A refrigeration system energy efficiency optimization method and system based on water temperature self-adaptation

Abstract

This invention discloses a method and system for optimizing the energy efficiency of a refrigeration system based on water temperature self-adaptation, belonging to the technical field of refrigeration system control. It includes the following steps: detecting refrigeration system data, calculating the temperature difference of the cooling capacity, calculating the optimized outlet water temperature based on the refrigeration system data after a set time interval, adjusting the chiller unit load, establishing a database, using the data stored in the database to train the chiller unit's refrigeration efficiency model, calling the stored data to fit and generate an ambient temperature curve and a chiller unit inlet water temperature curve, analyzing the curves to calculate the ambient temperature and chiller unit inlet water temperature after the next set time interval, obtaining the chiller unit load and chiller unit outlet water temperature by combining the chiller unit refrigeration efficiency model, and adjusting the chiller unit load using the obtained chiller unit load and chiller unit outlet water temperature. This invention can pre-adjust the outlet water temperature, effectively improving the energy efficiency optimization effect.

Description

Technical Field

[0001] This invention relates to the field of refrigeration system control, and in particular to a method and system for optimizing the energy efficiency of a refrigeration system based on water temperature adaptive control. Background Technology

[0002] Currently, large-scale refrigeration systems typically include cooling towers and chiller units, as well as cooling water pumps to pump water to the upper part of the cooling tower. After the user sets the cooling temperature, the chiller unit produces chilled water. This chilled water absorbs heat from the target and cools it down before returning to the cooling tower for further cooling. The cooled water is then introduced back into the chiller unit for the next cycle. Optimizing the energy efficiency of refrigeration systems has been a long-term research goal for manufacturers, aiming to reduce cooling water consumption and lower refrigeration costs. Water temperature adaptive systems are a commonly used energy efficiency optimization method for chiller units. They achieve energy efficiency optimization by intelligently adjusting the outlet water temperature to precisely meet the cooling requirements of the refrigeration equipment.

[0003] The existing technical solutions mentioned above have the following drawbacks: When using water temperature adaptive regulation for energy efficiency optimization, if there are fluctuations in water temperature or sudden changes in ambient temperature, the water temperature adaptive regulation algorithm may lag, resulting in insufficient control accuracy and a decrease in energy efficiency optimization effect. Summary of the Invention

[0004] To address the lag issue in the water temperature adaptive regulation algorithm and effectively improve energy efficiency optimization, this application provides a water temperature adaptive refrigeration system energy efficiency optimization method and system.

[0005] On the one hand, the energy efficiency optimization method for a refrigeration system based on water temperature adaptation provided in this application adopts the following technical solution: A method for optimizing the energy efficiency of a refrigeration system based on water temperature adaptation includes the following steps: The system detects ambient temperature, chiller inlet water temperature, chiller outlet water temperature, chiller load, and refrigeration equipment return water temperature to obtain the difference between the set refrigeration temperature and the standard temperature. The difference between the chiller outlet water temperature and the refrigeration equipment return water temperature is then calculated as the refrigeration capacity temperature difference. After the set interval, the PID algorithm is used to calculate and optimize the outlet water temperature based on the ambient temperature, chiller load, cooling capacity temperature difference, and standard temperature difference. The chiller load is then adjusted according to the optimized outlet water temperature to make the chiller outlet water temperature reach the optimized outlet water temperature. Establish a database to store the current data each time the optimal outlet water temperature is calculated; The system uses the ambient temperature, chiller inlet water temperature, chiller outlet water temperature and chiller load stored in the database to train a neural network model to obtain the unit cooling efficiency model. The unit cooling efficiency model uses the ambient temperature and chiller inlet water temperature as input parameters and the chiller load and chiller outlet water temperature as output parameters. The stored ambient temperature and chiller inlet water temperature are retrieved and combined with the current ambient temperature and chiller inlet water temperature to generate an ambient temperature curve and a chiller inlet water temperature curve. The function formulas of the ambient temperature curve and the chiller inlet water temperature curve are analyzed, and the ambient temperature and chiller inlet water temperature after the next set time are calculated. The calculated ambient temperature and chiller inlet water temperature are then imported into the chiller cooling efficiency model to obtain the chiller load and chiller outlet water temperature. Adjust the chiller load using the obtained chiller load and chiller outlet water temperature.

[0006] By adopting the above scheme, after energy efficiency optimization, future temperature changes are predicted based on historical data. The prediction results are combined with intelligent model processing to obtain the adjustment plan for the next energy efficiency optimization. The chiller unit is then pre-adjusted according to the adjustment plan, realizing the advance control of energy efficiency optimization technology. This effectively solves the problem of lag in the water temperature adaptive regulation algorithm and effectively improves the energy efficiency optimization effect.

[0007] Preferably, after the step of "obtaining the chiller unit load and the chiller unit outlet water temperature", the following step is also included: The calculated ambient temperature and chiller inlet water temperature were verified by using a PID algorithm, along with the obtained chiller load and chiller outlet water temperature. If the calculated ambient temperature, chiller inlet water temperature, and chiller load cannot be used to calculate the chiller outlet water temperature using the PID algorithm, an alarm will be issued and the current adjustment of the chiller load will be suspended.

[0008] By adopting the above approach, the predicted data needs to be tested and verified before pre-adjustment to reduce the possibility of adjustment errors.

