Turn Problem Loops Into Performing Loops

Understanding the five essential pieces of a control loop contributes to process performance and can turn problem loops into performing loops.

By Dave Harrold, CONTROL ENGINEERING February 1, 1999

E ach control system control loop contains five pieces: a sensing element, transmitter, controller, final control element, and process. Only when all five elements are performing their best will the control system meet expectations.

Frequently process control requires controlling one of four variables; flow, pressure, temperature, or level.

Flow measurement devices available today are more forgiving in their installation requirements than devices available even five years ago; so it may be that replacing a flow measurement device is justified on its ability to perform in the current installation.
When auditing existing flow measurement installations, information necessary to understand performance expectations includes:

  • Full range (0-100%) is the minimum and maximum flow that passes by the measurement point;

  • Rangeability (turndown) is the ratio between the maximum and minimum control points;

  • Repeatability, frequently described in percent, is the ability to achieve the same output when the same input, coming from the same direction is provided; and

  • Accuracy, often stated in either percent of full scale or percent of reading.

If, for example, flow was originally to be controlled at 1,400 gpm (gallons per minute) (5,300 lpm (liters per minute)) at

Flow measurement devices can be segregated into two categories: devices measuring flow rate and those measuring velocity.

Devices measuring flow are often head-type devices that depend on measuring pressure differences across inline interferences (i.e., orifice plate) and typically provide about 3:1 rangeability. Accurate flow measurements in head-type devices occurs as long as the pressure and/or temperature of the flowing media remains at design (base or standard) conditions.

Velocity based measurement devices frequently provide measurements in mass, mole, or volume terms; and include magnetic-, vortex-, mass-, and turbine-meters. Devices in the velocity category rely on the basic formula Mass

Mass-flow measurement relies on the laws of nature that prohibit a stream from accumulating or loosing mass; thus mass-flow measurements are independent of changes in temperature, pressure, or pipe size. Units used in mass-flow measurement are usually pounds or kilograms with time periods of seconds, minutes, or hours such as pounds per hour (pph) or kilograms per second (kg/s).

When volume measurements are made assuming design conditions, an inaccurate measurement is produced. Overcoming these inaccuracies requires on-line compensation for density changes. For liquids, that means temperature compensation; and for vapors or gases, that means pressure and temperature compensation. Volume measurements are usually in cubic feet, cubic meters, cubic liters, or gallons with time periods of seconds, minutes, or hours, i.e., cfh (cubic feet per hour), m3/s (cubic meters per second), or gpm (gallons per minute).

Mole-flow measurements are determined by the formula

Rangeability = Maximum control point

Rangeability of velocity based flowmeters is improving, but a few years ago the rules-of-thumb were: magnetic meters (30:1), vortex devices (15:1), massflow meters (100:1), and turbine meters (10:1).

Flow Installation Rules of Thumb
Flow meter type Straight pipe diameter requirements
Up-stream Down-stream
Magnetic meter 5 2
Mass flow meter 1 1
Vortex meter 10 25
Turbine meter 15 10
Orifice plate with 0.5 orifice to pipe diameter ratio 25 4
Orifice plate with 0.7 orifice to pipe diameter ratio 40 5
Note: Pipe diameter must match flowmeter size
Source: Control Engineering

Streamline or turbulent flow
Osborne Reynolds (1842-1912) determined turbulence influenced obtaining repeatable flow measurements. Reynolds developed a formula that when basic units are assigned to each quantity, the ratio is a dimensionless number (see Reynolds number formula). Reynolds determined that turbulence essentially disappeared and flow became streamline (laminar) at about 2,000. Between 2,000 and 4,000 measurement performance is questionable. Above 4,000, turbulence is good.

Reynolds number = pipe diameter x velocity x density

Producing turbulent flow is not enough for accurate flow measurements; how the turbulence is developed also influences the measurement. Valves, pumps, or piping configurations located close to flow sensors can cause unwanted flow stream influences.

Determining if a specific flowmeter installation can provide the required capability is best determined by referring to specific device installation instructions. When instructions are unavailable, ‘rules-of-thumb’ may help decide if sufficient up- and down-stream runs of pipe the same size as the flowmeter are installed (see Flow installation rules of thumb).

