Multi-condenser water cooler and refrigeration method thereof

Abstract

The invention relates to the technical field of refrigeration, in particular to a multi-condenser water chiller and a refrigeration method thereof, and relates to the technical field of refrigeration, in particular to a water chiller which comprises a refrigerant circulation loop, the refrigerant circulation loop comprises a compressor, a valve assembly, an evaporator and two condensers, and the refrigerant circulation loop is used for refrigeration circulation. The refrigeration cycle comprises the steps that after a compressor compresses a refrigerant into a high-temperature and high-pressure state, the refrigerant takes away heat by ambient air when flowing through a condenser, and then the refrigerant enters an evaporator after being throttled and cooled through a valve assembly; the circulating water loop comprises an evaporator, a water opening, a water tank and a load, and the closed-loop circulation comprises the steps that after the circulating water absorbs cold energy in the evaporator, the circulating water is conveyed to the load through a water pump to be cooled and then returns to the evaporator to form the closed-loop circulation. According to the multi-condenser cooling-water machine and the refrigerating method thereof, on the premise that the equipment size is not obviously increased, the refrigerating performance of the whole machine is improved, the energy consumption is reduced, and the operation stability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of refrigeration technology, and in particular to a multi-condenser chiller and its refrigeration method. Background Technology

[0002] Chillers are widely used in industrial production, medical and health care, building air conditioning and other fields. Their main function is to provide precise and stable temperature control. Traditional chillers usually use a large condenser heat exchanger, which has the following drawbacks: First, it has high energy consumption under low load conditions; second, it occupies a large space, as a large heat exchange condenser usually requires a large heat exchange area to ensure the heat exchange capacity, which limits the size of the chiller and makes it impossible to miniaturize it; third, under low load conditions and long-term operation, the compressor has the risk of liquid slugging, difficulty in oil return, and frequent start-stop problems.

[0003] For example, Chinese patent publication number CN207635650U, entitled "A Chiller and Refrigeration System," includes a housing, a compressor, a condenser, a pressure sensor, and a water sprayer. The compressor, condenser, pressure sensor, and water sprayer are installed inside the housing. The compressor is connected to the condenser, and the condenser is connected to the water sprayer, which cleans the condenser. The water sprayer is electrically connected to the pressure sensor, which controls the opening and closing of the water sprayer. The disadvantage is poor cooling performance and an inability to precisely adjust the cooling capacity distribution. Summary of the Invention

[0004] To address the problem of poor refrigeration performance in existing water chillers, this invention provides a multi-condenser water chiller and its refrigeration method, which improves the overall refrigeration performance, reduces energy consumption, and enhances operational stability without significantly increasing the size of the equipment.

[0005] To achieve the above-mentioned technical objectives, the present invention provides a technical solution: a multi-condenser chiller, comprising: The refrigerant circulation loop includes a compressor, a valve assembly, an evaporator, and two condensers. The refrigerant circulation loop can realize a refrigeration cycle. The refrigeration cycle includes the compressor compressing the refrigerant to a high temperature and high pressure state, the refrigerant flowing through the condenser having its heat carried away by the ambient air, and then entering the evaporator after being throttled and cooled by the valve assembly. The circulating water circuit includes an evaporator, a water inlet, a water tank, and a load. The water inlet includes a return water inlet, a water outlet, and a drain outlet. A water pump is installed between the water tank and the water outlet. The circulating water circuit can achieve closed-loop circulation. The closed-loop circulation includes circulating water absorbing cold energy in the evaporator, being pumped to the load for cooling, and then returning to the evaporator to form a closed-loop circulation.

[0006] In this technical solution, a dual-condenser layout is adopted, which improves the overall cooling performance, reduces energy consumption, enhances operational stability, and effectively saves space resources while keeping the overall size of the equipment unchanged.

[0007] The present invention is further configured such that: the valve assembly includes a solenoid valve and an electronic expansion valve, the number of solenoid valves is two, the two solenoid valves are connected in series with two condensers respectively to control the opening and closing of the condensers respectively, the electronic expansion valve includes electronic expansion valve A and electronic expansion valve B, electronic expansion valve A is connected in series with the solenoid valve and the condenser, and electronic expansion valve B is connected in parallel with electronic expansion valve A.

[0008] In this technical solution, the design of two solenoid valves connected in series with two condensers gives the multi-condenser chiller the ability to precisely control the opening and closing of the condensers.

