A high-efficiency control method for a multi-core suspension fan

Abstract

The application discloses a kind of high-efficiency control methods of multi-core suspension fan, belong to fan energy-saving control technical field;The method sets acquisition cycle, total air demand is issued to PLC by host computer, and outlet air pressure is determined as rated pressure by pressure instrument;In constant pressure mode, the adiabatic efficiency of each core full-flow discrete parameter is collected, the efficiency and flow function are fitted by using nonlinear least square method, and efficiency contour is generated to construct single machine efficiency characteristic matching library;Based on the library, trial method and Lagrange multiplier method are used for double optimization, the optimal allocation of multi-machine flow and dynamic variable number strategy are solved, the system comprehensive adiabatic efficiency is maximized, and the optimal scheme library is formed for real-time regulation and control by actual measurement calibration;The application breaks the traditional equal load strategy, realizes the global energy efficiency optimal allocation of multi-machine parallel connection, and significantly reduces the energy consumption of aeration system.

Description

Technical Field

[0001] This invention belongs to the field of magnetic levitation fan control technology, specifically relating to a high-efficiency control method for a multi-core levitation fan. Background Technology

[0002] Wastewater aeration often employs multiple magnetic levitation blowers connected in parallel. Existing control methods largely rely on manufacturers' theoretical curves to adjust the speed to maintain pipeline pressure, but this approach has the following shortcomings: the performance model is out of touch with actual operating conditions; load allocation lacks a global energy efficiency assessment; and closed-loop control struggles to balance response speed and long-term stability.

[0003] Specifically, the following technical issues are raised: Existing performance evaluation relies on theoretical curves and lacks experimental modeling based on real pipe networks, making it difficult to construct smooth, continuous analytical models and three-dimensional efficiency maps, resulting in a lack of reliable computational basis for global optimization. Existing multi-machine parallel connection mostly adopts the load sharing strategy, lacks a global energy efficiency optimization mechanism, and does not fully consider the potential of dynamic change in the number of units and asymmetric distribution, making the system prone to deviating from the high efficiency zone; Existing control methods struggle to balance real-time response and long-term stability. Online optimization can lead to response delays, and the lack of a measured feedback calibration mechanism can cause control effectiveness to degrade over long-term operation. To address this, we propose a high-efficiency control method for multi-core suspended fans. Summary of the Invention

[0004] The purpose of this invention is to provide a high-efficiency control method for multi-core suspended fans to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency control method for a multi-core suspended fan, comprising: Step 1: Set the process parameter acquisition cycle. The host computer provides the total air volume requirement and transmits it to the PLC controller. Synchronously update the fixed air volume command for the air volume control cycle. Measure the fan outlet air pressure as the rated operating pressure of the aeration system. Step 2: Lock the constant pressure operation mode, collect discrete flow point parameters for each fan core within the full flow range, calculate the adiabatic efficiency at each operating point, and construct flow-efficiency data pairs; use the nonlinear least squares method to perform polynomial fitting to obtain the efficiency-flow function, and generate efficiency contour curves to form an efficiency feature matching library. Step 3: Based on the efficiency feature matching library, the optimal allocation scheme of air mass flow rate of multi-core system is solved by two methods: trial algorithm for full-condition traversal optimization and Lagrange multiplier method for analytical optimization, so as to maximize the overall thermal efficiency of the system. The two methods are compared and the optimal one is selected. Start-up and shutdown and air volume adjustment commands are generated and corrected by actual measurement. Finally, the optimal operation scheme library is formed for real-time control.

[0006] Preferably, the specific process of setting the process parameter acquisition cycle, having the host computer provide the total air volume requirement and transmit it to the programmable logic controller, and synchronously updating the fixed air volume command for the air volume control cycle is as follows: Multiple process parameter acquisition cycles are set. In each cycle, the host computer provides the total air volume requirement of a single fan and transmits it to the programmable logic controller. For each air volume control cycle, the total air volume requirement obtained in the latest acquisition cycle is used as the current updated fixed air volume instruction, which is updated synchronously with the acquisition cycle.

[0007] Preferably, the specific process of measuring the blower outlet air pressure as the rated operating pressure of the aeration system is as follows: During each air volume control cycle, the actual air pressure at the outlet of the multi-core suspended blower is measured in real time using a pressure measuring instrument. This measured air pressure value is then directly used as the rated operating pressure of the aeration system in the wastewater treatment plant. Subsequently, this rated operating pressure is established as the benchmark for constant pressure control, thereby locking in the constant pressure operation mode.

