A refrigeration unit operation efficiency statistical system of an ice machine station

Abstract

This invention belongs to the field of ice plant technology, specifically a system for statistically analyzing the operating efficiency of refrigeration units in ice plants. Addressing the problem that existing methods for statistically analyzing the operating efficiency of refrigeration units cannot achieve real-time, comprehensive, and intelligent closed-loop management from accurate assessment and optimized scheduling to predictive maintenance, this invention proposes the following solution: a visualization operation module; a data acquisition module connected to a data processing module, which in turn is connected to an energy efficiency model library module, which is connected to an efficiency calculation module; and an operating condition analysis module connected to an equipment evaluation module. This invention constructs an intelligent system integrating data, models, optimization, and diagnostics, achieving closed-loop management from data perception to optimized decision-making and proactive execution, ultimately leading to continuous optimization of the overall energy efficiency of the ice plant and a fundamental improvement in operational reliability.

Description

Technical Field

[0001] This invention relates to the field of ice plant technology, and in particular to a statistical system for the operating efficiency of refrigeration units in an ice plant. Background Technology

[0002] In industrial and commercial buildings, refrigeration units (ice machines) are the core energy-consuming equipment in central air conditioning systems or process cooling systems. Their operating efficiency directly affects the energy consumption, operating costs, and carbon emission levels of the entire system.

[0003] In existing technologies, the process of calculating the operating efficiency of refrigeration units cannot achieve closed-loop management from accurate assessment and optimized scheduling to predictive maintenance in a real-time, comprehensive and intelligent manner. To address this issue, we propose a refrigeration unit operating efficiency calculation system for ice storage stations. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies in the process of calculating the operating efficiency of refrigeration units, which cannot achieve real-time, comprehensive, and intelligent closed-loop management from accurate assessment and optimized scheduling to predictive maintenance. Therefore, this invention proposes a system for calculating the operating efficiency of refrigeration units in ice-making stations.

[0005] The refrigeration unit operating efficiency statistics system for an ice-making station provided in this application adopts the following technical solution:

[0006] A system for statistically analyzing the operating efficiency of refrigeration units in an ice plant, including a visualization operation module;

[0007] The data acquisition module is connected to a data processing module, which in turn is connected to an energy efficiency model library module, which is connected to an efficiency calculation module.

[0008] The operating condition analysis module is connected to an equipment evaluation module, which in turn is connected to a statistical report module, which is connected to an analysis and early warning module, and which is connected to a push module.

[0009] A cooling load forecasting module is connected to an analysis and early warning module. The cooling load forecasting module is connected to an optimization scheduling module. The optimization scheduling module is connected to a collaborative optimization module. The collaborative optimization module is connected to a balance optimization module.

[0010] The fault diagnosis module is connected to the analysis and early warning module, and is also connected to a fault knowledge base module and a maintenance trigger module. The maintenance trigger module is connected to the equipment evaluation module.

[0011] Furthermore, the efficiency calculation module includes a real-time energy efficiency ratio calculation unit, a coefficient generation unit, and a comprehensive efficiency output unit. The real-time energy efficiency ratio calculation unit is connected to the coefficient generation unit, and the coefficient generation unit is connected to the comprehensive efficiency output unit.

[0012] The real-time energy efficiency ratio calculation unit is used to calculate the energy efficiency ratio of the chiller unit, which is the ratio of cooling capacity to power consumption. The formula is:

[0013] ;

[0014] Wherein: Cooling capacity (unit: kW) can be calculated using flow rate (m³ / h) and temperature difference (°C):

[0015] ;

[0016] For example, the specific heat capacity of water is approximately 4.186 kJ / kg·℃. If the flow rate is 100 m³ / h and the temperature difference is 5℃, then the cooling capacity is:

[0017] ;

[0018] Power consumption (unit: kW) can be obtained directly from equipment operating data;

[0019] The coefficient generation unit generates a correction coefficient based on the difference between the real-time energy efficiency ratio and the benchmark energy efficiency ratio (obtained from the energy efficiency model library module). This correction coefficient is used for subsequent calculations of overall efficiency. The formula is as follows:

[0020] ;

[0021] Among them: the benchmark energy efficiency ratio is based on the standard energy efficiency ratio of the unit under the current operating conditions stored in the energy efficiency model library module;

[0022] The integrated efficiency output unit combines the real-time energy efficiency ratio with the correction factor to output the integrated efficiency, which more comprehensively reflects the actual operating efficiency of the unit. The formula is:

[0023] ;

[0024] Wherein: α is a weighting coefficient (0<α<1), used to balance the weights of real-time energy efficiency ratio and benchmark energy efficiency ratio in overall efficiency.

