Energy-saving control method for fan cooling system in galvanized steel sheet production process

Abstract

This invention relates to the field of energy-saving control technology in galvanized steel sheet production, specifically to an energy-saving control method for a fan cooling system during galvanized steel sheet production. The method includes the following steps: pre-recording basic process parameters of the production line and deploying various sensors to collect real-time data on production, equipment, environment, and production line operation, outputting multi-dimensional data; pre-setting a load identification model, dividing the production into four types of operating conditions based on the multi-dimensional data, and calculating the real-time cooling load and target heat dissipation power under each condition; using minimum total energy consumption as the optimization core, and combining multiple process and equipment constraints, iteratively solving for the optimal matching control parameters; employing hierarchical collaborative logic to dynamically adjust the cooling airflow, and iteratively optimizing based on multi-dimensional data; real-time monitoring of the production line status, predicting changes in cooling load, and making pre-adjustments; through the above methods, adapting to all production conditions of galvanized steel sheets and achieving multi-parameter collaborative control, thereby adapting to the changing production needs of the production line.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology in galvanized steel sheet production, and in particular to an energy-saving control method for a fan cooling system during the production of galvanized steel sheets. Background Technology

[0002] In the production process of hot-dip galvanized and electro-galvanized steel sheets, after the steel sheets are galvanized and cured at high temperatures in the galvanizing furnace, the surface temperature can reach 200~350℃. It is necessary to use a cooling fan unit to force the cooling down to reduce the temperature of the steel sheet to the required 45~65℃ before the coiling and transfer process can be completed. The cooling effect directly determines the surface flatness of the galvanized steel sheet, the adhesion of the galvanized layer, and the product qualification rate.

[0003] The production process of galvanizing production lines is subject to significant fluctuations in operating conditions. The heat dissipation load of steel plates varies greatly at different stages, such as start-up preheating, steady-state production, speed reduction standby, and shutdown finishing. At the same time, the cooling air volume required for galvanized steel plates of different thicknesses and widths also varies significantly. However, most galvanized steel plate production lines currently use traditional industrial frequency constant speed operation or fixed gear control mode for their fan cooling systems. The entire cooling fan unit operates at full load or fixed gear all year round, which cannot adapt to the changing operating conditions of the production line. Summary of the Invention

[0004] The purpose of this invention is to provide an energy-saving control method for the fan cooling system in the production process of galvanized steel sheets. This method aims to solve the technical problem that the fan cooling systems of existing galvanized steel sheet production lines all adopt traditional constant speed operation or fixed gear control mode, and the entire cooling fan unit operates at full load or fixed gear all year round, which cannot adapt to the changing production requirements of the production line.

[0005] To achieve the above objectives, the present invention provides an energy-saving control method for a fan cooling system during the production of galvanized steel sheets, comprising the following steps: Pre-record basic process parameters of the production line and deploy multiple types of sensors to collect production, equipment, environment, and production line operation data in real time, and output multi-dimensional data; A pre-defined load identification model is used to classify four types of production conditions based on multi-dimensional data, and to calculate the real-time cooling load and target heat dissipation power under each condition. The four types of production conditions include heating and preheating conditions, steady-state production conditions, speed reduction and standby conditions, and shutdown buffer conditions. With minimizing total energy consumption as the core optimization, and combining multiple process and equipment constraints, the optimal matching control parameters for fan grouping, operating frequency, and damper opening are iteratively solved. It adopts a hierarchical collaborative logic of rapid damper fine-tuning, variable frequency fan fine-tuning, and fan group group optimization to dynamically control the cooling air volume, and relies on multi-dimensional data for iterative optimization; Real-time monitoring of production line start-up, shutdown, speed increase, speed decrease, and specification change; prediction of cooling load changes; and pre-adjustment of fan frequency, damper opening, and fan grouping.