[0009] The present invention is further configured such that: the chiller also includes an intelligent control system, the intelligent control system includes sensors, a controller and an adaptive learning module, the sensors are capable of real-time monitoring of refrigerant temperature, pressure, flow rate and customer load side temperature.

[0010] In this technical solution, the sensors equipped with the intelligent control system can monitor key parameters such as refrigerant temperature, pressure, flow rate, and customer load side temperature in real time and accurately.

[0011] The present invention is further configured such that: the intelligent control system can predict the system's heat load and heat dissipation demand based on sensor data and through a thermal management model, dynamically adjust the opening degree of the solenoid valve and electronic expansion valve, and optimize the refrigerant flow distribution to ensure the system operates efficiently and stably.

[0012] In this technical solution, the intelligent control system, with the help of a thermal management model, can accurately predict the system's heat load and heat dissipation requirements based on rich data collected by sensors, such as refrigerant temperature, pressure, flow rate, and customer load side temperature.

[0013] The present invention is further configured such that the adaptive learning module can automatically optimize the parameters of the thermal control model and the flow resistance simulation model based on long-term operating data and deviation correction records.

[0014] In this technical solution, the adaptive learning module automatically optimizes the parameters of the thermal control model and the flow resistance simulation model based on long-term operating data and deviation correction records.

[0015] Another technical solution provided by the present invention is a refrigeration method for a multi-condenser chiller, comprising the following steps: S1 monitors the temperature changes and refrigerant parameters on the load side in real time through sensors. The controller generates a thermal management model based on historical data, current environmental conditions and system configuration to predict the system heat load. S2, depending on the amount of heat on the load side, activate either high load mode or low load mode; S3 continuously monitors changes in client load and refrigerant parameters, adjusts the opening of solenoid valves and electronic expansion valves in real time according to the thermal management model, and compensates for deviations in sensor data in real time through a deviation correction strategy.

[0016] In this technical solution, through precise sensing, prediction, mode switching and dynamic adjustment, the chiller can accurately control the refrigeration process according to actual needs, avoiding energy waste and unnecessary equipment damage, thereby significantly improving refrigeration efficiency.

[0017] The present invention is further configured such that: in the high-load mode, both solenoid valves are open, the two microchannel condensers operate in parallel, and the intelligent flow control valve is adjusted to the maximum flow rate; in the low-load mode, one of the solenoid valves is open, the refrigerant exchanges heat through one condenser, and the intelligent flow control valve reduces the flow rate.

[0018] In this technical solution, when the system is in high-load mode, both solenoid valves are open, allowing the two microchannel condensers to operate in parallel. This parallel operation significantly increases the heat dissipation area of ​​the condensers, enabling efficient heat exchange through these two parallel condensers. This rapid heat transfer to the external environment ensures the chiller can promptly remove heat generated on the load side, meeting high-load cooling demands and preventing overheating, performance degradation, or even malfunctions due to insufficient heat dissipation. In low-load mode, only one solenoid valve is open, allowing the refrigerant to exchange heat through one condenser. Simultaneously, intelligent flow control valves reduce the flow rate, significantly lowering system energy consumption. Because the number of operating condensers and refrigerant flow is reduced, the load on compressors, fans, and other equipment is also reduced, thus lowering power consumption. Adopting low-load mode effectively saves energy, reduces operating costs, and aligns with the development concept of green energy conservation.

[0019] The present invention is further configured such that: the generated thermal management model includes a controller collecting parameters such as temperature of different customer load sides, temperature, pressure and flow rate of refrigerant at different locations under different load conditions during long-term operation, obtaining ambient temperature, humidity and system configuration information, and using load conditions, environmental conditions and system configuration as input features according to a machine learning algorithm, and using the cooling capacity and cooling efficiency when the system reaches a stable state in actual operation as output targets, and adjusting the weights and parameters of the neural network.

[0020] In this technical solution, the controller collects various parameters under different load conditions during long-term operation, covering different time periods, different customer load side temperatures, and refrigerant temperature, pressure, and flow parameters at different locations.

[0021] The present invention is further configured such that: the predicted system heat load includes, based on the generated thermal management model, real-time monitored load-side temperature changes, refrigerant parameters and current environmental conditions data as input, which are substituted into the thermal management model for calculation; the thermal management model matches the input data with the system operation rules learned through pre-training to predict the system heat load under the current operating conditions.