[0025] Furthermore, the maintenance triggering module includes a threshold judgment unit, an association unit, and a push unit. The threshold judgment unit is connected to the association unit, and the association unit is connected to the push unit.

[0026] Furthermore, the visualization operation module is used to present the underlying data, analysis results and optimization strategies to the operation and management personnel in a graphical and intuitive way, while the data acquisition module is responsible for acquiring raw operating data from the distributed control systems and instruments on site.

[0027] Furthermore, the equipment evaluation module is used to evaluate the internal health status of the refrigeration unit itself, and the statistical report module is used to organize, store, and format the efficiency data for presentation.

[0028] Furthermore, the fault diagnosis module is used to perform intelligent root cause analysis on efficiency anomalies or equipment alarms, and the fault knowledge base module is used to store diagnostic rules and experience.

[0029] Furthermore, the analysis and early warning module is used to monitor anomalies and trends and trigger alarms, while the push module is used to proactively deliver key information to relevant responsible persons.

[0030] Furthermore, the data processing module is used to clean, integrate, and perform preliminary calculations on the collected raw data, while the energy efficiency model library module stores benchmark performance data of various units under different operating conditions, providing a reference for efficiency calculation and evaluation.

[0031] Furthermore, the operating condition analysis module includes a flow analysis unit, a temperature difference analysis unit, and an operating condition correction unit. The flow analysis unit is connected to the temperature difference analysis unit, and the temperature difference analysis unit is connected to the operating condition correction unit.

[0032] Furthermore, the optimization scheduling module includes an optimization function construction unit, a load allocation unit, and a strategy generation and distribution unit. The function construction unit is connected to the load allocation unit, and the load allocation unit is connected to the strategy generation and distribution unit.

[0033] In summary, this application includes at least one of the following beneficial technical effects:

[0034] 1. This solution combines real-time data with baseline operating condition data in the energy efficiency model library and uses weighted calculation to obtain comprehensive efficiency, eliminating the one-sidedness of single operating point evaluation, making the efficiency evaluation results more scientific and better reflecting the true performance and health status of the unit, and providing a reliable basis for refined management.

[0035] 2. Through the cooling load forecasting and optimized scheduling module, this solution enables the system to automatically formulate and distribute unit start-up and shutdown and load allocation strategies for future periods based on the principle of optimal efficiency. This avoids the blindness of manual scheduling, ensures that the units always operate in the high-efficiency range, and significantly reduces the overall energy consumption and operating costs of the system.

[0036] 3. When efficiency remains low or an abnormal trend appears, this solution can automatically initiate fault diagnosis, perform root cause analysis based on the knowledge base, and ultimately trigger a targeted maintenance work order.

[0037] This invention constructs an intelligent system that integrates data, models, optimization, and diagnosis, achieving closed-loop management from data perception to optimization decision-making and proactive execution, ultimately leading to continuous optimization of the overall energy efficiency of the ice plant and a fundamental improvement in operational reliability. Attached Figure Description

[0038] Fig. 1 This is a structural block diagram of a refrigeration unit operating efficiency statistical system for an ice station proposed in this invention;

[0039] Fig. 2 This is a structural block diagram of the efficiency calculation module of a refrigeration unit operation efficiency statistics system for an ice station proposed in this invention;

[0040] Fig. 3 This is a structural block diagram of the optimized scheduling module of the refrigeration unit operation efficiency statistics system for an ice station proposed in this invention;