Error sources
Primary sensors are frequently connected to transmitters that are used as transducers, receiving information in one form and converting it to another form. For example, an RTD (resistance thermal detector) primary sensor provides temperature measurements in ohms/deg. Connected to a transmitter (transducer) the ohms/deg value is converted to 4-20 mA and transmitted to an indicator, recorder or controller.

A source of error in control loops occurs when transmitter calibrations are made at the electronics, and do not include the sensor. For example, it is common to find older thermocouples have drifted several degrees. Substituting a calibrating source for the thermocouple input will not reveal an inaccurate thermocouple.

Sensor interchangeability is another source of temperature measurement error. Standard temperature sensors allow for a reasonable tolerance around an ‘ideal’ sensor curve. Matched temperature sensors cost more but deliver significantly better accuracy. Be aware that some manufacturers deliver high accuracy systems by matching the sensor and transmitter to form a system. Extra care is required in maintaining systems using matched sensors to preserve the ‘paid for’ capability.

Primary sensors are the number one influence on control-loop performance-but final control elements rank a close second.

Final control elements
Final control elements come in a variety of shapes and sizes including variable-speed drives, heaters, and valves. Valves include globe, characterized ball, quarter-turn, butterfly, eccentric-disk, and knife gate.

Final control elements can be segregated into three performance classes; linear, equal percentage, and quick opening (see Final element performance classes diagram). Linear elements include globe and eccentric-disk valves, variable-speed drives, and heaters. Equal-percentage elements include globe, characterized ball, and butterfly valves. Included in the quick-opening class are globe, quarter-turn ball, knife gates, and dampers.

Globe valves appear in all three classes because they can be fitted with a variety of plug, seat, and cage designs to meet a broad range of applications; a point worth remembering as process audits are conducted and the need for changes analyzed.

Control valves have long been the primary final element installed to control flow, temperature, pressure, and level. Experience reveals that about 60% of installed control loops reach 100% of the measured variable range with only 30% of the final control element travel. That means a lot of businesses have bought more control valve capacity than necessary. When sizing and selecting control valves, it is best to calculate minimum, maximum, and normal flow for all three characteristics (see Example control valve calculations chart). With results side-by-side, the characteristic that provides the most uniform process gain is easier to determine. A valve that’s too small will not pass the required flow, while one that is too large may result in unstable performance as it tries to control at very low increments of travel.

Since the goal is to reduce process variability, ensuring smooth control valve performance is critical to success.

Making permanent improvements to control-loop performance requires verifying that data are repeatable. Processes unable to repeat data often indicate problems in the measurement system and/or control valves. Two common causes of nonlinear response in control valves are excess hysteresis and stick-slip.

Hysteresis is the inability of a device to return to a previously established position when the input to the device is repeated. In control valves, hysteresis is distinguished from deadband by expecting that small reversals of input may not produce reversals of valve travel. Integrating processes often demonstrate an oscillatory behavior caused by control valves with excess hysteresis; self-regulating processes rarely do.

Sources to check for excess hysteresis include:

  • Valve packing-gland tightness;

  • Seal friction in rotary valves;

  • Worn or loose linkages and couplings;

  • Defective I/P (current-to-pneumatic) transducer;

  • Inadequate supply air pressure;

  • Defective positioner;

  • Incorrect valve actuator bench set; and

  • Undersized actuator.

Stick-slip cycling occurs when controller integral action continuously increases the controller output without a corresponding change in the actual valve position (stick phase). When the valve finally moves, it ‘pops’ and the process variable overshoots the setpoint (slip phase). Controller integral action drives the output in the other direction, setting up a distinctive continuous oscillation. (See sidebar story Common cause of control loop cycling for more information.)

