chemical engineering process control basics of investing
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Chemical engineering process control basics of investing

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As flows are changed, the process is affected, as seen by process observations. Process observations are measured as variables like temperatures, pressures, levels, compositions and flowrates. As process designs are optimized for energy recovery and minimization of both capital cost and operating cost for a plant, they incorporate increasing integration between process streams. If variability is not controlled in a highly integrated process with a high degree of process interactions, there are more pathways for it to create quality issues.

Therefore, it is increasingly important for design and control engineers to work together to ensure operability and strategize how to attenuate variability. Control basics Most process responses can be classified into self-regulating and non-self-regulating or integrating. Self-regulating processes respond to a change in process input by settling into a new steady-state value.

For example, if steam is increased to a heat exchanger, the material being heated will rise to a new temperature. In a heat exchanger, for example, when the steam valve is opened, more steam enters the heat exchanger. First, the steam pressure in the exchanger rises and heat transfers to the tubes and finally to the colder stream. The temperature of the cold stream takes some time before it begins to rise. Then it rises gradually and increases its rate of change until it approaches the new steady-state temperature, where the temperature rise begins to slow.

The characteristic response is a SODT. Figure 1. Typical first-order plus deadtime FODT responses are characterized by a rapid initial response to a process input, followed by slowing response as a new steady state is reached The steam flow began increasing as soon as the valve started moving. But if a controller was telling the valve to open, there might have been a short delay before the valve actually moved and the steam flow changed.

The steam flow begins to increase quickly and begins to increase more slowly as the new steady-state flow is achieved. This is a typical FODT response. Self-regulating control loops can be tuned for closed-loop control response to assure that the process observation sometimes known as the process variable, or PV is driven to and maintained at its target setpoint.

The control response can be tuned for faster or slower response, but as the speed of response increases, so does the risk of overshoot or oscillation. Different measures of performance have been developed with tuning rules to approximately achieve these objectives. Early performance objectives focused on minimizing error, square of the error, or absolute error over time. Tuning to achieve quarter-amplitude damping was often described in early control literature. Zeigler-Nichols tuning rules were proposed to achieve this kind of response.

But this kind of aggressive tuning results in some cycling. Recent thought in automation prefers to attenuate variability, and that includes closed-loop oscillation, so most loops today should be tuned for a first-order response, with the response time being defined according to process requirements. The fastest-responding loops are limited by the point of critical damping. However, where loops interact, one can be slowed relative to another to prevent the interacting loops from fighting with each other.

A first-order process normally takes about four time constants, plus the deadtime, to reach steady-state at the target setpoint. Figure 3. Integrating, or non-self-regulating process variables do not settle into a new steady-state value within allowable operating limits Non-self-regulating process variables do not settle into a new steady-state value, at least not within allowable operating limits Figure 3.

Changing the rate of feed into a vessel will change the rate at which the level rises or falls. In the absence of some kind of control, the level would continue to rise until the tank overflowed. Usually integrating process responses can be described as deadtime and integrating gain or ramp rate. Sometimes, there may be a lead or lag associated with the ramp rate, but this is not common, and when it occurs, it tends to be minimal.

Fortunately, controllers can also be tuned on integrating processes to achieve a first-order response. However, the response does not look exactly like the response of a self-regulating process. Following a setpoint change, the PV will move to the new setpoint and overshoot slightly before turning around and settling back at the target value. Following a disturbance, the PV will deviate from the target setpoint until finally being arrested and returning to setpoint. Deadtime is the same as for a self-regulating process.

There is no open-loop time constant by definition. However, the closed-loop time constant for an integrating process is defined as the time it takes to first cross the target setpoint following a setpoint change or the arrest time for a disturbance.

An integrating process normally takes about six time constants, plus the deadtime, to reach steady-state at the target setpoint following either a setpoint change or a load disturbance. Feedback controllers Process control usually takes the form of a feedback controller Figure 4. Some process inputs can be manipulated in order to drive important process observations to targets or setpoints. Other process observations may not be controlled to a target setpoint, but they are not allowed to exceed upper or lower constraint limits.

A control-loop includes a measurement of the process observation to be controlled the PV , a final control element usually a control valve that varies the process flow to be manipulated and a controller that makes a move based on where the process observation is relative to its setpoint. Figure 4. Most process control is accomplished by using a feedback control loop The workhorse controller in the process industry remains the PID proportional-integral-derivative controller.

It is robust and a good fit for the job as long as the process response is not excessively nonlinear or characterized by a dominant deadtime dynamic. Proportional, integral and derivative are the actions the controller can apply to drive the PV to setpoint. Every controller manufacturer may employ a slightly different form, structure and options, but the functionality and results are the same. The proportional, integral and derivative parameters can be adjusted by the control engineer to provide the best controller response.

In order to properly tune a control loop, it is necessary to understand the things that influence loop performance and process profitability Figure 5. Figure 5. Several aspects of a process-control loop can influence performance and profitability Often, process inputs can impact more than one important process observation.

If the heat exchanger was the reboiler of a distillation column, increasing the steam could affect the levels in the base of the column and the reflux accumulator and compositions at the top and bottom of the column. It might also affect the column pressure and differential pressure, and will affect temperatures up and down the column. Similarly, a process observation might be affected by more than one process input. The distillate composition may be affected by the steam flow to the reboiler, the reflux flow, the feed flow, the product flows and other process inputs.

An interactive process requires that the controls be designed to minimize the detrimental impact of multivariable interaction, where two or more loops could fight with each other. One way to do this is with a decoupling strategy, which is something easily understood by process engineers. Feed-forward control and sometimes ratio-control strategies are used to decouple process interactions. Another way to decouple loop interactions is by tuning one loop for a relatively faster response and the other for a relatively slower response.