[0041] Fig. 4 This is a structural block diagram of the maintenance trigger module of a refrigeration unit operation efficiency statistics system for an ice machine station proposed in this invention;

[0042] Fig. 5 This is a structural block diagram of the operating condition analysis module of the refrigeration unit operating efficiency statistical system for an ice machine station proposed in this invention. Detailed Implementation

[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0044] Example 1

[0045] Reference Figs. 1-5 A system for statistically analyzing the operating efficiency of refrigeration units in an ice plant, including a visualization operation module;

[0046] The data acquisition module is connected to the data processing module, which in turn is connected to the energy efficiency model library module, which is connected to the energy efficiency calculation module.

[0047] The operating condition analysis module is connected to the equipment evaluation module, which in turn is connected to the statistical report module. The statistical report module is connected to the analysis and early warning module, which in turn is connected to the push module.

[0048] The cooling load forecasting module is connected to the analysis and early warning module. The cooling load forecasting module is connected to the optimization scheduling module. The optimization scheduling module is connected to the collaborative optimization module. The collaborative optimization module is connected to the balance optimization module.

[0049] The fault diagnosis module is connected to the analysis and early warning module. The fault diagnosis module is also connected to the fault knowledge base module and the maintenance trigger module. The maintenance trigger module is connected to the equipment evaluation module.

[0050] In this embodiment, the efficiency calculation module includes a real-time energy efficiency ratio calculation unit, a coefficient generation unit, and a comprehensive efficiency output unit. The real-time energy efficiency ratio calculation unit is connected to the coefficient generation unit, and the coefficient generation unit is connected to the comprehensive efficiency output unit.

[0051] The real-time energy efficiency ratio calculation unit is used to calculate the energy efficiency ratio of the chiller unit, which is the ratio of cooling capacity to power consumption. The formula is:

[0052] ;

[0053] Wherein: Cooling capacity (unit: kW) can be calculated using flow rate (m³ / h) and temperature difference (°C):

[0054] ;

[0055] For example, the specific heat capacity of water is approximately 4.186 kJ / kg·℃. If the flow rate is 100 m³ / h and the temperature difference is 5℃, then the cooling capacity is:

[0056] ;

[0057] Power consumption (unit: kW) can be obtained directly from equipment operating data;

[0058] The coefficient generation unit generates a correction coefficient based on the difference between the real-time energy efficiency ratio and the benchmark energy efficiency ratio (obtained from the energy efficiency model library module). This correction coefficient is used for subsequent calculations of overall efficiency. The formula is as follows:

[0059] ;

[0060] Among them: the benchmark energy efficiency ratio is based on the standard energy efficiency ratio of the unit under the current operating conditions stored in the energy efficiency model library module;

[0061] The integrated efficiency output unit combines the real-time energy efficiency ratio with the correction factor to output the integrated efficiency, which more comprehensively reflects the actual operating efficiency of the unit. The formula is:

[0062] ;

[0063] Wherein: α is a weighting coefficient (0<α<1), used to balance the weights of real-time energy efficiency ratio and benchmark energy efficiency ratio in overall efficiency.

[0064] In this embodiment, the optimization scheduling module includes an optimization function construction unit, a load allocation unit, and a strategy generation and distribution unit. The function construction unit is connected to the load allocation unit, and the load allocation unit is connected to the strategy generation and distribution unit. The maintenance triggering module includes a threshold judgment unit, an association unit, and a push unit. The threshold judgment unit is connected to the association unit, and the association unit is connected to the push unit. The operating condition analysis module includes a flow analysis unit, a temperature difference analysis unit, and an operating condition correction unit. The flow analysis unit is connected to the temperature difference analysis unit, and the temperature difference analysis unit is connected to the operating condition correction unit.

