Good temperature control
Temperature is one of the four most common types of loops. While the other common loops (flow, level, pressure) occur more often, temperature loops are generally more difficult and important.
Temperature is a critical condition for reaction, fermentation, combustion, drying, calcination, crystallization, extrusion, or degradation rate and is an inference of a column tray concentration in the process industries.
Tight temperature control translates to lower defects and greater yields during seeding, crystal pulling, and rapid thermal processing of silicon wafers for the semiconductor industry.
For boilers, temperature is important for water and air preheat, fuel oil viscosity, and steam superheat control. For incinerators, an optimum temperature often exists in terms of ensured destruction of hazardous compounds and minimum energy cost. For heat transfer fluids, such as cooling tower, chilled water, brine, or Therminol, good temperature control minimizes upsets to users.
Temperature control in cold rooms reduces the contamination and degradation rate in pharmaceutical, biochemical, beverage, and food research and production. Temperature control in plant growth chambers is important for studying the effects of hybridization, generic engineering, and plant growth regulators.
For aqueous solutions of acids, bases, and salts, the inferred concentration from conductivity and the pH of the solution often changes at the rate of about 20% and four tenths of a pH unit, respectively, per 10 degrees centigrade.
Good temperature control is important during the research, reaction, separation, processing, and storage of products and feeds and is thus a key to product quality. It is also important for environmental control and energy conservation.
Tight temperature control can extend the life of process equipment (e.g., reactor glass lining, scrubber fiberglass trays, or furnace firebrick) by preventing excursions beyond the temperature rating. However, abrupt changes in coolant or steam flow can shock equipment and upset other utility users. Thus, it is also important to monitor the controller output and use methods (e.g., set point velocity limits and split-range, criss-cross prevention logic) to prevent rapid changes or oscillations. Since you typically achieve temperature control by the direct or indirect manipulation of heat flow into or out of the system, a reduction in the overshoot and oscillation of temperature loops can also correspond to a decrease in energy consumption.
Control problems, sources
The slowness of the response of the temperature process is the biggest source of problems and opportunities for tight temperature control. The slowness makes it difficult to tune the controller because the persistence and patience required to obtain a good open- or closed-loop test exceeds the capability of most humans. At the same time, this slowness, in terms of a large major process time constant, enables gain settings larger than those permissible in other types of loops except for level.
The nonlinearity of the process further aggravates the tuning problem. Discussions have occurred on dependence of the process gain on operating conditions and load, but there has been no simplification and quantification in enough detail to facilitate online compensation.
The slowness of the response of the thermocouple or resistance temperature detector (RTD) in a thermowell slows down the ability of the controller to identify and react to upsets and affects all tuning settings (i.e., gain, reset, and rate) of temperature loops.
Once you properly implement and tune a temperature loop, the control error is often less than the tolerance (error limits) of the sensor. If you consider the accumulated error of an installed thermocouple or RTD system is about five times larger than the error limits of the sensor, you realize system measurement error seriously limits temperature loop performance.
Sensor, tuning, strategy options
The user can reduce errors significantly by changing installation or by using a breakthrough in technology. Control loop analyzers, auto tuners, and self-tuning controllers can alleviate most of the tuning problems.
You can also develop quantitative relationships for tuning temperature loops to see use with signal characterization and adaptive gain strategies to keep the loop well tuned for changing operating conditions and loads as they occur.
You can change the proportional-integral-derivative controller algorithm by simple terms to improve loops for batch operation and set point changes or excessive dead time. Alternatively, model predictive control can see use where normal derivative action is prohibited due to noise, inverse response, interaction, or dead time dominance. Also, cascade and feed-forward control can compensate for disturbances before they significantly affect the primary controlled variable.
For each application, the specifics as to the nature of the dynamics, nonlinearities, and special considerations of the loop can provide greater detail on how you can improve performance by using control strategies.
Source: Advanced Temperature Control, by Gregory K. McMillan, ISA, 1995.