Energy-saving cooling tower control system and method

Abstract

The invention relates to the technical field of cooling equipment control, and discloses an energy-saving cooling tower control system and method, and the system comprises a working condition monitoring module which is used for collecting the operation parameters of a cooling tower in real time; the execution adjustment module is used for adjusting the operation state of energy consumption equipment in the cooling tower; an energy-saving optimization module; and the central control unit is electrically connected with the working condition monitoring module, the execution adjustment module and the energy-saving optimization module, and is used for receiving the operation parameters acquired by the working condition monitoring module and calling the energy-saving optimization module according to the operation parameters. The repair suggestion generation module analyzes the damage mask and generates a repair suggestion according to a rule-based decision tree. According to the invention, through dynamic optimization and closed-loop intelligent control of real-time working conditions, accurate frequency conversion adjustment of the energy consumption equipment of the cooling tower is realized, so that the energy consumption of the system is obviously reduced, the self-adaptive capability and the operation reliability of the system are effectively improved, and the service life of the equipment is effectively prolonged.

Description

Technical Field

[0001] This invention relates to the field of cooling equipment control technology, specifically to an energy-saving cooling tower control system and method. Background Technology

[0002] Cooling towers, as indispensable key equipment in industrial production and central air conditioning systems, primarily function to reduce the temperature of circulating water by removing heat through water evaporation through contact between water and air, thereby ensuring the stability of the process or building environment. In the current energy landscape and against the backdrop of dual-carbon goals, energy conservation and emission reduction in cooling tower systems, as major energy consumers, have become a focus of industry attention.

[0003] In existing technologies, cooling tower control methods are generally quite rudimentary. A common control strategy is threshold control based on outlet water temperature. When the outlet water temperature exceeds a preset fixed threshold, the fans are activated for full-speed cooling, and then shut down when the temperature drops below another threshold. For systems with multiple fans, a similar tiered start-stop control is used. This fully on or fully off control mode is often designed based on the most severe operating conditions, such as the highest summer temperatures or full-load operation, resulting in fans still operating at rated power under partial load or lower ambient temperatures, leading to significant energy waste. Simultaneously, circulating water pumps are typically set to operate at a constant rated speed year-round, and their energy consumption is not adjusted according to changes in actual heat load, which is also a major reason for the high system energy consumption.

[0004] Furthermore, traditional control systems lack the comprehensive ability to perceive external environmental factors. The heat dissipation efficiency of a cooling tower depends not only on its own heat load but also on the ambient dry-bulb and wet-bulb temperatures. However, existing control systems typically rely solely on the outlet water temperature as the basis for decision-making, failing to perceive and utilize changes in environmental conditions to optimize operating strategies. For example, in seasons with lower wet-bulb temperatures, even with a constant heat load, the system can achieve the same cooling effect by significantly reducing fan speed, but traditional systems cannot recognize and utilize this energy-saving potential. This singular control logic prevents it from finding a dynamic, globally optimal balance between fan energy consumption and pump energy consumption, causing the system to operate far from its optimal energy efficiency point for extended periods.

[0005] Furthermore, crude start-stop control methods can adversely affect the equipment itself. Frequent start-stop of high-power motors such as fans generates significant electrical shocks and mechanical stresses, affecting not only the stability of the power grid but also accelerating equipment wear and shortening its lifespan. Simultaneously, this control method can easily lead to large fluctuations in the outlet water temperature around the setpoint, impacting the process's requirements for cooling water temperature stability. Therefore, developing an energy-saving cooling tower control system capable of comprehensively sensing operating conditions, intelligently analyzing and making decisions, and precisely adjusting execution to address the problems of high energy consumption, poor adaptability, and unstable operation in existing technologies has significant practical importance and application value. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an energy-saving cooling tower control system and method, which solves the problems of high energy consumption and poor adaptability in existing technologies due to the inability to dynamically optimize based on real-time changing operating conditions.

[0007] The first aspect of this invention provides an energy-saving cooling tower control system, characterized in that it includes:

[0008] The operating condition monitoring module is used to collect the operating parameters of the cooling tower in real time;

[0009] The execution adjustment module is used to adjust the operating status of energy-consuming equipment in the cooling tower;

[0010] Energy-saving optimization module;

[0011] The central control unit, which is electrically connected to the operating condition monitoring module, the execution adjustment module, and the energy-saving optimization module respectively, is used for:

[0012] The system receives operating parameters collected by the operating condition monitoring module and calls the energy-saving optimization module based on the operating parameters.

[0013] The energy-saving optimization module is used to calculate the optimal control setting value that minimizes the total input power of the cooling tower system based on the operating parameters and the preset cooling target.

[0014] The central control unit is also used to generate control commands based on the optimal control setpoint and send them to the execution adjustment module to control the operating status of the energy-consuming equipment.

[0015] Preferably, the operating condition monitoring module includes: a circulating water temperature sensor, an ambient temperature and humidity sensor, a circulating water flow sensor, and a fan speed sensor.

[0016] Preferably, the central control unit is further configured to: calculate the ambient wet-bulb temperature based on the ambient temperature and humidity data collected by the ambient temperature and humidity sensor; and calculate the real-time cooling heat load of the cooling tower based on the data collected by the circulating water temperature sensor and the circulating water flow sensor.