[0065] In this embodiment, the visualization module presents underlying data, analysis results, and optimization strategies to operation and management personnel in a graphical and intuitive manner; the data acquisition module is responsible for acquiring raw operating data from distributed control systems and instruments on-site; the data processing module cleans, integrates, and performs preliminary calculations on the acquired raw data; the energy efficiency model library module stores benchmark performance data of various units under different operating conditions, providing a reference for efficiency calculation and evaluation; the equipment evaluation module evaluates the internal health status of the refrigeration unit itself; the statistical report module organizes, stores, and formats the efficiency data for presentation; the analysis and early warning module monitors anomalies and trends and triggers alarms; the push module proactively delivers key information to relevant responsible persons; the fault diagnosis module performs intelligent root cause analysis on efficiency anomalies or equipment alarms; and the fault knowledge base module stores diagnostic rules and experience.

[0066] The implementation principle in this embodiment is as follows: During use, the data acquisition module acquires the raw operating parameters of each chiller unit in real time, such as power, flow rate, and inlet / outlet water temperature. The data processing module cleans, calibrates, and integrates the data. The operating condition analysis module calculates the current actual cooling capacity and identifies the operating condition point. The energy efficiency model library module provides the theoretical benchmark energy efficiency ratio of the unit under the current operating conditions (such as cooling water temperature and load rate). The efficiency calculation module first calculates the real-time COP, and then generates a correction coefficient reflecting the performance deviation by comparing the real-time COP with the benchmark COP. Finally, the weighted comprehensive efficiency value is output. This value is used by the equipment evaluation module to assess the health of the unit and is archived by the statistical report module. The analysis and early warning module continuously monitors the comprehensive efficiency and key parameters. Once it is found to be below the set threshold or a deterioration trend is observed, an alarm is immediately triggered. The alarm is notified to relevant personnel through the push module, and two parallel processes are initiated:

[0067] Optimization process: The cooling load forecasting module predicts future cooling demand based on historical data and external factors (such as weather forecasts and production plans). The optimization scheduling module uses this forecast and the current efficiency of each unit as a basis, with the goal of minimizing the total power consumption of the system or maximizing the overall energy efficiency. It uses optimization algorithms to calculate the optimal unit combination and load allocation scheme, and coordinates the execution of each unit through the collaborative optimization module and the balance optimization module to achieve dynamic energy-saving operation.

[0068] Diagnosis and maintenance process: After receiving an efficiency warning, the fault diagnosis module calls the rule base and case base in the fault knowledge base module to reason and sort the possible causes of the fault (such as condenser scaling, insufficient refrigerant, sensor drift, etc.) and give a diagnostic conclusion. If it is determined that maintenance is required, the maintenance trigger module will automatically generate maintenance suggestions or work orders, associate them with specific equipment, and push them to the maintenance department to form a management closed loop.

[0069] Example 2

[0070] The difference between this embodiment and Embodiment 1 is that the operating condition analysis module is connected to the correlation analysis module. The correlation analysis module is used to analyze the operating status and energy consumption of auxiliary systems (such as cooling towers, cooling water pumps, and chilled water pumps) that are closely related to the efficiency of the chiller unit, and to evaluate their impact on the overall energy efficiency of the chiller unit and the station building, providing more comprehensive system boundary conditions for the operating condition analysis.

[0071] Example 3

[0072] The difference between this embodiment and Embodiment 1 is that the equipment evaluation module is connected to a cost analysis module. The cost analysis module is used to track the efficiency degradation, maintenance costs, and energy consumption costs of a single unit from commissioning to decommissioning from the perspective of equipment asset management, providing data support for equipment upgrade and overhaul decisions, and expanding the time and economic dimensions of equipment evaluation.

[0073] Example 4

[0074] The difference between this embodiment and Embodiment 1 is that the energy efficiency model library module is connected to the energy efficiency insight module. The energy efficiency insight module is used to use machine learning and big data analysis technologies to deeply mine massive amounts of historical operating data, automatically discover potential energy efficiency improvement patterns, abnormal patterns and optimization opportunities, and provide self-learning capabilities for the model library.

[0075] Example 5

[0076] The difference between this embodiment and Embodiment 1 is that the visualization operation module is connected to an external interface module. The external interface module serves as a data bridge and business collaboration entry point with other external management systems, enabling information sharing and process integration. It provides external data sources and output channels for the data acquisition module, cold load prediction module, push module, etc., thereby enhancing the openness and integrability of the system.