[0022] The present invention is further configured such that: the deviation correction strategy includes collecting the load-side temperature, refrigerant temperature, pressure, and flow rate monitored by the sensors, comparing them in real time with the data predicted by the thermal management model, calculating the deviation value between the actual data and the predicted data, setting a deviation threshold range, and when the deviation value of a certain parameter exceeds the preset threshold range, starting the deviation analysis program, and establishing a deviation correction algorithm based on the deviation analysis results.

[0023] In this technical solution, multi-condenser chillers will face various operating conditions and environmental conditions in actual operation. The deviation correction strategy enables the system to better adapt to these changes through real-time monitoring and adjustment. Whether it is a large fluctuation in the external ambient temperature, a sudden change in the customer load, or the aging of the equipment itself, the system can make timely adjustments according to the deviation correction algorithm to maintain stable operating performance.

[0024] The beneficial effects of the present invention are as follows: (1) The dual condenser layout significantly improves the overall cooling performance of the machine while keeping the overall size of the equipment unchanged, and effectively saves space resources; (2) The built-in intelligent program can identify the client load in real time and flexibly adjust the number of condensers and the cooling mode accordingly, so as to ensure that the chiller can operate efficiently and stably under different load conditions; (3) For low load conditions, by reducing the number of condensers and adjusting the system parameters, the power consumption of the chiller is effectively reduced, and significant energy-saving effect is achieved. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of a multi-condenser chiller according to the present invention; Figure 2 This is a flowchart of a refrigeration method for a multi-condenser chiller according to the present invention. Detailed Implementation

[0026] like Figure 1 , Figure 2 As shown, as an embodiment of the present invention, a multi-condenser chiller includes: The refrigerant circulation loop includes a compressor, a valve assembly, an evaporator, and two condensers. The refrigerant circulation loop can realize a refrigeration cycle. The refrigeration cycle includes the compressor compressing the refrigerant to a high temperature and high pressure state, the refrigerant flowing through the condenser having its heat carried away by the ambient air, and then entering the evaporator after being throttled and cooled by the valve assembly. The circulating water circuit includes an evaporator, a water inlet, a water tank, and a load. The water inlet includes a return water inlet, a water outlet, and a drain outlet. A water pump is installed between the water tank and the water outlet. The circulating water circuit can achieve closed-loop circulation. The closed-loop circulation includes circulating water absorbing cold energy in the evaporator, being pumped to the load for cooling, and then returning to the evaporator to form a closed-loop circulation.

[0027] In this embodiment, the dual-condenser design of the multi-condenser chiller significantly increases the heat exchange area between the refrigerant and the external environment. After the compressor compresses the refrigerant to a high-temperature, high-pressure state, the refrigerant can flow through both condensers simultaneously. More heat can be quickly carried away by the ambient air, making the heat dissipation process in the refrigeration cycle more efficient. After the refrigerant passes through the valve assembly for throttling and cooling, it enters the evaporator at a lower temperature and in a more suitable state to participate in subsequent cycles, thereby significantly improving the overall cooling capacity of the unit and meeting the cooling requirements of larger loads or higher demands. This solution, through its innovative dual-condenser layout, rationally plans the internal space and cleverly arranges two condensers without increasing the overall size of the equipment. This not only avoids making the equipment bulky by adding components but also effectively saves space resources, allowing the chiller to exert a more powerful cooling function within a limited space. This provides a good cooling solution for environments with strict space requirements.

[0028] In one embodiment of the present invention, the valve assembly includes a solenoid valve and an electronic expansion valve. There are two solenoid valves, which are connected in series with two condensers to control the opening and closing of the condensers respectively. The electronic expansion valve includes electronic expansion valve A and electronic expansion valve B. Electronic expansion valve A is connected in series with the solenoid valve and the condenser, and electronic expansion valve B is connected in parallel with electronic expansion valve A.