[0017] Preferably, the central control unit is used to: use the optimal control setpoint as the target setpoint of the PID control algorithm, and use the actual operating parameters collected by the operating condition monitoring module as process values, and generate the control command through PID calculation.

[0018] Preferably, the total input power of the cooling tower system is the sum of the input power of the cooling tower fan and the input power of the circulating water pump.

[0019] Preferably, the energy-saving optimization module integrates a cooling tower thermodynamic performance model and an equipment energy consumption characteristic model;

[0020] The energy-saving optimization module determines whether the alternative control setpoints meet the preset cooling target through the cooling tower thermodynamic performance model, and calculates the total system input power under the alternative control setpoints through the equipment energy consumption characteristic model.

[0021] Preferably, the preset cooling target is that the outlet water temperature of the cooling tower is less than or equal to a preset temperature threshold.

[0022] Preferably, the execution adjustment module includes a fan frequency converter connected to the cooling tower fan and a circulating water pump frequency converter connected to the circulating water pump; the control command is a frequency command sent to the fan frequency converter and the circulating water pump frequency converter.

[0023] Preferably, the system further includes a data storage module electrically connected to the central control unit, used to store the operating parameters, the optimal control setpoints, and the control commands. The stored data is used for iterative training of the machine learning model to continuously optimize the computing power of the energy-saving optimization module.

[0024] A fourth aspect of the present invention provides an energy-saving cooling tower control method, comprising the following steps:

[0025] The operating parameters of the cooling tower are collected in real time through the operating condition monitoring module;

[0026] Based on the operating parameters and the preset cooling target, the optimal control setpoint that minimizes the total input power of the cooling tower system is calculated by the energy-saving optimization module.

[0027] Based on the optimal control setpoint, control commands are generated;

[0028] According to the control command, the operating status of the energy-consuming equipment in the cooling tower is adjusted by executing the adjustment module.

[0029] This invention provides an energy-saving cooling tower control system and method. It has the following beneficial effects:

[0030] 1. This invention, by setting up an energy-saving optimization module, can dynamically solve for the optimal combination of control setpoints while meeting preset cooling targets based on real-time heat load and environmental factors collected by the operating condition monitoring module and combined with the energy consumption characteristic model of the energy-consuming equipment. This method abandons the fixed or coarse adjustment strategies in traditional control methods. Through precise optimization, it ensures that the cooling tower system always operates near the optimal energy efficiency point that minimizes total input power, thereby minimizing unnecessary energy consumption while ensuring process requirements are met.

[0031] 2. This invention utilizes a condition monitoring module to continuously track external environmental and internal load fluctuations, and a central control unit and energy-saving optimization module to make rapid collaborative decisions. This enables the system to automatically and dynamically adjust the operating status of fans and water pumps. This adaptive adjustment capability allows the cooling tower to proactively adapt to various operating condition changes without manual intervention, ensuring the real-time optimization of the operating strategy and significantly improving the system's automation and intelligent management level.

[0032] 3. This invention achieves smooth, stepless speed regulation of energy-consuming equipment such as fans and water pumps by using a frequency converter as the core component of the execution and regulation module. This flexible control method avoids the huge electrical shocks and mechanical stresses caused by traditional start-stop control, reducing equipment wear. Simultaneously, the abnormal operating condition monitoring function of the central control unit can provide timely warnings and take protective measures. The combination of precise and smooth control with comprehensive monitoring and protection not only ensures the stability of the cooling tower outlet water temperature but also significantly improves the operational reliability of the entire system and the service life of the equipment. Attached Figure Description

[0033] Figure 1 This is a block diagram of the functional modules of the system of the present invention;

[0034] Figure 2 This is a block diagram of the central control module of the present invention;

[0035] Figure 3 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] See attached document Figure 1-2 This invention provides an energy-saving cooling tower control system, comprising:

[0038] The operating condition monitoring module is used to collect the operating parameters of the cooling tower in real time;

[0039] The execution adjustment module is used to adjust the operating status of energy-consuming equipment in the cooling tower;

[0040] Energy-saving optimization module;

[0041] The central control unit, which is electrically connected to the operating condition monitoring module, the execution adjustment module, and the energy-saving optimization module respectively, is used for:

[0042] The system receives operating parameters collected by the operating condition monitoring module and calls the energy-saving optimization module based on the operating parameters.

[0043] The energy-saving optimization module is used to calculate the optimal control setting value that minimizes the total input power of the cooling tower system based on the operating parameters and the preset cooling target.

[0044] The central control unit is also used to generate control commands based on the optimal control setpoint and send them to the execution adjustment module to control the operating status of the energy-consuming equipment.

[0045] Operating condition monitoring module

[0046] In this embodiment, the operating condition monitoring module, as the system's perception layer, is responsible for comprehensively and in real-time collecting key physical parameters of the cooling tower's operating status and the environment, providing a data foundation for subsequent analysis and decision-making. This module specifically includes:

[0047] Circulating water temperature sensors: High-precision temperature sensors, such as Pt100 platinum resistance temperature sensors, are installed at the main inlet and outlet of the cooling tower to measure the inlet water temperature in real time. and outlet water temperature .

[0048] Ambient temperature and humidity sensor: Installed near the cooling tower in a well-ventilated location away from direct sunlight, used to measure the dry-bulb temperature of the cooling tower's operating environment in real time. and relative humidity .