[0077] Experimental Example

[0078] I. Experimental Objective

[0079] By comparing the operating efficiency of refrigeration units under different operating conditions, this study verifies the performance advantages of the system in real-time energy efficiency assessment, optimized scheduling, and fault diagnosis, as well as the effect of closed-loop management on overall energy efficiency improvement.

[0080] II. Experimental Equipment and Conditions

[0081] Experimental equipment: Select a typical model of refrigeration unit (assuming it is a certain brand and model A), and equip it with necessary monitoring equipment such as flow meter, temperature sensor, and power meter;

[0082] Experimental operating conditions settings:

[0083] Operating condition 1: Cooling water temperature is 30℃, load rate is 60%;

[0084] Operating Condition 2: Cooling water temperature is 35℃, load rate is 80%;

[0085] Operating Condition 3: Cooling water temperature is 32℃, load rate is 70% (simulating normal operating conditions);

[0086] Experiment duration: Each operating condition was run for 2 hours continuously, and data was collected and analyzed before and after the experiment;

[0087] III. Experimental Procedure

[0088] Data Acquisition and Preprocessing:

[0089] The data acquisition module is used to obtain the raw operating parameters of the chiller unit in real time, such as power, flow rate, and inlet and outlet water temperature.

[0090] The data processing module cleans, calibrates, and integrates the collected data to ensure its accuracy and integrity.

[0091] Real-time energy efficiency assessment:

[0092] Calculate the real-time energy efficiency ratio (COP real-time) and overall efficiency under each operating condition according to the formula;

[0093] Compare the real-time energy efficiency ratio with the benchmark energy efficiency ratio (obtained from the energy efficiency model library module) and analyze the differences.

[0094] Optimize scheduling and simulation:

[0095] The cooling load forecasting module combines historical data and external factors (such as weather forecasts) to predict future cooling demand.

[0096] The optimization scheduling module calculates the optimal unit combination and load allocation scheme based on the forecast results and the current unit efficiency.

[0097] Simulate the optimized operating conditions and record the optimized energy efficiency data;

[0098] Fault diagnosis and triggering:

[0099] Under operating condition 2, a refrigerant shortage fault is simulated artificially, and it is observed whether the system can trigger fault diagnosis and maintenance work orders in a timely manner.

[0100] The fault diagnosis module calls the fault knowledge base module to perform intelligent root cause analysis and verify the accuracy of the diagnosis results;

[0101] Results Comparison and Analysis:

[0102] By comparing the energy efficiency data of the unoptimized and optimized systems under various operating conditions, the effectiveness of the system in improving energy efficiency is analyzed.

[0103] Evaluate the response speed and diagnostic accuracy of the fault diagnosis module;

[0104] IV. Comparison of Experimental Data

[0105] Operating condition Cooling water temperature (°C) Load rate (%) Real-time refrigerating capacity (kW) Real-time power consumption (kW) Real-time energy efficiency ratio (COP real-time) Reference energy efficiency ratio (COP reference) Correction coefficient Overall efficiency Optimized overall efficiency Fault diagnosis result Operating condition 1 30 60 50.0 10.0 5.0 5.5 1.1 5.25 5.4 - Operating condition 2 35 80 60.0 12.0 5.0 5.2 1.04 5.12 5.3 Insufficient refrigerant Operating condition 3 32 70 55.0 11.0 5.0 5.3 1.06 5.18 5.35 -

[0106] V. Data Analysis

[0107] The above experimental design and data comparison table can intuitively demonstrate the performance of the refrigeration unit operating efficiency statistics system of this ice machine station under different operating conditions, as well as the effectiveness of the optimized scheduling and fault diagnosis functions.