[0029] In this technical solution, the design of two solenoid valves connected in series with two condensers respectively gives the multi-condenser chiller precise control over the opening and closing of the condensers. In actual operation, the cooling requirements vary significantly depending on the operating conditions. Under low-load conditions with low ambient temperature and light load, only one condenser needs to be turned on to meet the heat dissipation requirements. At this time, by controlling the on / off state of the corresponding solenoid valve, the unnecessary condenser can be easily shut off, avoiding unnecessary energy loss. Under high-load conditions, such as high ambient temperature and heavy load, both solenoid valves open simultaneously, allowing the two condensers to work together, significantly improving heat dissipation capacity and ensuring stable operation of the chiller. This allows the chiller to dynamically adjust according to actual operating conditions, effectively improving the adaptability and energy efficiency of the equipment. The layout of electronic expansion valve A connected in series with the solenoid valve and condenser, and electronic expansion valve B connected in parallel with electronic expansion valve A, enables fine regulation of the refrigerant flow. Electronic expansion valve A precisely controls the refrigerant flow entering the condenser connected in series with it based on the load conditions of that condenser. When the load on the condenser is small, the electronic expansion valve A reduces its opening to decrease the refrigerant flow, preventing excessive refrigerant buildup in the condenser and reducing system pressure. When the load increases, the electronic expansion valve A increases its opening to increase the refrigerant flow to meet heat dissipation requirements.

[0030] Electronic expansion valve B is connected in parallel with electronic expansion valve A, serving a supplementary and regulatory function. When a condenser or its corresponding electronic expansion valve malfunctions, electronic expansion valve B can participate in regulation to maintain the basic refrigerant flow rate and ensure the basic operation of the system. Simultaneously, under normal operating conditions, electronic expansion valve B can also fine-tune the refrigerant flow rate according to the overall system operation, further optimizing the system's cooling effect and ensuring the chiller always operates at high efficiency.

[0031] In one embodiment of the present invention, the chiller further includes an intelligent control system, which includes sensors, a controller and an adaptive learning module. The sensors are capable of real-time monitoring of refrigerant temperature, pressure, flow rate and customer load side temperature.

[0032] In this technical solution, the sensors equipped with the intelligent control system can monitor key parameters such as refrigerant temperature, pressure, flow rate, and customer load side temperature in real time and accurately.

[0033] The temperature, pressure, and flow rate of the refrigerant directly reflect the operating status of the refrigeration cycle. By comprehensively and accurately acquiring this data, the intelligent control system can gain a deep understanding of the chiller's real-time operating status, providing reliable and detailed information for subsequent intelligent regulation and ensuring that regulatory measures are targeted and effective. Based on real-time monitoring data from sensors, the controller, as the core of the intelligent control system, intelligently manages the chiller. It can analyze and judge the monitored parameters according to preset algorithms and logic. It can dynamically adjust the equipment's operating parameters according to actual working conditions, keeping the chiller in optimal working condition at all times. This avoids the adjustment lag or inaccuracy problems that may exist in traditional manual control methods, thereby significantly improving the equipment's operating efficiency and stability. The adaptive learning module can automatically analyze and summarize the operating patterns of the equipment under different operating conditions based on data collected during long-term operation and actual operating results. By analyzing the refrigerant parameters and load-side temperature changes under different time periods and load conditions, the adaptive learning module can gradually optimize the control strategy, enabling the controller to make more accurate and efficient control decisions when facing similar operating conditions. Over time, the adaptive learning module continuously accumulates experience and optimizes the performance of the equipment, enabling the chiller to better adapt to various complex and changing working environments, further improving energy utilization efficiency and reducing operating costs.

[0034] In one embodiment of the present invention, the intelligent control system can predict the system's heat load and heat dissipation demand based on sensor data and a thermal management model, dynamically adjust the opening of the solenoid valve and electronic expansion valve, and optimize the refrigerant flow distribution to ensure efficient and stable system operation.

[0035] In this technical solution, the intelligent control system, leveraging a thermal management model and based on abundant data collected by sensors, such as refrigerant temperature, pressure, flow rate, and customer load-side temperature, can accurately predict the system's heat load and heat dissipation requirements. Through the forward-looking predictions of the thermal management model, the intelligent control system can anticipate the system's heat dissipation needs at different times, making ample preparations for subsequent dynamic adjustments. This avoids insufficient or excessive cooling due to lag in response, ensuring the chiller always operates at its optimal state to cope with various operating conditions. Based on the prediction results of the thermal management model, the intelligent control system can dynamically adjust the opening degrees of the solenoid valves and electronic expansion valves. By dynamically adjusting the valve opening degrees, the intelligent control system optimizes the refrigerant flow distribution.

[0036] The adaptive learning module can automatically optimize the parameters of the thermal control model and the flow resistance simulation model based on long-term operating data and deviation correction records.

