Process Control_ Modeling, Design and Simulation - B. Wayne spawdelacseopror.ga Process Control: Modeling, Design, and Simulation teaches the field's most. [ Team LiB ] \u Table of Contents Process Control: Modeling, Design, and Simulation By B. Wayne Bequette Publisher: Prentice Hall PTR Pub Date. process design and process control perspectives. It is easy to design a treatment of process dynamics, including modeling, analysis, and simulation. This text-.
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The fight has left congressional leaders unsure of whether they will eventually come to an agreement. Some remain optimistic that Trump can be walked back to the political center and will cut a deal that expands border security while protecting those under the Deferred Action for Childhood Arrivals DACA program, which Trump has ordered ended.
Trump complained that there wasn't enough money included in the deal for his promised wall along the U. He also objected that Democratic proposals to adjust the visa lottery and federal policy for immigrants with temporary protected status were going to drive 3. Attendees who were alarmed by the racial undertones of Trump's remarks were further disturbed when the topic of the Congressional Black Caucus CBC came up, these people said.
At one point, Durbin told the president that members of that caucus — an influential House group — would be more likely to agree to a deal if certain countries were included in the proposed protections, according to people familiar with the meeting. Trump was curt and dismissive, saying he was not making immigration policy to cater to the CBC and did not particularly care about that bloc's demands, according to people briefed on the meeting.
Kelly was in the room and was largely stone-faced, not giving any visible reaction when Trump said "shithole countries" or when he said Haitians should not be part of any deal, White House advisers said. At one point, Graham told Trump he should use different language to discuss immigration, people briefed on the meeting said. As Trump batted back the Democrats, he was urged on by Republican lawmakers. Bob Goodlatte R-Va. Durbin was not interested, White House officials said. After Graham left, he told associates that he was disturbed by what he heard in the Oval Office, according to people who spoke with him, and that it was evident the deal's antagonists had gotten to Trump.
Graham and Durbin also told allies that they were stunned that the other lawmakers were present — and that Trump's tone seemed so different than it had been days or even hours before, according to people close to them.
Graham declined to comment on the president's reported obscenity. He has told others in his circle that commenting would only hurt the chance of a deal and that he wants to keep a relationship with the president. There had initially been hope for the Thursday meeting.
Trump had told lawmakers during a partially televised session two days earlier that he was flexible. He even said he would be willing to lock the door of the Cabinet room if they wanted to negotiate at the White House, according to people who heard his comments. Trump went on to say at the earlier meeting that he wanted a deal and that even those in the conservative House Freedom Caucus should work with Durbin.
In the hours and days afterward, a bipartisan group of senators — Graham, Durbin, Sen. Jeff Flake R-Ariz. Robert Menendez D-N.
Michael F. But some White House officials, including conservative adviser Stephen Miller, feared that Graham and Durbin would try to trick Trump into signing a bill that was damaging to him and would hurt him with his political base. As word trickled out Thursday morning on Capitol Hill that Durbin and Graham were heading over to the White House, legislative affairs director Marc Short began to make calls to lawmakers and shared many of Miller's concerns.
Soon, Goodlatte, one of the more conservative House members on immigration, was headed to the White House. David Perdue R-Ga. In the late morning, before Durbin and Graham arrived, Kelly — who had already been briefed on the deal — talked to Trump to tell him that the proposal would probably not be good for his agenda, White House officials said. Kelly, a former secretary of homeland security, has taken an increasingly aggressive and influential role in the immigration negotiations, calling lawmakers and meeting with White House aides daily — more than he has on other topics.
He has "very strong feelings," in the words of one official. But he's not a lone voice. Trump in recent weeks has also been talking more to conservatives such as Rep.
Mark Meadows R-N. At low velocity, algorithms exist to account for nonlinearity. Vortex frequency is an input, fluid velocity is an out- The control valve should be fail-closed. Increasing put. Loss of b. Orifice—plate flow meters air to the valve will cause it to close. The gain of the Please see vortex—shedding flow meters for a repre- valve is positive, because an increase in the signal to sentative answer. Mass flow meters h. Please see vortex—shedding flow meters for a repre- It is important from a safety perspective to have a sentative answer.