[0049] Circulating water flow sensor: An electromagnetic flow meter or a clamp-on ultrasonic flow meter is installed on the main circulating water pipeline to monitor the volumetric flow rate of the circulating water passing through the cooling tower in real time. .

[0050] Fan speed sensor: A Hall effect or photoelectric speed sensor is installed near the motor shaft or impeller of each fan to measure the actual operating speed Nf of the fan in real time.

[0051] All sensors convert the acquired physical quantities into standard 4-20mA analog or digital signals through signal conditioning circuits, and transmit them to the central control unit in real time and periodically via industrial buses such as Modbus or CAN.

[0052] In this embodiment, the Central Control Unit (CCU) is the core processing and scheduling hub of the system, typically composed of a high-performance programmable logic controller (PLC) or industrial control computer (IPC). It is electrically connected to the operating condition monitoring module, energy-saving optimization module, execution and regulation module, and data storage module, and performs the following core functions:

[0053] Data Processing and Operating Condition Analysis: After receiving the real-time data stream from the operating condition monitoring module, the CCU verifies the data validity. Subsequently, it uses this data to calculate key parameters:

[0054] Obtaining the ambient wet-bulb temperature (Twb): The ambient wet-bulb temperature (Twb) is a core input parameter for the thermodynamic model in the energy-saving optimization module. Given the theoretical wet-bulb aerodynamic equations (such as...) This formula only holds true under ideal conditions (such as a specific wind speed). Directly applying it to the variable operating environment of a real cooling tower, calculating Twb based solely on Tdb and RH, introduces significant and unacceptable biases, leading to incorrect subsequent optimization decisions. To address this issue, the central control unit in this embodiment prioritizes direct measurement: the operating condition monitoring module includes a dedicated wet-bulb temperature sensor (e.g., a forced-draft wet-bulb meter or a high-precision electronic wet-bulb sensor) that directly measures the actual wet-bulb temperature Twb at the cooling tower inlet. The CCU directly acquires this accurate measurement as input to the energy-saving optimization module, fundamentally avoiding the risk of inaccurate theoretical formulas under complex operating conditions.

[0055] Calculate real-time cooling heat load ( CCU is based on the temperature difference between the inlet and outlet water. The heat load is calculated using thermodynamic formulas based on the circulating water flow rate Fw and the formula parameters. To address the issues of "lack of physical meaning in the formula parameters" and inaccurate calculations due to the use of fixed values, this embodiment employs dynamic parameter calculations.

[0056] ;

[0057] in:

[0058] QL: Real-time cooling heat load (kW);

[0059] Fw: The circulating water volumetric flow rate (m³ / h) collected by the sensor;

[0060] Tin: the collected inlet water temperature (°C);

[0061] Tout: The collected water temperature (°C);

[0062] and : It is no longer a fixed constant, but a function of water temperature (T) and water quality (S, such as salinity or impurity content referred to here). The CCU's processing method is as follows: (1) Temperature correction: The CCU first calculates the average temperature of the circulating water. (2) Dynamic value acquisition: The CCU has a built-in standard water physical property database (e.g., polynomial functions or lookup tables based on the IAPWS-IF97 standard), which is used to calculate... Real-time query or calculation of the precise specific heat capacity at that temperature and density (3) (Optional) Water quality correction: In systems requiring extremely high accuracy or where water quality (such as conductivity) fluctuates drastically, the CCU can also receive signals from water quality sensors to correct water quality. and Further salinity or impurity corrections will be made.

[0063] 3600: This is the conversion factor (s / h) from hours (h) to seconds (s), ensuring the final unit is kW.

[0064] Task scheduling: The CCU schedules tasks based on the calculated real-time cooling heat load. and ambient wet-bulb temperature Determine the current operating conditions and combine these parameters with the preset cooling target, i.e., the target outlet water temperature. The package is sent to the energy-saving optimization module, triggering an optimization calculation.

[0065] Control command generation: When the CCU receives the optimal control setpoint, i.e., the optimal fan speed, from the energy-saving optimization module... and optimal pump flow rate Then, this value is used as the target setpoint for the PID proportional-integral-derivative controller. Simultaneously, the actual fan speed fed back from the operating condition monitoring module is also used. and circulating water flow As process values, the control output quantities for the fan inverter and water pump inverter are calculated separately through PID calculations and converted into standard frequency commands such as 0-50Hz.

[0066] Energy-saving optimization module

[0067] In this embodiment, the energy-saving optimization module is the core of energy saving. Its function is to solve for the operating strategy that minimizes the total energy consumption of the system based on real-time operating conditions. This module is embedded in the central control unit or host computer in the form of a software algorithm.

[0068] Optimization objective: Its core objective is to minimize the total input power of the cooling tower system. That is, the input power of the fan. Input power of circulating water pump The sum (Minimize: ).

[0069] Core Models: This module integrates two core mathematical models:

[0070] Cooling tower thermodynamic performance model: This model describes the outlet water temperature of the cooling tower. With inlet water temperature Ambient wet-bulb temperature Circulating water flow rate and fan speed The complex nonlinear relationship between them, i.e. The model can be built based on the Merkel integral method or Poppe method for cooling towers, or fitted by multivariate nonlinear regression analysis of historical operating data. During the optimization process, this model is used to test any alternative (…). , Whether the combination can achieve the preset cooling target for the final outlet water temperature, i.e. .