[0037] In this technical solution, the adaptive learning module automatically optimizes the parameters of the thermal control model and the flow resistance simulation model based on long-term operating data and deviation correction records.

[0038] like Figure 2 As shown in Embodiment 2 of the present invention, a refrigeration method for a multi-condenser chiller includes the following steps: S1 monitors the temperature changes and refrigerant parameters on the load side in real time through sensors. The controller generates a thermal management model based on historical data, current environmental conditions and system configuration to predict the system heat load. S2, depending on the amount of heat on the load side, activate either high load mode or low load mode; S3 continuously monitors changes in client load and refrigerant parameters, adjusts the opening of solenoid valves and electronic expansion valves in real time according to the thermal management model, and compensates for deviations in sensor data in real time through a deviation correction strategy.

[0039] In this technical solution, in step S1, sensors monitor the load-side temperature changes and refrigerant parameters in real time. Real-time data acquisition accurately captures subtle changes during system operation, providing rich and accurate information for subsequent heat load prediction and control strategy adjustment. Step S2 automatically activates high-load or low-load mode based on the load-side heat level. Intelligent mode switching based on actual load conditions allows the chiller to flexibly adjust its operating status according to different working scenarios and needs. Step S3 continuously monitors client load changes and refrigerant parameters, and adjusts the opening of the solenoid valve and electronic expansion valve in real time according to the thermal management model. The opening of the solenoid valve and electronic expansion valve directly affects the refrigerant flow rate and direction. By adjusting their opening in real time, the refrigerant distribution can be precisely controlled, enabling the refrigeration system to respond quickly to load changes. Through precise sensing, prediction, mode switching, and dynamic adjustment, the chiller can accurately control the refrigeration process according to actual needs, avoiding energy waste and unnecessary equipment wear, thereby significantly improving refrigeration efficiency. (It is understood that refrigerant parameters include temperature, pressure, and flow rate.)

[0040] In the high-load mode, both solenoid valves are open, the two microchannel condensers operate in parallel, and the intelligent flow control valve adjusts the flow rate to the maximum. In the low-load mode, one of the solenoid valves is open, the refrigerant exchanges heat through one condenser, and the intelligent flow control valve reduces the flow rate.

[0041] In this technical solution, when the system is in high-load mode, both solenoid valves are open, allowing the two microchannel condensers to operate in parallel. This parallel operation significantly increases the heat dissipation area of ​​the condensers, enabling efficient heat exchange through these two parallel condensers. This rapid heat transfer to the external environment ensures the chiller can promptly remove heat generated on the load side, meeting high-load cooling demands and preventing overheating, performance degradation, or even malfunctions due to insufficient heat dissipation. In low-load mode, only one solenoid valve is open, allowing the refrigerant to exchange heat through one condenser. Simultaneously, intelligent flow control valves reduce the flow rate, significantly lowering system energy consumption. Because the number of operating condensers and refrigerant flow is reduced, the load on compressors, fans, and other equipment is also reduced, thus lowering power consumption. Adopting low-load mode effectively saves energy, reduces operating costs, and aligns with the development concept of green energy conservation.

[0042] The generation of the thermal management model includes the controller collecting parameters such as temperature on different customer load sides, temperature, pressure, and flow rate of refrigerant at different locations under different load conditions during long-term operation, acquiring ambient temperature, humidity, and system configuration information, and using load conditions, environmental conditions, and system configuration as input features based on machine learning algorithms, and taking the cooling capacity and cooling efficiency when the system reaches a steady state in actual operation as output targets, and adjusting the weights and parameters of the neural network.

[0043] The controller collects various parameters under different load conditions during long-term operation, covering temperatures at different time periods and customer load sides, as well as refrigerant temperature, pressure, and flow parameters at different locations. Load conditions reflect the customer's cooling demand, environmental conditions are external factors affecting system performance, and system configuration determines the system's hardware capabilities. Using these factors as input allows the model to comprehensively consider various factors affecting system operation. Employing machine learning algorithms, the thermal management model is constructed by adjusting the weights and parameters of a neural network. This model possesses strong learning and adaptive capabilities; the neural network can automatically extract features and patterns from large amounts of data, and by continuously adjusting weights and parameters, the model's predictions gradually approach actual values. An accurate thermal management model can predict the system's cooling capacity and efficiency under different operating conditions, allowing the controller to adjust system operating parameters in advance based on the predictions, such as the opening degree of solenoid valves and electronic expansion valves, and the compressor's operating frequency. By adjusting system parameters in real time, the thermal management model helps maintain stable system operation. In the event of changes in the external environment or sudden changes in customer load, the model can react quickly, adjusting the system's operating state to keep the system near its optimal operating point, reducing system fluctuations and failures caused by parameter mismatches. Moreover, accurate prediction and optimized control can reduce system energy consumption, thereby reducing operating costs.

