce the flow of water into the boiler. Once boiler pressure is restored the steam bubbles contract, and the measured water level drops suddenly. The level control system responds to this by increasing the flow of water into the boiler, which effectively deluges the boiler with relatively cold water, and boiling is arrested. Some of the steam bubbles in the boiler water collapse, and the boiler water level drops significantly

– possibly to a low-level alarm or lockout. By adding water and steam flow measurement into the control system, we can identify any major disparity between the two, and make compensation to the measured water level. This means that any transient peak demands on the boiler are recognized as such, and the feedforward control is appropriately applied.

Incidentally, it is possible to achieve the same results using a two-element control system (level and steam flow), but it is easier to commission three-element systems.

Three Element Boiler Drum Level Control system are sometimes referred to as ―fe d forward‖ control. This is because the system identifies a transient high demand for steam before it has any effect on the boiler water level, and

therefore starts to put extra water into the boiler in anticipation of demand.

​

In this control philosophy, there are three process variables.

​

1. Boiler Level,
2. Feed water flow and
3. Steam Flow to control boiler Drum Level.

​

Boiler Three Element Controller

Understanding of diagram :

Here, LT1, LT2 and LT3 are three different Level transmitter. reason for using three level transmitters is simple that, in case of failure of any transmitter(s), control wont be affected. LT is average of three LTs.

Water density changes with pressure. So density compensation is there for every level transmitter.

LIC is first PID block with LT as process variable.

FT1 is the steam flow leaving the steam drum. Here we have done pressure and temperature correction.

Output of LIC and FT1 goes to one calculation block. Output of this block is our remote set point for Flow controller (FIC).

​

FT2 is feed water flow to the boiler drum and process variable for FIC.

SS is selector switch. By this controlling philosophy can be selected either single or three element.

​

FCV is feed flow control valve.

​

Types of Control:

​

Single Element Control:

During lower boiler loads or <30% steam flow, drum level signal LT and the fixed local set point LSP are compared in LIC and the controller output is fed to feed water control valve FCV

​

Three Element Control:

The steam flow signal sensed by the steam flow transmitter FT1 acts as a feed forward signal and takes care of the shrink & swell effect.

​

The steam flow transmitter is connected across flow nozzle, and the signal is then

compensated for pressure and temperature.

LIC is the primary controller in the three element level control function. When the steam drum water level is below the set point, controller LIC will further increase the remote set point of the feed water flow controller to increase the feed water flow. When the level is too high the reverse action will take place.

The Level controller LIC output signal is added with the compensated steam flow signal at calculation block.

​

The following equation is implemented in summing block

​

Remote SP for (FIC) % = (LIC) O/P + Steam Flow (FT1) PV in % – 50%

FIC is the secondary controller in the three element level control. When the feed water flow is below the set point, controller FIC will further increase the feed water flow by opening the feed water control valve. When the flow is too high the reverse action will take place.

​

Introduction to multiloop control schemes

Process that have only one controlled variable and one manipulated variable is called single input, single output (SISO) process. This is called single loop process control system.

In Industry many process have more number of output variables which can be controlled and may have more number of input variables which can be manipulated. Such a system is called Multi Input Multi Output (MIMO) Process.

The MIMO process is further divided into Multiloop process control system and Multivariable process control system.

​

Multiloop control

Each manipulated variable depends on only a single controlled variable, i.e., each conventional feedback controller will control each output variable.

Multivariable control

Each manipulated variable can depend on two or more controlled variables. A

single controller will control all the output variables.

Examples: Decoupling control and model predictive control

​

Multiloop control

The multiloop approach, using multiple single loop controllers using was the first approach used for MIMO control in process industries.

The first advantage is simple controllers are used and easy to implement. The second advantage is ease of understanding by plant operator. Since each controller uses only one measured controlled variable and adjust only one manipulated variable, the actions of the controllers are relatively easy to monitor. A third advantage is that the hardware and software are readily available for multiloop controllers.

A MIMO process is said to have interaction when process input (manipulated) variables affect more than one process output (controlled) variable.

In most industrial MIMO process, each manipulated variable (input signal) may affect several controlled variables(output signals) causing interaction between the input/output loops. For this reason, control of MIMO process is typically much more difficult compared to the SISO process. It is therefore of great importance to quantity the degree of interaction so that proper input/output pairs that minimize the impact of the interaction can be formed. For this, dedicated interaction measures can be used.