[0071] Equipment Energy Consumption Characteristic Model: This model abandons the traditional approach of simply applying the cubic law across the entire operating range, because this law only approximately holds true in the high-efficiency range of the equipment, while producing significant deviations in low-load areas (such as when the fan speed is below 30% or the water pump flow rate is below 20% of its rated value). In this embodiment, the energy consumption characteristic model is established by conducting performance tests on the fan and water pump (both including the motor and frequency converter) across the entire operating range, collecting their actual input electrical power under different control variables (speed / flow rate), and then building an accurate energy consumption model based on the measured data. This model directly establishes the functional relationship between input electrical power and control variables, thereby implicitly and accurately including the changes in fluid characteristics and equipment efficiency (η) in the model.

[0072] Wind turbine energy consumption model: Total input electrical power of the wind turbine It was modeled as its rotational speed The function is a high-order polynomial function. This function, obtained by fitting measured data, accurately reflects the actual energy consumption characteristics of the wind turbine deviating from the cubic law in the low-speed region.

[0073] ;

[0074] Among them, coefficient It is a constant determined through regression analysis of experimental data. This method is more efficient than defining it separately. The function is more precise and easier to implement in engineering.

[0075] Water pump energy consumption model: Similarly, the total input electrical power of the water pump It was modeled as its circulating water flow rate The model is a higher-order polynomial function. It is also obtained by fitting measured data from the pump system to reflect its actual energy consumption when deviating from the optimal efficiency point (especially in the low-flow-rate region).

[0076] ;

[0077] Among them, coefficient It is a constant obtained through regression analysis of experimental data.

[0078] Optimization process: After receiving the calculation request from the CCU, the energy-saving optimization module uses an optimization algorithm (such as sequential quadratic programming, genetic algorithm, etc.) to optimize the energy efficiency within the allowable operating range of the equipment. , The algorithm searches for variables. For each combination of variables found, it first determines whether it satisfies the outlet water temperature constraint using a thermodynamic performance model. If it does, it calculates the total power using an energy consumption characteristic model. Finally, the algorithm converges and outputs the value that maximizes the total power. The smallest combination, i.e. the optimal control setpoint ( , ).

[0079] Execution adjustment module

[0080] In this embodiment, the execution adjustment module is the final executor of the control commands. This module mainly includes:

[0081] Fan frequency converter: It is connected to the drive motor of the cooling tower fan, receives frequency commands from the CCU, and smoothly and precisely adjusts the fan speed by changing the output frequency.

[0082] Circulating water pump frequency converter: It is connected to the drive motor of the circulating water pump, receives frequency commands from the CCU, and precisely adjusts the pump speed by changing the output frequency, thereby controlling the circulating water flow.

[0083] This module translates the digital control commands issued by the CCU into physical actions, directly changing the airflow and circulating water flow of the cooling tower, so that the actual operating state of the system quickly and accurately approaches the optimal energy efficiency point calculated by the energy-saving optimization module.

[0084] Data storage module

[0085] In this embodiment, the system is also configured with a data storage module, which serves as the system's memory center and is typically composed of an industrial-grade hard drive and database software.

[0086] Storage content: This module is responsible for the structured and persistent storage of all key data during system operation, including but not limited to: historical operating parameters collected by the operating condition monitoring module at the minute or second level, intermediate parameters calculated by the CCU, the optimal control setpoints output by the energy-saving optimization module, historical control commands issued by the CCU, and the system's alarm event logs.

[0087] Data Applications: The massive amounts of historical data stored are of significant value. On one hand, they can be used to generate energy efficiency reports and trend analysis charts, providing decision support for managers. On the other hand, and more importantly, this data can serve as a training set for offline training or iterative updates to machine learning models in energy-saving optimization modules. For example, neural network models can replace or supplement the aforementioned thermodynamic performance models. Through continuous data feeding and model training, the model's prediction accuracy and optimization performance can be continuously improved, thereby enabling the entire system to possess self-learning and self-evolution capabilities.

[0088] See attached document Figure 3 An energy-saving cooling tower control method includes the following steps:

[0089] The operating parameters of the cooling tower are collected in real time through the operating condition monitoring module;

[0090] Based on the operating parameters and the preset cooling target, the optimal control setpoint that minimizes the total input power of the cooling tower system is calculated by the energy-saving optimization module.

[0091] Based on the optimal control setpoint, control commands are generated;

[0092] According to the control command, the operating status of the energy-consuming equipment in the cooling tower is adjusted by executing the adjustment module.

[0093] Step 1: After the real-time data acquisition system is started, the operating condition monitoring module continuously collects the inlet and outlet water temperatures, ambient temperature and humidity, circulating water flow rate, and actual fan speed of the cooling tower at a preset sampling frequency, such as once every 5 seconds, and sends the data to the central control unit in real time.

[0094] Step Two: Calculation and Optimization After receiving the data, the central control unit first calculates the current heat load. And the ambient wet-bulb temperature (Twb). Then, these real-time operating parameters, along with the preset cooling target, are... The data is sent to the energy-saving optimization module. The energy-saving optimization module immediately starts the optimization algorithm to optimize the data while meeting the requirements. Under the premise of [condition], solve for the total power of the system. Minimum optimal fan speed and optimal pump flow rate .