Example Control Valve Flow Characteristics
Minimum Normal Maximum Rangeability
Quick opening 10% open 25% open 50% open 5:1
Linear 10% open 50 % open 80% open 8:1
Equal percentage 5% open 70% open 90% open 18:1
Assumes a minimum control point of 375 GPM, a normal control of 750 GPM, and a maximum of 1,500 GPM.
Source: Control Engineering

Controller influences
Controllers exist to maintain a measured variable equal to a setpoint. The affects filtering have on controller performance is a seldom addressed topic. For example, transmitters often provide means of ‘snubbing’ the measured variable. Snubbing is accomplished using an adjustable orifice (or partially closed isolation valve) in the sensing line to reduce process pulsations from reaching the sensing element.

Some transmitters provide electronic filtering of the output signal. Many digital control systems allow users to apply one or more filters on input signals.

Regardless of the form, filters add lag to the signal and, when inappropriately used, can mask measurement variability and create unsafe conditions.

If examination of the raw measured-variable input indicates frequent, random spikes which cannot be eliminated at their source, then application of a filter as near the source of the noise is appropriate. The amount of filter should be the minimum necessary to filter excessive, but not all the noise from the signal. Use extra care when applying filters to integrating processes. The additional lag introduced by the filter can be canceled by the controller’s derivative action.

Temperature measurements seldom contain excessive noise because of process measurement lags. If high-frequency noise is discovered on temperature measurement signals the cause is likely improper shielding of thermocouple leads. Source of the noise should be fixed, rather than applying filters. When filters must be applied to temperature measurement signals, they should be very small values.

Controller scan periods can be a source of poor control loop performance. As a rule-of-thumb, controller scan periods should be at least eight times faster than the loop time constant (see Loop time constant diagram).

Conducting systematic audits of the five parts of existing control loops can pay big dividends in understanding the ‘knobs’ available to operations and the influence each contributes to process variability.

Common cause of control loop cycling

A s many as one in five control loops demonstrates a continuous cycling at steady state when tuned with the optimum PI or PID tuning parameters calculated using any of the popular methods including Lamda and Ziegler-Nichols. In most cases, the cycling can be directly traced to nonlinear behavior of pneumatically actuated control valves. The two most common types of nonlinear control valve responses are hysteresis with deadband and stick-slip.

Hysteresis with deadband will cause steady-state cycling in properly tuned integrating loops, while stick-slip causes the same in self-regulating loops.

Stick-slip response is common in pneumatically actuated control valves using pneumatic positioners.

By design, pneumatic positioners are nonlinear devices. When a constant ramp input signal is applied to a pneumatic positoner, the gain is small and loads the actuator slowly. When the ramp input exceeds a predetermined value, the gain increases and loads the actuator dome at a faster rate.

Stick-slip occurs when the controller integral action continuously increases the controller output without a corresponding change in the actual valve position (see Closed loop stick-slip cycling illustration). When the valve finally moves, it pops and the process variable overshoots the setpoint. The error becomes negative and the controller integral action drives the output in the other direction. This results in the distinctive continuous limit cycle known as a stick-slip cycle. The process variable appears as a square wave oscillating around the setpoint. The controller output appears as a triangular wave with a frequency dependent on the tuning parameters, the valve, and the process gain.

There are three traditional solutions to stick-slip cycling problems.

  • Repair or replace the valve;

  • Place the controller in manual; or

  • Detune the controller integral setting.

Detuning the integral setting eliminates stick-slip cycling but also slows the control loop’s ability to respond to setpoint changes.

Techmation Inc. (Scottsdale, Ariz.) has developed a deadband reset scheduling (DSR) algorithm that adjust controller integral settings depending on the size of the error between the setpoint and process variable.

David Ender, president,
Techmation Inc.

Recent Control Engineering Relevant Articles
reference back issues at www.controleng.com
November 1997 How software tools simplify loop tuning
March 1998 How to control processes with large deadtimes
March 1998 Basics of proportional-integral-derivative control
May 1998 Back to basics: Electrical noise – what a racket
June 1998 Back to basics: Control loop is automation essence
June 1998 Sensor matching boosts performance
August 1998 Back to basics: Ziegler-Nichols methods facilitate loop tuning
October 1998 4-20 mA transmitters alive and kicking
November 1998 Product focus on valves
January 1999 Designing for six sigma capability

E-mail dharrold@cahners.com