Method and apparatus for measuring and improving efficiency in refrigeration systems

Abstract

An apparatus for optimizing an efficiency of a refrigeration system, comprising means for measuring a refrigeration efficiency of an operating refrigeration system; means for altering a process variable of the refrigeration system during efficiency measurement; and a processor for calculating a process variable level which achieves an optimum efficiency. The process variables may include refrigerant charge and refrigerant oil concentration in evaporator.

Claims

1. An adaptive model-based control system for a refrigeration system, comprising: a sensor input, configured to receive refrigeration system operating parameters; a memory configured to store an adaptively-derived mathematical model of the refrigeration system, based on analysis over time of at least a response of the refrigeration system represented in the sensor input to at least one control signal; at least one automated processor configured to: receive the sensor input; determine if the adaptively-derived mathematical model is predictive of an actual refrigeration system performance based on at least the sensor input and the at least one control signal, and: if the adaptively-derived mathematical model is predictive of an actual refrigeration system performance, generate at least one control signal for controlling the refrigeration system in dependence on at least the model and the sensor input; and if the adaptively-derived mathematical model is not predictive of the actual refrigeration system performance, controlling the refrigeration system to selectively adaptively update the model based on at least the sensor input to increase predictiveness of the adaptively-derived mathematical model; and a control output configured to communicate the at least one control signal to the refrigeration system from the at least one automated processor.

2. The adaptive model-based control system according to claim 1, further comprising a memory configured to store probable normal operational limits; wherein the at least one automated processor is further configured to determine, based on at least the adaptively-derived mathematical model and the probable normal operational limits, a probability of system malfunction.

3. The adaptive model-based control system according to claim 1, wherein at least one timeconstant of a response the refrigeration system to the at least one control signal is represented in the model.

4. The adaptive model-based control system according to claim 3, wherein the at least one timeconstant varies over a range of conditions of the refrigeration system, and the at least one automated processor is further configured to update the model with respect to the at least one timeconstant.

5. The adaptive model-based control system according to claim 1, wherein the at least one automated processor is configured to employ a linear computational method to perform temporal calculations.

6. The adaptive model-based control system according to claim 1, wherein the at least one automated processor is configured to determine at least one first derivative of a series of sensor data.

7. The adaptive model-based control system according to claim 6, wherein the at least one automated processor is configured to determine at least one second derivative of the series of sensor data.

8. The adaptive model-based control system according to claim 1, wherein the at least one automated processor is configured to predict a most efficient operational state of the refrigeration system, and to produce the at least one control signal to alter the state of the refrigeration system toward the predicted most efficient operational state.

9. The adaptive model-based control system according to claim 1, wherein: the sensor input is selected from the group consisting of at least one of a pressure, a liquid level, a power, and a lubricant parameter; the at least one control signal comprises at least two control signals, each adapted to independently control different physical elements of the refrigeration system in dependence on the adaptively-derived mathematical model, at least one of the at least two control signals being selected from the group consisting of a proportional control signal, a valve control signal, a speed control signal, an oil control signal, and a refrigerant charge control signal.

10. The adaptive model-based control system according to claim 1, further comprising a memory configured to store information representing at least two different independent costs relating to operation of the refrigeration system, wherein the at least one automated processor is further configured to perform a cost-optimization with respect to the at least two different independent costs relating to operation of the refrigeration system, and the adaptively-derived mathematical model, to generate the at least one control signal.

11. The adaptive model-based control system according to claim 10, wherein the at least two different independent costs relating to operation of the refrigeration system comprise an energy cost for operating the refrigeration system, and a value attributed to removing heat by the refrigeration system.

12. The adaptive model-based control system according to claim 10, wherein the at least two different independent costs relating to operation of the refrigeration system comprise an energy cost for operating the refrigeration system, and a service cost for improving an efficiency of the refrigeration system.

13. The adaptive model-based control system according to claim 1, wherein the at least one automated processor is configured to employ the model to account for a time response of the refrigeration system, and to selectively damp an oscillation of the refrigeration system that would result from failure to account for the time response of the refrigeration system, while controlling the refrigeration system.

14. An adaptive model-based control method for controlling a refrigeration system, comprising: receiving a sensor input, representing refrigeration system operating parameters; storing an adaptively-derived mathematical model of the refrigeration system, based on analysis over time of at least a response of the refrigeration system represented in the sensor input to at least one control signal; determining by at least one automated processor if the adaptively-derived mathematical model is predictive of an actual refrigeration system performance, based on at least the sensor input and the at least one control signal, and: if the adaptively-derived mathematical model is predictive, generating the at least one control signal for controlling the refrigeration system in dependence on at least the model and the sensor input; and if the adaptively-derived mathematical model is not predictive of the actual refrigeration system performance, controlling the refrigeration system to selectively adaptively update the model based on at least the sensor input, to increase predictive accuracy of the adaptively-derived mathematical model; and communicating the at least one control signal to the refrigeration system selectively dependent on said determining.

15. The method according to claim 14, further comprising determining, based on at least the adaptively-derived mathematical model and predetermined probable normal operational limits, a probability of system malfunction.

16. The method according to claim 14, wherein at least one timeconstant of a response the refrigeration system to the at least one control signal is represented in the adaptively-derived mathematical model, further comprising determining at least one time-derivative of a sensor signal input to the adaptively-derived mathematical model.

