Method and apparatus for measuring and improving efficiency in refrigeration systems

• US 10,041,713 B1
• Filed: 12/28/2015
• Issued: 08/07/2018
• Est. Priority Date: 08/20/1999
• Status: Expired due to Term

• Current Assignee: Hudson Technologies, Inc.
• Original Assignee: Hudson Technologies, Inc.
• Inventors: Zugibe, Kevin, Schmidt, Douglas
• Primary Examiner(s): PATEL, RAMESH B
• Application Number:
  US14/980,962
• Time in Patent Office: 953 Days
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/32 Responding to malfunctions or emergencies
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 2110/00 Control inputs relating to air properties
• CPC Class Codes: F24F 2130/40 Noise
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: F25B 2339/0242 having tubular elements
• CPC Class Codes: F25B 2500/05 Cost reduction
• CPC Class Codes: F25B 2500/18 Optimization, e.g. high integration of refrigeration components
• CPC Class Codes: F25B 2500/19 Calculation of parameters
• CPC Class Codes: F25B 2600/2523 Receiver valves
• CPC Class Codes: F25B 2700/03 Oil level
• CPC Class Codes: F25B 2700/151 of the compressor motor
• CPC Class Codes: F25B 2700/195 of the condenser
• CPC Class Codes: F25B 2700/197 of the evaporator
• CPC Class Codes: F25B 2700/2116 of a condenser
• CPC Class Codes: F25B 2700/2117 of an evaporator
• CPC Class Codes: F25B 2700/21171 of the fluid cooled by the evaporator
• CPC Class Codes: F25B 31/004 oil recirculating arrangements
• CPC Class Codes: F25B 39/02 : Evaporators
• CPC Class Codes: F25B 43/02 : for separating lubricants from the refrigerant
• CPC Class Codes: F25B 45/00 : Arrangements for charging or discharging refrigerant
• CPC Class Codes: F25B 49/02 : for compression type machines, plants or systems
• CPC Class Codes: F25B 6/04 : arranged in series
• CPC Class Codes: G05B 13/041 : in which a variable is automatically adjusted to optimise the performance

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.