CHAPTER 2
LITEARATURE REVIEW
This chapter includes a fully-referenced review and discussion of previous studies which are relevant to the research.
2.1
Distillation Co Column
Distillation is a physical process of separating the mixture into two or more products that have have differ different ent boilin boiling g points points,, by prefere preferenti ntially ally boilin boiling g more more volatil volatilee compon component entss of the mixture. When the liquid mixture of two volatile material is heated, the vapour that comes off will have higher concentrations concentrations of volatile volatile (i.e., lower boiling boiling point of liquid material material from where it has grown. !n the other hand, if the vapour is cooled, the less volatile (i.e., the higher the boiling point materials have a tendency to condense on a larger share of the more volatile substances.
2.1.1
Model Des Design
"eed is introduced approximately centrally into the global vertical cascade. #team rising in section at the top of the feed was washed with a liquid to remove or absorb the less volatile component. #ince no external substances are added, as in the case of absorption, washing
liquid in this case is provided by the condensation of steam removing from the top, which is rich in the more volatile component. The liquid on the top tower is called reflux, and materials are removed permanently is distillate, which may be vapour or liquid, rich in the more volatile component. $n the bottom of the liquid feed was stripped of volatile components with steam generated at the bottom by partial evaporation of liquid in the bottom reboiler. %ich liquid produced in less volatile component, or the bottom of the tray. $nside the tower, liquid and vapour are always in bubble point and dew point, respectively, so that the highest temperature is at the bottom and the lowest at the top. The figure below shows the example of a typical distillation column model&
Figure 1: Distillation Column Model
2.1.2
Model Assumptions
'. onstant relative volatility. $n this case the vapour-liquid equilibrium between any two components, where ) is independent of composition. This assumption holds well for the separation of similar components, for example, for alcohols or for hydrocarbons.
*. onstant molar flows. $n this case the molar flows of liquid and vapour along the column do not change from one stage to the next, that is, if there is no feed or product removal between stages i and i+'. 2.1. Design Pa!amete!s
olumn proposal in this wor is designed to produce a distillate product with D /.0 mol 1 min and the composition of the product yds /.22 and 3 bottom with /.0 mol 1 min and composition x3 /./', from feed equimolal " ' mol 1 min of methanol 1 ethanol at 004. #o we have the following nominal conditions&
•
"eed rate " ' (mol 1 min "ood composition 5" /.0 (unit mole fraction "eed temperature T" 004 6D /.22, 7 /.00 mol 1 min x3 /./', 3 /.00 mol 1 min With 5" /.0 and T" 004 we calculate the pet food q& q './8/0 %eflux stream is 9 *.:;;2 mol 1 min and flow boilup is < =.**28 mol 1 min. Detainees nominal liquid is >i /.0 mol for all levels, including reboiler and
•
condenser. The relative volatility of methanol-ethanol system is considered to be '.0.
• • • • • • •
2.1."
#pe!ating Points
$ndustrial process usually wors with different quality in their products. $n this case, we will considers that the distillation column should get three different quality, that is, three different operating point& !perating 7oint ' * =
x3 /./' /./' /./0
yD
D
3
9
<
/.22 /.2: /.22
(mol1min /.00 /.00 /.00
(mol1min /.00 /.00 /.00
(mol1min *.:;;2 *.*0=? *.=*8=
(mol1min =.**28 *.;'// *.;8=0
2.2
$imulation
#imulation studies are often used to examine the operational behaviour of distillation columns. @ rigorous model for the simulation of the dynamic behaviour of distillation column has been developed. This model allows the analysis of the influence of different disturbances (failures on the dynamic behaviour of a methanol1water column.
2.2.1
$imulin%
#imulin is an environment for multi domain simulation and >odel-3ased Design for dynamic and embedded systems. $t provides an interactive graphical environment and a customiAable set bloc libraries that let you design, simulate, implement, and test a variety of time varying systems, including communications, controls, signal processing, video processing, and image processing. #imulin is integrated with >@T9@3 , providing immediate access to various tools that lets you develop algorithms , analyse and visualiAe simulations, create batch processing scripts, customiAe the modelling environment, and define signal, parameter, and test data. #imulin provides a graphical user interface (BC$ for building models as bloc diagrams, allows you to draw a model as you would with pencil and paper. #imulin also includes omprehensive bloc libraries sins, sources, linear and nonlinear component, and connector. The interactive graphical environment simplifies the process of modelling, eliminate the need to devise a different, and the differences or similarities in the language program. $t is also hierarchical, so you can build them using both top-down and bottom-up approach. 6ou can see the system at a high level and then double-clic the bloc to see the level of improvement detailed model. This approach provides insight into how the model organiAed and how its parts interact.
