Title of Invention

A METHOD AND SYSTEM FOR OPTIMIZING REFRIGERATION SYSTEMS

Abstract A refrigeration system comprising a compressor (100) for compressing a refrigerant, a condenser (107) for condensing refrigerant to a liquid, an evaporator (103) for evaporating liquid refrigerant from the condenser (107) to a gas, an inner control loop for optimizing a supply of liquid refrigerant to the evaporator (103), and an outer control loop for optimizing a level of refrigerant in the evaporator (103), said outer control loop defining a supply rate for said inner control loop based on an optimization including measurement of evaporator (103) performance, and said inner control loop optimizing liquid refrigerant supply based on said defined supply rate. Independent variables, such as proportion of oil in refrigerant, amount of refrigerant, contaminates, non-condensibles, scale and other deposits on heat transfer surfaces, may be estimated or measured. A model of the system and/or a thermodynamic model approximating the system, for example derived from temperature and pressure gages (155, 156), as well as power computations or measurements, is employed to determine or estimate the effect on efficiency of deviance from an optimal state. Various methods are provided for returning the system to an optimal state, and for calculating a cost-effectiveness of employing such processes.
Full Text METHOD AND APPARATUS FOR OPTIMIZING REFRIGERATION SYSTEMS
RELATED APPLICATIONS
The present application claims benefit of priority from U.S. Provision Patent Application
Nos. 60/431,901, filed December 9, 2002, and 60/847, filed December 19, 2002, each of
which is expressly incorporated herein by reference.
FIELD OF THE INVENTION
The present invention relates to the field of methods and systems for optimization of
refrigeration system operation.
BACKGROUND OF THE INVENTION
In large industrial scale systems, efficiency may be a critical aspect of operations. Even
small improvement of system efficiency can lead to significant cost savings; likewise, loss of
efficiency may lead to increased costs or even system failure. Chillers represent a significant
type of industrial system, since they are energy intensive to operate, and are subject to variation
of a number of parameters which influence system efficiency and capacity.
The vast majority of mechanical refrigeration systems operate according to similar, well
known principles, employing a closed-loop fluid circuit through which refrigerant flows, with a
source of mechanical energy, typically a compressor, providing the motive forces for pumping
heat from an evaporator to a condenser. In a chiller, water or brine is cooled in the evaporator
for use in a process. In a common type of system, discussed in more detail below, the evaporator
is formed as a set of parallel tubes, forming a tube bundle, within a housing. The tubes end on
either side in a separator plate. The water or brine flows through the tubes, and the refrigerant is
separately provided on the outside of the tubes, within the housing.
The condenser receives hot refrigerant gas from the compressor, where it is cooled. The
condenser may also have tubes, which are, for example, filled with water which flows to a
cooling tower. The cooled refrigerant condenses as a liquid, and flows by gravity to the bottom
of the condenser, where it is fed through a valve or orifice to the evaporator.
The compressor therefore provides the motive force for active heat pumping from the
evaporator to the condenser. The compressor typically requires a lubricant, in order to provide
extended life and permit operation with close mechanical tolerances. The lubricant is an oil
which miscible with the refrigerant. Thus, an oil sump is provided to feed oil to the compressor,
and a separator is provided after the compressor to capture and recycle the oil. Normally, the
gaseous refrigerant and liquid lubricant are separated by gravity, so that the condenser remains
relatively oil free. However, over time, lubricating oil migrates out of the compressor and its
lubricating oil recycling system, into the condenser. Once in the condenser, the lubricating oil
becomes mixed with the liquefied refrigerant and is carried to the evaporator. Since the
evaporator evaporates the refrigerant, the lubricating oil accumulates at the bottom of the
evaporator.
The oil in the evaporator tends to bubble, and forms a film on the walls of the evaporator
tubes, In some cases, such as fin tube evaporators, a small amount of oil enhances heat transfer
and is therefore beneficial. In other cases, such as nucleation boiling evaporator tubes, the
presence of oil, for example over 1%, results in reduced heat transfer. See, Schlager, L.M., Pate,
M.B., and Berges, A.E., "A Comparison of 150 and 300 SUS Oil Effects on Refrigerant
Evaporation and Condensation in a Smooth Tube and Micro-fin Tube", ASHRAE Trans. 1989,
95(l):387-97; Thome, J.R., "Comprehensive Thermodynamic Approach to Modelling
Refrigerant-Lubricating Oil Mixtures", Intl. J. HVAC&R Research (ASHRAE) 1995, 110-126;
Poz, M. Y., "Heat Exchanger Analysis for Nonazeotropic Refrigerant Mixtures", ASHRAE
Trans. 1994, 10Q(1)727:735 (Paper No. 95-5-1).
A refrigeration system is typically controlled at a system level in one of two ways: by
regulating the temperature of the gas phase in the top of the evaporator (the superheat), or by
seeking to regulate the amount of liquid (liquid level) within the evaporator. As the load on the
system increases, the equilibrium within the evaporator changes. Higher heat load will increase
temperatures in the headspace. Likewise, higher load will boil more refrigerant per unit time,
and lead to lower liquid levels.
For example, US 6,318,101, expressly incorporated herein by reference, relates to a
method for controlling an electric expansion valve based on cooler pinch and discharge
superheat. This system seeks to infer the level of refrigerant in the evaporator and control the
system based thereon, while preventing liquid slugging. A controlled monitors certain variables
which are allegedly used to determine the optimal position of the electronic expansion valve, to
optimize system performance, the proper discharge superheat value, and the appropriate
refrigerant charge. Sec also, US Patent No. 6,141,980, expressly incorporated herein by
reference.
US Patent No. 5,782,131, expressly incorporated herein by reference, relates to a
refrigeration system having a flooded cooler with a liquid level sensor.
Each of these strategies provides a single fixed setpoint which is presumed to be the
normal and desired setpoint for operation. Based on this control variable, one or more
parameters of operation are varied. Typically, a compressor will either have a variable speed
drive or a set of variable angle vanes which deflect gaseous refrigerant from the evaporator to the
compressor. These modulate the compressor output. Additionally, some designs have a
controllable expansion valve between the condenser and evaporator. Since there is a single main
control variable, the remaining elements are controlled together as an inner loop to maintain the
control variable at the setpoint.
Typical refrigerants are substances that have a boiling point (at the operating pressure)
below the desired cooling temperature, and therefore absorb heat from the environment while
evaporating (changing phase) under operational conditions. Thus, the evaporator environment is
cooled, while heat is transferred to another location, the condenser, where the latent heat of
vaporization is shed. Refrigerants thus absorb heat via evaporation from one area and reject it via
condensation into another area. In many types of systems, a desirable refrigerant provides an
evaporator pressure as high as possible and, simultaneously, a condenser pressure as low as
possible. High evaporator pressures imply high vapor densities, and thus a greater system heat
transfer capacity for a given compressor. However, the efficiency at the higher pressures is
lower, especially as the condenser pressure approaches the critical pressure of the refrigerant.
The overall efficiency of the refrigeration system is influenced by the heat transfer
coefficients of the respective heat exchangers. Higher thermal impedance results in lower
efficiency, since temperature equilibration is impaired, and a larger temperature differential must
be maintained to achieve the same heat transfer. The heat transfer impedance generally increases
as a result of deposits on the walls of the heat exchangers, although, in some cases, heat transfer
may be improved by various surface treatments and/or an oil film.
Refrigerants must satisfy a number of other requirements as best as possible including:
compatibility with compressor lubricants and the materials of construction of refrigerating
equipment, toxicity, environmental effects, cost availability, and safety. The fluid refrigerants
commonly used today typically include halogenated and partially halogenated alkanes, including
chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HFCFs), and less commonly
hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs). A number of other refrigerants are
known, including propane and fluorocarbon ethers. Some common refrigerants are identified as
R11, R12, R22, R500, and R502, each refrigerant having characteristics that make them suitable
for different types of applications.
In an industrial chiller, the evaporator heat exchanger is a large structure, containing a
plurality of parallel tubes in a bundle, within a larger vessel comprising a shell. The liquid
refrigerant and oil form a pool in the bottom of the evaporator, boiling and cooling the tubes and
their contents. Inside the tubes, an aqueous medium, such as brine, circulates and is cooled,
which is then pumped to another region where the brine cools the industrial process. Such an
evaporator may hold hundreds or thousands of gallons of aqueous medium with an even larger
circulating volume. Since evaporation of the refrigerant is a necessary part of the process, the
liquid refrigerant and oil must fill only part of the evaporator.
It is also known to periodically purge a refrigeration or chiller system, recycling purified
refrigerant through the system to clean the system. This technique, however, generally permits
rather large variance in system efficiency and incurs relatively high maintenance costs. Further,
this technique generally does not acknowledge that there is an optimum (non-zero) level of oil in
the evaporator and, for example, the condenser. Thus, typical maintenance seeks to produce a
"clean" system, which may be suboptimal, subject to incremental changes after servicing.
Refrigerant from a refrigeration system may be reclaimed or recycled to separate oil and provide
clean refrigerant, in a manual process that requires system shutdown.
US patent No. 6,260,378, expressly incorporated herein by reference, relates to a
refrigerant purge system, in particular to control removal of non-condensable gases.
The oil in the evaporator tends to accumulate, since the basic design has no inherent path
for returning the oil to the sump. For amounts in excess of the optimum, there are generally
reduced system efficiencies resulting from increasing oil concentration in the evaporator. Thus,
buildup of large quantities of refrigerant oil within an evaporator will reduce efficiency of the
system.
In-line devices may be provided to continuously remove refrigerant oil from the
refrigerant entering the evaporator. These devices include so-called oil eductors, which remove
oil and refrigerant from the evaporator, returning the oil to the sump and evaporated refrigerant
to the compressor. The inefficiency of these continuous removal devices is typically as a result
of the bypassing of the evaporator by a portion of the refrigerant, and potentially a heat source to
vaporize or partially distill the refrigerant to separate the oil. Therefore, only a small proportion
of the refrigerant leaving the condenser may be subjected to this process, resulting in poor
control of oil level in the evaporator and efficiency loss. There is no adequate system for
controlling the eductor. Rather, the eductor may be relatively undersize and run continuously.
An oversize eductor would be relatively inefficient, since the heat of vaporization is not
efficiently used in the process.
Another way to remove oil from the evaporator is to provide a shunt for a portion of
mixed liquid refrigerant and oil in the evaporator to the compressor, wherein the oil is subject to
the normal recycling mechanisms. This shunt, however, may be inefficient and is difficult to
control. Further, it is difficult to achieve and maintain low oil concentrations using this method.
US Patent No. 6,233,967, expressly incorporated herein by reference, relates to a
refrigeration chiller oil recovery system which employs high pressure oil as an eductor motive
fluid. See also, US Patent Nos. 6,170,286 and 5,761,914, expressly incorporated herein by
reference.
In both the eductor and shunt, as the oil level reaches low levels, e.g., about 1%, 99% of
the fluid being separate is refrigerant, leading to significant loss of process efficiency.
It is noted that it is difficult to accurately sample and determine the oil concentration in
the evaporator. As the refrigerant boils, oil concentration increases. Therefore, the oil
concentration near the top of the refrigerant is higher than the bulk. However, as the boiling
liquid churns, inhomogeneities occur, and accurate sampling becomes difficult or impossible.
Further, it is not clear that the average bulk oil concentration is a meaningful control variable,
apart from the effects of the oil on the various components. Since it is difficult to measure the
oil concentration, it is also difficult to measure the amount of refrigerant in the evaporator. A
difficulty of measurement of the amount of refrigerant is compounded by the fact that, during
operation, the evaporator is boiling and froths; measuring the amount during a system shutdown
must account for any change in distribution of the refrigerant between the other system
components.
It is known that the charge conditions of a chiller may have a substantial effect on both
system capacity and system operating efficiency. Obviously, if the amount of liquid refrigerant
in the evaporator is insufficient, the system cannot meet its cooling needs, and this limits
capacity. Thus, in order to handle a larger heat load, a greater quantity of refrigerant, at least in
the evaporator, is required. However, in typical designs, by providing this large refrigerant
charge, the operating efficiency of the system at reduced loads is reduced, thus requiring more
energy for the same BTU cooling. Bailey, Margaret B., "System Performance Characteristics of
a Helical Rotary Screw Air-Cooled Chiller Operating Over a Range of Refrigerant Charge
Conditions", ASHRAE Trans. 1998 104(2), expressly incorporated herein by reference.
Therefore, by correctly selecting the "size" (e.g., cooling capacity) of the chiller, efficiency is
enhanced. Typically the chiller capacity is determined by the maximum expected design load,
and thus for any given design load, the quantity of refrigerant charge in a typical design is
dictated. Therefore, in order to achieve improved system efficiency, a technique of modulation
recruitment is employed, in which one or more of a plurality of subsystems are selectively
activated depending on the load, to allow efficient design of each subsystem while permitting a
high overall system load capacity with all subsystems operational. See, Trane "Engineer's
Newsletter" December 1996, 25(5): 1-5. Another known technique seeks to alter the rotational
speed of the compressor. See, U.S. Patent No. 5,651,264, expressly incorporated herein by
reference. It is also possible to control compressor speed using an electronic motor control, or
system capacity, by restricting refrigerant flow into the compressor.
Chiller efficiency generally increases with chiller load. Thus, an optimal system seeks to
operate system near its rated design. Higher refrigerant charge level than the nominal full level,
however, results in deceased efficiency. Further, chiller load capacity sets a limit on the
minimum refrigerant charge level. Therefore, it is seen that there exists an optimum refrigerant
charge level for maximum efficiency. As stated above, as oil level increases in the evaporator, it
both displaces refrigerant and has an independent effect on system efficiency.
Systems are available for measuring the efficiency of a chiller, i.e., a refrigeration system
which cools water or a water solution, such as brine. In these systems, the efficiency is
calculated based on Watt-hours of energy consumed (Volts x Amps x hours) per cooling unit,
typically tons or British Thermal Unit (BTU) (the amount of energy required to change the
temperature of one British ton of water 1┬░ C). Thus, a minimal measurement of efficiency
requires a power meter (timebase, voltmeter, ammeter), and thermometers and flowmeters for
the inlet and outlet water. Typically, further instruments are provided, including a chiller water
pressure gage, gages for the pressure and temperature of evaporator and condenser. A data
acquisition system processor is also typically provided to calculate the efficiency, in BTU/kWH.
U.S. Patent Nos. 4,437,322; 4,858,681; 5,653,282; 4,539,940; 4,972,805; 4,382,467;
4,365,487; 5,479,783; 4,244,749; 4,750,547; 4,645,542; 5,031,410; 5,692,381; 4,071,078;
4,033,407; 5,190,664; and 4,747,449, expressly incorporated herein by reference, relate to heat
exchangers and the like.
There are a number of known methods and apparatus for separating refrigerants,
including U.S. 2,951,349; 4,939,905; 5,089,033; 5,110,364; 5,199,962; 5,200,431; 5,205,843;
5,269,155; 5,347,822; 5,374,300; 5,425,242; 5,444,171; 5,446,216; 5,456,841; 5,470,442;
5,534,151; and 5,749,245, expressly incorporated herein by reference. In addition, there are a
number of known refrigerant recovery systems, including U.S. 5,032,148; 5,044,166; 5,167,126;
5,176,008; 5,189,889; 5,195,333; 5,205,843; 5,222,369; 5,226,300; 5,231,980; 5,243,831;
5,245,840; 5,263,331; 5,272,882; 5,277,032; 5,313,808; 5,327,735; 5,347,822; 5,353,603;
5,359,859; 5,363,662; 5,371,019; 5,379,607; 5,390,503; 5,442,930; 5,456,841; 5,470,442;
5,497,627; 5,502,974; 5,514,595; and 5,934,091, expressly incorporated herein by reference.
Also known are refrigerant property analyzing systems, as shown in U.S. 5,371,019; 5,469,714;
and 5,514,595, expressly incorporated herein by reference.
SUMMARY OF THE INVENTION
The present invention provides a system and method for optimizing operation of a
refrigeration system.
In most known refrigeration systems, control is exerted principally to assure that liquid
refrigerant is not returned to the compressor, and otherwise to assure that the level of refrigerant
in the evaporator is presumed to be at a predetermined set level.
According to the present the optimum level of refrigerant arid oil in the -
evaporator is not predetermined] The problem of the prior art is that, over time, the system
characteristics may change, as well as the load characteristics, and that an optimal control
requires more complexity. simularly direct measurements of the effective
levels of relevant parameters may not be measurable, and thus surrogates may be provided. Thus
According to the present invention, a pair of control loops, an inner loop and an outer
loop, are provided. The inner loop controls the compressor, than is, the motive force for
pumping heat. This inner control loop receives a single input from the outer loop, and optimizes
the compressor operation in accordance therewith, for example compressor speed, duty cycle,
inlet vane position, and the like. If present, a controllable expansion valve (typically located
between the condenser and evaporator) is also encompassed within this inner control loop.
Thus, the inner control loop controls the rate of supply of liquid refrigerant to the evaporator.
The outer control loop controls the partitioning of refrigerant between the evaporator and
a refrigerant accumulator element within the system. The accumulator is typically not a
"functional" system element, in that the amount of refrigerant in the accumulator is not critical,