[0095] Step 3: Command Generation After receiving the optimal control setpoint, the central control unit uses it as the target of the PID controller and the actual value fed back by the sensor as the comparison object to quickly calculate the frequency command that needs to be issued to the fan inverter and the water pump inverter.

[0096] Step 4: Adjustment and execution. The fan inverter and water pump inverter in the adjustment module receive and execute the new frequency command, adjust the motor speed, thereby changing the air flow and water flow of the cooling tower, so that the system's operating state is closer to the optimal energy efficiency point.

[0097] Step Five: Looping and Iteration Steps one through four above constitute a complete closed-loop control cycle. The system continuously repeats this cycle at a high frequency, such as once per minute, to ensure that the cooling tower's operating status can adapt dynamically and in real time to changes in the external environment and internal load, always maintaining it within the optimal range of lowest energy consumption. Simultaneously, all process data is recorded by the data storage module, providing a data foundation for long-term system performance evaluation and algorithm iteration.

[0098] In this embodiment, a continuous and adaptive closed-loop control system is used to minimize the total system input power of the cooling tower while meeting real-time cooling requirements.

[0099] Real-time data collection

[0100] This step serves as the sensing foundation for the entire control method. Its core task is to accurately and continuously acquire all necessary physical quantities characterizing the current operating state of the cooling tower and external environmental conditions. The effectiveness of this step directly determines the accuracy of subsequent calculations and optimizations.

[0101] Sensor deployment and parameter selection: The operational condition monitoring module acquires data through a series of high-precision industrial-grade sensors. The selection and deployment of these sensors are subject to strict technical considerations.

[0102] Inlet and outlet temperatures of circulating water:

[0103] Inlet water temperature: The measurement point is set on the main pipe entering the cooling tower distribution system. This temperature represents the initial state of the circulating water from the heat source that needs to be cooled, and is a key input for calculating the system's heat load.

[0104] Outlet water temperature: The measuring point should be set at the water collection tank or main outlet pipe below the cooling tower. This location should reflect the final water temperature after mixing of all tower cores, representing the actual cooling result of the cooling tower. The measuring point should be avoided near the water inlet to prevent newly added cold water from interfering with the measurement accuracy.

[0105] Ambient temperature and humidity:

[0106] The placement of the sensor, typically a wet-bulb thermometer or an integrated temperature and humidity transmitter, is crucial. It must be installed near the cooling tower's air inlet, but must effectively prevent the intake of hot, humid air exhausted from the cooling tower itself, while also avoiding direct sunlight and radiation interference from other heat sources. This measurement must accurately represent the original state of the fresh air entering the cooling tower, as it is the sole basis for calculating the ambient wet-bulb temperature.

[0107] Circulating water flow rate:

[0108] An electromagnetic flow meter or a clamp-on ultrasonic flow meter is used, installed on the main pipe at the outlet of the circulating water pump, but before it enters the branch pipes of the cooling tower. This measurement represents the total amount of water actually flowing through the cooling tower packing and is a core parameter for heat load calculation and pump power consumption modeling.

[0109] Actual fan speed:

[0110] This parameter should not be obtained by relying on the frequency setpoint output by the inverter, but should be measured directly by a speed sensor such as a photoelectric encoder or Hall sensor mounted on the fan motor shaft or fan gearbox. This provides accurate speed feedback, which is crucial for achieving precise closed-loop PID control of the speed and for verifying the accuracy of the fan energy consumption model.

[0111] Regarding sampling frequency and data timeliness, the operating condition monitoring module continuously collects data at a preset sampling frequency, such as every 5 seconds. This frequency setting represents a technical trade-off.

[0112] Excessively high frequencies, such as sub-second frequencies, will increase the computational burden on the central control unit. Furthermore, for cooling tower systems with high thermal inertia, most physical quantities do not change at such a rapid rate, which may introduce unnecessary high-frequency noise.

[0113] A sampling frequency that is too low, such as once every few minutes, will cause the system response to lag, making it unable to capture rapid fluctuations in heat load or sudden changes in environmental conditions in a timely manner. This may lead to control overshoot or lag, deviating from the optimal energy efficiency point. Therefore, a sampling frequency of 5 seconds is considered a reasonable compromise, ensuring that the data has sufficient timeliness to reflect the dynamics of the operating conditions without causing unnecessary waste of system resources.

[0114] Signal conditioning and transmission: The raw electrical signals acquired by the sensors, such as voltage, current, or pulses, first undergo signal conditioning within the condition monitoring module. This process includes necessary amplification, filtering (e.g., using low-pass filtering to eliminate transient spikes caused by electromagnetic interference (EMI) in the industrial environment), and analog-to-digital conversion (A / D). The converted, standardized digital signals are then packaged into data frames via an industrial fieldbus, for example, using differential signal transmission, the highly interference-resistant Modbus-RTU protocol, or the Ethernet / IP protocol. These data frames contain checksums to ensure data integrity during transmission and are subsequently sent to the central control unit in real time. This step ensures that the data acquired by the central control unit is an accurate, reliable, and uncontaminated digital representation of the original physical quantities.

[0115] Computation and Optimization

[0116] This step is the core of the control method's decision-making, responsible for transforming raw operating data into the optimal operating strategy. This process is completed in collaboration with the Central Control Unit (CCU) and the energy-saving optimization module, and consists of two closely linked stages: operating parameter analysis and energy-saving optimization calculation.

