Energy-saving control system of closed cooling tower

Abstract

The invention relates to the technical field of cooling tower energy-saving control, and discloses a closed cooling tower energy-saving control system, which comprises a multi-dimensional sensing module, an intelligent decision module, a precise execution module and a self-diagnosis optimization module, and is characterized in that the multi-dimensional sensing module acquires target parameters in real time and transmits the target parameters to the intelligent decision module; the intelligent decision-making module analyzes and processes the collected parameters and outputs a control instruction based on a preset working condition classification rule in combination with the dynamic energy efficiency model and the dual-threshold judgment logic; the precise execution module adjusts the operation state of a core component of the closed cooling tower according to the control instruction and feeds back real-time operation data to the intelligent decision module; the self-diagnosis optimization module monitors the equipment state based on feedback data, the system can flexibly adjust the operation state according to environment parameters and heat exchange loads through cooperative frequency conversion adjustment of a draught fan and a spraying pump and a dynamic adaptation strategy according to different working conditions, and compared with a traditional fixed rotating speed control mode, the comprehensive energy consumption of the closed cooling tower is effectively reduced.

Description

Technical Field

[0001] This application relates to the field of energy-saving control technology for cooling towers, and in particular to an energy-saving control system for closed-loop cooling towers. Background Technology

[0002] Closed-circuit cooling towers are key heat exchange equipment in industrial refrigeration and air conditioning systems, and their operating efficiency directly affects the energy consumption of the entire refrigeration system. Currently, most closed-circuit cooling towers operate at a fixed speed and have simple start-stop control, which cannot be dynamically adjusted according to actual conditions such as wet-bulb temperature and heat exchange load, resulting in high energy consumption. At the same time, the monitored parameters are relatively simple, focusing only on basic indicators such as inlet and outlet water temperatures, while ignoring important factors affecting heat exchange performance such as water quality, packing condition, and dust concentration, which can easily lead to problems such as packing scaling and a gradual decline in heat exchange efficiency.

[0003] Existing closed-loop cooling towers have weak seasonal adaptability and lack specific control schemes for different seasons. In winter, pipes are prone to freezing and cracking due to low water temperature, while in summer, insufficient heat exchange may affect the overall efficiency of the system. Moreover, the level of automation and intelligence is not high, and it mainly relies on manual inspection and maintenance, making it difficult to detect equipment failures or abnormal parameters in a timely manner. In addition, the lack of dynamic optimization mechanisms leads to significant energy efficiency degradation after long-term operation. Summary of the Invention

[0004] The closed-loop cooling tower energy-saving control system provided in this application adopts the following technical solution:

[0005] An energy-saving control system for a closed-loop cooling tower includes a multi-dimensional sensing module, an intelligent decision-making module, a precise execution module, and a self-diagnosis and optimization module. The multi-dimensional sensing module collects target parameters in real time and transmits them to the intelligent decision-making module. The intelligent decision-making module analyzes and processes the collected parameters and outputs control commands based on preset operating condition classification rules, combined with a dynamic energy efficiency model and dual-threshold judgment logic. The precise execution module adjusts the operating status of the core components of the closed-loop cooling tower according to the control commands and feeds back the real-time operating data to the intelligent decision-making module. The self-diagnosis and optimization module monitors the equipment status based on the feedback data, generates fault warnings or parameter correction suggestions, and dynamically optimizes the control strategy.

[0006] Preferably, the target parameters collected by the multi-dimensional sensing module include: environmental parameters including dry-bulb temperature, wet-bulb temperature, atmospheric pressure, dust concentration; heat exchange parameters including cooling tower inlet water temperature Tin, outlet water temperature Tout, spray water temperature Ts, circulating water flow rate Qc; equipment status parameters including fan speed N, spray pump pressure P, pressure difference ΔP before and after the packing material, solenoid valve on / off status; water quality parameters including circulating water turbidity Z, pH value; and energy consumption parameters including fan power consumption Wf and spray pump power consumption Wp.

[0007] Preferably, the operating condition classification rule of the intelligent decision-making module is as follows: based on the relationship between the air wet-bulb temperature Tw and the set threshold, it is divided into three categories of operating conditions: cooling season Tw≥20℃, transition season 10℃<Tw<20℃, and winter Tw≤10℃. Each category of operating conditions corresponds to different energy efficiency optimization targets and control priorities.