simply that this element allows a variation in the amount of refrigerant elsewhere in the system.
The accumulator may be a lower portion of the condenser, a separate accumulator, or even a
reserve portion of the evaporator which does not significantly particulate in the cooling process. During steady state operation, the feed of liquid refrigerant from the condenser will equal
the rate of gaseous intake to the compressor. Thus, the rate of heat absorption in the evaporator
will effectively control the inner control loop for the compressor. Typically, this heat absorption
may be measured or estimated from a variety of system sensors, including evaporator discharge
temperature and pressure, evaporator water/brine inlet and outlet temperature and pressure, and
possibly condenser headspace temperature and pressure.
The outer control loop determines an optimal level of refrigerant in the evaporator. A
direct measurement of refrigerant level in the evaporator is difficult for two reasons: First, the
evaporator is filled with refrigerant and oil, and a direct sampling of the evaporator contents,
such as by using an optical sensor for oil concentration, does not typically yield useful results
during system operation. During system shutdown, the oil concentration may be accurately
measured, but such shutdown conditions typically allow a repartitioning of refrigerant within the
various system components. Second, during operation, the refrigerant and oil bubble and froth,
and therefore there is no simple level to be determined. Rather a preferred method for inferring
the amount of refrigerant in the evaporator, especially changes over a relatively short period of
time, is to monitor the level of refrigerant in the accumulator, which is preferably a lower portion
of the condenser or associated with the condenser. Since this refrigerant is relatively pure, and
held under condensing conditions, the level is relatively easy to measure. Since the remaining
system components include principally refrigerant gas, a measurement of the condenser or
accumulator refrigerant level will provide useful information for measuring changes in
evaporator refrigerant level. If the starting levels of both the accumulator or condenser and
evaporator are known (even during a shutdown state), than an absolute measurement may be
calculated.
Of course, there are other means for measuring or calculating the amount of refrigerant in
the evaporator, and broad embodiments of the invention are not limited to the preferred method
of measurement.
The present invention provides, however, that there is a partitioning of refrigerant, with
variable control over the amount within the evaporator. The outer loop controls this level to
achieve an optimum state.
In a refrigeration system, efficiency is calculated in terms of energy per unit heat transfer.
Energy may be supplied as electricity, gas, coal, steam, or other source, and may be directly
measured. Surrogate measurements may also be employed, as known in the art. Heat transfer
may also be calculated in known manner. For example, the heat transfer to the cooled process
water is calculated by measuring or estimating the flow rate and the inlet and outlet
temperatures.
While it is possible to map the control algorithm in terms of desired partitioning of
refrigerant under a variety of load circumstances, a preferred embodiment of the invention
provides an adaptive control. This adaptive control determines, during system transients, which
may be normally occurring or induced, the charge in system efficiency with changes in
refrigerant partitioning at a given operating point. For example, if the process changes, requiring
a different heat load dissipation, this will be represented by a change in inlet water temperature
and/or flow rate. This change will result in a different rate of refrigerant evaporation in the
evaporator, and thus a transient change in partitioning. Before or in conjunction with correcting
the refrigerant partitioning, the control monitors the system efficiency. This monitoring allows
the control to develop a system model, which then allows it to anticipate an optimum control
surface. The outer loop repartitions the refrigerant to achieve optimum efficiency. It is noted
that, while efficiency is typically considered to be kW/ton, other measurements of efficiency may
be substituted without materially altering the control strategy. For example, instead of
optimizing the refrigeration system itself, the industrial process may be included. In this case,
the production parameters or economics of the process may be calculated, to provide a more
global optimization.
In a global optimization, other systems may also require control or serve as inputs. These
may be accommodated in known manner.
Over time, oil migrates from the oil sump of the compressor to the evaporator. One
aspect of the invention provides a control system which measures oil consumption, in order to
estimate oil level in the evaporator. This control system therefore measures oil replenishment
into the sump, oil return from the outlet of the compressor, and oil return from the eductor. It is
noted that the oil in the sump may be mixed with refrigerant, and therefore a simple level gage
will likely require compensation, such as by boiling a sample of oil to remove refrigerant, or by
using an oil concentration sensor, such as an optical type sensor. Thus, it is possible to estimate
the amount of oil migration into the evaporator, and with a known starting state or clean system,
to estimate a total amount of oil. Using measurements of evaporator discharge temperature and
pressure, as well as water inlet and outlet temperature and pressure, it is further possible to
estimate heat transfer coefficients in the tube bundle, and impairments thereof. The refrigerant,
oil and heat transfer impairments arc the principle internal variables which control the efficiency
of the evaporator. Over the short term (and assuming that oil is not intentionally added to the
evaporator), refrigerant is the only effective and available control variable. Over longer periods,
an oil eductor may be controlled based on inferred or measured oil concentration to return the oil
level in the evaporator to an optimal level. Over extended intervals, maintenance may be
performed to correct heat transfer impairments and purify the refrigerant. Such maintenance
requirements may be indicated as an output from the control system. For example, the control
system operates automatically to immediately tune the control variable to an optimum'state.
This tuning is triggered by a change in process conditions or some adaptive auto-tuning process.
In addition, overtime, the optimization control surface will vary. As this surface varies to reduce
overall efficiency, secondary correction controls may be invoked, such as oil eductor, non-
condensable gas purge (typically from the condenser), or the like. Over a longer term, the
control may model significant parameters of system operation with respect to a model, and
determine when a service is required, either because the system is failing, or substantial
inefficiencies are apparent, such as impaired heat transfer through the tube bundle.
As stated above, the inner control loop is generally insulated from direct response to
changes in process. Further, since the evaporator is generally outside of the inner control loop,
this control loop generally does not suffer adverse changes over time, except buildup of non-
condensable gasses in the condenser, which are relatively easy to infer based on a superheat
value, and relatively easy to purge. Thus, the inner control loop may typically operate according
to a predetermined control strategy, and need not be adaptive. This, in turn, allows multivariate
control, for example, motor speed, inlet vane position, and expansion valve control, to be
effected based on a static system model, to achieve optimal efficiency under a variety of
conditions.
On the other hand, the outer control loop seeks to control the short term system response
principally based on an optimization of a single variable, refrigerant partitioning, with variations
in system load. While a static system model is difficult or impossible to implement, while
achieving the required accuracy, such a control is readily implemented in an adaptive fashion, to
compensate for changes in the system, and indeed, over a period of time, to correct deviations in
system parameters which adversely effect system efficiency.
It is, of course, apparent that these control loops and their algorithmic implementation
may be merged, and indeed hybridized, the general strategy remains the same. At any operating
point, the partitioning of refrigerant is controlled to achieve a maximum efficiency. The system
senses or tests efficiency as a function of the control variable, in order to compensate for changes
in system response.
A more detailed analysis of the basis for refrigerant partitioning as a control strategy is
provided. Chiller efficiency depends on several factors, including subcooling temperature and
condensing pressure, which, in turn, depend on the level of refrigerant charge, nominal chiller
load, and the outdoor air temperature. First, subcooling within the thermodynamic cycle will be
examined. Fig. 6A shows a vapor compression cycle schematic and Fig. 6B shows an actual
temperature-entropy diagram, wherein the dashed line indicates an ideal cycle. Upon exiting the
compressor at state 2, as indicated in Fig 6A, a high-pressure mixture of hot gas and oil passes
through an oil separator before entering the tubes of the remote air-cooled condenser where the
refrigerant rejects heat (Qh) to moving air by forced convection (or other cooling medium). In
the last several rows of condenser coils, the high-pressure saturated liquid refrigerant should be
subcooled, e.g., 10F to 20F (5.6C to 11.1 C), according to manufacturer's recommendations, as
shown by state 3 in Fig. 6B. This level of subcooling allows the device following the condenser,
the electronic expansion valve, to operate properly. In addition, the level of subcooling has a
direct relationship with chiller capacity. A reduced level of subcooling results in a shift of state 3
(in Fig. 6B) to the right and a corresponding shift of state 4 to the right, thereby reducing the heat
removal capacity of the evaporator (Q1).
As the chiller's refrigerant charge increases, the accumulation of refrigerant stored in the
condenser on the high-pressure side of the system also increases. An increase in the amount of
refrigerant in the condenser also occurs as the load on the chiller decreases due to less refrigerant
flow through the evaporator, which results in increased storage (accumulation) in the condenser.
A flooded condenser causes an increase in the amount of sensible heat transfer area used for
subcooling, and a corresponding decrease in the surface area used for latent or isothermal heat
transfer associated with condensing. Therefore, increasing refrigerant charge level and
decreasing chiller load both result in increased subcooling temperatures and condensing
temperatures.
According to the present invention, therefore, the condenser or accumulator are provided
to reduce any inefficiency resulting from variable storage of the refrigerant. This can be
achieved by a static mechanical configuration, or a controlled variable configuration.
Increased outdoor air or other heat sink (condenser heat rejection medium) temperatures
have an opposite effect on the operation of the condenser. As the heat sink temperature
increases, more condenser surface area is used for latent or isothermal heat transfer associated
with condensing and a corresponding decrease in sensible heat transfer area used for subcooling.
Therefore, increases in heat sink temperature result in decreased subcooling temperatures and
increased condensing temperatures.
Referring to Fig. 6B, an increase in subcooling drives state 3 to the left, while an increase
in condensing temperature shifts the curve connecting states 2 and 3 upward. High condensing
temperatures can ultimately lead to compressor motor overload and increased compressor power
consumption or lowered efficiency. As subcooling increases, heat is added to the evaporator,
resulting in an upward shift of the curve connecting states 4 and 1. As the evaporating
temperature increases, the specific volume of the refrigerant entering the compressor also
increases, resulting in increased power input to the compressor. Therefore, increased levels of
refrigerant charge and decreased chiller load conditions result in increased subcooling, which
leads to increased compressor power input.
Superheat level is represented by the slight increase in temperature after the refrigerant
leaves the saturation curve, as shown at state 1 in Fig. 6B. Vaporized refrigerant leaves the
chiller's evaporator and enters the compressor as a superheated vapor. According to the present
invention, the amount of superheat is not constant, and may vary based on operating conditions
to achieve efficiency. In some systems, it is preferred that a minimum superheat be provided,
e.g., 2.2C, to avoid premature failure from droplet pitting and erosion, or liquid slugging.
However, any amount of superheat generally represents an inefficiency. According to the
present invention, the "cost" of low superheat levels may optionally be included in the
optimization, in order to account for this factor. Otherwise, systems may be provided to reduce
or control such problems, allowing low operating superheat levels.
Superheat level in the condenser may be increased, for example, by an accumulation of
non-condensable gasses, which cause thermodynamic inefficiency. Therefore, according to one
aspect of the invention, superheat level is monitored, and if it increases beyond a desired level, a
non-condensable gas purge cycle, or other refrigerant purification, may be conducted. Non-
condensable gases may be removed, for example, by extracting a gas phase from the condenser,
and subjecting it to significant sub-cooling. The head-space of this sample will be principally
non-condensing gasses, while refrigerant in the sample will liquefy. The liquefied refrigerant
may be returned to the condenser or fed to the evaporator.
As discussed previously, an increase in heat sink temperature causes an increase in
discharge pressure, which, in turn, causes the compressor's suction pressure to increase. The
curves connecting states 2 and 3 and states 4 and 1 on Fig. 6B 3 both shift upward due to
increases in heat sink temperature. An upward shift in curves 4 through 1 or an increase in
refrigerant evaporating temperature results in a decrease in the evaporating approach
temperature. As the approach temperature decreases, the mass flow rate through the evaporator
must increase in order to remove the proper amount of heat from the chilled water loop.
Therefore, increasing heat sink temperatures cause evaporating pressure to increase, which leads
to increased refrigerant mass flow rate through the evaporator. The combined effect of higher
refrigerant mass flow rate through the evaporator and reduced approach temperature causes a
decrease in superheat temperatures. Therefore, an inverse relationship exists between heat sink
temperature and superheat temperatures.
With decreasing refrigerant charge, the curve connecting states 2 and 3 in Fig. 6B shifts
downward and the subcooling level decreases or state 3 on the T-s diagram in Fig. 6B moves to
the right. Bubbles begin to appear in the liquid line leading to the expansion device due to an
increased amount of gaseous refrigerant leaving the condenser. Without the proper amount of
subcooling in the refrigerant entering the expansion device (state 3 in Fig. 6B), the device does
not operate optimally. In addition, a decrease in refrigerant charge causes a decrease in the
amount of liquid refrigerant that flows into the evaporator and a subsequent decrease in capacity
and increase in superheat and suction pressure. Thus, an inverse relationship exists between
refrigerant charge level and superheat temperature.
According to the present invention, the discharge from the condenser includes a
compliant reservoir, and thus may provide increased opportunity to achieve the desired level of
subcooling. Likewise, because a reservoir is provided, the refrigerant charge is presumed to be
in excess of that required under all operating circumstances, and therefore it will not be limiting.
It is also possible to have a hybrid control strategy, wherein the reservoir is undersize, and
therefore under light load, refrigerant accumulates in a reservoir, while under heavy load, the
refrigerant charge is limiting. The control system according to the present invention may, of
course, compensate for this factor in known manner. However, preferably, when the refrigerant
charge is not limiting, the superheat temperature is independently controlled. Likewise, even
where the refrigerant charge is sufficient, the evaporator may be artificially starved as a part of
the control strategy.
Under extreme refrigerant undercharge conditions (below -20% charge), refrigerant
undercharge causes an increase in suction pressure. In general, the average suction pressure
increases with increasing refrigerant charge during all charge levels above -20%. Refrigerant
charge level is a significant variable in determining both superheat temperature and suction
pressure.
A system and method for measuring, analyzing and manipulating the capacity and
efficiency of a refrigeration system by instrumenting the refrigeration system to measure
efficiency, selecting a process variable for manipulation, and altering the process variable is
provided. The process variable may be varied during operation of the refrigeration system while
measuring efficiency thereof.
In an industrial process, a refrigeration system must have sufficient capacity to cool the
target to a desired level. If the capacity is insufficient, the underlying process may fail,
sometimes catastrophically. Thus, maintaining sufficient capacity, and often a margin of
reserve, is a critical requirement. Therefore, it is understood that where capacity is limiting,
deviations from optimal system operation may be tolerated or even desired in order to maintain
the process within acceptable levels. Over the long term, steps to ensure that the system has
adequate capacity for efficient operation may be taken. For example, system maintenance to
reduce tube bundle scale or other heat transfer impediment, cleaning of refrigerant (e.g., to
remove excess oil), and refrigerant-side heat transfer surfaces, and purging of non-condensable
gases may be performed alone or in combination.
Efficiency is also important, although an inefficient system does not necessarily fail.
Efficiency and system capacity are often related, since inefficiency typically reduces system
capacity.
According to another embodiment of the invention, a set of state measurements are taken
of the refrigeration system, which are then analyzed for self-consistency and to extract
fundamental parameters, such as efficiency. Self-consistency, for example, assesses
presumptions inherent in the system model, and therefore may indicate deviation of the actual
system operation from the model operation. As the actual system deviates from the model, so
too will the actual measurements of system parameters deviate from their thermodynamic
theoretical counterparts. For example, as heat exchanger performance declines, due for example
to scale accumulation on the tube bundle, or as compressor superheat temperature increases, for
example due to non-condensable gases, these factors will be apparent in an adequate set of
measurements of a state of the system. Such measurements may be used to estimate the capacity
of the refrigeration system, as well as factors which lead to inefficiency of the system. These, in
turn, can be used to estimate performance improvements which can be made to the system by
returning it to an optimal state, and to perform a cost-benefit analysis in favor of any such
efforts.
Typically, before extensive and expensive system maintenance is performed, it is
preferable to instrument the system for real time performance monitoring, rather than simple