[0008] Preferably, in the constant pressure operation mode, the specific process of collecting discrete flow point parameters for each fan core across the entire flow range is as follows: In the locked constant pressure mode, each fan core is individually debugged. Discrete flow point parameters are collected at preset steps within the minimum to maximum operating flow range, including inlet air absolute temperature, inlet and outlet air absolute pressure, actual drive shaft power, and outlet air mass flow rate, and integrated to form a parameter group.

[0009] Preferably, the specific process for calculating the adiabatic efficiency at each operating point and constructing flow rate-efficiency data pairs is as follows: For each data acquisition point, the operating parameters in the corresponding discrete flow point parameter group are normalized and dimensionless, and then substituted into the blower adiabatic efficiency calculation logic to obtain the core adiabatic efficiency corresponding to each flow point. The air mass flow rate of the blower core at each data acquisition point is combined with the corresponding core adiabatic efficiency to construct a core flow rate and adiabatic efficiency data pair, and then organized to form the efficiency dataset of the current constant pressure operation mode.

[0010] Preferably, the specific process of obtaining the efficiency and flow functions by polynomial fitting using the nonlinear least squares method is as follows: Using each data pair in the core efficiency dataset as the fitting sample, a nonlinear least squares method is used to perform polynomial fitting. The polynomial fitting coefficients are solved by minimizing the sum of squared errors, and the goodness of fit is calculated. Different polynomial degrees are selected in sequence. When the goodness of fit is greater than or equal to the corresponding preset threshold, the current polynomial degree and the corresponding polynomial fitting coefficient are determined, and the efficiency and flow function of the core under the current constant pressure mode are obtained.

[0011] Preferably, the specific process of generating efficiency contour curves and forming an efficiency feature matching library is as follows: Based on the current constant pressure mode, a three-dimensional operating condition coordinate system is constructed with the absolute pressure at the fan outlet, the mass flow rate of the air mass at the core outlet, and the overall adiabatic efficiency of the core as the three vertical coordinate axes. Within the effective range of flow rate values, each flow rate value is substituted into the efficiency and flow rate function to calculate the corresponding efficiency, coordinate points are constructed and projected onto the three-dimensional coordinate system, and efficiency contour curves are generated through curve smoothing. All curves under constant pressure mode are organized to form an efficiency feature matching library.

[0012] Preferably, the specific process of finding the optimal allocation scheme of air mass flow rate for multi-core systems through trial and error across all operating conditions is as follows: Enumerate all possible core operation combinations. For each operation combination, use the average allocated air mass flow rate as the initial allocated air mass flow rate. Adjust the allocated air mass flow rate of each core with a preset perturbation step size to keep the total allocated air mass flow rate equal to the total demand. Substitute the efficiency and flow function into each allocation combination to calculate the comprehensive adiabatic efficiency. After traversing all combinations, select the allocation flow rate combination with the largest comprehensive adiabatic efficiency value as the optimal solution of the trial algorithm.

[0013] Preferably, the specific process of analytically finding the optimal allocation scheme of air mass flow rate for multi-core engines using the Lagrange multiplier method is as follows: Construct a Lagrange function and introduce Lagrange multipliers and total air mass flow constraints. Calculate the first-order partial derivatives of the air mass flow allocated to each core and the Lagrange multipliers, and set them to zero. Solve based on the optimality condition of zero first-order partial derivatives to obtain the optimal air mass flow allocated to each core and the corresponding comprehensive adiabatic efficiency. This physical meaning is clearly defined as the energy-saving optimal operating point where the system's comprehensive adiabatic efficiency reaches its maximum and the input shaft power is minimized.

[0014] Preferably, the optimal method is selected by comparing the two methods, generating start / stop and airflow adjustment commands, and then performing actual measurement, calibration, and correction to finally form an optimal operation scheme library for real-time control. The specific process is as follows: The optimal integrated adiabatic efficiency obtained by comparing the trial-and-error method and the Lagrange multiplier method is selected as the final optimal operating scheme, and start-stop and air volume adjustment commands are generated accordingly. If the measured integrated adiabatic efficiency deviates from the theoretical maximum value and exceeds the allowable range, the efficiency, flow function and allocation parameters are calibrated and corrected. The correction schemes under various constant pressure modes are integrated to form an optimal operating scheme library for real-time control.

[0015] Compared with the prior art, the beneficial effects of the present invention are: (1) The high-efficiency control method of the multi-core suspended fan is to collect discrete parameters of the entire flow range and calculate the adiabatic efficiency under the locked constant pressure operation mode, and use the nonlinear least squares method to perform polynomial fitting to construct the efficiency-flow function, and then generate three-dimensional efficiency contour curves to form an efficiency feature matching library. This measured modeling method based on real pipeline network conditions effectively improves the performance deviation caused by the reliance on the manufacturer's theoretical curves in the existing technology, and constructs a continuous analytical model with good fitting degree and calculation smoothness, providing a more reliable mathematical calculation basis for subsequent multi-machine global optimization.