[0008] Preferably, the dynamic energy efficiency model includes a fan optimal speed calculation model and a spray optimal flow rate calculation model, wherein the fan optimal speed calculation model is as follows: In the formula: Nopt is the optimal fan speed; Nmax is the rated fan speed; Ttarget is the target outlet water temperature of the cooling tower; Tw is the wet-bulb air temperature; the optimal spray flow rate calculation model is as follows: Qs,opt is the optimal flow rate for spraying; Qs,max is the rated flow rate of the spray pump; ΔPset is the maximum allowable differential pressure of the packing; Zset is the maximum allowable turbidity of the circulating water; Z is the actual turbidity.

[0009] Preferably, the dual threshold judgment logic includes: temperature threshold judgment: when the inlet and outlet water temperature difference ΔT = Tin - Tout > the set upper limit ΔTmax, the fan speed and spray flow rate are increased first; when ΔT < the set lower limit ΔTmin, the fan speed is reduced or some spray branches are shut down; load threshold judgment: the load rate β = Qc / Qc,max is calculated based on the ratio of the circulating water flow rate Qc to the rated flow rate Qc,max. When β ≥ 80%, the dual fan linkage mode is started; when β ≤ 30%, the single fan low-frequency operation + intermittent spray mode is switched.

[0010] Preferably, the adjustment actions of the precision execution module include: adjusting the speed of the fan variable frequency speed regulation from 30% to 100% of the rated speed, adjusting the flow rate of the spray pump variable frequency regulation from 20% to 100% of the rated flow rate, switching the solenoid valve group of the spray branch to achieve 3-level spray coverage area adjustment, starting and stopping the packing online cleaning device, starting and stopping the winter antifreeze heating device, starting when Tout≤5℃, and emergency shutdown in case of failure.

[0011] Preferably, the fault warning logic of the self-diagnosis optimization module is as follows: when the packing pressure difference ΔP ≥ ΔPset × 1.1, a packing scaling warning is issued and online cleaning is triggered; when the circulating water turbidity Z ≥ Zset × 1.2, a water quality exceeding standard warning is issued; when the fan speed and power consumption deviate from the energy efficiency curve by ±15%, a fan fault warning is issued.

[0012] Preferably, the industrial communication protocol includes 5G, Modbus TCP, and MQTT protocols. The multi-dimensional sensing module and the intelligent decision-making module use the 5G protocol to achieve real-time data transmission. The intelligent decision-making module and the precision execution module interact with control commands through the Modbus TCP protocol. The equipment operation data is uploaded to the cloud platform through the MQTT protocol to achieve remote monitoring.

[0013] In summary, this application includes the following beneficial technical effects:

[0014] 1. Through coordinated frequency conversion regulation of the fan and spray pump, and a dynamic adaptation strategy based on different operating conditions, the system can flexibly adjust its operating status according to environmental parameters and heat exchange load. Compared with the traditional fixed speed control method, this effectively reduces the overall energy consumption of the closed-circuit cooling tower. In different usage scenarios, reduced equipment operating energy consumption ensures that the cooling effect meets actual needs while reducing electricity costs. This energy-saving operation mode aligns with the current development needs for energy conservation and carbon reduction, achieving efficient energy utilization while ensuring normal equipment operation and helping users control operating costs.

[0015] 2. By flexibly responding to the climate characteristics of different seasons, such as the cooling season, transitional season, and winter, as well as load fluctuations, the system can adjust operating parameters accordingly, improving the problem of insufficient seasonal adaptability of traditional cooling towers. During the high temperatures of summer, heat exchange regulation can be enhanced to ensure stable cooling output under high loads; during the suitable temperatures of the transitional season, it automatically switches to energy-saving operation mode to reasonably control the intensity of equipment operation; in the low-temperature environment of winter, a temperature-linked protection mechanism reduces the risk of pipe freezing and cracking. This adaptability allows the equipment to maintain good operating conditions under diverse climatic conditions and load changes, reducing the need for manual adjustments due to environmental changes, improving the long-term stability and practicality of the equipment, and adapting to the needs of multiple usage scenarios.

[0016] 3. Through its self-diagnostic function, the system monitors the status of the packing material, water quality, and equipment operating parameters in real time. It can promptly detect issues such as packing material scaling, excessive water quality, or abnormal component operation, issuing warnings and simultaneously coordinating with online cleaning devices for targeted treatment, extending the packing material's lifespan. Combined with remote monitoring and data traceability, users can check the equipment's operating status at any time, reducing the manpower required for frequent on-site inspections. Furthermore, by analyzing equipment operating trends, the system can predict potential faults and provide maintenance reminders, avoiding losses caused by sudden downtime and making equipment management more worry-free and efficient, significantly reducing overall maintenance costs. Attached Figure Description

[0017] Figure 1 This is a diagram of the closed-loop cooling tower energy-saving control system architecture of the present invention.