state analysis. Such real time performance modeling is typically expensive, and not apart of
normal system operation; whereas adequate information for a state analysis may be generally
available from system controls. By employing a real time monitoring system, analysis of
operational characteristics in a fluctuating environment may be assessed.
This scheme may also be used in other types of systems, and is not limited to
refrigeration systems. Thus, a set of sensor measurements are obtained and analyzed with
respect to system model. The analysis may then be used to tune system operational parameters,
instigate a maintenance procedure, or as part of a cost-benefit analysis. Systems to which this
method may be applied include, among others, internal combustion engines, turbomachinery,
hydraulic and pneumatic systems.
Preferably, the efficiency is recorded in conjunction with the process variables. Thus, for
each system, the actual sensitivity of efficiency, detected directly or by surrogate measures, to a
process variable, may be measured.
According to a further aspect of the invention, a business method is provided for
maintaining complex systems based on a cost-savings basis, rather than the typical cost of
service or flat fee basis. According to this aspect of the invention, instead of servicing and
maintaining a system for a fee based on a direct cost thereof, compensation is based on a system
performance metric. For example, a baseline system performance is measured. Thereafter, a
minimum system capacity is defined, and the system is otherwise serviced at the significant
discretion of the service organization, presumably based on the cost-benefit of such service, with
the service organization being compensated based on the system performance, for example a
percentage of cost savings over the baseline. According to the present invention, data from the
control system may be used to determine degradation of system parameters from an efficient
stale. The invention also allows monitoring of system performance, and communication of such
performance data remotely to a service organization, such as through radio uplink, modem
communication over telephone lines, or computer network. This communication may also permit
immediate notification to the service organization of process shift, potentially in time to prevent
subsequent and consequent system failure.
In this case, the system is performance monitored frequently or continuously, and if the
system capacity is sufficient, decisions are made whether, at any time, it would be cost efficient
to perform certain maintenance services, such as refrigerant purification, evaporator descaling or
cleaning, purging of non-condensing gasses, or the like. Typically, if system capacity is
substantially diminished below a prespecified reserve value (which may vary seasonally, or
based on other factors), service is required. However, even in this case, degradation in system
capacity may be due to a variety of factors, and the most efficient remediation may then be
selected to cost-efficiently achieve adequate system performance.
After system service or maintenance, the control system may be initialized or retimed to
ensure that pre-service or pre-maintenance parameters do not erroneously govern system
operation.
According to a second main embodiment of the present invention, multivariate
optimization and control may be conducted. In the case of multivariate analysis and control,
interaction between variables or complex sets of time-constants may require a complex control
system. A number of types of control may be implemented to optimize the operation of the
system. Typically, after the appropriate type of control is selected, it must be tuned to the
system, thus defining efficient operation and the relation of the input variables from sensors on
the efficiency of the system. Often, controls often account for time delays inherent in the
system, for example to avoid undesirable oscillation or instability. In many instances,
simplifying presumptions, or segmentations are made in analyzing the operating space to provide
traditional analytic solutions to the control problems. In other instances, non-linear techniques
are employed to analyze the entire range of input variables. Finally, hybrid techniques are
employed using both non-linear techniques and simplifying presumptions or segmentation of the
operating space.
For example, in the second main embodiment of the invention, it is preferred that the
range of operating conditions be segmented along orthogonal delineations, and the sensitivity of
the system to process variable manipulation be measured for each respective variable within a
segment. This, for example, permits a monotonic change in each variable during a testing or
training phase, rather than requiring both increasing and decreasing respective variables in order
to map the entire operating space. On the other hand, in the case of a single variable, it is
preferred that the variable be altered continuously while measurements are talcing place in order
to provide a high speed of measurement.
Of course, it may not be possible to measure orthogonal (non-interactive) parameters.
Therefore, another aspect of the invention provides a capability for receiving a variety of data
relating to system operation and performance, and analyzing system performance based on this
data. Likewise, during a continuous system performance monitoring, it may be possible to
employ existing (normally occurring) system perturbations to determine system characteristics.
Alternately, the system may be controlled to include a sufficient set of perturbations to determine
the pertinent system performance parameters, in a manner which does not cause inefficient or
undesirable system performance.
In an adaptive control system, the sensitivity of the operating efficiency to small
perturbations in the control variables are measured during actual operation of the system, rather
than in a testing or training mode, as in an autotuning system, which may be difficult to arrange
and which may be inaccurate or incomplete if the system configuration or characteristics change
after training or testing. Manual tuning, which requires an operator to run different test or trial
and error procedures to determine the appropriate control parameters, is typically not feasible,
since the characteristics of each installation over the entire operating range are not often fully
characterized and are subject to change over time. Some manual tuning methods are described
in D. E. Seborg, T. F. Edgar, and D. A. Mellichamp, Process Dynamics and Control, John Wiley
& Sons, New York (1989) and A. B. Corripio, Tuning of Industrial Control Systems, Instrument
Society of America, Research Triangle Park, N.C. (1990).
Autotuning methods require a periodically initiated tuning procedure, during which the
controller will interrupt the normal process control to automatically determine the appropriate
control parameters. The control parameters thus set will remain unchanged until the next tuning
procedure. Some autotuning procedres are described in K. J. Astrom and T. Hagglund,
Automatic Tuning of PID Controllers, Instrument Society of America, Research Triangle Park,
N.C. (1988). Autotuning controllers may be operator or self initiated, either at fixed periods,
based on an external event, or based on a calculated deviance from a desired system
performance.
With adaptive control methods, the control parameters are automatically adjusted during
normal operation to adapt to changes in process dynamics. Further, the control parameters are
continuously updated to prevent the degraded performance which may occur between the tunings
of the other methods. On the other hand, adaptive control methods may result in inefficiency
due to the necessary periodic variance from an "optimal" condition in order to test the
optimally. Further, adaptive controls may be complex and require a high degree of intelligence.
Advantageously, the control may monitor system operation, and select or modify appropriate
events for data acquisition. For example, in a system operating according to a pulse-width
modulation paradigm, the pulse width and/or frequency may be varied in particular manner in
order to obtain data about various operational states, without causing the system to unnecessarily
deviate from acceptable operational ranges.
Numerous adaptive control methods have been developed. See, for example, C. J. Harris
and S. A. Billings, Self-Tuning and Adaptive Control: Theory and Applications, Peter
Peregrinus LTD (1981). There are three main approaches to adaptive control: model reference
adaptive control ("MRAC"), self-tuning control, and pattern recognition adaptive control
("PRAC"). The first two approaches, MRAC and self-tuning, rely on system models which are
generally quite complex. The complexity of the models is necessitated by the need to anticipate
unusual or abnormal operating conditions. Specifically, MRAC involves adjusting the control
parameters until the response of the system to a command signal follows the response of a
reference model. Self-tuning control involves determining the parameters of a process model on
line and adjusting the control parameters based upon the parameters of the process model.
Methods for performing MRAC and self-tuning control are described in K. J. Astrom and B.
Wittenmark, Adaptive Control, Addison-Wesley Publishing Company (1989). In industrial
chillers, adequate models of the system are typically unavailable for implementing the control, so
that self-tuning controls are preferred over traditional MRAC. On the other hand, a sufficient
model may be available for estimating system efficiency and capacity, as discussed above.
With PRAC, parameters that characterize the pattern of the closed-loop response are
determined after significant setpoint changes or load disturbances. The control parameters are
then adjusted based upon the characteristic parameters of the closed-loop response. A pattern
recognition adaptive controller known as EXACT is described by T. W. Kraus and T. J. Myron,
"Self-Tuning PID Controller uses Pattern Recognition Approach," Control Engineering, pp. 106-
111, June 1984, E. H. Bristol and T. W. Kraus, "Life with Pattern Adaptation," Proceedings
1984 American Control Conference, pp. 888-892, San Diego, Calif. (1984), and K. J. Astrom
and T. Hagglund, Automatic Tuning of PID Controllers, Instrument Society of America,
Research Triangle Park, N.C. (1988). See also U.S. Pat. No. Re. 33,267, expressly incorporated
herein by reference. The EXACT method, like other adaptive control methods, does not require
operator intervention to adjust the control parameters under normal operation. Before normal
operation may begin, EXACT requires a carefully supervised startup and testing period. During
this period, an engineer determines the optimal initial values for controller gain, integral time,
and derivative time. The engineer also determines the anticipated noise band and maximum wait
time of the process. The noise band is a value representative of the expected amplitude of noise
on the feedback signal. The maximum wait time is the maximum time the EXACT algorithm
will wait for a second peak in the feedback signal after detecting a first peak. Further, before an
EXACT-bascd controller is put into normal use, the operator may also specify other parameters,
such as the maximum damping factor, the maximum overshoot, the parameter change limit, the
derivative factor, and the step size. In fact, the provision of these parameters by an expert
engineer is generally appropriate in the installation process for any control of an industrial
chiller, and therefore such a manual definition of initial operating points is preferred over
techniques which commence without a priori assumptions, since an unguided exploration of the
operating space may be inefficient or dangerous.
According to the present invention, the system operational parameters need not be
limited to an a priori "safe" operating range, where relatively extreme parameter values might
provide improved performance, while maintaining a margin of safety, while detecting or
predicting erroneous or artifact sensor data. Thus, using a model of the system constructed
during operation, possibly along with manual input of probable normal operational limits, the
system may analyze sensor data to determine a probability of system malfunction, and therefore
with greater reliability adopt aggressive control strategies. If the probability exceeds a threshold,
an error may be indicated or other remedial action taken.
A second known pattern recognition adaptive controller is described by Chuck Rohrer
and Clay G. Nelser in "Self-Tuning Using a Pattern Recognition Approach," Johnson Controls,
Inc., Research Brief 228 (Jun. 13, 1986). The Rohrer controller calculates the optimal control
parameters based on a damping factor, which in turn is determined by the slopes of the feedback
signal, and requires an engineer to enter a variety of initial values before normal operation may
commence, such as the initial values for a proportional band, an integral time, a deadband, a tune
noise band, a tune change factor, an input filter, and an output filter. This system thus
emphasizes temporal control parameters.
Manual tuning of loops can take a long time, especially for processes with slow
dynamics, including industrial and commercial chillers. Different methods for autotuning PID
controllers are described by Astrom, K. J., and T. Hagglund, Automatic Tuning of PID
Controllers, Instrument Society of American, Research Triangle Park, N.C., 1988, and Seborg,
D. E. T., T. F. Edgar, and D. A. Mellichamp, Process Dynamics and Control, John Wiley &
sons, 1989. Several methods are based on the open loop transient response to a step change in
controller output and other methods are based on the frequency response while under some form
of feedback control. Open loop step response methods are sensitive to load disturbances, and
frequency response methods require a large amount of time to tune systems with long time
constants. The Ziegler-Nichols transient response method characterizes the response to a step
change in controller output, however, implementation of this method is sensitive to noise. See
also, Nishikawa, Yoshikazu, Nobuo Sannomiya, Tokuji Ohta, and Haruki Tanaka, "A Method
for Autotuning of PID Control Parameters," Automatica, Volume 20, No. 3, 1984.
For some systems, it is often difficult to determine if a process has reached a steady-state.
In many systems, if the test is stopped too early, the time delay and time constant estimates may
be significantly different than the actual values. For example, if a test is stopped after three time
constants of the first order response, then the estimated time constant equals 78% of the actual
time constant, and if the test is stopped after two time constants, then the estimated time constant
equals 60% of the actual time constant. Thus, it is important to analyze the system in such a way
as to accurately determine time-constants. Thus, in a self-tuning system, the algorithm may
obtain tuning data from normal perturbations of the system, or by periodically testing the
sensitivity of the plant to modest perturbations about the operating point of the controlled
variable(s). If the system determines that the operating point is inefficient, the controlled
variable(s) are altered in order to improve efficiency toward an optimal operating point. The
efficiency may be determined on an absolute basis, such as by measuring kWatt hours consumed
(or other energy consumption metric) per BTU of cooling, or through surrogate measurements of
energy consumption or cooling, such as temperature differentials and flow data of refrigerant
near the compressor and/or water in the secondary loop near the evaporator/heat exchanger.
Where cost per BTU is not constant, either because there are different sources available, or the
cost varies over time, efficiency may be measured in economic terms and optimized accordingly.
Likewise, the efficiency calculation may be modified by including other relevant "costs".
A full power management system (PMS) is not required in order to optimize the
efficiency. However, this PMS may be provided depending on cost and availability, or other
considerations.
In many instances, parameters will vary linearly with load and be independent of other
variables, thus simplifying analysis and permitting traditional (e.g., linear, proportional-integral-
differential (PID)) control design. See, U.S. Patent Nos. 5,568,377, 5,506,768, and 5,355,305,
expressly incorporated herein by reference. On the other hand, parameters which have
multifactorial dependencies are not easily resolved. In this case, it may be preferable to segment
the control system into linked invariant multifactorial control loops, and time-varying simple
control loops, which together efficiently control the entire system, as in the preferred
embodiment of the invention.
Alternately, a neural network or fuzzy-neural network control may be employed. In order
to train a neural network, a number of options are available. One option is to provide a specific
training mode, in which the operating conditions are varied, generally methodically, over the
entire operating space, by imposing artificial or controlled loads and extrinsic parameters on the
system, with predefined desired system responses, to provide a training set. Thereafter, the
neural network is trained, for example by back propagation of errors, to produce an output that
moves the system toward an optimal operating point for the actual load conditions. The
controlled variables may be, for example, oil concentration in the refrigerant and/or refrigerant
charge. See, U.S. Patent No. 5,579,993, expressly incorporated herein by reference.
Another option is to operate the system in a continual learning mode in which the local'
operating space of the system is mapped by the control during operation, in order to determine a
sensitivity of the system to perturbations in process variables, such as process load, ambient
temperature, oil concentration in the refrigerant and/or refrigerant charge. When the system
determines that the present operating point is suboptimal, it alters the operating point toward a
presumable more efficient condition. The system may also broadcast an alert that specific
changes are recommended to return the system to a more efficient operating mode, where such
changes are not controlled by the system itself. If the process has insufficient variability to
adequately map the operating point, the control algorithm may conduct a methodical search of
the space or inject a pseudorandom signal into one or more controlled variables seeking to detect
the effect on the output (efficiency). Generally, such search techniques will themselves have
only a small effect on system efficiency, and will allow the system to learn new conditions,
without explicitly entering a learning mode after each alteration in the system.
Preferably, the control builds a map or model of the operating space from experience,
and, when the actual system performance corresponds to the map or model, uses this map or
model to predict an optimal operating point and directly control the system to achieve the
predicted most-efficient state. On the other hand, when the actual performance does not
correspond to the map or model, the control seeks to generate a new map or model. It is noted
that such a map or model may itself have little physical significance, and thus is generally useful
only for application within the specific network which created it. See, U.S. Patent No.
5,506,768, expressly incorporated herein by reference. It may also be possible to constrain the
network to have weights which correspond to physical parameters, although this constraint may
lead to either control errors or inefficient implementation and realization.
See, also:
A. B. Corripio, "Tuning of Industrial Control Systems", Instrument Society of America.
Research Triangle Park, NC (1990) pp. 65-81.
C. J. Harris & S. A. Billings, "Self-Tuning and Adaptive Control: Theory and
Applications", Peter Peregrinus LTD (1981) pp. 20-33.
C. Rohrer & Clay Nesler, "Self-Tuning Using a Pattern Recognition Approach", Johnson
Controls, Inc., Research Brief 228 (Jun. 13, 1986).