[0044] The predicted system heat load includes the generation of a thermal management model, which takes real-time monitored load-side temperature changes, refrigerant parameters, and current environmental conditions as inputs and substitutes them into the thermal management model for calculation. The thermal management model matches the input data with the system operating rules learned through pre-training to predict the system's heat load under the current operating conditions.

[0045] Real-time monitoring of load-side temperature changes is crucial, as load-side temperature is the most direct indicator of actual cooling demand. Incorporating load-side temperature data into the prediction system allows the model to more accurately capture the trend of heat load changes over time. Refrigerant parameters are a key indicator of the system's internal operating status. During circulation, refrigerant parameters change with load conditions and environmental factors. By monitoring refrigerant parameters in real-time, the model can gain a deeper understanding of the system's internal heat exchange, thus predicting the heat load more accurately. Real-time monitoring of load-side temperature changes, refrigerant parameters, and current environmental conditions enables the prediction system to obtain the latest system operating status and environmental information promptly. After accurately predicting the system's heat load, the controller can adjust system operating parameters in a timely manner based on the prediction results, such as the opening of solenoid valves, the adjustment of electronic expansion valves, and the compressor's operating frequency. When an increase in system heat load is predicted, the controller can increase the refrigerant flow rate and increase the compressor's operating frequency in advance to enhance the system's cooling capacity and prevent the system from failing to meet cooling demands due to excessive heat load. When a decrease in heat load is predicted, energy consumption can be reduced accordingly, lowering operating costs. By predicting heat load based on a thermal management model, the system can always maintain optimal operating conditions. This avoids overload or underload operation caused by inaccurate heat load prediction. Overload operation increases equipment wear and energy consumption, and may even lead to failure; underload operation results in energy waste. Accurate heat load prediction allows the system to allocate energy rationally according to actual needs, improving energy utilization efficiency, reducing operating costs, and extending equipment lifespan.

[0046] The deviation correction strategy includes collecting load-side temperature, refrigerant temperature, pressure, and flow rate monitored by sensors, comparing them in real time with the data predicted by the thermal management model, calculating the deviation between the actual data and the predicted data, setting a deviation threshold range, and starting the deviation analysis program when the deviation of a certain parameter exceeds the preset threshold range. Based on the deviation analysis results, a deviation correction algorithm is established.

[0047] The system collects multi-dimensional data from sensors, including load-side temperature, refrigerant temperature, pressure, and flow rate. Load-side temperature directly reflects the actual cooling demand; changes in load-side temperature under different scenarios indicate whether the cooling effect meets requirements. Refrigerant temperature, pressure, and flow rate parameters reflect the refrigerant's circulation status and heat exchange efficiency within the system. Real-time data collection allows for timely monitoring of the system's dynamic operation, providing rich and accurate information for subsequent deviation analysis. A reasonable threshold range can be determined based on the chiller's design parameters, operating experience, and actual needs. When a deviation of a parameter exceeds a preset threshold, it indicates an abnormal system operation, alerting operators or the control system. In actual operation, multi-condenser chillers face various operating conditions and environmental factors. The deviation correction strategy, through real-time monitoring and adjustment, enables the system to better adapt to these changes. Whether it's significant fluctuations in ambient temperature, sudden changes in customer load, or equipment aging, the system can adjust promptly based on the deviation correction algorithm to maintain stable performance. The deviation correction strategy effectively reduces fluctuations and instability in system operation. By promptly identifying and correcting deviations, system performance degradation or malfunctions caused by the accumulation of deviations are avoided. Implementing deviation correction strategies ensures that the system operates within a stable range, improving its reliability and stability.