[0006] Among the steps, the following steps are involved: pre-recording basic process parameters of the production line, deploying various types of sensors to collect real-time data on production, equipment, environment, and production line operation, and outputting multi-dimensional data: Enter the basic process parameters of the galvanized steel sheet production line to complete the initial parameter configuration; the basic process parameters include steel sheet specification parameters, production process parameters, and temperature control thresholds. Temperature sensors, wind speed sensors, equipment load acquisition modules, and environmental monitoring modules are installed in the cooling section of the galvanizing production line to build a comprehensive data acquisition hardware system. During the operation of the production line, real-time data on steel plate temperature, air duct temperature, fan operating load, ambient temperature and humidity, and production line feed rate are captured, summarized and integrated to form multi-dimensional data, and then output.

[0007] The process involves using a pre-defined load identification model to classify four production conditions based on multi-dimensional data, and calculating the real-time cooling load and target heat dissipation power for each condition. These four production conditions include: preheating, steady-state production, speed-reduction standby, and shutdown buffering. Build and pre-set a cooling load identification model adapted to the galvanizing production scenario, and bind the judgment criteria and load calculation logic corresponding to various working conditions; Based on multi-dimensional data and judgment criteria, the production line operation status is divided into four types of working conditions: heating and preheating, steady-state production, speed reduction and standby, and shutdown buffer. For each of the various production conditions that have been defined, the corresponding heat dissipation logic is matched based on the cooling load identification model to calculate the current real-time cooling load requirements of the production line.

[0008] After calculating the real-time cooling load requirements of the production line based on the cooling load identification model and matching the corresponding heat dissipation logic for the various production conditions that have been defined: Based on the target heat dissipation power range matched to the real-time cooling load, the basic heat dissipation control standards of the cooling system under each operating condition are defined.

[0009] Among them, in the step of iteratively solving for the optimal matching control parameters of fan grouping, operating frequency, and damper opening, with the minimum total energy consumption as the core optimization and in combination with multiple process and equipment constraints: The core optimization objective of minimizing the overall energy consumption of the cooling system is used as the optimization benchmark for all parameter adjustments. In conjunction with steel plate cooling process standards, wind speed uniformity requirements, fan rated operating range, and damper adjustment limits, operating constraint rules are set. Within the constraints, for the current real-time cooling load, a multi-dimensional parameter iterative comparison is performed on the fan operation combination mode, the operating frequency of a single fan, and the opening and closing degree of the cooling damper.

[0010] Within the constraints, after performing multi-dimensional parameter iterative comparisons of the fan operation combination, single fan operating frequency, and cooling damper opening degree for the current real-time cooling load: Based on the comparison results, the parameter combination that is suitable for the current working conditions, meets the process requirements, and has the lowest energy consumption is selected, and the optimal matching control parameters for fan grouping, operating frequency, and damper opening are output.

[0011] Among them, the following steps are involved: employing a hierarchical collaborative logic that combines rapid damper fine-tuning, variable frequency fan tuning, and optimized fan group grouping to dynamically control cooling airflow and iteratively optimize based on multi-dimensional data: A hierarchical collaborative control logic is constructed, and the damper fine-tuning is set as a rapid response level. For small fluctuations in steel plate temperature, the cooling air volume is quickly adapted by adjusting the damper opening. Set the fan frequency converter as the control level. When the damper opening is adjusted to the upper or lower limit threshold, the fan operating frequency is finely adjusted. To optimize the control level, the number of operating fans is dynamically increased or decreased based on the fan operating status and real-time cooling load, thereby optimizing the overall fan group grouping mode.

[0012] Among them, after setting the fan group grouping as an optimized control level, dynamically increasing or decreasing the number of operating fans according to the fan operating status and real-time cooling load, and optimizing the overall fan group grouping mode: The hierarchical regulation logic and control parameters are dynamically and iteratively optimized based on multi-dimensional data.

[0013] Among these steps, the real-time monitoring of production line start-up, shutdown, speed increase, speed decrease, and specification change, the prediction of cooling load changes, and the pre-adjustment of fan frequency, damper opening, and fan grouping are as follows: Real-time monitoring of production line operation status switching signals; among which, the switching scenarios of operation status switching signals include equipment start-up and shutdown, production rate increase / decrease, and steel plate specification change; Based on the switching scenarios of operating conditions and combined with the relationship between historical operating data and operating load, the trend of increase or decrease in cooling load can be predicted.