[0016] (2) The high-efficiency control method of the multi-core suspended fan solves the optimal distribution scheme of air mass flow rate of the multi-core by combining the trial algorithm for full-condition traversal optimization and the Lagrange multiplier method for analytical optimization. This mechanism changes the conventional strategy of average load distribution or fixed number of operating units in the traditional multi-unit parallel connection, and gives full play to the adjustment potential of dynamic adjustment of the number of operating units and asymmetric flow distribution, so that the parallel system can be better maintained in the comprehensive high-efficiency operating range, and effectively reduce the overall operating energy consumption of the system.

[0017] (3) The high-efficiency control method of the multi-core suspended fan integrates the optimal scheme obtained by optimization into the optimal operation scheme library for real-time table lookup and matching control, and introduces a deviation comparison and calibration mechanism between the measured comprehensive adiabatic efficiency and the theoretical value. This design transforms the complex online multivariate optimization calculation into efficient offline library building and online matching. While ensuring the system's response speed to pipeline pressure fluctuations, it can effectively compensate for the performance drift caused by long-term operation of the equipment and maintain the stability of the control strategy in long-term operation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the efficiency-flow fitting curve and three-dimensional efficiency contour lines of a single wind turbine core in this invention; Figure 3 This is a three-dimensional efficiency contour map of the multi-core parallel operation in this invention; Figure 4 This is a schematic diagram of the hardware architecture of the high-efficiency control system for multi-core suspended wind turbines of the present invention. Detailed Implementation

[0019] The technical solutions of 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.

[0020] Example 1; Please see Figure 1-4 This invention provides a high-efficiency control method for a multi-core suspended fan, comprising: Step 1: Set the process parameter acquisition cycle. The host computer provides the total air volume requirement and transmits it to the PLC controller; synchronously update the fixed air volume command for the air volume control cycle; measure the blower outlet air pressure as the rated operating pressure of the aeration system. The specific process is as follows: Several process parameter acquisition cycles are set (i.e., the sampling cycle of the real-time total air volume demand dynamically output by the intelligent aeration system of the sewage treatment biochemical tank or the plant-level DCS system). Within each process acquisition cycle, the host computer provides the total air volume demand of a single blower and transmits it to the PLC controller of the machine through communication.

[0021] For each air volume control cycle, the total air volume requirement of the latest acquisition cycle within the air volume control cycle is used as the updated fixed air volume command for the current air volume control cycle; wherein, the fixed air volume command of the air volume control cycle is updated synchronously with the update of the process parameter acquisition cycle. For each air volume control cycle, the air pressure at the outlet of the multi-core suspended blower is measured by a pressure measuring instrument and used as the rated operating pressure of the aeration system in the wastewater treatment plant.

[0022] It should be noted that by setting the process parameter acquisition cycle and the air volume control cycle and keeping the instructions updated synchronously, the core is to establish a real-time mapping relationship between the dynamic air volume demand of the wastewater treatment biochemical tank and the air volume supply of the multi-core suspended blower, so as to ensure that the air volume instructions received by the programmable logic controller always accurately reflect the actual oxygen demand of the current process. Meanwhile, by measuring the blower outlet pressure in real time and using it as the rated operating pressure of the aeration system, the core purpose is to obtain the true resistance characteristics of the current pipeline network, thereby establishing a benchmark for constant pressure control.

[0023] The above design achieves dynamic and precise matching between air demand and air supply, effectively avoiding energy waste caused by insufficient air supply or excessive aeration, and improving the treatment efficiency of the biological treatment tank and the response speed of the system. On the other hand, using measured wind pressure as a constant pressure benchmark eliminates the error of the theoretically set pressure not matching the actual operating conditions. This provides a stable and reliable pressure operating environment and data foundation for the subsequent construction of a high-precision efficiency characteristic matching library and the optimal allocation of multi-core air mass flow. This ensures that the multi-core suspended fan system can always maintain the energy-saving optimal operating condition with the maximum comprehensive thermal insulation efficiency.