[0018] Figure 2 This is a flowchart of the control logic of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] The closed-loop cooling tower energy-saving control system of this invention achieves full-condition energy-saving control through multi-module collaboration. Example 1 is a basic control scheme adapted to a single small-to-medium-sized closed-loop cooling tower. During system setup, the multi-dimensional sensing module integrates environmental, heat exchange, equipment status, water quality, and energy consumption sensors to collect target parameters in real time, such as air dry / wet bulb temperature, cooling tower inlet and outlet water temperature, circulating water flow rate, packing pressure difference, spray pump pressure, circulating water turbidity and pH value, and power consumption of the fan and spray pump. The sensors are connected to the intelligent decision-making module via shielded cables, and a preset sampling frequency ensures data real-time performance and stability. The intelligent decision-making module uses an industrial-grade embedded controller with built-in operating condition classification rules, a dynamic energy efficiency model, and a dual-threshold judgment logic algorithm. It supports industrial communication protocols such as ModbusTCP and MQTT to achieve data interaction with various modules and local data caching, ensuring normal operation in the event of a network outage. The precision execution module is equipped with a fan frequency converter, a spray pump frequency converter, a spray branch solenoid valve group, an online packing cleaning device, and a winter anti-freeze heating device. The fan speed adjustment covers 30%-100% of the rated speed, the spray flow adjustment range is 20%-100% of the rated flow, and the solenoid valve group realizes three-level spray coverage area switching to meet the adjustment needs under different loads. The self-diagnosis optimization module and the intelligent decision-making module are integrated. Through software algorithms, the deviation between the equipment operating parameters and preset thresholds and energy efficiency curves is analyzed to complete status diagnosis, audible and visual warnings, and linkage processing.

[0021] During the implementation of the core control logic, the intelligent decision-making module automatically classifies the operating conditions into three categories: cooling season, transitional season, and winter, based on the air wet-bulb temperature. Each operating condition corresponds to different energy efficiency optimization targets and control priorities. A delayed judgment mechanism is used to avoid frequent adjustments during operating condition switching. Dual threshold judgment logic operates synchronously. In the temperature threshold judgment stage, when the temperature difference between the cooling tower inlet and outlet water exceeds the set upper limit, the fan speed and spray flow are prioritized to be increased; when the temperature difference is below the set lower limit, the fan speed is reduced or some spray branches are shut down. In the load threshold judgment stage, the load rate is calculated based on the ratio of the actual circulating water flow to the rated flow. Under high load, a multi-component linkage mode is activated; under low load, it switches to a low-frequency operation + intermittent spray mode. The dynamic energy efficiency model provides data support for the control strategy, and the optimal fan speed is determined by the formula... The optimal spray flow rate was calculated using the formula... Once confirmed, the intelligent decision-making module, based on the operating condition type and the dual threshold judgment results, calls the corresponding model to calculate the optimal control parameters and outputs instructions.

[0022] The system workflow follows a closed-loop control logic. During initialization, the multi-dimensional sensing module collects initial parameters and transmits them to the intelligent decision-making module. The decision-making module identifies the current operating condition and loads the corresponding control strategy, while the precision execution module resets to its initial operating state. In the real-time control phase, the sensing module continuously collects parameters at a preset frequency and transmits them to the decision-making module via an industrial communication protocol. The decision-making module periodically performs analysis and calculations, generates optimal control commands, and sends them to the execution module. After completing the adjustment, the execution module feeds back the real-time operating data to the decision-making module, forming a closed loop. In the special operating condition handling phase, during winter, when the outlet water temperature is below the antifreeze threshold, the antifreeze heating device is automatically activated. When equipment parameters malfunction, the system switches to emergency operation mode, ensuring basic heat exchange needs while triggering an early warning.

[0023] Example 2 is an intelligent upgrade control solution adapted to commercial buildings or industrial parks. Building upon Example 1, it adds 5G communication, AI learning algorithms, and cloud platform monitoring functions, adapting to the cluster control of multiple closed cooling towers. After the system upgrade, the multi-dimensional sensing module adds a dust concentration sensor to collect environmental dust data for correcting the spray flow strategy and reducing dust accumulation in the packing material. A new 5G industrial communication module enables high-speed data transmission and simultaneously uploads equipment operation data to the cloud platform via the MQTT protocol, supporting remote monitoring, parameter adjustment, and early warning pushes. The intelligent decision-making module embeds a load forecasting model based on machine learning. By analyzing historical operating data, the dynamic energy efficiency model coefficients are optimized to improve the adaptability of control strategies. In terms of cluster coordination control, a cluster coordination controller is added to achieve balanced load distribution based on the operating status of each cooling tower and the regional load distribution, avoiding overload or idleness of a single device. The remote management function allows users to view operating parameters, energy consumption data, and early warning information in real time through a mobile APP or computer cloud platform. Target parameters can be set remotely, and control thresholds can be adjusted to achieve unattended management. The fault prediction function uses AI algorithms to analyze equipment operating trends, predict potential faults in advance, and issue preventive maintenance prompts to reduce downtime risks.