[0014] Among them, after the step of predicting the trend of cooling load increase or decrease based on the switching of operating conditions and the correspondence between historical operating data and operating load: Based on the predicted load change trend, adjust the fan operating frequency and cooling damper opening, simultaneously optimize the fan group operation formation, and pre-adjust cooling parameters during the switching of operating conditions.

[0015] This invention discloses an energy-saving control method for a fan cooling system during the production of galvanized steel sheets. The method involves pre-recording basic process parameters of the production line and deploying various sensors to collect real-time data on production, equipment, environment, and production line operation, outputting multi-dimensional data. A pre-set load identification model is used to classify four production conditions based on the multi-dimensional data, and the real-time cooling load and target heat dissipation power under each condition are calculated. The four production conditions include preheating, steady-state production, speed-reduction standby, and shutdown buffer conditions. The method optimizes for minimum total energy consumption, combining multiple process and design parameters. Given constraints, the optimal matching control parameters for fan grouping, operating frequency, and damper opening are iteratively solved. A hierarchical collaborative logic of rapid damper fine-tuning, variable frequency fan tuning, and fan group group optimization is adopted to dynamically regulate the cooling air volume, and iterative optimization is performed based on multi-dimensional data. The start-up, shutdown, speed increase, speed decrease, and specification change of the production line are monitored in real time to predict changes in cooling load and pre-adjust the fan frequency, damper opening, and fan grouping. Through the above methods, multi-parameter collaborative control is achieved to adapt to all production conditions of galvanized steel sheets, thereby adapting to the production needs of the production line under changing conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the steps of the energy-saving control method for the fan cooling system in the galvanized steel sheet production process of the present invention.

[0018] Figure 2 This is a flowchart of steps S100 of the present invention.

[0019] Figure 3 This is a flowchart of steps S200 of the present invention.

[0020] Figure 4 This is a flowchart of steps S300 of the present invention.

[0021] Figure 5 This is a flowchart of steps S400 of the present invention.

[0022] Figure 6 This is a flowchart of steps S500 of the present invention.

[0023] Figure 7 This is a schematic diagram of the energy-saving control system of the fan cooling system in the galvanized steel sheet production process of the present invention.

[0024] Figure 8 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0026] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0028] Please see Figures 1-6 This invention provides an energy-saving control method for a fan cooling system during the production of galvanized steel sheets, comprising the following steps: S100: Pre-records basic process parameters of the production line and deploys multiple types of sensors to collect real-time data on production, equipment, environment, and production line operation, outputting multi-dimensional data; the specific process is as follows: S101: Enter the basic process parameters of the galvanized steel sheet production line and complete the initial parameter configuration; among which, the basic process parameters include steel sheet specification parameters, production process parameters and temperature control thresholds; S102: Install temperature sensors, wind speed sensors, equipment load acquisition modules, and environmental monitoring modules in the cooling section of the galvanizing production line to build a comprehensive data acquisition hardware system. S103: During the operation of the production line, real-time data of steel plate temperature, air duct temperature, fan operating load, ambient temperature and humidity, and production line feed rate are captured, summarized and integrated to form multi-dimensional data, and output.

[0029] In the above process, the inherent fixed parameters of the galvanized steel sheet production line are manually categorized and entered into the storage database of the main control system. The parameters are entered and verified in a structured manner according to three categories: The first category is steel plate specifications, including the thickness range, width range, and standard for single-sided and double-sided galvanized coating of steel plates produced by the production line. The second category is production process parameters, including the rated steady-state operating speed of the production line, the curing temperature range of the galvanizing furnace, and the standard air supply conditions of the cooling section. The third category is temperature control threshold parameters, which include the maximum allowable temperature of the steel plate entering the cooling section, the upper limit of the qualified temperature for cooling discharge, the lower limit of the qualified temperature for cooling discharge, and the allowable temperature fluctuation range of the process.

[0030] All entered parameters underwent a second manual verification and system validation to eliminate abnormal values. Valid parameters were then solidified as permanent benchmark constants, serving as a unified process standard for subsequent data comparison, operating condition determination, and parameter optimization.