[0009] Preferably, the step "retrieving the stored ambient temperature and chiller inlet water temperature, and combining them with the current ambient temperature and chiller inlet water temperature to generate an ambient temperature curve and a chiller inlet water temperature curve" includes the following steps: Preset the number of time segments to select; The system retrieves the latest ambient temperature and chiller inlet water temperature stored in the database, which are equal to the number of time intervals selected. A two-dimensional coordinate system is generated with time as the horizontal axis. The retrieved ambient temperature and chiller inlet water temperature, as well as the current ambient temperature and chiller inlet water temperature, are marked on the two-dimensional coordinate system. The marked points of the ambient temperature and the chiller inlet water temperature are fitted to generate the ambient temperature curve and the chiller inlet water temperature curve, respectively.

[0010] By adopting the above scheme, when generating the ambient temperature curve and the unit inlet water temperature curve, the number of data groups called is limited by the number of time period segments, and the ambient temperature curve and the unit inlet water temperature curve are synchronized by the time axis.

[0011] Preferably, the step of "analyzing the functional formulas of the ambient temperature curve and the unit inlet water temperature curve" further includes the following steps: Preset the first maximum temperature difference and the second maximum temperature difference; Calculate the temperature difference between the last and previous marked points on the ambient temperature curve, and the temperature difference between the last and previous marked points on the computer group inlet water temperature curve. Compare the calculated temperature difference of the ambient temperature curve with the first maximum temperature difference, and compare the calculated temperature difference of the ambient temperature curve with the second maximum temperature difference with the first maximum temperature difference. If the calculated temperature difference of the ambient temperature curve is less than the first maximum temperature difference and the calculated temperature difference of the ambient temperature curve is less than the second maximum temperature difference, then the analysis of the function formulas of the ambient temperature curve and the unit inlet water temperature curve will be paused. If the calculated temperature difference of the ambient temperature curve is greater than the first maximum temperature difference, or the calculated temperature difference of the ambient temperature curve is greater than the second maximum temperature difference, then proceed to the next step.

[0012] By adopting the above scheme, before making pre-adjustments, it is first determined whether the current temperature change is large enough. If the temperature change is large, pre-adjustment is performed; if it is not large, pre-adjustment is not performed, thus saving computing power and reducing the number of times the chiller unit needs to be adjusted.

[0013] Preferably, the following steps are also included: Preset load alarm values ​​and cooling thresholds; After optimizing the outlet water temperature, determine whether the chiller unit load exceeds the load alarm value; When the chiller unit load exceeds the load alarm value, the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of the chiller unit is calculated as the cooling tower cooling efficiency value. The difference between the current return water temperature of the refrigeration equipment and the ambient temperature is calculated. The natural cooling efficiency value is calculated based on the difference between the current return water temperature of the refrigeration equipment and the ambient temperature. The relationship between the cooling tower cooling efficiency value and the natural cooling efficiency value is then determined. An alarm will be issued if the natural cooling efficiency is less than the cooling tower cooling efficiency. If the natural cooling efficiency value is greater than the cooling tower cooling efficiency value, then calculate whether the absolute value of the difference between the ambient temperature and the chiller inlet water temperature is higher than the cooling threshold. If the absolute value of the difference between the ambient temperature and the chiller inlet water temperature is higher than the cooling threshold, the power of the cooling water pump should be increased.

[0014] By adopting the above scheme, when the chiller unit is pre-adjusted, if the chiller unit is overloaded, the system will judge the water temperature at various points in the current refrigeration system. If abnormal data is found, an alarm will be issued to remind the user that the system has an error. If the data is normal, it may be that the inlet water temperature is too high. The system will try to increase the water flow to increase the cooling rate of the cooling tower, so as to avoid damage to the chiller unit due to overload or failure to reach the set value of cooling.

[0015] On the other hand, the energy efficiency optimization system for a refrigeration system based on water temperature adaptation provided in this application adopts the following technical solution: A cooling system energy efficiency optimization system based on water temperature adaptive includes a cooling tower, a cooling water pump connected to the cooling tower, a chiller unit connected to the cooling tower, and a control system. The control system includes a data acquisition module, an energy efficiency optimization module, a data storage module, a model training module, a curve generation module, and a prediction and adjustment module. The data acquisition module detects the ambient temperature, chiller inlet water temperature, chiller outlet water temperature, chiller load, and refrigeration equipment return water temperature, obtains the difference between the set refrigeration temperature and the standard temperature, and transmits it to the energy efficiency optimization module and the data storage module. The energy efficiency optimization module is preset with a set time interval. After the set time interval, the energy efficiency optimization module calculates the difference between the outlet water temperature of the chiller unit and the return water temperature of the refrigeration equipment as the cooling capacity temperature difference. Based on the ambient temperature, chiller unit load, cooling capacity temperature difference, and standard temperature difference, the module uses a PID algorithm to calculate the optimized outlet water temperature. Based on the optimized outlet water temperature, the chiller unit load is adjusted to make the chiller unit outlet water temperature reach the optimized outlet water temperature. The cooling capacity temperature difference is then transmitted to the data storage module. The data storage module stores the received data after receiving the temperature difference of the cooling capacity. The model training module calls the ambient temperature, chiller inlet water temperature, chiller outlet water temperature and chiller load stored in the data storage module to train the neural network model and obtain the unit cooling efficiency model. The unit cooling efficiency model uses the ambient temperature and chiller inlet water temperature as input parameters and the chiller load and chiller outlet water temperature as output parameters. The unit cooling efficiency model is transmitted to the prediction and adjustment module. The curve generation module calls the ambient temperature and chiller inlet water temperature stored in the data storage module and the ambient temperature and chiller inlet water temperature collected by the data acquisition module, and performs fitting to generate an ambient temperature curve and a chiller inlet water temperature curve, and transmits the fitted ambient temperature curve and chiller inlet water temperature curve to the prediction and adjustment module. The prediction and adjustment module parses the function formulas of the ambient temperature curve and the chiller inlet water temperature curve, calculates the ambient temperature and chiller inlet water temperature after the next set time, imports the calculated ambient temperature and chiller inlet water temperature into the chiller cooling efficiency model, obtains the chiller load and chiller outlet water temperature, and uses the obtained chiller load and chiller outlet water temperature to adjust the chiller load.