[0024] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change.

[0025] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0026] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0027] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A closed-loop cooling tower energy-saving control system, comprising a multi-dimensional sensing module, an intelligent decision-making module, a precise execution module, and a self-diagnosis and optimization module, wherein the multi-dimensional sensing module collects target parameters in real time and transmits them to the intelligent decision-making module; The intelligent decision-making module analyzes and processes the collected parameters and outputs control commands based on preset operating condition classification rules, combined with dynamic energy efficiency model and dual threshold judgment logic; The precision execution module adjusts the operating status of the core components of the closed cooling tower according to control commands and feeds back the real-time operating data to the intelligent decision-making module; The self-diagnosis optimization module monitors the equipment status based on feedback data, generates fault warnings or parameter correction suggestions, and dynamically optimizes the control strategy.

2. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The target parameters collected by the multi-dimensional sensing module include: environmental parameters such as dry-bulb temperature, wet-bulb temperature, atmospheric pressure, and dust concentration; heat exchange parameters such as cooling tower inlet water temperature Tin, outlet water temperature Tout, spray water temperature Ts, and circulating water flow rate Qc; equipment status parameters such as fan speed N, spray pump pressure P, pressure difference ΔP before and after the packing, and solenoid valve on / off status; water quality parameters such as circulating water turbidity Z and pH value; and energy consumption parameters such as fan power consumption Wf and spray pump power consumption Wp.

3. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The operating condition classification rule of the intelligent decision-making module is as follows: based on the relationship between the air wet-bulb temperature Tw and the set threshold, it is divided into three types of operating conditions: cooling season Tw≥20℃, transition season 10℃<Tw<20℃, and winter Tw≤10℃. Each type of operating condition corresponds to different energy efficiency optimization targets and control priorities.

4. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The dynamic energy efficiency model includes a fan optimal speed calculation model and a spray optimal flow rate calculation model. The fan optimal speed calculation model is as follows: In the formula: Nopt is the optimal fan speed; Nmax is the rated fan speed; Ttarget is the target outlet water temperature of the cooling tower; Tw is the wet-bulb air temperature; the optimal spray flow rate calculation model is as follows: Qs,opt is the optimal flow rate for spraying; Qs,max is the rated flow rate of the spray pump; ΔPset is the maximum allowable differential pressure of the packing; Zset is the maximum allowable turbidity of the circulating water; Z is the actual turbidity.

5. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The dual threshold judgment logic includes: temperature threshold judgment: when the inlet and outlet water temperature difference ΔT = Tin - Tout > the set upper limit ΔTmax, the fan speed and spray flow rate are increased first; when ΔT < the set lower limit ΔTmin, the fan speed is reduced or some spray branches are shut down; load threshold judgment: the load rate β = Qc / Qc,max is calculated based on the ratio of the circulating water flow rate Qc to the rated flow rate Qc,max. When β ≥ 80%, the dual fan linkage mode is started; when β ≤ 30%, it switches to single fan low-frequency operation + intermittent spray mode.

6. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The adjustment actions of the precision execution module include: adjusting the speed of the fan variable frequency speed regulation from 30% to 100% of the rated speed; adjusting the flow rate of the spray pump variable frequency regulation from 20% to 100% of the rated flow rate; switching the solenoid valve group of the spray branch to achieve three-level spray coverage area adjustment; starting and stopping the packing online cleaning device; starting and stopping the winter antifreeze heating device; starting when Tout≤5℃; and emergency shutdown in case of failure.

7. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The fault warning logic of the self-diagnosis optimization module is as follows: when the packing pressure difference ΔP ≥ ΔPset × 1.1, a packing scaling warning is issued and online cleaning is triggered; when the circulating water turbidity Z ≥ Zset × 1.2, a water quality exceeding standard warning is issued; when the fan speed and power consumption deviate from the energy efficiency curve by ±15%, a fan fault warning is issued.

8. The closed-loop cooling tower energy-saving control system according to claim 1, characterized in that: The industrial communication protocols include 5G, Modbus TCP, and MQTT protocols. The multi-dimensional sensing module and the intelligent decision-making module use the 5G protocol to achieve real-time data transmission. The intelligent decision-making module and the precision execution module interact with control commands through the Modbus TCP protocol. Equipment operation data is uploaded to the cloud platform through the MQTT protocol to achieve remote monitoring.

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