[0024] Step Two: Locking the constant pressure operation mode, discrete flow point parameters are collected for each fan core across the full flow range. The adiabatic efficiency at each operating point is calculated, and flow-efficiency data pairs are constructed. A nonlinear least squares method is used for polynomial fitting to obtain the efficiency-flow function, and efficiency contour curves are generated to form an efficiency feature matching library. The specific process is as follows: The rated operating pressure of the determined aeration system is used as the constant pressure control benchmark to complete the constant pressure operation mode locking. Under the locked constant pressure operation mode, each fan core is individually debugged, specifically as follows: Within the full flow range from the minimum to the maximum operating flow of the wind turbine core, discrete flow point operating parameters are collected according to a preset flow step size. Let the sequence number of the collected operating point be m, and the m-th collected operating point be denoted as m_m. ; Operating parameters include: absolute temperature of air at the fan inlet, absolute pressure of air at the inlet, absolute pressure of air at the outlet, actual shaft power of the drive shaft, and mass flow rate of air at the outlet. The parameters collected from each discrete flow point are integrated to form a discrete flow point parameter group. For each data acquisition point, the operating parameters in the corresponding discrete flow point parameter group are normalized and dimensionless, and then substituted into the blower adiabatic efficiency calculation formula: The core adiabatic efficiency corresponding to each flow point is obtained. ; in: The measured air mass flow rate at the fan outlet at the m-th data collection point; The preset air constant pressure specific heat capacity (a pre-calibrated value, for example: 1005); Let m be the absolute temperature at the fan inlet at the m-th data collection point. The fan shaft power at the m-th data collection point; Let be the absolute pressure at the fan outlet at the m-th data collection point; Let m be the absolute pressure at the fan inlet at the m-th data collection point. The preset air insulation index (a pre-calibrated value, for example: 1.4); A constant term derived from the air adiabatic index (e.g., 0.2857); By combining the air mass flow rate of the fan core at each data collection point with the corresponding core adiabatic efficiency, a core flow rate-adiabatic efficiency data pair is constructed. ; Compare the flow rate-adiabatic efficiency data of all fan cores. The data is organized to form an efficiency dataset of the core under the current constant voltage operating mode; Based on the core efficiency dataset, each To fit the samples, a nonlinear least squares method is used for polynomial fitting: ; in: The overall thermal efficiency of the fan core (a continuous function value after fitting); q represents the mass flow rate of the air at the engine core outlet. n is the degree of the fitted polynomial, and is the highest power of the polynomial. The coefficients of the polynomial to be solved are denoted as . The fitting process involves solving for the polynomial fitting coefficients by minimizing the sum of squared errors, S. The formula for calculating the sum of squared errors S is: ; in: M represents the total number of data pairs in the core efficiency dataset, i.e., the total number of data points collected. The measured air mass flow rate at the fan outlet at the m-th data collection point; To measure the air mass flow rate at the m-th data collection point The efficiency prediction value is obtained by substituting into the fitted polynomial. Solving for the goodness of fit of a polynomial fit The calculation process is as follows: , in: For all core efficiency datasets The arithmetic mean; By sequentially selecting different polynomial degrees n, the goodness of fit of the corresponding polynomials is calculated. When the goodness of fit is greater than or equal to the corresponding preset threshold, determine the current polynomial degree n and the corresponding polynomial fitting coefficients. The efficiency-flow function of the core under the current constant pressure mode is obtained. ; Based on the current constant pressure mode, a three-dimensional operating condition coordinate system is established with the absolute pressure at the fan outlet, the mass flow rate of the air at the core outlet, and the overall thermal insulation efficiency of the core corresponding to the constant pressure mode as the three vertical coordinate axes. Within the effective range of the mass flow rate at the engine core outlet, each flow rate value, divided according to a preset flow rate step size, is substituted into the efficiency-flow function. Calculate the overall adiabatic efficiency of the corresponding core. Thus, several Coordinate points; All The data points are projected onto a three-dimensional operating condition coordinate system, and through curve smoothing, an efficiency contour curve for the current constant pressure mode is generated.

[0025] The efficiency contour curves under all constant pressure modes will be compiled to form an efficiency characteristic matching library for multi-core suspended fans.

[0026] It should be noted that the core purpose of this step is to establish a high-precision performance analysis model for each wind turbine core by combining on-site measurements with mathematical modeling, and to construct a global multi-dimensional efficiency map.

[0027] By performing single-unit commissioning and discrete parameter acquisition of each fan core within the full flow range under a locked constant pressure operating mode, it is possible to accurately obtain the operating boundary and physical performance data of each core in a real pipeline network environment.

[0028] This method of calculating adiabatic efficiency based on field measurements and using nonlinear least squares polynomial fitting effectively eliminates performance deviations caused by wind turbine manufacturing tolerances, long-term operational wear, and the idealization of theoretical curves by manufacturers. Simultaneously, by dynamically verifying the goodness of fit to determine the polynomial order, it ensures that the continuous functions of efficiency and flow rate possess both extremely high fitting accuracy and avoid overfitting, thus providing a reliable and smooth mathematical analytical foundation for subsequent complex optimization calculations.