This technique is very effective and is naturally applied when tuning cascade loops. That difference is called the error, and the most basic controller would be a proportional controller. The error is multiplied by a proportional gain and that result is the new output.

The proportional gain may be an actual gain in terms of percent change of output per percent change of error or in terms of proportional band. Proportional band is the same as gain divided by , so the effect is the same, even if the units and value are different. When tuning a control system, it is important to know whether the proportional tuning parameter used in the controller being tuned is gain or proportional band.

When the error does not change, there is no change in output. This results in an offset for any load beyond the original load for which the controller was tuned. This results in a permanent off-set. Integral action overcomes the off-set by calculating the integral of error or persistence of the error. This action drives the controller error to zero by continuing to adjust the controller output after the proportional action is complete. In reality, these two actions are working in tandem.

The integral of the error is multiplied by a gain that is actually in terms of time. Again, different controllers have defined the integral parameter in different ways. One is directly in time and the other is the inverse of time or repeats of the error per unit of time.

They are functionally equivalent, but when calculating tuning parameters, the correct units must be used. Adding further complication, the time can be expressed in different units, although seconds or minutes are usually the design choice. And finally, there is a derivative term that considers the rate of change of the error. However, again the units of time may be seconds or minutes.

Derivative is not often required, but can be helpful in processes that can be modelled as multiple capacities or second order. For that reason, derivative action is rarely used on noisy processes and if it is needed, then filtering of the PV is recommended. Since a setpoint change can look to the controller like an infinite rate of change and processes usually change more slowly, many controllers have an option to disable derivative action on setpoint changes and instead of multiplying the rate of change of the error, the rate of change of the PV is multiplied by the derivative term.

There are two steps to tuning a controller. First the process dynamics must be identified. This can be done with an open-loop or closed-loop step test. In open loop, the controller is put in manual mode and the output is stepped. The PV is observed and the process deadtime, gain, and time constants are estimated.

Several steps should be made to identify any nonlinearity and to ensure the response is not being affected by an unmeasured disturbance. In closed loop, the controller is forced to oscillate in a fixed cycle by stepping the output, forcing it to oscillate with an amplitude that will be dependent on the process gain and step size. This can be achieved with a controller by zeroing the integral and derivative terms and adjusting the proportional gain until the cycle is repeating, or by using logic that switches the output when the cycling PV crosses the setpoint value.

The second step is calculating the tuning parameters. There are different guidelines proposed by different authors and even software that will calculate the tuning parameters for the tuner to achieve the desired response. Lambda refers to the closed loop time constant in a controller response. The advantage of this kind of tuning is that the tuner is free to choose the speed of response or the aggressiveness of the controller tuning. There is a tradeoff in loop tuning.

As noted earlier, faster response or more aggressive tuning may result in some overshoot or even cycling response that is undesirable and the loop could become completely unstable if there is any nonlinearity in the process. Therefore, robustness is the sacrifice for more aggressive control and lambda can be used to strike an optimal balance between robustness and aggressiveness.

A general term for types of controls more elaborate than the basic loops. A complex control strategy often involving more than one PID controller examples include cascade control, ratio control, feed-forward control, override control and inferential control CV — Control variable. A process observation that has a setpoint that may be provided by the operator or by a supervisory controller DCS — Distributed control system. A digital process control platform in which various controllers are distributed throughout the system DV — Disturbance variable.

Measured process inputs that are not manipulated by the controller also known as feed-forward inputs HC — Hand control. Used to indicate a manually positioned valve LV — Limit variable. A term describing a multivariable controller with one output MPC 1 — Model predictive controller. A controller that controls future error rather than current error.

To do this, it incorporates process response models that describe the dynamic behavior of process observations CVs and LVs to changes in process inputs MVs and DVs. This allows the controller to plan moves in the future to provide control over a future time interval. This technology is helpful with processes that have long process delays or a high degree of interaction between multiple process inputs and outputs. It is the ideal platform for constraint optimization 2 Multivariable process control.

Means controlling more than one measurement or variable at a time from the same calculation. Since most model-predictive controllers are also multivariable controllers, the definitions are often used interchangeably MV — Manipulated Variable. A process input that is an output of a controller PID — Proportional, integral, derivative.

The name used for the most common control loop algorithm seen in the process industries. Computer for industrial control PV — Process value. A term describing an uncommon multivariable controller with one process output an example would be split-range control SISO — Single-input, single-output controller. The target value for the control variable TSS — Time to steady state.

This includes all of the links to on-line lectures and most of the links to industrial or governmental websites. Clarity rating: 2 The prose is written in a manner that most engineers could understand it. However, there is a problem with the sequence of the chapters. The chapters on the different types of instrumentation refer to control techniques that are not covered until you go pages further into the book. It would see where this would cause problems for most students. Consistency In the sections I reviewed, the text was consistent.

Modularity rating: 5 If anything this book is too modular. It comes across as a compendium of notes from various courses that have been merged into one document. Control concepts are cited in the first pages of the book, but are not explained to the reader until you get pages further into the text. Interface rating: 2 The equations in this book are all a bit fuzzy looking.

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Introduction to Process Control

AdTD Ameritrade Investor Education Offers Immersive Curriculum, Videos, and Training and Support to Help You Succeed in Your Learning Objectives. At level #1, the essential process measurements for underflow control are: 1. rake torque with a torque meter fixed to the rakes, 2. bed level by using a simple float or vertical position . Feb 18,  · Part I Process Control Introduction. Chapter 1: Overview. Chapter 2: Modeling Basics. Chapter 3: Sensors and Actuators. Chapter 4: Piping and Instrumentation Diagrams. .