17. The method according to claim 14, wherein at least one timeconstant of a response the refrigeration system to the at least one control signal is represented in the adaptively-derived mathematical model, which varies over a range of conditions of the refrigeration system, further comprising updating the model with respect to the at least one timeconstant.

18. The method according to claim 14, further comprising: storing information representing at least two different independent costs relating to operation of the refrigeration system in a memory; predicting, with the at least one automated processor, a most cost-efficient operational state of the refrigeration system; and controlling the refrigeration system to move toward a predicted most cost-efficient operational state.

19. The method according to claim 14, further comprising employing at least one timeconstant represented in the adaptively-derived mathematical model to selectively damp oscillations of the refrigeration system due to changes in the at least one control signal.

20. A control system for a thermodynamic system, comprising: a sensor input, configured to receive refrigeration system operating parameters; a memory configured to store cost information and an adaptively-derived mathematical representation of the thermodynamic system, based on analysis over time of at least a time- and amplitude response of the thermodynamic system represented in the sensor input to at least one control signal, wherein the mathematical representation accounts for a plurality of timeconstants; at least one automated processor configured to determine if the adaptively-derived mathematical representation is predictive of an actual thermodynamic system performance based on at least the sensor input and the at least one control signal, and: if the adaptively-derived mathematical representation is predictive of an actual thermodynamic system performance, generating the at least one control signal for controlling the thermodynamic system to achieve a predicted cost-effective optimum operating point based on the adaptively-derived mathematical representation and the sensor input; and if the adaptively-derived mathematical representation is not predictive of the actual thermodynamic system performance, controlling the thermodynamic system to selectively adaptively update the adaptively-derived mathematical representation by operating the thermodynamic system over a range of conditions, to determine responses of the thermodynamic system to the at least one control signal as represented in the sensor input, and costs over the range of operating conditions; and a control output configured to output the at least one control signal selectively dependent to the determination by the at least one automated processor.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The invention will now be described with reference to the accompanying drawings, in which:

(2) FIG. 1 is a schematic view of a known tube in shell heat exchanger evaporator;

(3) 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;

(4) FIG. 3 shows a schematic drawing of a partial distillation system for removing oil from a refrigerant flow stream;

(5) FIG. 4 shows a schematic of a chiller efficiency measurement system;

(6) FIG. 5 shows a stylized representative efficiency graph with respect to changes in evaporator oil concentration;

(7) FIGS. 6A and 6B show, respectively, a schematic of a vapor compression cycle and a temperature-entropy diagram; and

(8) FIGS. 7A, 7B and 7C show, respectively, different block diagrams of a control according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

(9) 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

(10) 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.

(11) 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. Pat. No. 5,377,499, expressly incorporated herein by reference.

(12) The compressor 100 compresses the refrigerant to a hot, dense gas. The condenser 107, sheds the heat in the gas, resulting from the compressor 100. A small amount of compressor oil is carried with the hot gas to the condenser 107, where it condenses, with the refrigerant, into a mixed liquid. The liquefied, cooled refrigerant (and oil) exits the condenser through line 108. Isolation valves 102, 109 are provided to selectively allow insertion of a partial distillation apparatus 105 within the refrigerant flow path. As shown, a fitting 14 receives the flow of refrigerant contents from the condenser 107 of the refrigeration system, though line 108. The refrigerant from the partial distillation apparatus 105 is received by the evaporator 103 through the isolation valve 102.

(13) The partial distillation apparatus 105 is capable of boiling contaminated refrigerant in a distillation chamber 130 without the need for external electrical heaters. Furthermore, no cooling water is required. The distillation temperature is controlled by throttling the refrigerant vapor. The distillation is accomplished by feeding contaminated refrigerant, represented by directional arrow 110, through an inlet 112 and a pressure regulating valve 114. The contaminated refrigerant flows into distillation chamber 116, to establish liquid level 118 of contaminated refrigerant liquid 120. 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. In turn, the temperature control unit controls the position of three-way valve 128, so that the distillation temperature will be set at a constant value at approximately thirty degrees Fahrenheit (for R22 refrigerant). Temperature control valve 128 operates in a manner, 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. This creates a hot gas discharge at the output 138 of compressor 136, such that those hot gases feed through three-way valve 128, under the control of temperature control 126. In those situations where thermocouple 124 indicates a distillation temperature above thirty degrees Fahrenheit, as an example, bypass conduit 130 will receive some flow of hot gases from compressor 136. Conversely, in those situations where thermocouple 124 indicates a temperature below thirty degrees Fahrenheit, as an example, the flow of hot gases will proceed as indicated by arrow 140 into helical coil 122. When thermometer 124 indicates certain values of temperature near thirty degrees Fahrenheit, hot gases from the compressor are allowed to flow partially along the bypass conduit and partially into the helical coil to maintain the thirty degree temperature. For differing refrigerants or mixtures, the desired boiling temperature may vary, and thus the temperature may be controlled accordingly. 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.

(14) 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.

(15) 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.

(16) 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.

(17) 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

(18) 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 Ill. 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.

(19) 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

(20) A second embodiment of the control system employs feedfoward 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, will 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.

(21) 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

(22) 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.

(23) 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 nave strategy after a few encounters.

(24) 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 optimal output variables. Alternately, a relatively simple non-linear network, such as a neural network, may be employed.

(25) 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.

(26) 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.

(27) 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 III processor running Windows NT or a real time operating system, and therefore specialized hardware is typically not necessary.

(28) 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.

(29) 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.