Claims

1. A multi-condenser chiller, characterized in that, include: The refrigerant circulation loop includes a compressor, a valve assembly, an evaporator, and two condensers. The refrigerant circulation loop can realize a refrigeration cycle. The refrigeration cycle includes the compressor compressing the refrigerant to a high temperature and high pressure state, the refrigerant flowing through the condenser having its heat carried away by the ambient air, and then entering the evaporator after being throttled and cooled by the valve assembly. The circulating water circuit includes an evaporator, a water inlet, a water tank, and a load. The water inlet includes a return water inlet, a water outlet, and a drain outlet. A water pump is installed between the water tank and the water outlet. The circulating water circuit can achieve closed-loop circulation. The closed-loop circulation includes circulating water absorbing cold energy in the evaporator, being pumped to the load for cooling, and then returning to the evaporator to form a closed-loop circulation.

2. A multi-condenser chiller according to claim 1, characterized in that, The valve assembly includes a solenoid valve and an electronic expansion valve. There are two solenoid valves, which are connected in series with two condensers to control the opening and closing of the condensers respectively. The electronic expansion valve includes electronic expansion valve A and electronic expansion valve B. Electronic expansion valve A is connected in series with the solenoid valve and the condenser, and electronic expansion valve B is connected in parallel with electronic expansion valve A.

3. A multi-condenser chiller according to claim 1, characterized in that, The chiller also includes an intelligent control system, which includes sensors, a controller, and an adaptive learning module. The sensors are capable of real-time monitoring of refrigerant temperature, pressure, flow rate, and customer load-side temperature.

4. A multi-condenser chiller according to claim 3, characterized in that, The intelligent control system can predict the system's heat load and heat dissipation demand based on sensor data and a thermal management model, dynamically adjust the opening of the solenoid valve and electronic expansion valve, and optimize the refrigerant flow distribution to ensure efficient and stable system operation.

5. A multi-condenser chiller according to claim 3 or 4, characterized in that, The adaptive learning module can automatically optimize the parameters of the thermal control model and the flow resistance simulation model based on long-term operating data and deviation correction records.

6. A refrigeration method for a multi-condenser chiller, applicable to the multi-condenser chiller according to any one of claims 1 to 5, characterized in that, Includes the following steps: S1 monitors the temperature changes and refrigerant parameters on the load side in real time through sensors. The controller generates a thermal management model based on historical data, current environmental conditions and system configuration to predict the system heat load. S2, depending on the amount of heat on the load side, activate either high load mode or low load mode; S3 continuously monitors changes in client load and refrigerant parameters, adjusts the opening of solenoid valves and electronic expansion valves in real time according to the thermal management model, and compensates for deviations in sensor data in real time through a deviation correction strategy.

7. The refrigeration method of a multi-condenser chiller according to claim 6, characterized in that, The high-load mode involves both solenoid valves being open, with two microchannel condensers operating in parallel, and the intelligent flow control valve adjusting to the maximum flow rate. The low-load mode involves one solenoid valve being open, with the refrigerant exchanging heat through one condenser, and the intelligent flow control valve reducing the flow rate.

8. The refrigeration method of a multi-condenser chiller according to claim 6, characterized in that, The generated thermal management model includes the controller collecting parameters such as temperature on different customer load sides, temperature, pressure, and flow rate of refrigerant at different locations under different load conditions during long-term operation, acquiring ambient temperature, humidity, and system configuration information, and using load conditions, environmental conditions, and system configuration as input features based on machine learning algorithms, and taking the cooling capacity and cooling efficiency when the system reaches a stable state in actual operation as output targets, and adjusting the weights and parameters of the neural network.

9. A refrigeration method for a multi-condenser chiller according to claim 6 or 7, characterized in that, The predicted system heat load includes a generated thermal management model. Real-time monitored load-side temperature changes, refrigerant parameters, and current environmental conditions are used as inputs and substituted into the thermal management model for calculation. The thermal management model matches the input data with the system operation rules learned through pre-training to predict the system's heat load under the current operating conditions.

10. A refrigeration method for a multi-condenser chiller according to claim 6 or 7, characterized in that, The deviation correction strategy includes collecting load-side temperature, refrigerant temperature, pressure, and flow rate monitored by sensors, comparing them in real time with the data predicted by the thermal management model, calculating the deviation value between the actual data and the predicted data, setting a deviation threshold range, and starting a deviation analysis program when the deviation value of a certain parameter exceeds the preset threshold range. Based on the deviation analysis results, a deviation correction algorithm is established.

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