[0029] Furthermore, a three-dimensional coordinate system for operating conditions is constructed using pressure, flow rate, and efficiency as dimensions, and efficiency contour curves are generated. Essentially, this transforms the complex nonlinear performance of the wind turbine into an intuitive multi-dimensional topological structure. The resulting efficiency feature matching library comprehensively characterizes the efficiency distribution features of multi-core suspended wind turbines under different constant pressure conditions.

[0030] This design enables the global flow allocation optimization process during multi-machine parallel operation to be quickly retrieved and analyzed directly using this high-precision matching library, which significantly reduces the computational dimension and time cost of multi-variable coupling optimization. This ensures that the control system can accurately locate and lock the operating point with the best overall energy efficiency in real time under dynamically changing process requirements.

[0031] Step 3: Based on the efficiency feature matching library, the optimal allocation scheme of air mass flow rate for multiple cores is solved by two methods: trial-and-error algorithm for full-condition optimization and Lagrange multiplier method for analytical optimization, so as to maximize the overall thermal efficiency of the system. The two methods are compared and the optimal one is selected. Start-up and shutdown and air volume adjustment commands are generated and corrected by actual measurement. Finally, the optimal operation scheme library is formed for real-time control. The specific process is as follows: For each constant pressure mode, it is substituted into the efficiency characteristic matching library of the multi-core suspended fan for matching, and the efficiency-flow function and efficiency contour curve corresponding to a single core are output; based on this, the optimal allocation of air mass flow rate for the multi-core is carried out, and the specific process is as follows: Obtain the total number of blowers in the multi-core suspended blower system of the wastewater treatment plant, and label it as N. Let the core number be k, k=1, 2, N; define the allocated air mass flow rate of the k-th core as... The allocated air mass flow rate is the target air mass flow rate allocated to this core; where, ,like This indicates that the nuclear power plant is out of service. The allocated air mass flow rate for each core meets the following requirements: , in, This represents the total air volume requirement for the updated cycle after the current air volume control cycle. Furthermore, considering the altitude differences between different projects (there are significant differences in air density and inlet absolute pressure under high-altitude and low-altitude operating conditions), before carrying out flow distribution calculations, the efficiency-flow function and efficiency contour curve under the current operating conditions are calibrated and corrected based on the measured shaft power of each machine to ensure the accuracy of efficiency calculations and flow distribution; among them, mass flow rate is the basic parameter for flow distribution, and shaft power is the calibration benchmark under different altitude operating conditions. Obtain the adiabatic efficiency of each core and the corresponding allocated air mass flow rate, and then use the comprehensive efficiency calculation formula: , in, Distribute air mass flow rate to the kth unit The adiabatic efficiency is reduced. Let be the efficiency-flow function corresponding to the kth core; The optimal allocation of flow is solved by maximizing the overall efficiency E, specifically as follows: Trial and error algorithm for full-case optimization: Enumerate all possible combinations of machine core operation, i.e., select m machine cores to operate, m=1,2,N; for each operation combination, distribute the air mass flow rate evenly. As the initial allocation of air mass flow rate, air mass flow rate is allocated to each core with a preset perturbation step size. Regulate and maintain the total allocated air mass flow rate equal to ; For each group of assigned air mass flow rate combinations, substitute the corresponding core efficiency-air mass flow rate function. Calculate the overall adiabatic efficiency E. After traversing all combinations, select the distribution flow combination with the largest overall adiabatic efficiency E value, and record its corresponding overall adiabatic efficiency as the optimal overall adiabatic efficiency of the trial algorithm. The running scheme corresponding to this combination is the optimal running scheme of the trial algorithm. ; Analytical optimization using the Lagrange multiplier method: Construct the Lagrangian function: , in, It is a Lagrange function; For Lagrange multipliers; This is the total air mass flow constraint value within the current air volume control cycle (i.e., the real-time total air mass flow demand in steady state during the cycle). Distribute air mass flow rate to each core Find the first-order partial derivative of the Lagrange multiplier λ and set it to 0: ; in, Assigning air mass flow rate to the k-th machine core using the Lagrange function The first-order partial derivative; The core efficiency of the k-th machine is a function of air mass flow rate. For the allocation of air mass flow The first-order partial derivative; For Lagrange functions with respect to Lagrange multipliers The first-order partial derivative; This is the total air mass flow constraint value within the current air volume control cycle (i.e., the real-time total air mass flow demand in steady state during the cycle). The optimal air mass flow rate for each engine core is obtained by solving the problem. and corresponding comprehensive thermal insulation efficiency ; Furthermore, in this solution process, based on the optimality condition that the first-order partial derivative is 0, its physical meaning is clearly defined as: the system's overall adiabatic efficiency reaches its highest point, and the corresponding energy-saving optimal operating point is the point where the system's input shaft power is minimized. Comparative algorithm for optimal overall adiabatic efficiency Optimal overall adiabatic efficiency using the Lagrange multiplier method The larger value is taken as the maximum overall thermal efficiency of the system. The corresponding solution is the final optimal operating solution. ; Based on the final optimal operating scheme Generate start / stop commands and airflow adjustment commands. The commands include: the core number k and the corresponding optimal flow rate. ; Issue a shutdown command to the out-of-operation unit; open the intake valve of the operating unit at a preset rate and perform variable frequency start-up regulation based on the corresponding optimal allocation of air mass flow rate; Among them, if the measured efficiency deviates from the theoretically calculated maximum value of the system's comprehensive thermal insulation efficiency, performance testing will be conducted in the test room to verify whether the measured characteristic curve and efficiency curve of the machine are within the allowable deviation range from the theoretical design value. If they exceed the allowable deviation range, the efficiency-flow function and flow distribution parameters will be calibrated and corrected, and the corrected final optimal operating scheme will be used as the optimal operating parameter scheme for each fan core of the multi-core suspended fan under the current constant pressure mode.