D. E. Seborg, T. F. Edgar, & D. A. Mellichamp, "Process Dynamics and Control", John
Wiley & Sons, NY (1989) pp. 294-307, 538-541.
E. H. Bristol & T. W. Kraus, "Life with Pattern Adaptation", Proceedings 1984 American
Control Conference, pp. 888-892, San Diego, CA (1984).
Francis Schied, "Shaum's Outline Series-Theory & Problems of Numerical Analysis",
McGraw-Hill Book Co., NY (1968) pp. 236, 237, 243, 244, 261.
K. J. Astrom and B. Wittenmark, "Adaptive Control", Addison-Wesley Publishing
Company (1989) pp. 105-215.
K. J. Astrom, T. Hagglund, "Automatic Tuning of PID Controllers", Instrument Society
of America, Research Triangle Park, NC (1988) pp. 105-132.
R. W. Haines, "HVAC Systems Design Handbook", TAB Professional and Reference
Books, Blue Ridge Summit, PA (1988) pp. 170-177.
S. M. Pandit & S. M. Wu, "Timer Series & System Analysis with Applications", John
Wiley & Sons, Inc., NY (1983) pp. 200-205.
T. W. Kraus 7 T. J. Myron, "Self-Tuning PID Controller Uses Pattern Recognition
Approach", Control Engineering, pp. 106-111, Jun. 1984.
G F Page, J B Gomm & D Williams: "Application of Neural Networks to Modelling and
Control", Chapman & Hall, London, 1993.
Gene F Franklin, J David Powell & Abbas Emami-Naeini: "Feedback Control of
Dynamic Systems", Addison-Wesley Publishing Co. Reading, 1994.
George E P Box & Gwilym M Jenkins: "Time Series Analysis: Forecasting and Control",
Holden Day, San Francisco, 1976.
Sheldon G Lloyd & Gerald D Anderson: "Industrial Process Control", Fisher Controls
Co., Marshalltown, 1971.
Kortegaard, B. L., "PAC-MAN, a Precision Alignment Control System for Multiple
Laser Beams Self-Adaptive Through the Use of Noise", Los Alamos National Laboratory, date
unknown.
Kortegaard, B. L., "Superfine Laser Position Control Using Statistically Enhanced
Resolution in Real Time", Los Alamos National Laboratory, SPIE-Los Angeles Technical
Symposium, Jan. 23-25, 1985.
Donald Specht, IEEE Transactions on Neural Networks, "A General Regression Neural
Network", Nov. 1991, Vol. 2, No. 6, pp. 568-576.