[0016] By adopting the above scheme, after energy efficiency optimization, future temperature changes are predicted based on historical data. The prediction results are combined with intelligent model processing to obtain the adjustment plan for the next energy efficiency optimization. The chiller unit is then pre-adjusted according to the adjustment plan, realizing the advance control of energy efficiency optimization technology. This effectively solves the problem of lag in the water temperature adaptive regulation algorithm and effectively improves the energy efficiency optimization effect.

[0017] Preferably, the prediction and adjustment module obtains the chiller unit load and chiller unit outlet water temperature, and verifies the calculated ambient temperature and chiller unit inlet water temperature with the obtained chiller unit load and chiller unit outlet water temperature using a PID algorithm. If the calculated ambient temperature, chiller unit inlet water temperature and chiller unit load cannot be used to calculate the chiller unit outlet water temperature using the PID algorithm, an alarm is issued and the current adjustment of the chiller unit load is suspended.

[0018] By adopting the above approach, the predicted data needs to be tested and verified before pre-adjustment to reduce the possibility of adjustment errors.

[0019] Preferably, the curve generation module has a preset number of time period segments. It calls the latest ambient temperature and chiller inlet water temperature stored in the database that are equal to the number of time period segments, generates a two-dimensional coordinate system with time as the horizontal axis, marks the called ambient temperature and chiller inlet water temperature as well as the current ambient temperature and chiller inlet water temperature on the two-dimensional coordinate system, and fits the marked points of the ambient temperature and the marked points of the chiller inlet water temperature to generate the ambient temperature curve and the chiller inlet water temperature curve.

[0020] By adopting the above scheme, when generating the ambient temperature curve and the unit inlet water temperature curve, the number of data groups called is limited by the number of time period segments, and the ambient temperature curve and the unit inlet water temperature curve are synchronized by the time axis.

[0021] Preferably, the control system further includes a fault detection module. The fault detection module has a preset first maximum temperature difference value and a second maximum temperature difference value. The fault detection module calls the ambient temperature curve and the unit inlet water temperature curve of the curve generation module, calculates the temperature difference between the last and previous marked points of the ambient temperature curve, calculates the temperature difference between the last and previous marked points of the unit inlet water temperature curve, compares the calculated temperature difference of the ambient temperature curve with the first maximum temperature difference value, compares the calculated temperature difference of the ambient temperature curve with the second maximum temperature difference value with the first maximum temperature difference value, and if the calculated temperature difference of the ambient temperature curve is less than the first maximum temperature difference value and less than the second maximum temperature difference value, then a pause signal is transmitted to the prediction and adjustment module. After receiving the pause signal, the prediction and adjustment module pauses the analysis of the function formulas for the ambient temperature curve and the unit inlet water temperature curve.

[0022] By adopting the above scheme, before making pre-adjustments, it is first determined whether the current temperature change is large enough. If the temperature change is large, pre-adjustment is performed; if it is not large, pre-adjustment is not performed, thus saving computing power and reducing the number of times the chiller unit needs to be adjusted.

[0023] Preferably, the control system further includes a cooling monitoring module, and the energy efficiency optimization module transmits a detection signal to the cooling monitoring module when adjusting the chiller unit load according to the optimized outlet water temperature; The cooling monitoring module is preset with load alarm values ​​and cooling thresholds. After receiving the detection signal, the cooling monitoring module calls the latest data stored in the data storage module to determine whether the chiller unit load exceeds the load alarm value. When the chiller unit load exceeds the load alarm value, the module calculates the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of the chiller unit as the cooling tower cooling efficiency value. It also calculates the difference between the current return water temperature of the refrigeration equipment and the ambient temperature, and calculates the natural cooling efficiency value based on the difference between the current return water temperature of the refrigeration equipment and the ambient temperature. The module then determines the relationship between the cooling tower cooling efficiency value and the natural cooling efficiency value. If the natural cooling efficiency value is less than the cooling tower cooling efficiency value, an alarm is issued. If the natural cooling efficiency value is greater than the cooling tower cooling efficiency value, the module calculates whether the absolute value of the difference between the ambient temperature and the chiller unit inlet water temperature is higher than the cooling threshold. If the absolute value of the difference between the ambient temperature and the chiller unit inlet water temperature is higher than the cooling threshold, the power of the cooling water pump is increased.