​

Compared to SISO process, the control deign for MIMO process is more elaborate.

​

Interaction of control loops in CSTR

In CSTR, the temperature of the reactor is controlled by the flow of coolant in the jackect(Loop 2) while the effluent concentration is controlled by the inlet feed flow rate(loop 1). Assume that initially both effluent concentration and temperature are at their desired values.

Consider a change in inlet concentration (load change) or the desired effluent concentration (setpoint change). Loop 1 will compensated for these changes by manipulating the feed flow rate. However, this change in the feed rate also disturbs the reactor temperature away from the desired value. Then loop 2 attempts to compensate for the change in temperature by varying the coolant flow rate, which inturn affects the effluent concentration.

On the other hand, an attempt to compensate for changes in feed temperature (load change) or the desired setpoint of reactor temperature(setpoint change), it also causes the effluent concentration by varying the feed flow rate, which inturn disturbs the reactor temperature.

It is clear that the loop 1 interacts with loop 2 and also loop 2 interacts with loop 1 (i.e., I both directions).

Control schemes for Heat exchanger

The shell-and-tube is the most common type of heat exchanger used in petrochemical industries because it is suitable for low and high pressure applications (see Figure 1). It consists of an outer shell with a bundle of tubes inside. The tubes are oriented in a straight or in a "U" shape. One fluid runs through the tubes, and another fluid flows through the shell surrounding the tubes to transfer heat between the two fluids (see Figure 2). The set of tubes is known as a "tube bundle." Heat is transferred from one fluid to the other through the walls of the tubes.

Heat is transferred from the tube fluid to shell fluid to remove heat, or from the shell fluid to the tube fluid to heat the material inside. Fluids can be liquids or

gases on either the shell or the tube side. To transfer heat efficiently, many tubes are used, which increases the heat-transfer surface area between the two fluids.

​

Control objective

To develop a comprehensive control strategy for any control loop, it’s important to identify the process variable of interest—called the "controlled variable," the manipulated variable, and the different disturbance variables that directly affect the controlled variable.

Consider the heat exchanger shown in Figure. The shell side fluid is the process fluid that is required to be heated to a certain temperature setpoint. The resulting temperature is measured at the outlet of the heat exchanger T_1Out (controlled variable).

Heating is achieved by passing steam through the tube side. The more steam passing through the tubes, the more heat is transferred to the process fluid, and vice versa. Control of the steam flow F₂ (manipulated variable) is achieved by throttling a modulating valve installed on the steam inlet side.

Three major disturbances can affect the process fluid outlet temperature: Changes in process fluid flow rate, F₁

Changes in process fluid inlet temperature, T_1In

​

Changes in steam pressure, causing a change in steam flow rate, F₂.

The control objective is to maintain process fluid outlet temperature T_1Out at the desired setpoint—regardless of disturbances—by manipulating the steam flow rate F₂.

​

Feedback control

In the feedback control scheme, the process variable, T1_Out, is measured and applied to a proportional-integral-derivative (PID)-based feedback temperature controller (fbTC), which compares the process variable with the desired temperature setpoint and in turn calculates and generates the control action required to open or close the steam control valve (see Figure).

The most important advantage of the feedback control scheme is that regardless of the disturbance source, the controller will take corrective action. Employing feedback control requires very little knowledge of the process. Therefore, a process model is not necessary to set up and tune the feedback scheme, although it would be an advantage.

The major disadvantage of feedback control is its incapability to respond to disturbances—even major ones—until the controlled variable is already affected. Also, if too many disturbances occur with significant magnitude, they can create unrecoverable process instability.

​

Cascade control

In the cascade control scheme, instead of feeding the output of the PID temperature controller directly to the control valve, it is fed as a setpoint to a feedback PID-based, steam-flow controller (fbFC). This second loop is responsible for ensuring the flow rate of the steam doesn’t change due to uncontrollable factors, such as steam pressure changes or valve problems.