Fuzzy controllers may be trained in much the same way neural networks are trained,
using backpropagation techniques, orthogonal least squares, table look-up schemes, and nearest
neighborhood clustering. See Wang, L., Adaptive fuzzy systems and control, New Jersey:
Prentice-Hall (1994); Fu-Chuang Chen, "Back-Propagation Neural Networks for Nonlinear
Self-Tuning Adaptive Control", 1990 IEEE Control System Magazine.
Thus, while a system model may be useful, especially for large changes in system
operating parameters, the adaptation mechanism is advantageous in that it does not rely on an
explicit system model, unlike many of the on-line adaptation mechanisms such as those based on
Lyapunov methods. See Wang, 1994; Kang, H. and Vachtsevanos, G., "Adaptive fuzzy logic
control," IEEE International Conference on Fuzzy Systems, San Diego, Calif. (Mar. 1992);
Layne, J., Passino, K. and Yurkovich, S., "Fuzzy learning control for antiskid braking systems,"
IEEE Transactions on Control Systems Technology 1 (2), pp. 122-129 (1993).
The adaptive fuzzy controller (AFC) is a nonlinear, multiple-input multiple-output
(MIMO) controller that couples a fuzzy control algorithm with an adaptation mechanism to
continuously improve system performance. The adaptation mechanism modifies the location of
the output membership functions in response to the performance of the system. The adaptation
mechanism can be used off-line, on-line, or a combination of both. The AFC can be used as a
feedback controller, which acts using measured process outputs and a reference trajectory, or as
a feedback controller with feedforward compensation, which acts using not only measured
process outputs and a reference trajectory but also measured disturbances and other system
parameters. See, U.S. Patent Nos. 5,822,740, 5,740,324, expressly incorporated herein by
reference.
As discussed above, a significant process variable is the oil content of the refrigerant in
the evaporator. This variable may, in fact, be slowly controlled, typically by removal only, since
only on rare occasions will the oil content be lower than desired for any significant length of
time, and removing added oil is itself inefficient. To define the control algorithm, the process
variable, e.g., oil content, is continuously varied by partially distilling the refrigerant at, or
entering, the evaporator, to remove oil, providing clean refrigerant to the evaporator in an auto-
tuning procedure. Over time, the oil content will approach zero. The system performance is
monitored during this process. Through this method, the optimal oil content in the evaporator
and the sensitivity to changes in oil content can be determined. In a typical installation, the
optimum oil concentration in the evaporator is near 0%, while when the system is retrofitted
with a control system for controlling the oil content of the evaporator, it is well above optimum.
Therefore, the auto-tuning of the control may occur simultaneously with the remediation of the
inefficiency.
In fact, the oil content of the evaporator may be independently controlled, or controlled in
concert with other variables, such as refrigerant charge (or effective charge, in the case of the
preferred embodiment which provides an accumulator to buffer excess refrigerant and a control
loop to regulate level of refrigerant in the evaporator).
According to one design, an external reservoir of refrigerant is provided. Refrigerant is
withdrawn from the evaporator through a partial distillation apparatus into the reservoir, with the
oil separately stored. Based on the control optimization, refrigerant and oil are separately
returned to the system, i.e., refrigerant vapor to the evaporator and oil to the compressor loop. In
this way, the optimum oil concentration may be maintained for respective refrigerant charge
levels. It is noted that this system is generally asymmetric; withdrawal and partial distillation of
refrigerant is relatively slow, while charging the system with refrigerant and oil are relatively
quick. If rapid withdrawal of refrigerant is desired, the partial distillation system may be
temporarily bypassed. However, typically it is more important to meet peak loads quickly than
to obtain most efficient operating parameters subsequent to peak loads.
It is noted that, according to the second embodiment of the present invention, both
refrigerant-to-oil ratio and refrigerant fill may be independently controlled variables of system
operation.
The compressor may also be modulated, for example by controlling a compression ratio,
compressor speed, compressor duty cycle (pulse frequency, pulse width and/or hybrid
modulation), compressor inlet flow restriction, or the like.
While the immediate efficiency of the evaporator may be measured assuming a single
compartment within the evaporator, and therefore short time delay for mixing, it is also noted
that an oil phase may adhere to the evaporator tube walls. By flowing clean refrigerant through
the evaporator, this oil phase, which has a longer time-constant for release from the walls than a
mixing process of the bulk refrigerant, is removed. Advantageously, by modeling the evaporator
and monitoring system performance, by removing the oil phase from the refrigerant side of the
evaporator tub walls, a scale or other deposit on the water-side of the tube wall may be
estimated. This, it turns out, is a useful method for determining an effect on efficiency of such
deposits, and may allow an intelligent decision as to when an expensive and time consuming
descaling of the tube bundles is required. Likewise, by removing the excess oil film from the
tube wall, efficiency may be maintained, delaying the need for descaling.
The optimal refrigerant charge level may be subject to variation with nominal chiller load
and plant temperature, while related (dependent) variables include efficiency (kW/ton),
superheat temperature, subcooling temperature, discharge pressure, superheat temperature,
suction pressure and chilled water supply temperature percent error. Direct efficiency
measurement of kilowatt-hours per ton may be performed, or inferred from other variables,
preferably process temperatures and flow rates.
Complex interdependencies of the variables, as well as the preferred use of surrogate
variables instead of direct efficiency data, weigh in favor of a non-linear neural network model,
for example similar to the model employed in Bailey, Margaret B., "System Performance
Characteristics of a Helical Rotary Screw Air-Cooled Chiller Operating Over a Range of
Refrigerant Charge Conditions", ASHRAE Trans. 1998 104(2). In this case, the model has an
input layer, two hidden layers, and an output layer. The output layer typically has one node for
each controlled variable, while the input layer contains one node for each signal. The Bailey
neural network includes five nodes in the first hidden layer and two nodes for each output node
in the second hidden layer. Preferably, the sensor data is processed prior to input into the neural
network model. For example, linear processing of.sensor outputs, data normalization, statistical
processing, etc. may be performed to reduce noise, provide appropriate data sets, or to reduce the
topological or computational complexity of the neural network. Fault detection may also be
integrated in the system, either by way of further elements of the neural network (or a separate
neural network) or by analysis of the sensor data by other means.
Feedback optimization control strategies are may be applied to transient and dynamic
situations. Evolutionary optimization or genetic algorithms, which intentionally introduce small
perturbations of the independent control variable, to compare the result to an objective function,
may be made directly upon the process itself. In fact, the entire theory of genetic algorithms may
be applied to the optimization of refrigeration systems. See, e.g., US Patent Nos. 6,496,761;
6,493,686; 6,492,905; 6,463,371; 6,446,055; 6,418,356; 6,415,272; 6,411,944; 6,408,227
6,405,548; 6,405,122; 6,397,113; 6,349,293; 6,336,050; 6,324,530; 6,324,529; 6,314,412
6,304,862; 6,301,910; 6,300,872; 6,278,986; 6,278,962; 6,272,479; 6,260,362; 6,250,560
6,246,972; 6,230,497; 6,216,083; 6,212,466; 6,186,397; 6,181,984; 6,151,548; 6,110,214
6,064,996; 6,055,820; 6,032,139; 6,021,369; 5,963,929; 5,921,099; 5,946,673; 5,912,821
5,877,954; 5,848,402; 5,778,688; 5,775,124; 5,774,761; 5,745,361; 5,729,623; 5,727,130
5,727,127; 5,649,065; 5,581,657; 5,524,175; 5,511,158, each of which is expressly incorporated
herein by reference.
According to the present invention, the control may operate on multiple independent or
interdependent parameters. Steady state optimization may be used on complex processes
exhibiting long time constants and with disturbance variables that change infrequently. Hybrid
strategies are also employed in situations involving both long-term and short-term dynamics.
The hybrid algorithms are generally more complex and require custom tailoring for a truly
effective implementation. Feedback control can sometimes be employed in certain situations to
achieve optimal plant performance.
According to one embodiment of the invention, a refrigerant-side vs. water side heat
transfer impairment in an evaporator heat exchanger may be distinguished by selectively
modifying a refrigerant composition, for example to remove oil and other impurities. For
example, as the oil level of the refrigerant is reduced, oil deposits on the refrigerant side of the
heat exchanger tubes will also be reduced, since the oil deposit is generally soluble in the pure
refrigerant. The heat exchanger may then be analyzed in at least two different ways. First, if the
refrigerant-side is completely cleaned of deposits, then any remaining diminution of system
performance must be due to deposits on the water side. Second, assuming a linear process of
removing impairment on the refrigerant side, the amount of refrigerant-side impairment may be
estimated without actually removing the entire impairment. While, as stated above, a certain
amount of oil may result in more efficient operation than pure refrigerant, this may be added
back, if necessary. Since this process of purifying the refrigerant is relatively simpler and less
costly than descaling the evaporator to remove water-side heat exchange impairment, and is of
independent benefit to system operation, it therefore provides an efficient procedure to
determining the need for system maintenance. On the other hand, refrigerant purification
consumes energy, and may reduce capacity, and results in very low, possibly suboptimal, oil
concentrations in the evaporator, so continuous purification is generally not employed.
Thus, it is seen that a perturbation in system response in order to determine a parameter
of the system is not limited to compressor control, and, for example, changes in refrigerant
purity, refrigerant charge, oil level, and the like, may be made in order to explore system
operation.
Multivariate processes in which there are numerous interactive effects of independent
variables upon the process performance can best be optimized by the use of feedforward control.
However, an adequate predictive mathematical model of the process is required. This, for
example, may be particularly applicable to the inner compressor control loop. Note that the on-
line control computer will evaluate the consequences of variable changes using the model rather
than perturbing the process itself. Such a predictive mathematical model is therefore of
particular use in its failure, which is indicative of system deviation from a nominal operating
state, and possibly indicative of required system maintenance to restore system operation.
To produce a viable optimization result, the mathematical model in a feedforward
technique must be an accurate representation of the process. To ensure a one-to-one
correspondence with the process, the model is preferably updated just prior to each use. Model
updating is a specialized form of feedback in which model predictions are compared with the
current plant operating status. Any variances noted are then used to adjust certain key
coefficients in the model to enforce the required agreement. Typically, such models are based on
physical process elements, and therefore may be used to imply real and measurable
characteristics.
hi chillers, many of the relevant timeconstants are very long. While this reduces short
latency processing demands of a real time controller, it also makes corrections slow to
implement, and poses the risk of error, instability or oscillation if the timeconstants are
erroneously computed. Further, in order to provide a neural network with direct temporal
control sensitivity, a large number of input nodes may be required to represent the data trends.
Preferably, temporal calculations are therefore made by linear computational method, with
transformed time-varying data input to the neural network. The transform may be, for example,
in the time-frequency representation, or time-wavelet representation. For example, first and
second derivatives (or higher order, as may be appropriate) of sensor data or transformed sensor
data may be calculated and fed to the network. Alternately or additionally, the output of the
neural network may be subjected to processing to generate appropriate process control signals. It
is noted that, for example, if the refrigerant charge in a chiller is varied, it is likely that critical
timeconstants of the system will also vary. Thus, a model which presumes that the system has a
set of invariant timeconstants may produce errors, and the preferred system according to the
present invention makes no such critical presumptions. The control system therefore preferably
employs flexible models to account for the interrelation of variables.
Other potentially useful process parameters to measure include moisture, refrigerant
breakdown products, lubricant breakdown products, non-condensable gasses, and other known
impurities in the refrigerant. Likewise, there are also mechanical parameters which may have
optimizable values, such as mineral deposits in the brine tubes (a small amount of mineral
deposits may increase turbulence and therefore reduce a surface boundary layer), and air or water
flow parameters for cooling the condenser.
Typically, there are a set of process parameters which theoretically have an optimum
value of 0, while in practice, achieving this value is difficult or impossible to obtain or maintain.
This difficulty may be expressed as a service cost or an energy cost, but in any case, the control
system may be set to allow theoretically suboptimal parameter readings, which are practically
acceptable and preferable to remediation. A direct cost-benefit analysis may be implemented.
However, at some threshold, remediation is generally deemed efficient. The control system may
therefore monitor these parameters and either indicate an alarm, implement a control strategy, or
otherwise act. The threshold may, in fact, be adaptive or responsive to other system conditions;
for example, a remediation process would preferably be deferred during peak load periods if the
remediation itself would adversely affect system performance, and sufficient reserve capacity
exists to continue operation.
Thus, it is seen that in some instances, as exemplified by oil levels in the evaporator, an
initial (or periodic) determination of system sensitivity to the sensed parameter is preferred,
while in other instances, an adaptive control algorithm is preferred.
In the case of autotuning processes, after the optimization calculations are complete, the
process variable, e.g., the oil content of the evaporator, may be restored to the optimal level. It is
noted that the process variable may change over time, e.g., the oil level in the evaporator will
increase, so it is desired to select an initial condition which will provide the maximum effective
efficiency between the initial optimization and a subsequent maintenance to restore the system to
efficient operation. Therefore, the optimization preferably determines an optimum operating
zone, and the process variable established at the lower end of the zone after measurement. This
lower end may be zero, but need not be, and may vary for each system measured.
In this way, it is not necessary to continuously control the process variable, and rather the
implemented control algorithm may, for example, include a wide deadband and manual
implementation of the control process.
A monitor may be provided for the process variable, to determine when reoptimization is
necessary. During reoptimzation, it is not always necessary to conduct further efficiency
measurements; rather, the prior measurements may be used to redefine the desired operating
regime.
Thus, after the measurements are taken to a limit (e.g., near zero oil or beyond the
expected operating regime), the system is restored, if necessary, to achieve a desired initial
efficiency, allowing for gradual variations, e.g., accumulation of oil in the evaporator, while still
maintaining appropriate operation for a suitable period.
An efficiency measurement, or surrogate measurement(s) (e.g., compressor amperage,
thermodynamic parameters) may subsequently be employed to determine when process variable,
e.g., the oil level, has change or accumulated to sufficient levels to require remediation.
Alternately, a direct oil concentration measurement may be taken of the refrigerant in the
evaporator. In the case of refrigeration compressor oil, for example, the monitor may be an
optical sensor, such as disclosed in U.S. Patent No. 5,694,210, expressly incorporated herein by
reference.
A closed loop feedback device may seeks to maintain a process variable within a desired
range. Thus, a direct oil concentration gage, typically a refractometer, measures the oil content
of the refrigerant. A setpoint control, proportional, differential, integral control, fuzzy logic
control or the like is used to control a bypass valve to a refrigerant distillation device, which is
typically oversize, and operating well within its control limits. As the oil level increases to a
level at which efficiency is impaired, the refrigerant is distilled to remove oil. The oil is, for
example, returned to the compressor lubrication system, while the refrigerant is returned to the
compressor inlet. In this manner, closed loop feedback control may be employed to maintain the
system at optimum efficiency. It is noted that it is also possible to employ an active in-line
distillation process which does not bypass the evaporator. For example, the Zugibeast® system
(Hudson Technologies, Inc.) may be employed, however, this is system typically larger and more
complex than necessary for this purpose. U.S. Patent No. 5,377,499, expressly incorporated
herein by reference, thus provides a portable device for refrigerant reclamation. In this system,
refrigerant may be purified on site, rather than requiring, in each instance, transporting of the
refrigerant to a recycling facility. U.S. 5,709,091, expressly incorporated herein by reference,
also discloses a refrigerant recycling method and apparatus.
In the oil separating device, advantageously, the refrigerant is fed into a fractional
distillation chamber controlled to be at a temperature below its boiling point, and therefore
condenses into a bulk of liquid refrigerant remaining within the vessel. Relatively pure
refrigerant is present in the gas phase, while less volatile impurities remain in the liquid phase.
The pure refrigerant is used to establish the chamber temperature, thus providing a sensitive and
stable system. The fractionally distilled purified liquid refrigerant is available from one port,
while impurities are removed through another port. The purification process may be manual or
automated, continuous or batch.
One aspect of the invention derives from a relatively new understanding that the
optimum oil level in the evaporator of a refrigeration system may vary by manufacturer, model
and particular system, and that these variables are significant in the efficiency of the process and
may change over time. The optimal oil level need not be zero, for example in fin tube
evaporators, the optimal oil level may be between 1-5%, at which the oil bubbles and forms a
film on the tube surfaces, increasing heat transfer coefficient. On the other hand, so-called
nucleation boiling heat transfer tubes have a substantially lower optimal oil concentration,
typically less than about 1%.
Seeking to maintain a 0% oil concentration may itself be inefficient, since the oil
removal process may require expenditure of energy and bypass of refrigerant, and an operating
system has a low but continual level of leakage. Further, the oil level in the condenser may also
impact system efficiency, in a manner inconsistent with the changes in efficiency of the
evaporator.
Thus, this aspect of the invention does not presume an optimum level of a particular
process variable parameter. Rather, a method according to the invention explores the optimum
value, and thereafter allows the system to be set near the optimum. Likewise, the method
permits periodic 'tune-ups" of the system, rather than requiring continuous tight maintenance of
a control parameter, although the invention also provides a system and method for achieving
continuous monitoring and/or control.
The refrigeration systems or chillers may be large industrial devices, for example 3500
ton devices which draw 4160V at 500A max (2 MW). Therefore, even small changes in
efficiency may produce substantial savings in energy costs. Possibly more importantly, when
efficiency drops, it is possible that the chiller is unable to maintain the process parameter within
the desired range. During extended operation, for example, it is possible for the oil
concentration in the evaporator to increase above 10%, and the overall capacity of the system to
drop below 1500 tons. This can result in process deviations or failure, which may require
immediate or expensive remediation. Proper maintenance, to achieve a high optimum
efficiency, may be quite cost effective.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS:
The invention will now be described with reference to the accompanying drawings, in
which:
Fig. 1 is a schematic view of a known tube in shell heat exchanger evaporator;
Fig. 2 shows an end view of a tube plate, showing the radially symmetric arrangement of
tubes of a tube bundle, each tube extending axially along the length of the heat exchanger
evaporator;
Fig. 3 shows a schematic drawing of a partial distillation system for removing oil from a
refrigerant flow stream;
Fig. 4 shows a schematic of a chiller efficiency measurement system;
Fig. 5 shows a stylized representative efficiency graph with respect to changes in
evaporator oil concentration;
Fig. 6 A and 6B show, respectively, a schematic of a vapor compression cycle and a
temperature-entropy diagram;
Figs 7 A, 7B and 7C show, respectively, different block diagrams of a control according
to the present invention;
Fig. 8 shows a semi-schematic diagram of a refrigeration system controlled according to
the present invention; and
Fig. 9 shows a schematic diagram of a control for a refrigeration system according to the
present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
The foregoing and other objects, features and advantages of the present invention will
become more readily apparent to those skilled in the art to which the invention pertains upon
reference to the following detailed description of one of the best modes for carrying out the
invention, when considered in conjunction with the accompanying drawing in which preferred
embodiments of the invention are shown and described by way of illustration, and not of
limitation, wherein:
EXAMPLE 1
As shown in Figs. 1-2, a typical tube in shell heat exchanger 1 consists of a set of parallel
tubes 2 extending through a generally cylindrical shell 3. The tubes 2 are held in position with a
tube plate 4, one of which is provided at each end 5 of the tubes 2. The tube plate 4 separates a
first space 6, continuous with the interior of the tubes 7, from a second space 8, continuous with
the exterior of the tubes 2. Typically, a domed flow distributor 9 is provided at each end of the
shell 3, beyond the tube sheet 4, for distributing flow of the first medium from a conduit 10
through the tubes 2, and thence back to a conduit 11. In the case of volatile refrigerant, the
system need not be symmetric, as the flow volumes-and rates will differ at each side of the
system. Not shown are optional baffles or other means for ensuring optimized flow distribution
patterns in the heat exchange tubes.
As shown in Fig. 3, a refrigerant cleansing system provides an inlet 112 for receiving
refrigerant from the condenser, a purification system employing a controlled distillation process,
and an outlet 150 for returning purified refrigerant. This portion of the system is similar to the
system described in U.S. 5,377,499, expressly incorporated herein by reference.
The compressor 100 compresses the refrigerant, while condenser 107, sheds the heal in
the gas. A small amount of compressor oil is carried with the hot gas to the condenser 107,
where it cools and condenses into a mixed liquid with the refrigerant, and exits through line 108
and fitting 14. Isolation valves 102, 109 arc provided to selectively allow insertion of a partial
distillation apparatus 105 within the refrigerant flow path. The refrigerant from the partial
distillation apparatus 105 is received by the evaporator 103 through the isolation valve 102.
The partial distillation apparatus 105 is capable of boiling contaminated refrigerant in a
distillation chamber 130, with the distillation is controlled by throttling the refrigerant vapor.
Contaminated refrigerant liquid 120 is fed, represented by directional arrow 110, through an inlet
112 and a pressure regulating valve 114, into distillation chamber 116, to establish liquid level
118. A contaminated liquid drain 121 is also provided, with valve 123. A high surface area
conduit, such as a helical coil 122, is immersed beneath the level 118 of contaminated refrigerant
liquid. Thermocouple 124 is placed at or near the center of coil 122 for measuring distillation
temperature for purposes of temperature control unit 126, which controls the position of three-
way valve 128, to establish as fractional distillation temperature. Temperature control valve 128
operates, with bypass conduit 130, so that, as vapor is collected in the portion 132 of distillation
chamber 116 above liquid level 118, it will feed through conduit 134 to compressor 136, to
create a hot gas discharge at the output 138 of compressor 136, which are fed through three-way
valve 128, under the control of temperature control 126. In those situations where thermocouple
124 indicates a fractional distillation temperature above threshold, bypass conduit 130 receives
some of the output from compressor 136; below threshold, the output will flow as indicated by
arrow 140 into helical coil 122; near threshold, gases from the compressor output are allowed to
flow partially along the bypass conduit and partially into the helical coil to maintain that
temperature. Flow through bypass conduit 130 and from helical coil 122, in directions 142, 144,
respectively, will pass through auxiliary condenser 146 and pressure regulating valve 148 to
produce a distilled refrigerant outlet indicated by directional arrow 150. Alternatively,
condenser 146 is controlled by an additional temperature control unit, controlled by the
condenser output temperature. Thus, oil from the condenser 107 is removed before entering the
evaporator 105. By running the system over time, oil accumulation in the evaporator 103 will
drop, thus cleansing the system.
Fig. 4 shows an instrumented chiller system, allowing periodic or batch reoptimization,
or allowing continuous closed loop feedback control of operating parameters. Compressor 100
is connected to a power meter 101, which accurately measures power consumption by measuring
Volts and Amps drawn. The compressor 100 produces hot dense refrigerant vapor in line 106,
which is fed to condenser 107, where latent heat of vaporization and the heat added by the
compressor 100 is shed. The refrigerant carries a small amount of compressor lubricant oil. The
condenser 107 is subjected to measurements of temperature and pressure by temperature gage
155 and pressure gage 156. The liquefied, cooled refrigerant, including a portion of mixed oil, if
fed through line 108 to an optional partial distillation apparatus 105, and hence to evaporator
103. In the absence of the partial distillation apparatus 105, the oil from the condenser 107
accumulates in the evaporator 103. The evaporator 103 is subjected to measurements of
refrigerant temperature and pressure by temperature gage 155 and pressure gage 156. The
chilled water in inlet line 152 and outlet line 154 of the evaporator 103 are also subject to