[0024] By adopting the above scheme, when the chiller unit is pre-adjusted, if the chiller unit is overloaded, the system will judge the water temperature at various points in the current refrigeration system. If abnormal data is found, an alarm will be issued to remind the user that the system has an error. If the data is normal, it may be that the inlet water temperature is too high. The system will try to increase the water flow to increase the cooling rate of the cooling tower, so as to avoid damage to the chiller unit due to overload or failure to reach the set value of cooling.

[0025] In summary, the present invention has the following beneficial effects: 1. It enables advance control of energy efficiency optimization technology, effectively solving the problem of lag in the water temperature adaptive adjustment algorithm and effectively improving the energy efficiency optimization effect. Attached Figure Description

[0026] Figure 1 This is an overall flowchart of Embodiment 1 of this application.

[0027] Figure 2 This is an overall system block diagram of Embodiment 2 of this application.

[0028] Figure 3 This is a block diagram of the control system of Embodiment 2 of this application.

[0029] Explanation of reference numerals in the attached figures: 1. Control system; 11. Data acquisition module; 12. Energy efficiency optimization module; 13. Data storage module; 14. Model training module; 15. Curve generation module; 16. Prediction and adjustment module; 17. Fault detection module; 18. Cooling monitoring module; 2. Cooling tower; 3. Cooling water pump; 4. Chiller unit. Detailed Implementation

[0030] Example 1: This application discloses an energy efficiency optimization method for a refrigeration system based on water temperature adaptive design, such as... Figure 1 As shown, the specific steps are as follows: S100, preset time period selection quantity, first maximum temperature difference, second maximum temperature difference, load alarm value, and cooling threshold.

[0031] S101. Detect the ambient temperature, chiller unit 4 inlet water temperature, chiller unit 4 outlet water temperature, chiller unit 4 load, and refrigeration equipment return water temperature; obtain the difference between the set cooling temperature and the standard temperature; and calculate the difference between the chiller unit 4 outlet water temperature and the refrigeration equipment return water temperature as the cooling capacity temperature difference. The set cooling temperature is set by the user according to their needs. The standard temperature difference is the difference between the chiller unit 4 inlet water temperature and outlet water temperature under a preset standard environment.

[0032] S200 After the interval is set, the PID algorithm is used to calculate and optimize the outlet water temperature based on the ambient temperature, chiller unit 4 load, cooling capacity temperature difference, and standard temperature difference. The chiller unit 4 load is adjusted according to the optimized outlet water temperature to make the chiller unit 4 outlet water temperature reach the optimized outlet water temperature.

[0033] S300: Establish a database and store the current data each time the outlet water temperature is optimized.

[0034] S400: Use the ambient temperature, chiller 4 inlet water temperature, chiller 4 outlet water temperature and chiller 4 load stored in the database to train the neural network model and obtain the unit cooling efficiency model. The unit cooling efficiency model takes the ambient temperature and chiller 4 inlet water temperature as input parameters and the chiller 4 load and chiller 4 outlet water temperature as output parameters.

[0035] S500: The system retrieves the latest ambient temperature and chiller unit 4 inlet water temperature stored in the database, equal to the selected time period. A two-dimensional coordinate system is generated with time as the horizontal axis. The retrieved ambient temperature and chiller unit 4 inlet water temperature, along with the current ambient temperature and chiller unit 4 inlet water temperature, are marked on this coordinate system. Fittings are then performed on the marked points for both the ambient temperature and chiller unit 4 inlet water temperature to generate ambient temperature curves and chiller unit inlet water temperature curves, respectively. During the generation of these curves, the number of data sets retrieved is limited by the selected time period, and the ambient temperature curves and chiller unit inlet water temperature curves are synchronized via the time axis.

[0036] S501. Calculate the temperature difference between the last and previous marked points of the ambient temperature curve, and the temperature difference between the last and previous marked points of the computer group inlet water temperature curve.

[0037] S502. Compare the calculated temperature difference of the ambient temperature curve with the first maximum temperature difference, and compare the calculated temperature difference of the ambient temperature curve with the second maximum temperature difference with the first maximum temperature difference.

[0038] S503. If the calculated temperature difference of the ambient temperature curve is less than the first maximum temperature difference and the calculated temperature difference of the ambient temperature curve is less than the second maximum temperature difference, then the analysis of the function formulas of the ambient temperature curve and the unit inlet water temperature curve shall be suspended.

[0039] S504. If the calculated temperature difference of the ambient temperature curve is greater than the first maximum temperature difference, or the calculated temperature difference of the ambient temperature curve is greater than the second maximum temperature difference, then proceed to the next step. Before performing pre-adjustment, first determine whether the current temperature change is large enough. If the temperature change is large, then perform pre-adjustment; if not, then do not perform pre-adjustment to save computing power and reduce the number of adjustments to chiller unit 4.

[0040] S600, analyze the function formulas of the ambient temperature curve and the unit inlet water temperature curve, and calculate the ambient temperature and chiller 4 inlet water temperature after the next set time. Import the calculated ambient temperature and chiller 4 inlet water temperature into the unit cooling efficiency model to obtain the chiller 4 load and chiller 4 outlet water temperature.

[0041] S601. The calculated ambient temperature and chiller unit 4 inlet water temperature are compared with the obtained chiller unit 4 load and chiller unit 4 outlet water temperature using a PID algorithm for verification.

[0042] S602. If the calculated ambient temperature, chiller 4 inlet water temperature, and chiller 4 load cannot be used to calculate the chiller 4 outlet water temperature using the PID algorithm, an alarm will be issued and the current adjustment of chiller 4 load will be suspended. Before performing pre-adjustment, the predicted data needs to be tested and verified to reduce the possibility of adjustment errors.