[0032] The optimal operating parameter schemes of each fan core in the multi-core suspended fan under various constant pressure modes are integrated to form an optimal operating scheme library corresponding to each constant pressure mode. When regulating the operation of multi-core suspended fans, the system obtains the fixed air volume command and rated operating pressure corresponding to the current cycle update, and substitutes them into the optimal operation scheme library for matching based on the current constant pressure mode and total air volume demand. The system then outputs the corresponding optimal operating parameters and control commands, and regulates the start-up, shutdown and air volume distribution of each fan core based on the control commands.

[0033] It should be noted that the core purpose of this step is to construct a multivariate constrained optimization model and adopt a dual optimization mechanism of "discrete ergonomics and continuous analysis" and a closed-loop calibration strategy of "theory and experiment" to accurately solve the global energy efficiency optimal solution when multiple core suspended wind turbines are running in parallel.

[0034] In the design of the optimization mechanism, the trial-and-error algorithm's full-condition traversal and the analytical optimization using the Lagrange multiplier method complement each other highly. By enumerating core operation combinations and step size perturbations, the trial-and-error algorithm effectively overcomes the defects of local optima or non-differentiable intervals that may exist in the nonlinear efficiency-flow function, ensuring the completeness of the global search.

[0035] From a mathematical analytical perspective, the Lagrange multiplier rule utilizes the optimality condition that the first-order partial derivative is zero to precisely pinpoint the theoretical extreme point where the marginal efficiency of each core is equal and the power of the system input shaft is minimized.

[0036] By comparing the two methods and selecting the best one, the energy redundancy caused by the simple average distribution of flow or independent control of a single unit in the traditional multi-unit parallel operation is completely eliminated. At the same time, a calibration correction based on measured shaft power is introduced before the distribution calculation, which effectively eliminates the interference of air density and inlet pressure differences under different altitude conditions on the distribution air mass flow calculation, ensuring the physical authenticity of the underlying data.

[0037] Furthermore, by comparing the measured system comprehensive adiabatic efficiency with the theoretical maximum value, a closed-loop feedback mechanism for performance testing and parameter correction was constructed. This design can effectively compensate for the performance drift of the wind turbine core caused by long-term operating wear, component aging, or sudden environmental changes, ensuring the continuous high accuracy of the efficiency-flow function and the optimal operating parameter scheme throughout the entire life cycle.

[0038] The final integrated optimal operating scheme library cleverly transforms the extremely complex multivariate online nonlinear optimization calculation into efficient offline library construction and online table lookup matching. This not only significantly reduces the real-time computing load on the host computer and programmable logic controller, but also greatly improves the system's response speed and control stability in response to dynamic changes in the air demand of the biochemical tank, ensuring that the multi-core suspended fan system always operates under the optimal energy-saving condition that maximizes overall thermal efficiency.

[0039] For example: Please read Figure 2-3 The efficiency contour line for one core is as follows: Two cores require a total airflow of 120 cubic meters per minute. If both cores have an airflow of 60 cubic meters per minute, then the operating efficiency of this multi-core fan is 80%. According to our algorithm, we will find the maximum Emax = 86%, at which point only one core will operate.