Thermocouple based temperature measurements flame - this could cause the furnace firebox to fill with Please see vortex—shedding flow meters for a repre- fuel gas, which could then re-ignite under certain con- sentative answer. Although the combustion air is not shown, it e. Differential pressure measurements should be supplied with a small stoichiometric excess.
Please see vortex—shedding flow meters for a repre- If there is too much excess combustion air, energy is sentative answer. If there is too little excess air, combustion will not be f.
Control valves complete, causing fuel gas to be wasted and pollu- Please see vortex—shedding flow meters for a repre- tion to the atmosphere.
The process fluid is flowing to another unit. If the process fluid is not at the 1. This can also be seen visu- We are also given that the revamp will cost ally in Figure 2 below. We can now calculate the time required to payback the control system investment. Insulin is the ma- Therefore, we know it will take days to pay off nipulated input and blood glucose is the measured the investment. As performed by injection, the input is re- ally discrete and not continuous.
Also, glucose is 1. Disturbances include meal consumption and exercise. A fail-closed valve should be speci- fied. Bernard Goodwin Editorial assistant: Michelle Vincenti Marketing manager: For information regarding corporate and government bulk discounts please contact: Corporate and Government Sales or corpsales pearsontechgroup.
The MathWorks, Inc. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher. Pearson Education North Asia Ltd. Pearson Education Canada, Ltd. Formally launched as its own imprint in , our editors now publish over books annually, authored by leaders in the fields of computing, engineering, and business.
Our roots are firmly planted in the soil that gave rise to the technological revolution. Our bookshelf contains many of the industry's computing and engineering classics: PH PTR acknowledges its auspicious beginnings while it looks to the future for inspiration. We continue to evolve and break new ground in publishing by providing today's professionals with tomorrow's solutions. The focus of virtually all of these courses is on steady-state behavior; the rare exceptions include the analysis of batch reactors and batch distillation in the reaction engineering and equilibrium stage operations courses, respectively.
A concern of a practicing process engineer, on the other hand, is how to best operate a process plant where everything seems to be changing. The process dynamics and control course is where students must gain an appreciation for the dynamic nature of chemical processes and develop strategies to operate these processes. My experience is that students learn best with immediate simulation-based reinforcement of basic concepts.
Rather than simply present theory topics and develop analytical solutions, this textbook uses "interactive learning" through computer-based simulation exercises modules. Students, instructors, and practicing process engineers learning new model-based techniques can all benefit from the "feedback" provided by simulation studies. I wish for students to obtain the classic mode of understanding as analyzed so well by Robert Pirsig in Zen and the Art of Motorcycle Maintenance Bantam Books, This deeper understanding of process control can be obtained by rigorous analysis and by selected simulations where the student plays a direct role in the implementation of an algorithm or strategy of choice.
Still, it is probably too ambitious to cover the entire text during a typical week semester, so I recommend that instructors carefully choose the topics that best meet their personal objectives. At Rensselaer Polytechnic Institute, we teach the one-semester, 4-credit course in a studio-based format, with students attending two 2-hour sessions and one 2-hour recitation which also provides plenty of "catch-up" time each week. During these sessions we typically spend 45 minutes discussing a topic, then have the students spend the remaining hour performing analysis and computer simulation exercises, working in pairs.
During the discussion periods the students face the instructor station at the front of the room, and during the simulation periods they swivel in their chairs to the workstations on the countertops behind them. This textbook can also be used in a more traditional lecture-based course, with students working on the modules and solving homework problems on their own.
The development and use of models is very important in control systems engineering. Fundamental models are developed in Chapter 2, including the steady-state solution and linearization to form state space models. Chapter 3 focuses on the dynamic behavior of linear systems, starting with state space models and then covering transfer function-based models in detail.
Chapter 4 we cover the development of empirical models, including continuous and discrete transfer function models. Chapter 5 provides a more detailed introduction to feedback control, developing the basic idea of a feedback system, proportional, integral, derivative PID controllers, and methods of analyzing closed-loop stability.