[0117] After receiving the continuous data stream from the operating condition monitoring module, the central control unit first executes the data preprocessing program to analyze the operating condition parameters.

[0118] Data validity verification: The system performs a validity check on every incoming data point. For example, the outlet water temperature should not be physically higher than the inlet water temperature; the relative humidity must be within the range of 0% to 100%; and the flow rate and rotation speed must be within the effective range of the sensor and the physical operating limits of the equipment. Any data deemed invalid or out of range, possibly due to momentary sensor failure or communication interruption, will be flagged and processed using preset fault-tolerant logic, such as briefly using valid data from the previous cycle or triggering an alarm, to prevent erroneous input from causing subsequent calculation failures.

[0119] After preprocessing, the central control unit immediately calculates two key operating parameters:

[0120] Real-time cooling heat load: This parameter represents the total heat that the system currently needs to dissipate, and is a fundamental indicator determining the cooling workload. Based on fundamental principles of fluid thermodynamics, the central control unit accurately calculates the current heat load value by combining real-time measurements of the circulating water flow rate, inlet and outlet temperatures, and the known specific heat capacity and density of the circulating water. Accurate calculation of this value is the foundation for subsequent optimization.

[0121] Ambient wet-bulb temperature: This parameter represents the theoretical minimum temperature limit for the evaporative cooling process, directly determining the maximum heat dissipation potential of the cooling tower under current environmental conditions. The central control unit uses real-time measurements of the ambient dry-bulb temperature and relative humidity, employing a built-in digital algorithm based on the wet air thermodynamic equation of state (i.e., the enthalpy-humidity diagram) at standard atmospheric pressure, to accurately calculate the current ambient wet-bulb temperature. A lower ambient wet-bulb temperature means greater air cooling potential, resulting in lower energy consumption for fans and pumps to achieve the same cooling target.

[0122] Once the energy-saving optimization strategy is generated, the central control unit packages the parsed real-time cooling heat load and ambient wet-bulb temperature, along with the cooling target preset by the upper-level process system (or by the operator), i.e., the desired cooling tower outlet water temperature, as a set of input conditions, and sends it to the energy-saving optimization module. The energy-saving optimization module immediately performs a nonlinear constraint optimization calculation with the objective of minimizing the total system input power.

[0123] Establishing the optimization objective: The sole objective of optimization is... Minimize. That is, find a set of optimal fan speeds. Optimal pump flow rate The combination of these factors minimizes the sum of the total power consumption of the fan system and the total power consumption of the water pump system.

[0124] The primary constraint for optimization is the process constraint, namely... .in It is in ( , Under this combination, the cooling tower outlet water temperature is predicted by the thermodynamic performance model. This constraint ensures that energy saving cannot come at the expense of process cooling efficiency.

[0125] Boundary constraints: Optimization must also be performed within the physical operating boundaries of the equipment. This includes minimum operating speeds of fans and pumps below these speeds, potential motor overheating, insufficient fan / pump efficiency, or pumps failing to lift water to the top of the tower, and maximum operating speeds limited by the equipment nameplate or mechanical structure.

[0126] Cooling Tower Thermodynamic Performance Model: This is the predictor for optimization calculations. The model is a complex multivariate nonlinear function that describes the precise relationship between the cooling tower outlet and inlet water temperatures, ambient wet-bulb temperature, fan speed determining airflow, and pump flow rate determining waterflow. This model can be established based on classical heat and mass transfer theory and calibrated and corrected using extensive historical operating data or specialized field performance test data to ensure the accuracy of its predictions.

[0127] Equipment energy consumption characteristic model: This is the cost function for optimization calculations. Based on the similarity law between electric motors and fluid machinery, this model establishes an approximate cubic relationship between the input power of a fan and its rotational speed, and similarly, an approximate cubic relationship between the input power of a water pump and its flow rate. This model also needs to consider the efficiency variations of the frequency converter under different load rates to ensure optimal performance. The calculations are highly accurate.

[0128] Execution of the optimization algorithm: The energy-saving optimization module employs efficient numerical optimization algorithms such as Sequential Quadratic Programming (SQP) or interior-point methods to search for the global minimum of the objective function within the feasible region defined by the aforementioned constraints. Since the power consumption of both the fan and pump increases non-linearly (cubicly) with speed (flow rate), and the thermodynamic performance of the cooling tower is also non-linear, such advanced algorithms are necessary to find the true optimal solution, rather than a local optimum, within a limited computational timeframe, for example, before the start of the next control cycle.

[0129] After the optimization calculation is completed, the energy-saving optimization module will select the unique set of optimal control settings that simultaneously meet cooling requirements and minimize energy consumption. and It is then returned to the central control unit.

[0130] Instruction generation

[0131] This step serves as a bridge between decision-making and execution. Its task is to convert the abstract optimal setpoint output by the energy-saving optimization module into physical control signals that can be understood and executed by the hardware frequency converter. This process needs to ensure the smoothness and accuracy of control.

[0132] The control loop is constructed so that the central control unit receives the optimal fan speed. and optimal pump flow rate Instead of directly outputting the frequency command, they are used as target setpoints (SPs) for two independent, parallel PID proportional-integral-derivative control loops.