[0108] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A system for statistically analyzing the operating efficiency of refrigeration units in an ice-making station, characterized in that: include: Visual operation module; The data acquisition module is connected to a data processing module, which in turn is connected to an energy efficiency model library module, which is connected to an efficiency calculation module. The operating condition analysis module is connected to an equipment evaluation module, which in turn is connected to a statistical report module, which is connected to an analysis and early warning module, and which is connected to a push module. A cooling load forecasting module is connected to an analysis and early warning module. The cooling load forecasting module is connected to an optimization scheduling module. The optimization scheduling module is connected to a collaborative optimization module. The collaborative optimization module is connected to a balance optimization module. The fault diagnosis module is connected to the analysis and early warning module, and is also connected to a fault knowledge base module and a maintenance trigger module. The maintenance trigger module is connected to the equipment evaluation module.

2. The refrigeration unit operating efficiency statistical system for an ice-making station according to claim 1, characterized in that: The efficiency calculation module includes a real-time energy efficiency ratio calculation unit, a coefficient generation unit, and a comprehensive efficiency output unit. The real-time energy efficiency ratio calculation unit is connected to the coefficient generation unit, and the coefficient generation unit is connected to the comprehensive efficiency output unit. The real-time energy efficiency ratio calculation unit is used to calculate the energy efficiency ratio of the chiller unit, which is the ratio of cooling capacity to power consumption. The formula is: ； Wherein: Cooling capacity can be calculated from flow rate and temperature difference: ； The coefficient generation unit generates a correction coefficient based on the difference between the real-time energy efficiency ratio and the benchmark energy efficiency ratio, which is used for subsequent calculation of the overall efficiency. The formula is as follows: ； Among them: the benchmark energy efficiency ratio is based on the standard energy efficiency ratio of the unit under the current operating conditions stored in the energy efficiency model library module; The integrated efficiency output unit combines the real-time energy efficiency ratio with the correction factor to output the integrated efficiency, which more comprehensively reflects the actual operating efficiency of the unit. The formula is: ； Wherein: α is a weighting coefficient used to balance the weights of real-time energy efficiency ratio and benchmark energy efficiency ratio in overall efficiency.

3. The refrigeration unit operating efficiency statistical system for an ice-making station according to claim 2, characterized in that: The optimization scheduling module includes an optimization function construction unit, a load allocation unit, and a strategy generation and distribution unit. The function construction unit is connected to the load allocation unit, and the load allocation unit is connected to the strategy generation and distribution unit.

4. The refrigeration unit operating efficiency statistical system for an ice station according to claim 3, characterized in that: The maintenance triggering module includes a threshold judgment unit, an association unit, and a push unit. The threshold judgment unit is connected to the association unit, and the association unit is connected to the push unit.

5. The refrigeration unit operating efficiency statistical system for an ice station according to claim 4, characterized in that: The operating condition analysis module includes a flow analysis unit, a temperature difference analysis unit, and an operating condition correction unit. The flow analysis unit is connected to the temperature difference analysis unit, and the temperature difference analysis unit is connected to the operating condition correction unit.

6. The refrigeration unit operating efficiency statistical system for an ice station according to claim 5, characterized in that: The visualization module is used to present the underlying data, analysis results and optimization strategies to the operation and management personnel in a graphical and intuitive way, while the data acquisition module is responsible for acquiring raw operation data from the distributed control systems and instruments on site.

7. The refrigeration unit operating efficiency statistical system for an ice station according to claim 6, characterized in that: The data processing module is used to clean, integrate, and perform preliminary calculations on the collected raw data, while the energy efficiency model library module stores benchmark performance data of various units under different operating conditions, providing a reference for efficiency calculation and evaluation.

8. The refrigeration unit operating efficiency statistical system for an ice station according to claim 7, characterized in that: The equipment evaluation module is used to evaluate the internal health status of the refrigeration unit itself, and the statistical report module is used to organize, store, and format the efficiency data for presentation.

9. The refrigeration unit operating efficiency statistical system for an ice station according to claim 8, characterized in that: The analysis and early warning module is used to monitor anomalies and trends and trigger alarms, while the push module is used to proactively deliver key information to relevant responsible persons.

10. A system for statistically analyzing the operating efficiency of a refrigeration unit in an ice-making station according to claim 9, characterized in that: The fault diagnosis module is used to perform intelligent root cause analysis on efficiency anomalies or equipment alarms, and the fault knowledge base module is used to store diagnostic rules and experience.

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