Chapter 6 presents the Ziegler-Nichols closed-loop oscillation method for controller tuning, since the same basic concept is used in the automatic tuning procedures presented in Chapter Frequency response analysis techniques, important for determining control system robustness, are presented in Chapter 7. In recent years model-based control has lead to improved control loop performance.
One of the clearest model-based techniques is internal model control IMC , which is presented in Chapter 8. The PID controller remains the most widely used controller in industry; in Chapter 9 we show how to convert internal model controllers to classical feedback PID controllers.
In Chapter 10 the widely used cascade and feed-forward strategies are developed. Many control loops suffer from poor performance, either because they were not tuned well originally, or because the process is nonlinear and has changed operating conditions.
Two methods of dealing with these problems, automatic tuning and gain scheduling, are presented in Chapter The phenomenon of reset windup and the development of antireset windup strategies are also presented in Chapter Many control strategies must be able to switch between manipulated inputs or select from several measured outputs. Split-range, selective and override strategies are presented in Chapter Process units contain many control loops that generally do not operate independently.
The effects of these control-loop interactions are presented in Chapter The design of multivariable controllers is developed in Chapter The development of the control instrumentation diagram for an entire chemical process is challenging and remains somewhat of an art.
In Chapter 15 recycle systems are shown to cause unique and challenging steady-state and dynamic control problems. In addition, an overview of corporate-wide optimization and control problems is presented. Model predictive control MPC is the most widely applied advanced control strategy in industry.
The basic step response model- based MPC method is developed in Chapter This is followed by a discussion of the constrained version of MPC, and enhancements to improve disturbance rejection. The modules that follow focus on a particular unit operation, to provide a detailed demonstration of various control system design, analysis or implementation techniques.
Module 5 develops a simple isothermal CSTR model that is used in a number of the chapters. Module 7 presents a biochemical reactor with two possible desired operating points; one stable and the other unstable. The controller design and system performance is clearly different at each operating point. Issues discussed include recirculation heat transfer dynamics, cascade control, and split-range control.
Level control loops can be tuned for two different extremes of closed-loop performance, as shown in Module 9 steam drum, requiring tight level control and Module 10 surge drum, allowing loose level control to minimize outflow variation. Challenges associated with jacketed batch reactors are presented in Module Some motivating biomedical problems are presented in Module Challenges of control loop interaction are demonstrated in the distillation application of Module Module 14 provides an overview of several case study problems in multivariable control.
These case studies are meant to tie together many concepts presented in the text. Issues particular to flow control are discussed in Module 15, and digital control techniques are presented in Module First of all, Professor Jim Turpin at the University of Arkansas stimulated my interest in process dynamics and control when I took his course as an undergraduate.
As a neophyte process engineer for American Petrofina I had the opportunity to serve as a process operator during two work-stoppages. A newfound respect for control loop interaction led me to graduate study at the University of Texas, where Professor Tom Edgar provided the "degrees of freedom" for me to explore a range of control topics.
Collaborations at Merck, Inc. Research sponsored by the Whitaker Foundation and the National Science Foundation resulted in material presented in Modules 12 and My own graduate students have served as teaching assistants in the dynamics and control courses, and have provided me with valuable feedback on various versions of this textbook.
In particular, Lou Russo, now at ExxonMobil, helped me understand what works and what does not work in the classroom and in homework assignments. He certainly had a major positive impact on the education of many Rensselaer undergraduates. Professor Robert Parker at the University of Pittsburgh classroom tested this textbook, and made a number of valuable suggestions. My colleagues at Rensselaer have promoted an environment that provides an optimum mix of teaching and research; our department has published four textbooks during the past two years.
Various educational initiatives at Rensselaer have allowed me to develop an interactive learning approach to dynamics and control. In particular, the Control Engineering Studio environment gives me immediate feedback on the level of practical understanding on a particular topic and allows me to give immediate feedback to students.
Various Troy and Albany establishments have served to "gain schedule" my personal regulatory system and allowed me to obtain a better understanding of the pharma cokinetics and pharmacodynamics of caffeine and ethanol.