[0040] The efficiency difference is 1 - 86% / 80% = 7.5%.

[0041] The above algorithm is designed for multiple cores installed in a single chassis, where the combined airflow of all cores is treated as a single unit. This algorithm is also applicable to scenarios where multiple single-core suspended fans operate in parallel. It can obtain the optimal operating efficiency mode for multiple independent single-core fans.

[0042] It should be noted that, in conjunction with the above examples, the core of this control method lies in breaking away from the traditional mindset of "average load distribution" and "fixed number of operating units" in multi-machine parallel operation, and achieving a dynamic optimal combination of the number of operating units and the load of a single unit through global energy efficiency optimization.

[0043] Under conventional control strategies, when faced with total airflow demand, the system often defaults to activating multiple cores and distributing the flow evenly. This easily leads to the cores operating outside their high-efficiency range (e.g., in the example, two cores each handle 60 cubic meters per minute, resulting in an overall efficiency of only 80%). However, this algorithm, relying on a built-in efficiency feature matching library, can accurately identify the high-efficiency operating point of a single core when handling full load (e.g., in the example, selecting only one core increases efficiency to 86%), thus automatically avoiding inefficient zones. This asymmetric allocation and dynamic variable core number control resulted in a significant 7.5% improvement in the system's overall adiabatic efficiency, directly demonstrating the algorithm's outstanding performance in deeply exploring the energy-saving potential of equipment.

[0044] Furthermore, the algorithm boasts exceptional versatility and scalability in its architectural design. It is not only applicable to multi-core suspended fans physically integrated within a single chassis, managing the combined airflow of multiple cores as a whole; it can also be seamlessly adapted to cluster control scenarios where multiple independent single-core suspended fans operate in parallel. Regardless of the physical form and layout of the equipment, the algorithm can abstract it into a unified multivariate optimization model, solving for the optimal operating efficiency mode of the entire fan cluster.

[0045] This design, which balances deep energy saving with wide applicability, enables aeration systems in wastewater treatment plants and other applications to achieve considerable energy-saving and consumption-reducing economic benefits simply by upgrading the control logic without changing the existing hardware architecture. It provides a highly valuable engineering solution for the intelligent control of fan clusters in energy-intensive industries.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency control method for a multi-core suspended fan, characterized in that, include: Step 1: Set the process parameter acquisition cycle, and the host computer provides the total air volume requirement and transmits it to the PLC controller; Synchronously update the fixed air volume command of the air volume control cycle; measure the fan outlet air pressure as the rated operating pressure of the aeration system; Step 2: Lock the constant pressure operation mode, collect discrete flow point parameters for each fan core within the full flow range, calculate the adiabatic efficiency at each operating point, and construct flow-efficiency data pairs; use the nonlinear least squares method to perform polynomial fitting to obtain the efficiency-flow function, and generate efficiency contour curves to form an efficiency feature matching library. Step 3: Based on the efficiency feature matching library, the optimal allocation scheme of air mass flow rate of multi-core system is solved by two methods: trial algorithm for full-condition traversal optimization and Lagrange multiplier method for analytical optimization, so as to maximize the overall thermal efficiency of the system. The two methods are compared and the optimal one is selected. Start-up and shutdown and air volume adjustment commands are generated and corrected by actual measurement. Finally, the optimal operation scheme library is formed for real-time control.

2. The high-efficiency control method for a multi-core suspended fan according to claim 1, characterized in that: The specific process of setting the process parameter acquisition cycle, having the host computer provide the total air volume requirement and transmit it to the programmable logic controller, and synchronously updating the fixed air volume command for the air volume control cycle is as follows: Multiple process parameter acquisition cycles are set. In each cycle, the host computer provides the total air volume requirement of a single fan and transmits it to the programmable logic controller. For each air volume control cycle, the total air volume requirement obtained in the latest acquisition cycle is used as the current updated fixed air volume instruction, which is updated synchronously with the acquisition cycle.

3. The high-efficiency control method for a multi-core suspended fan according to claim 2, characterized in that: The specific process for measuring the blower outlet pressure as the rated operating pressure of the aeration system is as follows: During each air volume control cycle, the actual air pressure at the outlet of the multi-core suspended blower is measured in real time using a pressure measuring instrument. This measured air pressure value is then directly used as the rated operating pressure of the aeration system in the wastewater treatment plant. Subsequently, this rated operating pressure is established as the benchmark for constant pressure control, thereby locking in the constant pressure operation mode.