To understand how this works, consider that the heat exchanger is in steady-state operation, the outlet temperature matches the setpoint, and the controller output of fbTC is constant. A sudden increase in steam pressure will cause steam flow rate F₂ to ramp up (see Figure ). This will cause a gradual change in the controlled variable. Without the flow control loop, fbTC will not take corrective action until the outlet temperature is already affected.

​

By implementing the cascade strategy, the feedback flow control loop fbFC will

adjust the valve position immediately when the steam flow rate has changed to

bring the flow back to the value of the previous steady-state condition (because the flow setpoint given by the temperature controller didn’t change as the outlet temperature did not yet change), preventing a change in the outlet temperature before it happens.

Note that the flow control loop must be tuned to run much faster than the temperature control loop, therefore cancelling the effect of flow variance before it affects the process fluid outlet temperature.

​

Feedforward control

Unlike feedback control, feedforward takes a corrective action when a disturbance occurs. Feedforward control doesn’t see the process variable. It sees only the disturbances and responds to them as they occur. This enables a feedforward controller to quickly and directly compensate for the effect of a disturbance (see Figure ).

Control schemes for CSTR

Jacketed CSTR: Consider the CSTR, where the reaction is exothermic and the heat generated is removed by the coolant, which flows in the jacket around the tank.

​

The control objective is to keep the temperature of the reacting mixture T,

constant at a desired value.

Disturbances: Feed Temperature T_i and coolant Temperature T_c Manipulated variable: Coolant flow rate F_c

Simple feedback control: T will respond much faster to changes in T_i than to changes in T_c. Therefore the simple feedback control will be effective in compensating for changes in T_i and less effective in compensating for changes in

^Tc.

Cascade control: We can improve the response of simple feedback control to changes in the coolant temperature by measuring T_c and taking control action before the effect has been felt by the reacting mixture. Thus if T_c goes up, increase the flow rate of coolant to remove same amount of heat. Decrease F_c when T_c decreases.

There are two different control loops using two different measurements, T and T_c. But sharing common manipulated variable F_c. The loop that measures T (controlled variables) is the dominant or primary or master control loop and uses a setpoint supplied by the operator whereas the loop that measures T_c uses the output of the primary controller as its setpoint and called as the secondary or slave loop.

​

Feedforward control

​

The two disturbances: inlet concentration and temperature

The two manipulated variables: Product withdrawal flow rate and the coolant flow rate.

​

Two objectives: To maintain constant temperature and composition

Piping and Instrumentation (P & I) Diagram

- Process diagrams can be divided into two major

Introduction:

Process diagrams categories:1)process

flow diagrams (PFDs) and 2) process and instrument drawings (P&IDs), sometimes called piping and instrumentation drawings.

A process flow diagram (PFD) is a simple illustration that uses process symbols to describe the primary flow path through a unit. A process flow diagram provides a quick snapshot of the operating unit. Flow diagrams include all primary equipment and flows. A technician can use this document to trace the primary flow of chemicals through the unit. Secondary or minor flows are not included. Complex control loops and instrumentation are not included. The flow diagram is used for visitor information and new employee training.

A process and instrument drawing (PID) is more complex. The P&ID includes a graphic representation of the equipment, piping, and instrumentation. Modern process control can be clearly inserted into the drawing to provide a process technician with a complete picture of electronic and instrument systems. Process operators can look at their process and see how the engineering department has automated the unit. Pressure, temperature, flow, and level control loops are all included on the unit P&ID.

Basic Instrument Symbols

Process technicians use P&IDs to identify all of the equipment, instruments, and piping found in their units. New technicians use these drawings during their initial training period. Knowing and recognizing these symbols is important for a new technician. The chemical processing industry has assigned a symbol for each type of valve, pump, compressor, steam turbine, heat exchanger, cooling tower, basic instrumentation, reactor, distillation column, furnace, and boiler (shown in Figure below). There are symbols to represent major and minor process lines and pneumatic, hydraulic, or electric lines, and there is a wide variety of electrical symbols.

Process and Instrument Drawings

A P&ID is a complex representation of the various units found in a plant. It is used by people in a variety of crafts. The primary users of the document after plant startup are process technicians and instrument and electrical, mechanical, safety, and engineering personnel. In order to read a P&ID, the technician needs an understanding of the equipment, instrumentation, and technology.