Process Control_ Modeling, Design and Simulation - B. Wayne Bequette
The Daily Grind www. Seemingly innocuous problems assigned in the control class have led to interesting graduate research projects. Similarly, graduate research results have been brought into the undergraduate classroom. Naturally, completing this text would have been a struggle without the support of my wife, Pat Fahy, and the good sleeping habits of my kids, Brendan and Eileen. They have done their best to convince me that not all systems are controllable.
Introduction The goal of this chapter is to provide a motivation for, and an introduction to, process control and instrumentation. After studying this chapter, the reader, given a process, should be able to do the following: Determine possible control objectives, input variables manipulated and disturbance and output variables measured and unmeasured , and constraints hard or soft , as well as classify the process as continuous, batch, or semicontinuous Assess the importance of process control from safety, environmental, and economic points of view Sketch a process instrumentation and control diagram Draw a simplified control block diagram Understand the basic idea of feedback control Understand basic sensors measurement devices and actuators manipulated inputs Begin to develop intuition about characteristic timescales of dynamic behavior The major sections of this chapter are as follows: The objective of this textbook is to teach process engineers how to design and tune feedback controllers for the automated operation of chemical processes.
A conceptual process block diagram for a chemical process is shown in Figure Notice that the inputs are classified as either manipulated or disturbance variables and the outputs are classified as measured or unmeasured in Figure a.
To automate the operation of a process, it is important to use measurements of process outputs or disturbance inputs to make decisions about the proper values of manipulated inputs.
This is the purpose of the controller shown in Figure 1- 1b; the measurement and control signals are shown as dashed lines. These initial concepts probably seem very vague or abstract to you at this point.
Do not worry, because we present a number of examples in this chapter to clarify these ideas. Figure The development of a control strategy consists of formulating or identifying the following.
Process Control: Modeling, Design and Simulation
Control objective s. Input variables— classify these as a manipulated or b disturbance variables; inputs may change continuously, or at discrete intervals of time. Output variables— classify these as a measured or b unmeasured variables; measurements may be made continuously or at discrete intervals of time.
Constraints— classify these as a hard or b soft. Operating characteristics— classify these as a continuous, b batch, or c semicontinuous or semibatch. Safety, environmental, and economic considerations.
Control structure— the controllers can be feedback or feed forward in nature. Here we discuss each of the steps in formulating a control problem in more detail. The first step of developing a control strategy is to formulate the control objective s.
A chemical-process operating unit often consists of several unit operations. The control of an operating unit is generally reduced to considering the control of each unit operation separately.
Even so, each unit operation may have multiple, sometimes conflicting objectives, so the development of control objectives is not a trivial problem. Input variables can be classified as manipulated or disturbance variables. A manipulated input is one that can be adjusted by the control system or process operator.
A disturbance input is a variable that affects the process outputs but that cannot be adjusted by the control system. Inputs may change continuously or at discrete intervals of time. Output variables can be classified as measured or unmeasured variables. Measurements may be made continuously or at discrete intervals of time. Any process has certain operating constraints, which are classified as hard or soft. An example of a hard constraint is a minimum or maximum flow rate—a valve operates between the extremes of fully closed or fully open.
An example of a soft constraint is a product composition—it may be desirable to specify a composition between certain values to sell a product, but it is possible to violate this specification without posing a safety or environmental hazard. Operating characteristics are usually classified as continuous, batch, or semicontinuous semibatch.
Continuous processes operate for long periods of time under relatively constant operating conditions before being "shut down" for cleaning, catalyst regeneration, and so forth. For example, some processes in the oil-refining industry operate for 18 months between shutdowns.
Batch processes are dynamic in nature—that is, they generally operate for a short period of time and the operating conditions may vary quite a bit during that period of time. Example batch processes include beer or wine fermentation, as well as many specialty chemical processes. For a batch reactor, an initial charge is made to the reactor, and conditions temperature, pressure are varied to produce a desired product at the end of the batch time.
A typical semibatch process may have an initial charge to the reactor, but feed components may be added to the reactor during the course of the batch run. Another important consideration is the dominant timescale of a process. For continuous processes this is very often related to the residence time of the vessel.