[0031] To address the high-temperature, high-dust, and continuous operation characteristics of the galvanizing production line, standardized measurement points were deployed upstream and downstream of the cooling section, at the inlet and outlet of the air duct, at the fan equipment end, and at the production environment end. Infrared temperature sensors were installed at the galvanizing furnace outlet and the cooling section inlet to collect real-time surface temperatures of the high-temperature steel plates. Wind speed and temperature sensors were installed at the air inlet and outlet of the cooling air duct to collect ventilation status parameters. Each cooling fan was independently equipped with an operating load acquisition module to monitor the fan's operating current and workload status in real time. Environmental temperature and humidity monitoring modules were deployed in the cooling section production area to collect on-site environmental parameters. All sensing devices underwent unified wiring, address coding, communication protocol matching, and PLC communication binding. Single-point calibration and overall linkage debugging were completed to eliminate signal delay, signal interference, and data disconnection issues, constructing a comprehensive, highly synchronized, and highly reliable data acquisition hardware system.

[0032] The system continuously acquires raw analog signals from all sensors at a fixed high-frequency sampling period. First, the raw signals undergo data preprocessing and cleaning: a sliding mean filter is used to remove abnormal fluctuations caused by electromagnetic interference, dust obstruction, and momentary equipment vibration, and invalid values ​​exceeding limits are deleted. The sampling frequencies and data dimensions of different sensor types are standardized and converted to uniformly represent actual engineering values. Subsequently, multi-source data undergoes time synchronization and alignment to ensure that data on steel plate temperature, air temperature and velocity, fan load, environmental parameters, and production line operating speed are synchronized at the same time. Finally, all processed valid data is merged and packaged to form a standardized multi-dimensional real-time dataset containing production parameters, equipment parameters, and environmental parameters, which is continuously output to the downstream condition identification and optimization control module in real time.

[0033] S200: A preset load identification model is used to classify four production conditions based on multi-dimensional data, and to calculate the real-time cooling load and target heat dissipation power under each condition. The four production conditions include preheating, steady-state production, speed-reduction standby, and shutdown buffering. The specific process is as follows: S201: Build and pre-set a cooling load identification model adapted to the galvanizing production scenario, and bind the judgment criteria and load calculation logic corresponding to various working conditions; S202: Based on multi-dimensional data and judgment criteria, the production line operation status is divided into four types of working conditions: heating and preheating, steady-state production, speed reduction and standby, and shutdown buffer. S203: For the various production conditions that have been divided, the corresponding heat dissipation logic is matched based on the cooling load identification model to calculate the current real-time cooling load requirements of the production line. S204: Based on the real-time cooling load, match the corresponding target heat dissipation power range and divide the basic heat dissipation control standards of the cooling system under each operating condition.

[0034] In the aforementioned process, a dedicated cooling load identification model for galvanizing production scenarios was built from scratch, trained, calibrated for thresholds, and its program solidified. This process relied entirely on real-world production line operation patterns, without any external algorithm dependencies. First, long-term historical operational big data from the production line was collected, including batches of historical cooling operation data, fan energy consumption data, and qualified cooling data for different steel plate specifications, production speeds, ambient temperatures and humidity levels, and inlet plate temperatures, forming a model training sample library. Second, the sample data was tagged and categorized manually according to production stage into four sample sets: preheating samples, steady-state production samples, speed-reducing standby samples, and shutdown buffer samples. Temperature variation patterns, production line speed correlation patterns, and heat dissipation energy consumption variation characteristics were extracted for each type of operating condition. Third, by statistically analyzing the correlation between multi-dimensional parameters and cooling demand under various operating conditions, a threshold system for determining operating conditions and corresponding load rules were established. Based on the sample data, thresholds for start-up duration, temperature fluctuation, production line speed reduction ratio, and material feeding status were determined for the four types of operating conditions. Simultaneously, cooling load calculation rules corresponding to the coupling of multiple parameters under different operating conditions were obtained through fitting. Finally, the operating condition judgment logic, threshold standards, and load association rules are compiled and solidified into the control program to complete the construction of the cooling load identification model. The model can complete the operating condition identification and load quantification calculation based on real-time input data.