temperature and pressure measurement by temperature gage 155 and pressure gage 156. The
evaporated refrigerant from the evaporator 103 returns to the compressor through line 104.
The power meter 101, temperature gage 155 and pressure gage 156 each provide data to a
data acquisition system 157, which produces output 158 representative of an efficiency of the
chiller, in, for example, BTU/kWH. An oil sensor 159 provides a continuous measurement of
oil concentration in the evaporator 103, and may be used to control the partial distillation
apparatus 105 or determine the need for intermittent reoptimization, based on an optimum
operating regime. The power meter 101 or the data acquisition system 157 may provide
surrogate measurements to estimate oil level in the evaporator or otherwise a need for oil
removal.
As shown in Fig. 5, the efficiency of the chiller varies with the oil concentration in the
evaporator 103. Line 162 shows a non-monotonic relationship. After the relationship is
determined by plotting the efficiency with respect to oil concentration, an operating regime may
thereafter be defined. While typically, after oil is removed from the evaporator 103, it is not
voluntarily replenished, a lower limit 160 of the operating regime defines, in a subsequent
removal operation, a boundary beyond which it is not useful to extend. Complete oil removal is
not only costly and directly inefficient, it may also result in reduced system efficiency. Likewise,
when the oil level exceeds an upper boundary 161 of the operating regime, system efficiency
drops and it is cost effective to service the chiller to restore optimum operation. Therefore, in a
close loop feedback system, the distance between the lower boundary 160 and upper boundary
will be much narrower than in a periodic maintenance system. The oil separator (e.g., partial
distillation apparatus 105 or other type system) in a closed loop feedback system is itself
typically less efficient than a larger system typically employed during periodic maintenance, so
there are advantages to each type of arrangement.
EXAMPLE 2
Fig. 7A shows a block diagram of a first embodiment of a control system according to
the present invention. In this system, refrigerant charge is controlled using an adaptive control
200, with the control receiving refrigerant charge level 216 (from a level transmitter, e.g., Henry
Valve Co., Melrose Park IL LCA series Liquid Level Column with E-9400 series Liquid Level
Switches, digital output, or K-Tek Magnetostrictive Level Transmitters AT200 or AT600,
analog output), optionally system power consumption (kWatt-hours), as well as thermodynamic
parameters, including condenser and evaporator water temperature in and out, condenser and
evaporator water flow rates and pressure, in and out, compressor RPM, suction and discharge