2.2.2
Distillation Column Model
The original model uses an #-"unction bloc that calls a >@T9@3 function that implements the system of differential equations. This nonlinear model has four manipulated inputs (9, <, D and 3, three disturbances (", A" and q" and * states returned by #-"unction colas&
• • • • • •
#tate '& liquid composition in reboiler x' x3, Then follow the stage compositions xi up the column, i *, E, -' #tate & composition stage (condenser, x yD #tate +'& holdup reboiler, >' >3 Then follow the stage holdups up the column >i, i +*, E, *-' #tate *& condenser holdup, >* >D
Figure 2: Distillation Column Simulink original model.
2.2.
Distu!&an'es
#everal types of disturbances have been implemented in the simulation model. We can specify the time at which the disturbance will be activated and the siAe, type (spie, ramp and pulse and duration of the disturbance.
2.
Monito!ing
>onitoring of chemical processes is becoming increasingly difficult as a result of the more complex and larger scale operations. $n this experiment, 9ab<$FW is used to monitor the operation of a laboratory scale distillation column and to identify process state. !ccurrence of a fault will result in the deviation from the normal operating traGectory. %oot cause identification can also be performed through simple visualiAation of component planes.
2..1
La&VIEW
9ab<$FW software is suitable for any siAe or control system, and the heart of $ design platforms. $ntegrates all the tools that engineers and scientists need to develop a variety of applications in less dramatic, 9ab<$FW is a development environment for problem solving, accelerated productivity and continuous innovation. $ 9ab<$FW system design software is at the heart of the ational $nstruments platform. $t provides a comprehensive tool that you need to build any siAe or control applications in less time, 9ab<$FW is an ideal development environment for innovation, discovery, and accelerated results. ombining the power of 9ab<$FW and modular software, hardware reshaped to address the growing complexity involved in providing measurement and control system on time and under budget.
2."
P!o'ess Cont!ol
@ process is the conversion of feed materials for products using chemical and physical operations. $n practice, the term tends to be used for both processing operations and processing equipment. The main obGective is to maintain control of the process in the operating process required conditions, safely and efficiently, while satisfying environmental quality and product needs. The subGect of process control is concerned with how to achieve this goal. $n large-scale, integrated processing plants such as oil refineries or ethylene plants, thousands process variables such as composition, temperature, and pressure is measured and must controlled. "ortunately, a large number of process variables can usually be manipulated for this purpose. "eedbac control system compares the siAe with their then adGust the desired value and the manipulated variable accordingly.
2.".1
P!opo!tional 'ont!ol
The ey concepts behind proportional control are the following&
•
The controller gain can be adGusted to mae the controller output changes as sensitive
•
as desired to deviations between se point and controlled variable The sign of Hc can be chosen to mae the controller output increase (or decrease as the error signal increases.
@n inherent weaness of proportional only control is a steady-state error (or offset applies after the change set - points or disorder suffered. 3asically, the offset can be eliminated manually reset the set point y#7. Iowever, this approach is not appropriate because operator intervention is required and the new y#7 usually have to be sought by trial and error. "or control applications where offsets can be tolerated, proportional-only control is attractive because of its simplicity.
2.".2
Integ!al 'ont!ol
$ntegral control measures are widely used because it provides an important practical advantages, offset losses. When important measures were used, p automatically changes until it reaches the required to mae Aero steady-state error. %easonable condition always applies unless the controller output or final control elements saturates and therefore cannot be brought under control changed bac to set point. ontroller saturated or harassment occurs when set- point the change is so great that it is beyond the range of the manipulated variable. Therefore, an important control measures are commonly used in in conGunction with proportional control as proportional - integral (7$ controller.
!ne disadvantage of using integral control is that it tends to produce oscillations of responses controlled turns and reduce the stability of the feedbac control system. @ limited number of swing usually acceptable because it is often associated with a more rapid response. That undesirable effects were too many important actions can be avoided with proper tuning controller or by acts including derivatives that tend to counteract the effects of instability.
2.".
De!i(ati(e 'ont!ol
Derivative control action function is to anticipate the future behaviour of the error signal considering the rate of change. ote that a proportional controller reacts to temperature deviations only, without any difference to the time period in which the deviation is growing. $mportant control measures are not effective for sudden deviations in temperature due corrective action depends on the duration of the deviation. Therefore, the derivative control is never used aloneJ it is always used in conGunction with proportional or proportional control integral. To provide control measures expectations, modes tend issue to stabiliAe the controlled process. Therefore, it is often used to overcome the tendency of instability important mode.
2."."