The next step in using a P&ID is to memorize your plant’s process symbol list. This information can be found on the process legend. Process and instrument drawings have a variety of elements, including flow diagrams, equipment locations, elevation plans, electrical layouts, loop diagrams, title blocks and legends, and foundation drawings. The entire P&ID provides a three-dimensional look at the various operating units in a plant. The Scientific Apparatus Makers Association (SAMA) system of diagramming is used in piping and instrumentation diagrams like International Society of Automation (ISA) system. The intent of these diagrams is to identify all the instrumentation measuring and

final control devices and to show their locations in relation to those of the piping and major equipment.

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A P&ID should include:

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Instrumentation and designations

Mechanical equipment with names and numbers

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All valves and their identifications

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Process piping, sizes and identification

Miscellaneous - vents, drains, special fittings, sampling lines, reducers, increasers and swagers

Permanent start-up and flush lines Flow directions

Interconnections references Control inputs and outputs

Interfaces for class changes Vendor and contractor interfaces

Identification of components and subsystems delivered by others Intended physical sequence of the equipment

A P&ID should not include: manual switches

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equipment rating or capacity

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pressure temperature and flow data

elbow, tees and similar standard fittings

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extensive explanatory notes

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P & I DIAGRAM OF BOILER

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The Piping and instrumentation of a boiler is shown below.

Air is taken for combustion reaction through forced draft fan across air heater.

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The temperature of the air is transmitted by temperature transmitter (TT105).

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Flow rate of air is measured by differential pressure measuring flow transmitter (FT104).

The fuel, powdered coal is supplied from the pulverizer through mass flow meter (FT103).

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The fuel in flow is controlled by flow controller (FC300).

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The furnace temperature is measured by temperature transmitters (TT106).

Drum level of steam drum and mid drum is measured using level transmitter (LT108).

Feed water enters back into the boiler after being deaerated at the deaerator and pre –heated at the economizer through flow controller (FC301).

Pressure, flow and temperature of super heated steam are measured by using PT101, FT102 and TE111 respectively.

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Finally the super heated steam is passed on to the prime mover (turbine).

Flue gas the by-product of combustion reaction at the boiler is forced out through the stack.

The temperature of the flue gas is exchanged with feed water temperature and fed into the boiler. Flow rate of this water is measured using flow transmitter (FT109).

The flue gas temperature at the stack is measured by temperature element (TE110).

Also the flue gas is analysed for emission control purposes by analyzer transmitter (AT107)

Thus the piping and instrumentation diagram, drawn under ISA control diagramming system and SAMA control diagramming system shows graphically all equipments, piping & Instrumentation of boiler in a power plant.

10. ASSIGNMENTS

1. What is P&ID?

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• When to use P&IDs and who uses them

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• What are P&IDs all about?
• What’s the difference between a process flow diagram (PFD) and a piping & instrumentation diagram (P&ID)?

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• What are the limitations of P&ID?

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• What should a P&ID include?

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• What should a P&ID not include?

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• How to create a P&ID

11. PART-A

12. PART-B

13. SUPPORTIVE ONLINE CERTIFICATION COURSES

• Online Course: SWAYAM

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Course Name: Chemical Process control

Course Instructor: Prof. Sujit Jogwar, IIT Bombay Duration: 8 weeks

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AICTE approved FDP course

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• Online course:Coursera

Course Name: Sensor Manufacturing and Process Control

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University: University of Colorado Boulder

Course Instructor: James Zweighaft, Jay Mendelson Duration: 5 weeks

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• Online course:Udemy

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Course Name: Introduction to process control and Instrumentation

Course Instructor: WR Training, Petroleum Petrochemical & Chemical Engineering

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Duration: 10 sections,75 lectures

14. REAL TIME APPLICATIONS IN DAY TO DAY LIFE AND TO INDUSTRY

A.Detchrat,V.Tipuwanporn,A.Numsomran and S.Suvikath,Member;IAENG.IMC based PID controllers design for two mass system,International Multiconference of Engineers and Computer Scientists, 2, 2012.

A.Purna Chandra rao, Y.P Obulesu, CH. Saibabu, ,Robust internal model control strategy based PID controller for BLDCM, International journel of engineering science& technology, 2(11),2010.