pressure and temperature, and ambient pressure and temperature, all through a data acquisition
system for sensor inputs 201. These variables are fed into the adaptive control 200 employing a
nonlinear model of the system, based on neural network 203 technology. The variables are
preprocessed to produce a set of derived variables from the input set, as well as to represent
temporal parameters based on prior data sets. The neural network 203 evaluates the input data
set periodically, for example every 30 seconds, and produces an output control signal 209 or set
of signals. After the proposed control is implemented, the actual response is compared with a
predicted response based on the internal model defined by the neural network 203 by an adaptive
control update subsystem 204, and the neural network is updated 205 to reflect or take into
account the "error". A further output 206 of the system, from a diagnostic portion 205, which
may be integrated with the neural network or separate, indicates a likely error in either the
sensors and network itself, or the plant being controlled.
The controlled variable is, for example, the refrigerant charge in the system. In order to
remove refrigerant, liquid refrigerant from the evaporator 211 is transferred to a storage vessel
212 through a valve 210. In order to add refrigerant, gaseous refrigerant may be returned to the
compressor 214 suction, controlled by valve 215, or liquid refrigerant pumped to the evaporator
211. Refrigerant in the storage vessel 212 may be subjected to analysis and purification.
EXAMPLE 3
A second embodiment of the control system employs feedforward optimization control
strategies, as shown in Fig. 7B. Fig. 7B shows a signal-flow block diagram of a computer-based
feedforward optimizing control system. Process variables 220 are measured, checked for
reliability, filtered, averaged, and stored in the computer database 222. A regulatory system 223
is provided as a front line control to keep the process variables 220 at a prescribed and desired
slate of values. The conditioned set of measured variables are compared in the regulatory system
223 with the desired set points from operator 224A and optimization routine 224B. Errors
detected are then used to generate control actions that are then transmitted as outputs 225 to final
control elements in the process 221. Set points for the regulatory system 223 are derived either
from operator input 224A or from outputs of the optimization routine 224B. Note that the
optimizer 226 operates directly upon the model 227 in arriving at its optimal set-point slate
224B. Also note that the model 227 is updated by means of a special routine 228 just prior to use
by the optimizer 227. The feedback update feature ensures adequate mathematical process
description in spite of minor instrumentation errors and, in addition, wili compensate for
discrepancies arising from simplifying assumptions incorporated in the model 227. In this case,