Safety, environmental, and economic considerations are all very important. In many industries petroleum refining, for example , it is important to minimize energy costs while producing products that meet certain specifications.
Better process automation and control allows processes to operate closer to "optimum" conditions and to produce products where variability specifications are satisfied. The concept of "fail-safe" is always important in the selection of instrumentation. For example, a control valve needs an energy source to move the valve stem and change the flow; most often this is a pneumatic signal usually 3—15 psig.
If the signal is lost, then the valve stem will go to the 3-psig limit. If the valve is air-to-open, then the loss of instrument air will cause the valve to close; this is known as a fail-closed valve. If, on the other hand, a valve is air-to-close, when instrument air is lost the valve will go to its fully open state; this is known as a fail-open valve. The two standard control types are feed forward and feedback. A feed-forward controller measures the disturbance variable and sends this value to a controller, which adjusts the manipulated variable.
A feedback control system measures the output variable, compares that value to the desired output value, and uses this information to adjust the manipulated variable. For the first part of this textbook, we emphasize feedback control of single-input manipulated and single-output measured systems. Determining the feedback control structure for these systems consists of deciding which manipulated variable will be adjusted to control which measured variable.
The desired value of the measured process output is called the setpoint. A particularly important concept used in control system design is process gain.
The process gain is the sensitivity of a process output to a change in the process input. If an increase in a process input leads to an increase in the process output, this is known as a positive gain. If, on the other hand, an increase in the process input leads to a decrease in the process output, this is known as a negative gain.
The magnitude of the process gain is also important. For example, a change in power input of 0. The same input power change of 0. Once the control structure is determined, it is important to decide on the control algorithm.
The control algorithm uses measured output variable values along with desired output values to change the manipulated input variable. A control algorithm has a number of control parameters, which must be "tuned" adjusted to have acceptable performance.
Often the tuning is done on a simulation model before implementing the control strategy on the actual process. A significant portion of this textbook is on the use of model-based control, that is, controllers that have a model of the process "built in. Since many important concepts, such as control instrumentation diagrams and control block diagrams, are introduced in the next examples, it is important that you study them thoroughly.
Example 1. Surge Tank Surge tanks are often used as intermediate storage for fluid streams being transferred between process units. Consider the process flow diagram shown in Figure , where a fluid stream from process 1 is fed to the surge tank; the effluent from the surge tank is sent to process 2. Tank level problem. There are obvious constraints on the height in this tank.
If the tank overflows it may create safety and environmental hazards, which may also have economic significance. Let us analyze this system using a step-by-step procedure. Control objective: The control objective is to maintain the height within certain bounds.
If it is too high it will overflow and if it is too low there may be problems with the flow to process 2. Usually, a specific desired height will be selected. This desired height is known as the setpoint. Input variables: The input variables are the flow from process 1 and the flow to process 2. Notice that an outlet flow rate is considered an input to this problem. The question is which input is manipulated and which is a disturbance?
That depends. We discuss this problem further in a moment. Output variables: The most important output variable is the liquid level. We assume that it is measured.
There are a number of constraints in this problem. There is a maximum liquid level; if this is exceeded, the tank will overflow. There are minimum and maximum flow rates through the inlet and outlet valves. Operating characteristics: We assume that this is a continuous process, that is, that there is a continuous flow in and out of the tank. It would be a semicontinuous process if, for example, there was an inlet flow with no outlet flow if the tank was simply being filled.
Safety, environmental, and economic considerations: These aspects depend somewhat on the fluid characteristics. If it is a hazardous chemical, then there is a tremendous incentive from safety and environmental considerations to not allow the tank to overflow. Indeed, this is also an economic consideration, since injuries to employees or environmental cleanup costs money. Even if the substance is water, it has likely been treated by an upstream process unit, so losing water owing to overflow will incur an economic penalty.
Safety considerations play an important role in the specification of control valves fail-open or fail-closed. For this particular problem, the control-valve specification will depend on which input is manipulated. This is discussed in detail shortly. Control structure: There are numerous possibilities for control of this system. We discuss first the feedback strategies, then the feed-forward strategies.