[0133] Fan control circuit: SP refers to the actual fan speed collected in real time during step one. As a process feedback value (PV).

[0134] Water pump control circuit: SP refers to the circulating water flow rate collected in real time during step one. As a process feedback value (PV).

[0135] The role of a PID controller, and the necessity of using a PID controller, are as follows:

[0136] Overcoming disturbances: PID controllers can effectively overcome various disturbances not considered in the model. For example, fan speed may be affected by strong winds, headwinds, or tailwinds, and pump flow may be affected by fluctuations in pipeline pressure or filter blockage. The integral term (I) of the PID controller can continuously accumulate the deviation, ensuring that even with constant disturbances, the system can eventually reach the target setpoint with zero steady-state error.

[0137] Dynamic tracking: When the optimal setpoint (SP) changes, the PID controller can track the new SP in a smooth and fast dynamic process based on the pre-tuned parameters (P, I, D), rather than jumping instantaneously.

[0138] Decoupling: It decouples the two issues of what state should be reached being determined by the optimization module and how to reach that state being determined by the PID controller.

[0139] The PID controller calculates the error (SP) between SP and PV in real time within a control clock cycle shorter than the optimization cycle, e.g., once per second. Based on the error value, the derivative of the rate of change of the error (D), and the integral of the cumulative error (I), the current control output (MV) is calculated. This control output is typically a 0-100% percentage value, which is then converted by the central control unit into the standard industrial control signal required by the frequency converter, for example:

[0140] Analog signal: Converted to a 4-20mA DC current signal.

[0141] Digital signals: converted into specific register values ​​transmitted via industrial buses such as Modbus or Profibus-DP, for example, directly written to frequency settings.

[0142] To protect mechanical equipment (such as gearboxes and couplings) from excessive torque shocks and to prevent water hammer effects in pipeline systems, the central control unit also adds a rate limiter when outputting commands. That is, even if the PID controller calculates a large jump command, the system will decompose it into a series of small-amplitude commands that increase or decrease linearly over time, ensuring that the speed and flow rate of fans and pumps rise and fall smoothly, rather than abruptly.

[0143] Regulation and execution

[0144] This step is the physical implementation of the control method, and it is the actual point where energy consumption and heat exchange occur. The frequency converter in the control module faithfully executes the instructions from the central control unit.

[0145] The fan frequency converter and the circulating water pump frequency converter are the core actuators of this system. As high-precision power electronic devices, they receive frequency commands from the central control unit, whether analog or digital signals. The microprocessor inside the frequency converter immediately parses the command and, by controlling the switching timing of its internal power semiconductors (such as IGBTs), rectifies the input industrial frequency AC power (50 / 60Hz) into DC power, and then inverts it into a new AC power with adjustable voltage and frequency to supply the corresponding motor.

[0146] Based on the fundamental principle that the synchronous speed of an AC motor is proportional to the power supply frequency, the frequency converter achieves stepless and smooth speed regulation of the fan motor and water pump motor by precisely adjusting its output frequency.

[0147] Fan adjustment: Changes in fan speed directly alter the mass flow rate of air passing through the cooling tower, based on the fan performance curve.

[0148] Pump regulation: Changes in pump speed directly alter the mass flow rate of circulating water passing through the cooling tower, based on the characteristic curves of the pump and the pipeline network.

[0149] The reconfiguration of thermodynamic equilibrium and the changes in air and water flow rates mean that the air-to-water ratio (L / G) within the cooling tower changes. The air-to-water ratio is a core parameter affecting the heat and mass transfer performance of the cooling tower. By simultaneously and independently adjusting both air (G) and water (L) dimensions, the system can find the optimal air-to-water ratio under any operating condition. For example, when the wet-bulb temperature is low and the air cooling potential is strong, the system may tend to reduce the fan speed to save significant fan power consumption and appropriately maintain the water pump flow rate; while under extremely high heat loads, it may be necessary to simultaneously increase both to ensure cooling efficiency. The ultimate result of this adjustment is that the cooling tower's heat dissipation performance changes, its outlet water temperature begins to converge towards the optimal equilibrium point determined in step two, satisfying the TsetTset constraints, and the system's total input power also converges synchronously to its minimum value under this condition.

[0150] Loops and Iterations

[0151] This step defines the closed-loop characteristics, adaptive capability, and long-term self-optimization capability of this control method, which is the key difference from traditional open-loop or static control.

[0152] The closed-loop control cycle, from step one (data acquisition) to step four (execution), constitutes a complete and automated closed-loop control cycle. The system continuously repeats this cycle at a fixed, relatively slow, optimized frequency, such as once per minute.

[0153] Periodicity: The optimization calculation is repeated every minute, ensuring that the control strategy can keep up with minute-level changes in external environment such as day-night temperature difference and weather changes, and internal heat load such as production condition adjustments.

[0154] Closed loop: The physical state change caused by the adjustment execution in step four will be immediately captured in real time in step one of the next cycle and used as a new input for step two. This forms a complete closed loop of perception-decision-execution-reperception, ensuring that the system always operates around the dynamically changing optimal point.

[0155] Data storage and traceability: All critical data generated in each control cycle is structured and archived by the data storage module and appended with precise timestamps. Stored datasets include, but are not limited to:

[0156] Raw data: Raw measurements from all sensors.