the controlled variable may be, for example, compressor speed, alone or in addition to refrigerant
charge level.
The input variables are, in this case, similar to those in Example 2, including refrigerant
charge level, optionally system power consumption (kWatt-hours), as well as thermodynamic
parameters, including condenser and evaporator water temperature in and out, condenser and
evaporator water flow rates and pressure, in and out, compressor RPM, suction and discharge
pressure and temperature, and ambient pressure and temperature.
EXAMPLE 4
As shown in Fig. 7C, a control system 230 is provided which controls refrigerant charge
level 231, compressor speed 232, and refrigerant oil concentration 233 in evaporator. Instead of
providing a single complex model of the system, a number of simplified relationships are
provided in a database 234, which segment the operational space of the system into a number of
regions or planes based on sensor inputs. The sensitivity of the control system 230 to variations
in inputs 235 is adaptively determined by the control during operation, in order to optimize
energy efficiency.
Data is also stored in the database 234 as to the filling density of the operational space;
when the set of input parameters identifies a well populated region of the operational space, a
rapid transition is effected to achieve the calculated most efficient output conditions. On the
other hand, if the region of the operational space is poorly populated, the control 230 provides a
slow, searching alteration of the outputs seeking to explore the operational space to determine
the optimal output set. This searching procedure also serves to populate the space, so that the
control 230 will avoid the naive strategy after a few encounters.
In addition, for each region of the operational space, a statistical variability is determined.
If the statistical variability is low, then the model for the region is deemed accurate, and
continual searching of the local region is reduced. On the other hand, if the variability is high,
the control 230 analyzes the input data set to determine a correlation between any available input
235 and the system efficiency, seeking to improve the model for that region stored in the
database 234. This correlation may be detected by searching the region through sensitivity
testing of the input set with respect to changes in one or more of the outputs 231, 232, 233. For
each region, preferably a linear model is constructed relating the set of input variables and the
optima] output variables. Alternately, a relatively simple non-linear network, such as a neural
network, may be employed.
The operational regions, for example, segment the operational space into regions
separated by 5% of refrigerant charge level, from -40% to +20% of design, oil content of
evaporator by 0.5% from 0% to 10%, and compressor speed, from minimum to maximum in 10-
100 increments. It is also possible to provide non-uniformly spaced regions, or even adaptively
sized regions based on the sensitivity of the outputs to input variations at respective portions of
the input space.
The control system also provides a set of special modes for system startup and shutdown.
These are distinct from the normal operational modes, in that energy efficiency is not generally a
primary consideration during these transitions, and because other control issues may be
considered important. These modes also provide options for control system initialization and
fail-safe operation.
It is noted that, since the required update time for the system is relatively long, the neural
network calculations may be implemented serially on a general purpose computer, e.g., an Intel
Pentium IV or Athlon XP processor running Windows XP or a real time operating system, and
therefore specialized hardware (other than the data acquisition interface) is typically not
necessary.
It is preferred that the control system provide a diagnostic output 236 which "explains"
the actions of the control, for example identifying, for any given control decision, the sensor
inputs which had the greatest influence on the output state. In neural network systems, however,
it is often not possible to completely rationalize an output. Further, where the system detects an
abnormal state, either in the plant being controlled or the controller itself, it is preferred that
information be communicated to an operator or service engineer. This may be by way of a
stored log, visual or audible indicators, telephone or Internet telecommunications, control
network or local area network communications, radio frequency communication, or the like. In
many instances, where a serious condition is detected and where the plant cannot be fully
deactivated, it is preferable to provide a "failsafe" operational mode until maintenance may be
performed.
The foregoing description of the preferred embodiment of the invention has been
presented for purposes of illustration and description and is not intended to be exhaustive or to
limit the invention to the precise forms disclosed, since many modifications and variations are
possible in light of the above teaching. Some modifications have been described in the
specifications, and others may occur to those skilled in the art to which the invention pertains.
We Claim:
1. A method for optimizing operation of a refrigeration system having an
evaporator and having a control system for controlling a supply of liquid
refrigerant to the evaporator, comprising the steps of:
controlling a supply of refrigerant to the evaporator within an inner control
loop;
controlling a level of liquid refrigerant in the evaporator within an outer
control loop,
wherein the outer control loop modifies a supply rate of liquid refrigerant
to the inner control loop based on at least a measurement of evaporator
performance, and
wherein the inner control loop controls the refrigerant supply based on at
least the modified supply rate and a liquid refrigerant demand by the
evaporator,
thereby optimizing evaporator efficiency.
2. The method as claimed in claim 1, comprising the step of predicting a need for
refrigeration system service.
3. The method as claimed in any of claims 1 and 2, comprising the step of
providing a buffer for supply of refrigerant to the evaporator, the level of the
buffer being responsive to said outer control loop.
4. The method as claimed in any of claims 1 to 3, comprising the step of
estimating an oil migration into the evaporator.
5. The method as claimed in any of claims 1 to 4, wherein said outer control loop
is adaptive.
6. The method as claimed in any of claims 1 to 5, wherein said inner control loop
comprises a feed-forward characteristic.
7. The method as claimed in any of claims 1 to 6, wherein said outer control loop
compensates for oil migration into the evaporator.
8. The method as claimed in any of claims 1 to 7, wherein the outer control loop
compensates for alteration in refrigerant charge condition.
9. The method as claimed in any of claims 1 to 8, wherein at least one of the
inner control loop and the outer control loop perform a cost-optimization.
10. The method as claimed in any of claims 1 to 9, wherein at least one of the
inner control loop and the outer control loop perform a cost-optimization of a
process, said cost-optimization encompassing the refrigeration system and at
least one component of a plant employing the refrigeration system.
11. The method as claimed in any of claims 1 to 10, comprising the step of
modifying evaporator performance by separating oil from refrigerant in the
refrigeration system.
12. The method as claimed in any of claims 1 to 12, comprising the step of
providing an adaptive model of the refrigeration system for predicting a response
of the system to changes in a process variable.
13. A refrigeration system comprising a compressor for compressing a
refrigerant, a condenser for condensing refrigerant to a liquid, and an evaporator
for evaporating liquid refrigerant from the condenser to a gas, and a controller
which controls operation thereof, wherein the controller optimally controls both a
supply of liquid refrigerant to the evaporator and a level of refrigerant in the
evaporator.
14. The refrigeration system as claimed in claim 13, wherein the controller uses a
genetic algorithm to predict an optimal state.
15. The refrigeration system as claimed in any of claims 13 and 14, wherein said
controller comprises: an inner control loop for optimizing a supply of liquid
refrigerant to the evaporator; and an outer control loop for optimizing a level of
refrigerant in the evaporator, said outer control loop defining a supply rate for
said inner control loop based on an optimization including measurement of
evaporator performance, said inner control loop optimizing liquid refrigerant
supply based on said defined supply rate.
16. The refrigeration system as claimed in any of claims 13 to 15, comprising a
buffer for storing a reserve of liquid refrigerant.
17. The refrigeration system as claimed in claim 16, wherein a level of reserve
liquid refrigerant is controlled by said outer loop.
18. A method for controlling a refrigeration system having an evaporator,
comprising the steps of: measuring evaporator performance with respect to
quantity or composition of refrigerant in the evaporator; optimizing a target
quantity or composition of refrigerant in the evaporator based on the measured
evaporator performance; and optimizing a supply of volatile refrigerant
component of the refrigerant composition based on a refrigeration system
cooling demand and the optimized target.

A refrigeration system comprising a compressor (100) for compressing a
refrigerant, a condenser (107) for condensing refrigerant to a liquid, an
evaporator (103) for evaporating liquid refrigerant from the condenser (107) to a
gas, an inner control loop for optimizing a supply of liquid refrigerant to the
evaporator (103), and an outer control loop for optimizing a level of refrigerant
in the evaporator (103), said outer control loop defining a supply rate for said
inner control loop based on an optimization including measurement of evaporator
(103) performance, and said inner control loop optimizing liquid refrigerant
supply based on said defined supply rate. Independent variables, such as
proportion of oil in refrigerant, amount of refrigerant, contaminates, non-
condensibles, scale and other deposits on heat transfer surfaces, may be
estimated or measured. A model of the system and/or a thermodynamic model
approximating the system, for example derived from temperature and pressure
gages (155, 156), as well as power computations or measurements, is employed
to determine or estimate the effect on efficiency of deviance from an optimal
state. Various methods are provided for returning the system to an optimal state,
and for calculating a cost-effectiveness of employing such processes.

Documents:

1306-kolnp-2005-granted-abstract.pdf

1306-kolnp-2005-granted-assignment.pdf

1306-kolnp-2005-granted-claims.pdf

1306-kolnp-2005-granted-correspondence.pdf

1306-kolnp-2005-granted-description (complete).pdf

1306-kolnp-2005-granted-drawings.pdf

1306-kolnp-2005-granted-examination report.pdf

1306-kolnp-2005-granted-form 1.pdf

1306-kolnp-2005-granted-form 13.pdf

1306-kolnp-2005-granted-form 18.pdf

1306-kolnp-2005-granted-form 2.pdf

1306-kolnp-2005-granted-form 26.pdf

1306-kolnp-2005-granted-form 3.pdf

1306-kolnp-2005-granted-form 5.pdf

1306-kolnp-2005-granted-reply to examination report.pdf

1306-kolnp-2005-granted-specification.pdf


Patent Number 233646
Indian Patent Application Number 1306/KOLNP/2005
PG Journal Number 16/2009
Publication Date 17-Apr-2009
Grant Date 17-Apr-2009
Date of Filing 06-Jul-2005
Name of Patentee HUDSON TECHNOLOGIES, INC.
Applicant Address 275 NORTH MIDDLETOWN ROAD, PEAR RIVER, NY 10965
Inventors:
# Inventor's Name Inventor's Address
1 PAPAR, RIYAZ 14 SPLIT RAIL PLACE, THE WOODLANDS, TX 77382
2 ZUGIBE, KEVIN 38 EDGEMERE AVENUE, GREENWOOD, LAKE, NY 10925
PCT International Classification Number F25B 31/00
PCT International Application Number PCT/US2003/039175
PCT International Filing date 2003-12-09
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 10/730,791 2003-12-09 U.S.A.
2 60/431,901 2002-12-09 U.S.A.
3 60/434,847 2002-12-19 U.S.A.