Which input variable is manipulated depends on what is happening in process 1 and process 2. Let us consider two different scenarios. Scenario 1 Process 2 regulates the flow-rate F 2. This could happen, for example, if process 2 is a steam generation system and process 1 is a deionization process. Process 2 varies the flow rate of water F2 depending on the steam demand. As far as the tank process is concerned, F2 is a "wild" disturbance stream because the regulation of F2 is determined by another system.
In this case we would use F 1 as the manipulated variable; that is, F 1 is adjusted to maintain a desired tank height.
The control and instrumentation diagram for a feedback control strategy for scenario 1 is shown in Figure Notice that the level transmitter LT sends the measured height of liquid in the tank hm to the level controller LC. The LC compares the measured level with the desired level hsp, the height setpoint and sends a pressure signal Pv to the valve. This valve top pressure moves the valve stem up and down, changing the flow rate through the valve F1.
If the controller is designed properly, the flow rate changes to bring the tank height close to the desired setpoint. In this process and instrumentation diagram we use dashed lines to indicate signals between different pieces of instrumentation. Feedback control strategy 1.
The level is measured and the inlet flow rate valve position is manipulated. A simplified block diagram representing this system is shown in Figure Each signal and device or process is shown on the block diagram. We use a slightly different form for block diagrams when we use transfer function notation for control system analysis in Chapter 5. Note that each block represents a dynamic element.
We expect that the valve and LT dynamics will be much faster than the process dynamics. We also see clearly from the block diagram why this is known as a feedback control "loop.
The level is measured, and that value is fed back to the controller [which compares the measured level with the desired level setpoint ]. Feedback control schematic block diagram for scenario 1. F1 is manipulated and F 2 is a disturbance. Notice that the control valve should be specified as fail-closed or air-to-open, so that the tank will not overflow on loss of instrument air or other valve failure. Scenario 2 Process 1 regulates flow rate F 1.
This could happen, for example, if process 1 is producing a chemical compound that must be processed by process 2. Perhaps process 1 is set to produce F1 at a certain rate. F 1 is then considered "wild" a disturbance by the tank process. In this case we would adjust F 2 to maintain the tank height.
Notice that the control valve should be specified as fail-open or air-to-close, so that the tank will not overflow on loss of instrument air or other valve failure.
The process and instrumentation diagram for this scenario is shown in Figure The only difference between this and the previous instrumentation diagram Figure is that F2 rather than F1 is manipulated.
Feedback control strategy 2. Outlet flow rate is manipulated. The simplified block diagram shown in Figure differs from the previous case Figure only because F 2 rather than F1 is manipulated. F 1 is a disturbance input. Feedback control schematic block diagram for scenario 2. F2 is manipulated and F 1 is a disturbance. Feed-Forward Control The previous two feedback control strategies were based on measuring the output tank height and manipulating an input the inlet flow rate in scenario 1 and the outlet flow rate in scenario 2.
In each case the manipulated variable is changed after a disturbance affects the output. The advantage to a feed-forward control strategy is that a disturbance variable is measured and a manipulated variable is changed before the output is affected. Consider a case where the inlet flow rate can be changed by the upstream process unit and is therefore considered a disturbance variable.
If we can measure the inlet flow rate, we can manipulate the outlet flow rate to maintain a constant tank height. This feed-forward control strategy is shown in Figure , where FM is the flow measurement device and FFC is the feed-forward controller. The corresponding control block diagram is shown in Figure F 1 is a disturbance input that directly affects the tank height; the value of F1 is measured by the FM device, and the information is used by an FFC to change the manipulated input, F2.
Feed-forward control strategy. Inlet flow rate is measured and outlet flow rate is manipulated. Feed-forward control schematic block diagram. The main disadvantage to this approach is sensitivity to uncertainty. If the inlet flow rate is not perfectly measured or if the outlet flow rate cannot be manipulated perfectly, then the tank height will not be perfectly controlled.
With any small disturbance or uncertainty, the tank will eventually overflow or run dry. In practice, FFC is combined with feedback control to account for uncertainty.