[0035] The standardized multi-dimensional dataset output from step S100 is retrieved in real time. The real-time data is logically compared against various operating condition thresholds embedded in the load identification model: the cumulative running time of the production line is used to determine if it is in the preheating stage; the real-time temperature fluctuation of the steel plate and the stability of the production line's operating speed are used to determine if it is a steady-state production condition; the decrease in the real-time feed speed of the production line relative to the steady-state rated speed is used to determine if it is a speed-reducing standby condition; and whether the production line has stopped feeding steel plates or only left residual material is used to determine if it is a shutdown buffer condition. A unique matching principle is adopted, determining and outputting only one operating condition type at any given time, and labeling the current data frame with the corresponding operating condition category tag, achieving real-time, automatic, and accurate classification of production operating conditions.

[0036] Based on the identified operating condition type, the load identification and calculation logic specific to that condition is invoked, and multi-dimensional parameters such as real-time steel plate thickness, steel plate width, production line operating speed, steel plate inlet high temperature, and ambient temperature and humidity are synchronously input into the model. The model combines the parameter coupling relationships obtained from fitting historical samples, comprehensively considering the combined effects of steel plate heat capacity, strip conveying volume, environmental heat dissipation conditions, and duct heat dissipation efficiency, to quantify and calculate the real-time heat dissipation demand required for the entire cooling section, completing the accurate calculation of the real-time cooling load and outputting the precise cooling load value corresponding to the current operating condition.

[0037] A standardized target heat dissipation power range lookup table corresponding to different cooling loads is pre-stored. After obtaining the real-time cooling load value, the corresponding upper and lower limit ranges of the target heat dissipation power are matched. The lower limit of the range is the minimum heat dissipation power to ensure qualified cooling of the steel plate, and the upper limit of the range is the maximum reasonable heat dissipation power to meet the process margin, preventing insufficient or excessive heat dissipation. Finally, this power range is defined as the forced heat dissipation control standard of the cooling system under the current operating conditions, providing constraint targets and optimization boundaries for the subsequent S300 parameter optimization step.

[0038] S300: With minimizing total energy consumption as the core optimization principle, and considering multiple process and equipment constraints, the optimal matching control parameters for fan grouping, operating frequency, and damper opening are iteratively solved; the specific process is as follows: S301: The core optimization objective of minimizing the overall energy consumption of the cooling system is used as the optimization benchmark for all parameter adjustments. Combined with steel plate cooling process standards, wind speed uniformity requirements, fan rated operating range, and damper adjustment limits, operating constraint rules are set. S302: Within the constraints, for the current real-time cooling load, perform multi-dimensional parameter iterative comparison of fan operation combination mode, single fan operation frequency, and cooling damper opening degree; S303: Based on the comparison results, select the parameter combination that is suitable for the current working conditions, meets the process requirements, and has the lowest energy consumption, and output the optimal matching control parameters for fan grouping, operating frequency, and damper opening.

[0039] In the above process, standardized modeling of the optimization benchmark and all hard constraints was completed. The sole global optimization objective was established as "minimum total real-time energy consumption of all operating fans in the cooling system," and all parameter adjustments converged around this objective. Simultaneously, a multi-layered rigid constraint system was established and solidified as screening rules: process constraints included ensuring the final cooling temperature of the steel plate fell within the acceptable range and that the uniformity of the lateral wind speed in the cooling section met the allowable process deviation; equipment constraints included ensuring the operating frequency of a single fan was within the equipment's safe rated range, the opening of the electric damper was within the mechanically adjustable limit, and the fan's operating load did not exceed the rated safe load. All constraints were set as insurmountable hard thresholds; any parameter combination that violated the constraints was directly judged as an invalid solution, ensuring that production process and equipment safety were never sacrificed while guaranteeing energy-saving optimization.