[0043] S603. Adjust the load of chiller 4 using the obtained load and outlet water temperature of chiller 4.

[0044] S700: After optimizing the outlet water temperature, determine whether the load of chiller unit 4 exceeds the load alarm value.

[0045] S701. When the load of chiller unit 4 exceeds the load alarm value, the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of chiller unit 4 is calculated as the cooling efficiency value of cooling tower 2. The difference between the current return water temperature of the refrigeration equipment and the ambient temperature is calculated. The natural cooling efficiency value is calculated based on the difference between the current return water temperature of the refrigeration equipment and the ambient temperature. The relationship between the cooling efficiency value of cooling tower 2 and the natural cooling efficiency value is determined.

[0046] S702. If the natural cooling efficiency value is less than the cooling tower 2 cooling efficiency value, an alarm will be issued.

[0047] S703. If the natural cooling efficiency value is greater than the cooling tower 2 cooling efficiency value, then calculate whether the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit 4 is higher than the cooling threshold.

[0048] S704. If the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit 4 is higher than the cooling threshold, then increase the power of the cooling water pump 3.

[0049] The implementation principle of the energy efficiency optimization method and system of the refrigeration system based on water temperature adaptive in this application embodiment is as follows: After energy efficiency optimization, the future temperature change is predicted based on historical data. Based on the prediction results and combined with intelligent model processing, an adjustment plan for the next energy efficiency optimization is obtained. The chiller unit 4 is pre-adjusted according to the adjustment plan, so as to realize the advance control of energy efficiency optimization technology, effectively solve the problem of lag in water temperature adaptive adjustment algorithm, and effectively improve the energy efficiency optimization effect.

[0050] Example 2: This application discloses an energy efficiency optimization system for a refrigeration system based on water temperature adaptive design, such as... Figure 2 and Figure 3 As shown, the system includes a cooling tower 2, a cooling water pump 3 connected to the cooling tower 2, a chiller unit 4 connected to the cooling tower 2, and a control system 1. The cooling water pump 3 is used to control the flow of water in the refrigeration system. The chiller unit 4 generates chilled water and transmits it to the refrigeration equipment. The return water from the refrigeration equipment enters the cooling tower 2 for preliminary cooling. The cooled water then enters the chiller unit 4 for further cooling.

[0051] The control system 1 includes a data acquisition module 11, an energy efficiency optimization module 12, a data storage module 13, a model training module 14, a curve generation module 15, a prediction and adjustment module 16, a fault detection module 17, and a cooling monitoring module 18.

[0052] The data acquisition module 11 detects the ambient temperature, the inlet water temperature of the chiller unit 4, the outlet water temperature of the chiller unit 4, the load of the chiller unit 4, and the return water temperature of the refrigeration equipment, obtains the difference between the set refrigeration temperature and the standard temperature, and transmits it to the energy efficiency optimization module 12 and the data storage module 13.

[0053] The energy efficiency optimization module 12 has a preset time interval. After the preset time interval, the energy efficiency optimization module 12 calculates the difference between the outlet water temperature of the chiller unit 4 and the return water temperature of the refrigeration equipment as the cooling capacity temperature difference. Based on the ambient temperature, the load of the chiller unit 4, the cooling capacity temperature difference, and the standard temperature difference, it uses a PID algorithm to calculate the optimized outlet water temperature. Based on the optimized outlet water temperature, the load of the chiller unit 4 is adjusted to achieve the optimized outlet water temperature. The cooling capacity temperature difference is then transmitted to the data storage module 13. When the energy efficiency optimization module 12 adjusts the load of the chiller unit 4 based on the optimized outlet water temperature, it transmits a detection signal to the cooling monitoring module 18. The data storage module 13 stores the received data after receiving the cooling capacity temperature difference.

[0054] The model training module 14 calls the ambient temperature, chiller 4 inlet water temperature, chiller 4 outlet water temperature and chiller 4 load stored in the data storage module 13 to train the neural network model and obtain the unit cooling efficiency model. The unit cooling efficiency model uses the ambient temperature and chiller 4 inlet water temperature as input parameters, and the chiller 4 load and chiller 4 outlet water temperature as output parameters, and transmits the unit cooling efficiency model to the prediction and adjustment module 16.

[0055] The curve generation module 15 has a preset number of time period segments. It calls the latest ambient temperature and chiller unit 4 inlet water temperature stored in the database, equal to the number of time period segments. A two-dimensional coordinate system is generated with time as the horizontal axis. The called ambient temperature and chiller unit 4 inlet water temperature, as well as the current ambient temperature and chiller unit 4 inlet water temperature, are marked on the two-dimensional coordinate system. The marked points for the ambient temperature and chiller unit 4 inlet water temperature are fitted to generate the ambient temperature curve and the chiller unit 4 inlet water temperature curve, respectively. The curve generation module 15 transmits the fitted ambient temperature curve and chiller unit inlet water temperature curve to the prediction and adjustment module 16. When generating the ambient temperature curve and chiller unit inlet water temperature curve, the number of data groups called is limited by the number of time period segments, and the ambient temperature curve and chiller unit inlet water temperature curve are synchronized via the time axis.