Here, the feed-forward portion allows immediate corrective action to be taken before the disturbance inlet flow rate actually affects the output measurement tank height. The feedback controller adjusts the outlet flow rate to maintain the desired tank height, even with errors in the inlet flow-rate measurement.
The inlet flow rate is the measured disturbance, tank height is the measured output, and outlet flow rate is manipulated. Discussion of Level Controller Tuning and the Dominant Timescale Notice that we have not discussed the actual control algorithms; the details of control algorithms and tuning are delayed until Chapter 5.
Conceptually, would you prefer to tune level controllers for "fast" or "slow" responses? When tanks are used as surge vessels it is usually desirable to tune the controllers for a slow return to the setpoint. This is particularly true for scenario 2, where the inlet flow rate is considered a disturbance variable. The outlet flow rate is manipulated but affects another process.
In order to not upset the downstream process, we would like to change the outlet flow rate slowly, yet fast enough that the tank does not overflow or go dry. Related to the controller tuning issue is the importance of the dominant timescale of the process.
Consider the case where the maximum tank volume is gallons and the steady-state operating volume is gallons. Assume the inlet flow rate is a disturbance and outlet flow rate is manipulated Figure Clearly, controller tuning and concern about controller failure is different for these two cases.
The first example was fairly easy compared with most control-system synthesis problems in industry. Even for this simple example we found that there were many issues to be considered and a number of decisions specification of a fail-open or fail-closed valve, etc. Often there will be many and usually conflicting objectives, many possible manipulated variables, and numerous possible measured variables. It is helpful to think of common, everyday activities in the context of control, so you will become familiar with the types of control problems that can arise in practice.
The following activity is just such an example. Taking a Shower A common multivariable control problem that we face every day is taking a shower. A simplified process schematic is shown in Figure We analyze this process step by step. Control objectives: Control objectives when taking a shower include the following: Similar analysis can be performed for the other objectives. The manipulated input variables are hot-water and cold-water valve positions.
Some showers can also vary the velocity by adjustment of the shower head. Another input is body position—you can move into and out of the shower stream. Disturbance inputs include a drop in water pressure say, owing to a toilet flushing and changes in hot water temperature owing to "using up the hot water from the heater.
The "measured" output variables are the temperature and flow rate or velocity of the mixed stream as it contacts your body. There are minimum and maximum valve positions and therefore flow rates on both streams. The maximum mixed temperature is equal to the hot water temperature and the minimum mixed temperature is equal to the cold water temperature.
The previous constraints were hard constraints—they cannot be physically violated. An example of a soft constraint is the mixed-stream water temperature—you do not want it to be above a certain value because you may get scalded.
This is a soft constraint because it can physically happen, although you do not want it to happen. This process is continuous while you are taking a shower but is most likely viewed as a batch process, since it is a small part of your day. It could easily be called a semicontinuous semibatch process. Too high of a temperature can scald you—this is certainly a safety consideration. Economically, if your showers are too long, more energy is consumed to heat the water, costing money.
Environmentally and economically , more water consumption means that more water and wastewater must be treated. An economic objective might be to minimize the shower time. However, if the shower time is too short, or not frequent enough, your clothes will become dirty and must be washed more often—increasing your clothes-cleaning bill. This is a multivariable control problem because adjusting either valve affects both temperature and flow rate.
Control manipulations must be "coordinated," that is, if the hot-water flow rate is increased to increase the temperature, the cold-water flow rate must be decreased to maintain the same total flow rate. The measurement signals are continuous, but the manipulated variable changes are likely to be discrete unless your hands are continuously varying the valve positions.
Feedback control: As the body feels the temperature changing, adjustments to one or both valves is made. As the body senses a flow rate or velocity change, one or both valves are adjusted. Feed-forward control: Notice that you are making a manipulated variable change moving your body before the effect of an output temperature or flow rate change is actually detected.
Process schematic for taking a shower. Some showers may have a relatively large time delay or dead time between when a manipulated variable change is made and when the actual output change is measured. This could happen, for example, if there was a large pipe run between the mixing point and the shower head this would be considered an input time delay. Another type of time delay is measurement dead time, for example if your body takes a while to detect a change in the temperature of the stream contacting your body.