P!opo!tional)Integ!al)De!i(ati(e Cont!ol
The combination of proportional, integral, and derivative control mode as 7$D controllers can be done in many variations. The three most common form is the form of parallel and series. 7$D controllers are used in a loop where the signal is not noisy and where tight dynamic response is important. Derivative action helps to offset the lag in the loop. ontroller 7$D temperature in the reactor normally. The controller senses the rate of movement of set points and start moving the control valve earlier than the 7$ only.
2.*
Multi(a!iate $tatisti'al P!o'ess Cont!ol and Enginee!ing P!o'ess Cont!ol
#tatistical 7rocess ontrol (#7 and Fngineering 7rocess ontrol (F7 are two techniques that are used for improving process productivity and product quality by reducing the variability of process from target while eeping it stable and under control.
2.*.1
$tatisti'al P!o'ess Cont!ol +$PC,
#tatistical process control, a technique widely used, accomplishes the above tass to monitor and trac ey changes in the behaviour of the system. $t is an effective technique for monitoring process variables as far as possible otherwise advised by an independent statistical variable whose value falls around the deterministic.
2.*.2
Enginee!ing P!o'ess Cont!ol +EPC,
Fngineering process control attempts to reduce variability by controlling process variables to ensure output of the target process. $t is an ongoing procedure that adGusts the manipulatable variables to ensure output at set point or target.
2.*.
Integ!ation o- $PC and EPC
$ntegration of #tatistical 7rocess ontrol and Fngineering 7rocess ontrol got first attention in '2;; when (>acBregor '2;;, 3ox and Hramer '22* proposed this concept of integration and convinced the #7 research community that control charts can be used to monitor a KcontrolledL system. Ie reviewed the two schemes, their similarities, overlap, contradictions, reasons behind their isolation and the need to integrate them.
!ne of the popular schemes of #71F7 integration involves triggering of F7 controller only when the #7 signals the presence of faults or assignable cause as termed by statisticiansJ (embhard and >astrangelo were the earliest researchers who have advocated that F7-based process adGustments should only be triggered if #7 detects the Kout-ofcontrol stateL of the system which reflects the presence of fault(s.
The most powerful approach of #7 and F7 integration involves continuous adGustments using F7 and detection of assignable cause using #7 monitoring. #everal researchers have explored different F7 techniques along with different control charts for this purpose.
2.
Distu!&an'e
The type and magnitude of disorders affecting the distillation column has a direct effect on diversity of product. This is the main type of harassment faced in distillation column& 2..1
/eed 'omposition upsets
hanges in the composition of the feeds that represent the most significant disruption to the distillation column systems have to be deal on a continuous basis. hanges in feed composition switch composition profile through the column which resulted in a huge upset in the composition of the product. >ost of the industrial column has no feed composition analyserJ therefore, the composition of the feed disorders usually appear as unmeasured disturbance.
2..2
/eed -lo0 !ate upsets
The flow rate in the steady state model column with a continuous scale tray efficiency continues with a feed rate. Therefore, the ratio control is an effective means to address the upset flow rate of feed. Dynamic compensation is usually necessary to tae into account when the dynamics of the response does not match the composition of the product to feed and flow rate changes in response to changes in the ><.
2..
/eed entalp upsets
"or column that use a low reflux ratio, enthalpy changes can significantly alter the feed rate of vapour 1 liquid in the column, which led to maGor changes in the internal composition and profile, therefore, a significant upset in the composition of the product. This upset can be difficult to identify because most industrial column has no temperature measurements of feed
and though the measuring the temperature of feed available, it does not detect a change in enthalpy of feed for two-phase feed.
2.."
$u&'ooled !e-lu3 'anges
When a thundershower passes over a plant, the reflux temperatures for the columns can drop sharply. olumns that use finned-fan coolers as overhead condensers are particularly susceptible to rapid changes in ambient conditions. $f internal reflux control is not applied, severe upsets in the operation of the columns result because of maGor shifts in the composition profiles of the columns.
2..*
Loss o- !e&oile! steam p!essu!e
When a steep drop in steam header pressure occurs, certain columns experience a sharp drop in reboiler duty. This results in a sharp increase in the impurity levels in the products. When the steam header pressure returns to its normal level, the composition control system for the column attempts to return to the normal product purities. 3ecause of the severity of this upset, if the composition controllers are not properly tuned, the upset can be amplified by the composition controllers, requiring the operators to tae these controllers off-line to stabiliAe the column, greatly extending the duration of the period of production of off-specification products.
2..
Column p!essu!e upsets
olumn pressure has a direct effect on the relative volatility of the ey components in the column. Thus, changes in the column pressure can significantly affect product compositions. @ properly implemented pressure control scheme maintains column pressure close to its set point, with only short-term and low-amplitude departures. @ large class of columns is
operated at maximum condenser duty to maximiAe column separation, which minimiAes steam usage.