[0040] Within all constraints, and considering the current real-time cooling load requirements, a comprehensive enumeration and traversal of three core control parameters is performed: First, all feasible fan start-stop combinations are traversed, and effective operating configurations for single, dual, and multiple fans are listed one by one, eliminating configurations that exceed load requirements or cause equipment overload. Second, for each feasible fan group, different operating frequency levels of the fans are continuously traversed, covering the entire available range from low frequency to rated high frequency. Third, corresponding to the operating status of each fan group, all feasible opening and closing angles of the dampers are traversed segment by segment. Simulation operation verification is performed on each complete parameter combination of "fan group + fan frequency + damper opening," verifying whether the heat dissipation capacity under this combination matches the current cooling load, meets temperature and wind speed process requirements, and complies with equipment safety operation specifications, eliminating non-compliant, invalid, and substandard schemes one by one. Among all remaining valid and compliant solutions, the parameter ratios were repeatedly rotated and iterated multiple times. The overall energy consumption levels of different parameter combinations were compared horizontally to continuously narrow down the screening range and lock in the optimal parameter candidate range.

[0041] Among all the compliant parameter schemes after iterative screening, the total energy consumption of each group of schemes was compared, and the optimal parameter combination with the lowest total energy consumption and the heat dissipation capacity fully covering the target heat dissipation power range was selected. After confirming that this parameter combination can stably ensure the cooling quality of the steel plate, the uniformity of airflow, and the safe operation of the equipment, the optimal fan grouping method, the operating frequency of a single fan, and the cooling damper opening parameters corresponding to the current operating conditions were locked. The optimal control parameters were then uniformly output and sent to the lower-level machine actuator to complete the single-cycle parameter optimization solution.

[0042] S400: Employs a layered collaborative logic of rapid damper fine-tuning, variable frequency fan tuning, and optimized fan group grouping to dynamically regulate cooling airflow, and performs iterative optimization based on multi-dimensional data; the specific process is as follows: S401: Construct a hierarchical collaborative control logic, set the damper fine-tuning as a rapid response level, and prioritize the rapid adaptation of cooling air volume by adjusting the damper opening for small fluctuations in steel plate temperature. S402: Set the fan frequency converter to the control level. When the damper opening is adjusted to the upper or lower limit threshold and the heat dissipation demand cannot be met, the fan operating frequency is finely adjusted. S403: Set the fan group grouping to an optimized control level, dynamically increase or decrease the number of operating fans based on the fan operating status and real-time cooling load, and optimize the overall fan group grouping mode. S404: Dynamically iteratively optimize the hierarchical control logic and control parameters based on multi-dimensional data.

[0043] During the above process, the temperature fluctuation of the steel plate after cooling is monitored in real time. For small, low-frequency temperature deviations that occur during production, the damper priority adjustment mechanism is activated. Without changing the fan operation status, the damper opening fine-tuning command is precisely output according to the direction and amount of temperature deviation. By changing the effective ventilation cross-sectional area of ​​the air duct, the real-time ventilation volume is quickly adjusted. Utilizing the advantages of fast damper response, high adjustment accuracy, and no energy consumption fluctuations, slight cooling fluctuations are quickly smoothed out, ensuring stable cooling temperature and achieving rapid, low-energy control under small deviation conditions.

[0044] The system monitors the actual operating opening of the damper in real time. If adjusting the damper opening to the upper mechanical limit still fails to meet the heat dissipation requirements, or if adjusting it to the lower mechanical limit still results in overcooling, the current damper opening is immediately locked, and damper adjustment is stopped. Based on the current cooling load shortfall or excess, the system accurately calculates the fan frequency adjustment step size through adaptive adjustment logic, finely adjusting the frequency of the operating fan by increasing or decreasing it. By changing the fan speed, the system precisely matches the airflow demand, achieving refined airflow control under moderate load fluctuations and compensating for the limitations of a single damper adjustment range.

[0045] Continuously monitor the real-time operating frequency and load rate of each online fan to determine the overall operating condition of the fan group: when multiple fans operate at high frequency and full load for extended periods, resulting in insufficient heat dissipation, activate standby fans to redistribute the operating load of each fan and reduce the load pressure on individual units; when the entire fan group operates at low frequency, light load, and inefficiently for extended periods, shut down redundant fans and retain a suitable number of fans for efficient operation to avoid ineffective energy consumption caused by multiple units operating at light loads. By dynamically increasing or decreasing the number of operating fans, continuously optimize the fan group's grouping structure to maximize the overall operating efficiency of the fan group.