4. The high-efficiency control method for a multi-core suspended fan according to claim 3, characterized in that: The specific process of locking the constant pressure operation mode and collecting discrete flow point parameters for each fan core across the entire flow range is as follows: In the locked constant pressure mode, each fan core is individually debugged. Discrete flow point parameters are collected at preset steps within the minimum to maximum operating flow range, including inlet air absolute temperature, inlet and outlet air absolute pressure, actual drive shaft power, and outlet air mass flow rate, and integrated to form a parameter group.

5. The high-efficiency control method for a multi-core suspended fan according to claim 4, characterized in that: The specific process for calculating the adiabatic efficiency at each operating point and constructing flow rate-efficiency data pairs is as follows: For each data acquisition point, the operating parameters in the corresponding discrete flow point parameter group are normalized and dimensionless, and then substituted into the blower adiabatic efficiency calculation logic to obtain the core adiabatic efficiency corresponding to each flow point. The air mass flow rate of the blower core at each data acquisition point is combined with the corresponding core adiabatic efficiency to construct a core flow rate and adiabatic efficiency data pair, and then organized to form the efficiency dataset of the current constant pressure operation mode.

6. The high-efficiency control method for a multi-core suspended fan according to claim 5, characterized in that: The specific process of obtaining the efficiency and flow functions by polynomial fitting using the nonlinear least squares method is as follows: Using each data pair in the core efficiency dataset as the fitting sample, a nonlinear least squares method is used to perform polynomial fitting. The polynomial fitting coefficients are solved by minimizing the sum of squared errors, and the goodness of fit is calculated. Different polynomial degrees are selected in sequence. When the goodness of fit is greater than or equal to the corresponding preset threshold, the current polynomial degree and the corresponding polynomial fitting coefficient are determined, and the efficiency and flow function of the core under the current constant pressure mode are obtained.

7. The high-efficiency control method for a multi-core suspended fan according to claim 6, characterized in that: The specific process of generating efficiency contour curves and forming an efficiency feature matching library is as follows: Based on the current constant pressure mode, a three-dimensional operating condition coordinate system is constructed with the absolute pressure at the fan outlet, the mass flow rate of the air mass at the core outlet, and the overall adiabatic efficiency of the core as the three vertical coordinate axes. Within the effective range of flow rate values, each flow rate value is substituted into the efficiency and flow rate function to calculate the corresponding efficiency, coordinate points are constructed and projected onto the three-dimensional coordinate system, and efficiency contour curves are generated through curve smoothing. All curves under constant pressure mode are organized to form an efficiency feature matching library.

8. The high-efficiency control method for a multi-core suspended fan according to claim 7, characterized in that: The specific process of finding the optimal allocation scheme for air mass flow rate of a multi-core system by traversing all operating conditions using a trial-and-error algorithm is as follows: Enumerate all possible core operation combinations. For each operation combination, use the average allocated air mass flow rate as the initial allocated air mass flow rate. Adjust the allocated air mass flow rate of each core with a preset perturbation step size to keep the total allocated air mass flow rate equal to the total demand. Substitute the efficiency and flow function into each allocation combination to calculate the comprehensive adiabatic efficiency. After traversing all combinations, select the allocation flow rate combination with the largest comprehensive adiabatic efficiency value as the optimal solution of the trial algorithm.

9. A high-efficiency control method for a multi-core suspended fan according to claim 8, characterized in that: The specific process of analytically finding the optimal allocation scheme of air mass flow rate for multi-core processors using the Lagrange multiplier method is as follows: Construct a Lagrange function and introduce Lagrange multipliers and total air mass flow constraints. Calculate the first-order partial derivatives of the air mass flow allocated to each core and the Lagrange multipliers, and set them to zero. Solve based on the optimality condition of zero first-order partial derivatives to obtain the optimal air mass flow allocated to each core and the corresponding comprehensive adiabatic efficiency. This physical meaning is clearly defined as the energy-saving optimal operating point where the system's comprehensive adiabatic efficiency reaches its maximum and the input shaft power is minimized.

10. A high-efficiency control method for a multi-core suspended fan according to claim 9, characterized in that: The process of comparing the two methods, selecting the optimal one, generating start / stop and airflow adjustment commands, performing actual measurement calibration and correction, and finally forming an optimal operation scheme library for real-time control is as follows: The optimal integrated adiabatic efficiency obtained by comparing the trial-and-error method and the Lagrange multiplier method is selected as the final optimal operating scheme, and start-stop and air volume adjustment commands are generated accordingly. If the measured integrated adiabatic efficiency deviates from the theoretical maximum value and exceeds the allowable range, the efficiency, flow function and allocation parameters are calibrated and corrected. The correction schemes under various constant pressure modes are integrated to form an optimal operating scheme library for real-time control.

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