Athi thilagalakshmi, Vijay Anand, Simulation of Neuro PID controller for pressure process,International Journal of Computer Applications(0975-8887) Gediminas Liaucius,Vytautas Kaminskas, Adaptive digital PID control of pressure process,Energetika,2012

K.Ghousiya Begum,D.Mercy,H.Kiran Vedi,M.Ramathilagam, An intelligent model based level control of boiler drum,Iternational Journal Of Emerging Technology and Advanced Engineering.

K.Rajalakshmi,Ms.V.Mangaiyarkarasi Control of heat exchange using internal model control,ISOR Journal of Engineering

15. CONTENT BEYOND SYLLABUS

Adaptive Control

What is Adaptive Control ? An adaptive controller differs from an ordinary controller in that the controller parameters are variable, and there is a mechanism for adjusting these parameters on-line based on signals in the system. There are two main approaches for constructing adaptive controllers: so-called model- reference adaptive control method and so-called self-tuning method.

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Model-Reference Adaptive Control (MRAC)

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A MRAC can be schematically represented by Fig.

It is composed of four parts: a plant containing unknown parameters, a reference model for compactly specifying the desired output of the control system, a feedback control law containing adjustable parameters, and an adaptation mechanism for updating the adjustable parameters. The plant is assumed to have a known structure, although the parameters are unknown. - For linear plants, the numbers of poles and zeros are assumed to be known, but their locations are not. - For nonlinear plants, this implies that the structure of the dynamic equations is known, but that some parameters are not. A reference model is used to specify the ideal response of the adaptive control system to external command.

The choice of the reference model has to satisfy two requirements: - It should reflect the performance specification in the control tasks such as rise time, settling time, overshoot or frequency domain characteristics. - This ideal behavior should be achievable for the adaptive control system, i.e., there are some inherence constrains on the structure of reference model given the assumed structure of the plant model. The controller is usually parameterized by a number of adjustable parameters. The controller should have perfect tracking capacity in order to allow the possibility f tracking convergence. Existing adaptive control designs normally required linear parametrization of the controller in order to obtain adaptation mechanisms with guaranteed stability and tracking convergence. The adaptation mechanism is used to adjust the parameters in the control law. In MRAC systems, the adaptation law searches for parameters such that the response of the plant under adaptive control becomes the same as that of the reference model. The main difference from conventional control lies in the existence of this mechanism.

16. ASSESSMENT SCHEDULE (PROPOSED DATE & ACTUAL DATE)

17. Prescribed Text Books & Reference Books

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TEXT BOOKS:

1. Seborg, D.E., Edgar, T.F. and Mellichamp, D.A., ―Process Dynamics and Control‖, WileyJohn and Sons, 2nd Edition, 2003.
2. Bequette, B.W., ―Process Control Modeling, Design and Simulation‖, Prentice Hall of India, 2004.
3. Stephanopoulos, G., ―Chemical Process Control - An Introduction to Theory and Practice‖, Prentice Hall of India, 2005.

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REFERENCES:

1. Coughanowr, D.R., ―Process SystemsAnalysis and Control‖, McGraw - Hill International Edition,2004.
2. Curtis D. Johnson, ―Process Control Instrumentation Technology‖, 8th Edition, Pearson, 2006.
3. Considine, D.M., Process Instruments and Controls Handbook, Second Edition, McGraw, 1999.
4. Bela.G.Liptak., ―Process Control and Optimization‖., Instrument Engineers’ Handbook., volume 2, CRC press and ISA, 2005.
5. Ramesh C. Panda., T.Thyagarajan., ―AnIntroduction to Process Modelling Identification and Control for Engineers‖ Narosa Publishing house Pvt. Ltd, 2017.

18. MINI PROJECT SUGGESTIONS

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Project 1:

Design a IMC based PID controller for DC motor and compare with ZN method time domain specifications .

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Project 2:

Design IMC Based Design of PI Controller for Real Time Pressure Process.

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Project 3:

Design model based controller for level process.

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Project 4:

Using Matlab implement cascade control for Heat exchanger sand analysis the setpoint tracking of the system.

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Project 5:

Determine the closed loop response of Heat exchanger with feeback and feedforward control scheme Using Matlab

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Project 6:

Draw the Piping and Instrumentation Diagram of any one industrial process of your interest.

Thank you

Disclaimer:

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