Notice that the control strategy used has more manipulated variables two valve positions and body movement than measured outputs total mixed-stream flow rate and temperature.
In the shower example, the individual taking the shower served as the controller. The measurements and manipulations for this example are somewhat qualitative you do not know the exact temperature or flow rate, for example. Most of the rest of the textbook consists of quantitative controller design procedures, that is, a mathematical model of the process is used to develop the control algorithm.
This chapter has covered the important first step of control system development—identifying seven basic steps in analyzing a process control problem. We have used simple examples with which you are familiar. As you learn about more chemical and environmental processes, you should get in the habit of thinking about them from a process systems point of view, just as you have with these simple systems.
The sensor measured the tank level, the actuator changed the flow rate, and the controller determined how much to vary the actuator, based on the sensor signal. There are many common sensors used for chemical processes. These include temperature, level, pressure, flow, composition, and pH. The most common manipulated input is the valve actuator signal usually pneumatic. Each device in a control loop must supply or receive a signal from another device.
When these signals are continuous, such as electrical current or voltage, we use the term analog. If the signals are communicated at discrete intervals of time, we use the term digital.
Analog Analog or continuous signals provided the foundation for control theory and design and analysis. A common measurement device might supply either a 4- to mA or 0- to 5-V signal as a function of time.
Pneumatic analog controllers developed primarily in the s, but used in some plants today would use instrument air, as well as a bellows-and-springs arrangement to "calculate" a controller output based on an input from a measurement device typically supplied as a 3- to psig pneumatic signal. The controller output of 3—15 psig would be sent to an actuator, typically a control valve where the pneumatic signal would move the valve stem.
For large valves, the 3- to psig signal might be "amplified" to supply enough pressure to move the valve stem.
Electronic analog controllers typically receive a 4- to mA or 0- to 5-V signal from a measurement device, and use an electronic circuit to determine the controller output, which is usually a 4- to mA or 0- to 5-V signal. Again, the controller output is often sent to a control valve that may require a 3- to psig signal for valve stem actuation.
Syllabi, schedules, course notes
Digital Many devices and controllers are now based on digital communication technology. A sensor may send a digital signal to a controller, which then does a discrete computation and sends a digital output to the actuator. In the past few decades, digital control-system design techniques that explicitly account for the discrete rather than continuous nature of the control computations have been developed.
If small sample times are used, the tuning and performance of the digital controllers is nearly equal to that of analog controllers. Although many of the control computations performed on industrial processes are digital, the discrete sample time is usually small enough that virtually identical performance to analog control is obtained. Our understanding of chemical processes is based on ordinary differential equations, so it makes sense to continue to think of control in a continuous fashion.
We find that controller tuning is much more intuitive in a continuous, rather than discrete, framework. In Chapter 16 we spend some time discussing techniques that are specific to digital control systems, namely model predictive control MPC. Control Valve Placement In Example 1. It should be noted that virtually all control valves are actually installed in an arrangement similar to that shown in Figure When the control valve fails, the adjacent block valves can be closed; the control valve can then be removed and replaced.
During the interim, the bypass valve can be adjusted manually to maintain the desired flow rate. Generally, these control valve "stations" are placed at ground level for easy access, even if the pipeline is in a piperack far above the ground. Typical control valve arrangement. When the control valve needs to be taken out of service, the two block valves are closed and the control valve is removed.
The bypass valve can then be manually adjusted to control the flow. More specifically, we develop process models.Rather than simply present theory topics and develop analytical solutions, this textbook uses "interactive learning" through computer-based simulation exercises, employing for this the very popular "Matlab" engineering software package, and the "Simulink" block-diagram simulation environment. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads.
A typical control problem would be to manipulate one flow rate either in or out to maintain a desired drum pressure. However, if the shower time is too short, or not frequent enough, your clothes will become dirty and must be washed more often—increasing your clothes-cleaning bill. Discuss the objectives of this control strategy.
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