[0056] The fault detection module 17 is preset with a first maximum temperature difference and a second maximum temperature difference. The fault detection module 17 calls the ambient temperature curve and the unit inlet water temperature curve from the curve generation module 15, calculates the temperature difference between the last and previous marked points on the ambient temperature curve, and calculates the temperature difference between the last and previous marked points on the unit inlet water temperature curve. It compares the calculated temperature difference on the ambient temperature curve with the first maximum temperature difference, and compares the calculated temperature difference on the ambient temperature curve with the second maximum temperature difference. If the calculated temperature difference on the ambient temperature curve is less than the first maximum temperature difference and less than the second maximum temperature difference, a pause signal is transmitted to the prediction and adjustment module 16. Before performing pre-adjustment, it first determines whether the current temperature change is large enough. If the temperature change is large, pre-adjustment is performed; otherwise, it is not performed, saving computing power and reducing the number of adjustments to the chiller unit 4.

[0057] Upon receiving the pause signal, the prediction and adjustment module 16 pauses the analysis of the function formulas for the ambient temperature curve and the chiller inlet water temperature curve. The prediction and adjustment module 16 analyzes the function formulas for the ambient temperature curve and the chiller inlet water temperature curve, and calculates the ambient temperature and chiller unit 4 inlet water temperature after the next set time. It then imports the calculated ambient temperature and chiller unit 4 inlet water temperature into the chiller unit's cooling efficiency model. The prediction and adjustment module 16 obtains the chiller unit 4 load and chiller unit 4 outlet water temperature. It verifies the calculated ambient temperature and chiller unit 4 inlet water temperature against the obtained chiller unit 4 load and chiller unit 4 outlet water temperature using a PID algorithm. If the calculated ambient temperature, chiller unit 4 inlet water temperature, and chiller unit 4 load cannot be used to calculate the chiller unit 4 outlet water temperature using the PID algorithm, an alarm is issued and the current adjustment of the chiller unit 4 load is paused. The prediction and adjustment module 16 then uses the obtained chiller unit 4 load and chiller unit 4 outlet water temperature to adjust the chiller unit 4 load. Before making adjustments, the forecast data needs to be tested and verified to reduce the possibility of errors in the adjustment.

[0058] The cooling monitoring module 18 is preset with a load alarm value and a cooling threshold. After receiving the detection signal, the cooling monitoring module 18 calls the latest data stored in the data storage module 13 to determine whether the load of the chiller unit 4 exceeds the load alarm value. When the load of the chiller unit 4 exceeds the load alarm value, the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of the chiller unit 4 is calculated as the cooling efficiency value of the cooling tower 2. The difference between the current return water temperature of the refrigeration equipment and the ambient temperature is calculated. The natural cooling efficiency value is calculated based on the difference between the current return water temperature of the refrigeration equipment and the ambient temperature. The relationship between the cooling efficiency value of the cooling tower 2 and the natural cooling efficiency value is determined. If the natural cooling efficiency value is less than the cooling efficiency value of the cooling tower 2, an alarm is issued. If the natural cooling efficiency value is greater than the cooling efficiency value of the cooling tower 2, the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit 4 is calculated to see if it is higher than the cooling threshold. If the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit 4 is higher than the cooling threshold, the power of the cooling water pump 3 is increased. During the pre-adjustment of chiller unit 4, if chiller unit 4 experiences excessive load, the system will judge the water temperature at various points in the current refrigeration system. If abnormal data is detected, an alarm will be issued to remind the user that there is an error in the system. If the data is normal, it may be that the inlet water temperature is too high. The system will try to increase the water flow to improve the cooling rate of cooling tower 2 to avoid damage to chiller unit 4 due to excessive load or failure to reach the set temperature.

[0059] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the energy efficiency of a refrigeration system based on water temperature adaptation, characterized in that, Includes the following steps: Detect ambient temperature, chiller (4) inlet water temperature, chiller (4) outlet water temperature, chiller (4) load, and refrigeration equipment return water temperature, obtain the difference between the set refrigeration temperature and the standard temperature, and calculate the difference between the chiller (4) outlet water temperature and the refrigeration equipment return water temperature as the refrigeration capacity temperature difference. After the interval is set, the PID algorithm is used to calculate the optimized outlet water temperature based on the ambient temperature, chiller unit (4) load, cooling capacity temperature difference, and standard temperature difference. The chiller unit (4) load is adjusted according to the optimized outlet water temperature so that the outlet water temperature of the chiller unit (4) reaches the optimized outlet water temperature. Establish a database to store the current data each time the optimal outlet water temperature is calculated; The ambient temperature, chiller (4) inlet water temperature, chiller (4) outlet water temperature and chiller (4) load stored in the database are used to train the neural network model to obtain the unit cooling efficiency model. The unit cooling efficiency model takes the ambient temperature and chiller (4) inlet water temperature as input parameters and the chiller (4) load and chiller (4) outlet water temperature as output parameters. The stored ambient temperature and chiller unit (4) inlet water temperature are called and combined with the current ambient temperature and chiller unit (4) inlet water temperature to generate an ambient temperature curve and a chiller unit inlet water temperature curve; The function formulas of the ambient temperature curve and the unit inlet water temperature curve are analyzed, and the ambient temperature and the inlet water temperature of the chiller (4) after the next set time are calculated. The calculated ambient temperature and the inlet water temperature of the chiller (4) are imported into the unit cooling efficiency model to obtain the load of the chiller (4) and the outlet water temperature of the chiller (4). Adjust the load of chiller unit (4) using the obtained chiller unit (4) load and chiller unit (4) outlet water temperature.