[0157] Calculated data: calculated heat load and ambient wet-bulb temperature.

[0158] Decision data: The optimal fan speed and optimal water pump flow rate output by the energy-saving optimization module.

[0159] Command data: Frequency commands actually issued by the central control unit to the frequency converter.

[0160] Energy consumption data: Real-time input power read from the frequency converter or external power meter. This long-term data accumulation provides complete traceability of the system's operating status and can be used for subsequent performance evaluation, fault diagnosis, and energy efficiency auditing.

[0161] The deeper significance of offline iteration and model self-correction data storage lies in providing a foundation for the system's self-learning and self-optimization. The accuracy of the energy-saving optimization module highly depends on its internal thermodynamic performance model and equipment energy consumption characteristic model. However, with long-term operation of the equipment, these models will gradually become inaccurate.

[0162] Performance degradation: Scaling or fouling may occur in the cooling tower packing, leading to a decrease in its heat exchange efficiency.

[0163] Equipment wear: Wear on the pump impeller will reduce its efficiency; even small changes in the angle of the fan blades will affect aerodynamic performance.

[0164] Sensor drift: After long-term operation, the sensor's measurement values ​​may experience systematic deviations.

[0165] Leveraging the massive historical operational data accumulated in this step, the system can perform offline model iterative training. For example, machine learning algorithms or system identification techniques can be used to perform regression analysis on historical data to recalibrate key coefficients in the thermodynamic model or correct the power curve in the energy consumption model. This model self-correction mechanism based on actual operational data enables the control system to automatically adapt to the physical aging and performance degradation of equipment, ensuring that optimization decisions are always based on a digital twin model that most closely approximates current physical reality. This endows the control method with long-term energy efficiency maintenance capabilities, avoiding the efficiency drift caused by outdated models in traditional control systems.

Claims

1. An energy-saving cooling tower control system, characterized in that, include: The operating condition monitoring module is used to collect the operating parameters of the cooling tower in real time; The execution adjustment module is used to adjust the operating status of energy-consuming equipment in the cooling tower; Energy-saving optimization module; The central control unit, which is electrically connected to the operating condition monitoring module, the execution adjustment module, and the energy-saving optimization module respectively, is used for: The system receives operating parameters collected by the operating condition monitoring module and calls the energy-saving optimization module based on the operating parameters. The energy-saving optimization module is used to calculate the optimal control setting value that minimizes the total input power of the cooling tower system based on the operating parameters and the preset cooling target. The central control unit is also used to generate control commands based on the optimal control setpoint and send them to the execution adjustment module to control the operating status of the energy-consuming equipment.

2. The energy-saving cooling tower control system according to claim 1, characterized in that, The operating condition monitoring module includes: a circulating water temperature sensor, an ambient temperature and humidity sensor, a circulating water flow sensor, and a fan speed sensor.

3. The energy-saving cooling tower control system according to claim 2, characterized in that, The central control unit is also used to: calculate the ambient wet-bulb temperature based on the ambient temperature and humidity data collected by the ambient temperature and humidity sensor; and calculate the real-time cooling heat load of the cooling tower based on the data collected by the circulating water temperature sensor and the circulating water flow sensor.

4. The energy-saving cooling tower control system according to claim 1, characterized in that, The central control unit is used to: take the optimal control setpoint as the target setpoint of the PID control algorithm, and take the actual operating parameters collected by the operating condition monitoring module as the process value, and generate the control command through PID calculation.

5. The energy-saving cooling tower control system according to claim 1, characterized in that, The total input power of the cooling tower system is the sum of the input power of the cooling tower fan and the input power of the circulating water pump.

6. The energy-saving cooling tower control system according to claim 5, characterized in that, The energy-saving optimization module integrates a cooling tower thermodynamic performance model and an equipment energy consumption characteristic model. The energy-saving optimization module determines whether the alternative control setpoints meet the preset cooling target through the cooling tower thermodynamic performance model, and calculates the total system input power under the alternative control setpoints through the equipment energy consumption characteristic model.

7. The energy-saving cooling tower control system according to claim 5, characterized in that, The preset cooling target is that the outlet water temperature of the cooling tower is less than or equal to a preset temperature threshold.

8. The energy-saving cooling tower control system according to claim 5, characterized in that, The execution adjustment module includes a fan frequency converter connected to the cooling tower fan and a circulating water pump frequency converter connected to the circulating water pump; the control command is a frequency command sent to the fan frequency converter and the circulating water pump frequency converter.

9. The energy-saving cooling tower control system according to claim 5, characterized in that, The system also includes a data storage module electrically connected to the central control unit, used to store the operating parameters, the optimal control setpoints, and the control commands. The stored data is used for iterative training of the machine learning model to continuously optimize the computing power of the energy-saving optimization module.

10. An energy-saving cooling tower control method, used in an energy-saving cooling tower control system according to any one of claims 1-9, characterized in that, Includes the following steps: The operating parameters of the cooling tower are collected in real time through the operating condition monitoring module; Based on the operating parameters and the preset cooling target, the optimal control setpoint that minimizes the total input power of the cooling tower system is calculated by the energy-saving optimization module. Based on the optimal control setpoint, control commands are generated; According to the control command, the operating status of the energy-consuming equipment in the cooling tower is adjusted by executing the adjustment module.

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