2. The energy efficiency optimization method for a refrigeration system based on water temperature adaptation according to claim 1, characterized in that, After the step of "obtaining the load of the chiller unit (4) and the outlet water temperature of the chiller unit (4)", the following steps are also included: The calculated ambient temperature and chiller unit (4) inlet water temperature were verified by PID algorithm with the obtained chiller unit (4) load and chiller unit (4) outlet water temperature. If the calculated ambient temperature, chiller inlet water temperature and chiller load cannot be used to calculate the chiller outlet water temperature through the PID algorithm, an alarm will be issued and the current adjustment of chiller load will be suspended.

3. The energy efficiency optimization method for a refrigeration system based on water temperature adaptation according to claim 1, characterized in that, The step "Retrieving the stored ambient temperature and chiller unit (4) inlet water temperature and combining them with the current ambient temperature and chiller unit (4) inlet water temperature to generate an ambient temperature curve and a chiller unit inlet water temperature curve" includes the following steps: Preset the number of time segments to select; Each ambient temperature and the corresponding chiller unit (4) inlet water temperature are used as a set of experimental arrays. The latest experimental array stored in the database is equal to the number of time intervals. A two-dimensional coordinate system is generated with time as the horizontal axis. The ambient temperature and chiller unit (4) inlet water temperature, as well as the current ambient temperature and chiller unit (4) inlet water temperature, are marked on the two-dimensional coordinate system. The marked points of the ambient temperature and the marked points of the chiller unit (4) inlet water temperature are fitted respectively to generate the ambient temperature curve and the unit inlet water temperature curve.

4. The energy efficiency optimization method for a refrigeration system based on water temperature adaptation according to claim 1, characterized in that, Before the step "analyzing the function formulas of the ambient temperature curve and the unit inlet water temperature curve", the following steps are also included: Preset the first maximum temperature difference and the second maximum temperature difference; Calculate the temperature difference between the last and previous marked points on the ambient temperature curve, and the temperature difference between the last and previous marked points on the computer group inlet water temperature curve. Compare the calculated temperature difference of the ambient temperature curve with the first maximum temperature difference, and compare the calculated temperature difference of the ambient temperature curve with the second maximum temperature difference with the first maximum temperature difference. If the calculated temperature difference of the ambient temperature curve is less than the first maximum temperature difference and the calculated temperature difference of the ambient temperature curve is less than the second maximum temperature difference, then the analysis of the function formulas of the ambient temperature curve and the unit inlet water temperature curve will be paused. If the calculated temperature difference of the ambient temperature curve is greater than the first maximum temperature difference, or the calculated temperature difference of the ambient temperature curve is greater than the second maximum temperature difference, then proceed to the next step.

5. The energy efficiency optimization method for a refrigeration system based on water temperature adaptation according to claim 1, characterized in that, It also includes the following steps: Preset load alarm values ​​and cooling thresholds; After optimizing the outlet water temperature, determine whether the load of the chiller unit (4) exceeds the load alarm value; When the load of the chiller unit (4) exceeds the load alarm value, the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of the chiller unit (4) is calculated as the cooling efficiency value of the cooling tower (2). The difference between the current return water temperature of the refrigeration equipment and the ambient temperature is calculated. The natural cooling efficiency value is calculated based on the difference between the current return water temperature of the refrigeration equipment and the ambient temperature. The relationship between the cooling efficiency value of the cooling tower (2) and the natural cooling efficiency value is determined. If the natural cooling efficiency value is less than the cooling tower (2) cooling efficiency value, an alarm will be issued; If the natural cooling efficiency value is greater than the cooling tower (2) cooling efficiency value, then calculate whether the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit (4) is higher than the cooling threshold. If the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit (4) is higher than the cooling threshold, the power of the cooling water pump (3) will be increased.

6. A water temperature adaptive refrigeration system for energy efficiency optimization, characterized in that: It includes a cooling tower (2), a cooling water pump (3) connected to the cooling tower (2), a chiller unit (4) connected to the cooling tower (2), and a control system (1). The control system (1) includes a data acquisition module (11), an energy efficiency optimization module (12), a data storage module (13), a model training module (14), a curve generation module (15), and a prediction and adjustment module (16). The data acquisition module (11) detects the ambient temperature, the inlet water temperature of the chiller unit (4), the outlet water temperature of the chiller unit (4), the load of the chiller unit (4), and the return water temperature of the refrigeration equipment, obtains the difference between the set refrigeration temperature and the standard temperature, and transmits it to the energy efficiency optimization module (12) and the data storage module (13). The energy efficiency optimization module (12) is preset with a set time interval. After the set time interval, the energy efficiency optimization module (12) calculates the difference between the outlet water temperature of the chiller unit (4) and the return water temperature of the refrigeration equipment as the cooling capacity temperature difference. Based on the ambient temperature, the load of the chiller unit (4), the cooling capacity temperature difference, and the standard temperature difference, the PID algorithm is used to calculate the optimized outlet water temperature. Based on the optimized outlet water temperature, the load of the chiller unit (4) is adjusted to make the outlet water temperature of the chiller unit (4) reach the optimized outlet water temperature. The cooling capacity temperature difference is transmitted to the data storage module (13). The data storage module (13) stores the received data after receiving the temperature difference of the cooling capacity; The model training module (14) calls the ambient temperature, chiller (4) inlet water temperature, chiller (4) outlet water temperature and chiller (4) load stored in the data storage module (13) to train the neural network model and obtain the unit cooling efficiency model. The unit cooling efficiency model takes the ambient temperature and chiller (4) inlet water temperature as input parameters, and the chiller (4) load and chiller (4) outlet water temperature as output parameters, and transmits the unit cooling efficiency model to the prediction and adjustment module (16). The curve generation module (15) calls the ambient temperature and chiller unit (4) inlet water temperature stored in the data storage module (13) and calls the ambient temperature and chiller unit (4) inlet water temperature collected by the data acquisition module (11), and performs fitting to generate an ambient temperature curve and a chiller unit inlet water temperature curve, and transmits the fitted ambient temperature curve and chiller unit inlet water temperature curve to the prediction and adjustment module (16). The prediction and adjustment module (16) analyzes the function formulas of the ambient temperature curve and the unit inlet water temperature curve, and calculates the ambient temperature and chiller unit (4) inlet water temperature after the next set time. It then imports the calculated ambient temperature and chiller unit (4) inlet water temperature into the unit cooling efficiency model to obtain the chiller unit (4) load and chiller unit (4) outlet water temperature. Finally, it uses the obtained chiller unit (4) load and chiller unit (4) outlet water temperature to adjust the chiller unit (4) load.

7. The energy efficiency optimization system for a refrigeration system based on water temperature adaptation according to claim 6, characterized in that: The prediction and adjustment module (16) obtains the load of the chiller unit (4) and the outlet water temperature of the chiller unit (4). It verifies the calculated ambient temperature and inlet water temperature of the chiller unit (4) with the obtained load and outlet water temperature of the chiller unit (4) using a PID algorithm. If the calculated ambient temperature, inlet water temperature of the chiller unit (4) and load of the chiller unit (4) cannot be used to calculate the outlet water temperature of the chiller unit (4) using the PID algorithm, an alarm is issued and the current adjustment of the chiller unit (4) load is suspended.

8. The energy efficiency optimization system for a refrigeration system based on water temperature adaptation according to claim 6, characterized in that: The curve generation module (15) has a preset number of time period segments. It takes each ambient temperature and the corresponding chiller unit (4) inlet water temperature as a set of experimental arrays, calls the latest experimental array stored in the database that is equal to the number of time period segments, generates a two-dimensional coordinate system with time as the horizontal axis, marks the called ambient temperature and chiller unit (4) inlet water temperature as well as the current ambient temperature and chiller unit (4) inlet water temperature on the two-dimensional coordinate system, and fits the marked points of ambient temperature and chiller unit (4) inlet water temperature respectively to generate ambient temperature curve and chiller unit (4) inlet water temperature curve.

9. The energy efficiency optimization system for a refrigeration system based on water temperature adaptive design according to claim 6, characterized in that: The control system (1) further includes a fault detection module (17). The fault detection module (17) is preset with a first maximum temperature difference and a second maximum temperature difference. The fault detection module (17) calls the ambient temperature curve and the unit inlet water temperature curve of the curve generation module (15), calculates the temperature difference between the last and the previous mark point of the ambient temperature curve, calculates the temperature difference between the last and the previous mark point of the unit inlet water temperature curve, compares the calculated temperature difference of the ambient temperature curve with the first maximum temperature difference, compares the calculated temperature difference of the ambient temperature curve with the second maximum temperature difference with the first maximum temperature difference, and if the calculated temperature difference of the ambient temperature curve is less than the first maximum temperature difference and the calculated temperature difference of the ambient temperature curve is less than the second maximum temperature difference, then a pause signal is transmitted to the prediction and adjustment module (16). The prediction and adjustment module (16) pauses the analysis of the function formulas of the ambient temperature curve and the unit inlet water temperature curve after receiving the pause signal.

10. The energy efficiency optimization system for a refrigeration system based on water temperature adaptation according to claim 6, characterized in that: The control system (1) also includes a cooling monitoring module (18), and the energy efficiency optimization module (12) transmits a detection signal to the cooling monitoring module (18) when adjusting the load of the chiller unit (4) according to the optimized outlet water temperature. The cooling monitoring module (18) is preset with load alarm value and cooling threshold. After receiving the detection signal, the cooling monitoring module (18) calls the latest data stored in the data storage module (13) to determine whether the load of the chiller unit (4) exceeds the load alarm value. When the load of the chiller unit (4) exceeds the load alarm value, the difference between the current return water temperature of the refrigeration equipment and the inlet water temperature of the chiller unit (4) is calculated as the cooling efficiency value of the cooling tower (2). The difference between the current return water temperature of the refrigeration equipment and the ambient temperature is calculated according to the current system. The difference between the return water temperature of the cooling equipment and the ambient temperature is used to calculate the natural cooling efficiency value. The relationship between the cooling efficiency value of the cooling tower (2) and the natural cooling efficiency value is determined. If the natural cooling efficiency value is less than the cooling efficiency value of the cooling tower (2), an alarm is issued. If the natural cooling efficiency value is greater than the cooling efficiency value of the cooling tower (2), the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit (4) is calculated to see if it is higher than the cooling threshold. If the absolute value of the difference between the ambient temperature and the inlet water temperature of the chiller unit (4) is higher than the cooling threshold, the power of the cooling water pump (3) is increased.

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