[0046] Data on operating conditions, adjustment parameters, cooling effect, and energy consumption corresponding to each control action are collected to form a control effect database. Through continuously accumulated multi-dimensional operational data, the key thresholds and adjustment step sizes of the hierarchical control logic are iteratively optimized: dynamically correcting the high / low limit switching threshold of the damper, the fan frequency adjustment gradient, the fan start / stop judgment conditions, and the load matching coefficient. The control rules are continuously adaptively corrected, enabling the hierarchical collaborative control logic to constantly adapt to dynamic variables such as equipment aging, seasonal environmental changes, and production line process adjustments, maintaining optimal control accuracy and energy-saving effects in the long term.

[0047] S500: Real-time monitoring of production line start-up, shutdown, speed increase, speed decrease, and specification change; predicting changes in cooling load; and pre-adjusting fan frequency, damper opening, and fan grouping; the specific process is as follows: S501: Real-time monitoring of production line operation status switching signals; among which, the switching scenarios of operation status switching signals include equipment start-up and shutdown, production rate increase / decrease, and steel plate specification change; S502: Based on the operating condition switching scenario and combined with the relationship between historical operating data and operating load, predict the trend of cooling load increase or decrease; S503: Based on the predicted load change trend, adjust the fan operating frequency and cooling damper opening, simultaneously optimize the fan group operation group, and pre-adjust cooling parameters during the switching of operating conditions.

[0048] During the above process, the system continuously monitors production line operation status switching events by connecting the main control signals of the production line with the equipment operation sensor signals in real time. These events include five switching scenarios: production line start-up, production line shutdown, production speed increase, production speed decrease, and production of steel plates of different specifications being changed into coils. The system identifies, marks, and latches various switching signals in real time, accurately capturing the start time of the operating condition switch and providing a trigger reference signal for load prediction.

[0049] A full-condition historical load database is set up to store complete load change curves and parameter correspondences under various operating condition switching scenarios. When an operating condition switching event is identified, the load evolution pattern of similar historical switching scenarios is matched, and combined with the real-time load base of the current operating condition before the switch, the load rise and fall amplitude and change rate of historical data are compared to comprehensively predict the increase and decrease trend and magnitude of cooling load in a short period of time after this operating condition switch, so as to obtain the characteristics of cooling demand changes in the next stage in advance.

[0050] Based on the predicted load change trends, proactive pre-regulation is implemented before the actual changes in production line operating conditions take effect: for scenarios with increased load, such as speed increases or replacement of thicker plates, the operating frequency of fans is increased in advance, cooling dampers are opened wider, and standby fans are put into operation; for scenarios with decreased load, such as speed reduction, standby, or shutdown, the fan frequency is reduced in advance, dampers are closed smaller, and redundant operating fans are removed. Through pre-regulation before operating condition switching, the problem of airflow lag in traditional control methods is completely solved, achieving seamless dynamic matching between cooling airflow and production load, and avoiding problems such as uneven cooling, product quality fluctuations, and sudden surges in energy consumption during operating condition switching.

[0051] Corresponding to the aforementioned embodiments of the energy-saving control method for the fan cooling system in the galvanized steel sheet production process, this application also provides embodiments of the energy-saving control system for the fan cooling system in the galvanized steel sheet production process.

[0052] Figure 7 This is a structural schematic diagram of an energy-saving control system for a fan cooling system in the production process of galvanized steel sheet, according to an exemplary embodiment. (Refer to...) Figure 7 The system may include: a real-time data acquisition module, a cooling load calculation module, a multi-parameter collaborative optimization module, a hierarchical collaborative energy-saving control module, and a working condition switching adjustment module; wherein: The real-time data acquisition module is used to pre-record basic process parameters of the production line, and deploy multiple types of sensors to collect production, equipment, environment, and production line operation data in real time, and output multi-dimensional data. The cooling load calculation module is used to preset a load identification model, divide four types of production conditions according to multi-dimensional data, and calculate the real-time cooling load and target heat dissipation power under each condition; among which, the four types of production conditions include heating and preheating condition, steady-state production condition, speed reduction standby condition, and shutdown buffer condition. The multi-parameter collaborative optimization module is used to iteratively solve the optimal matching control parameters of fan grouping, operating frequency, and damper opening, taking the minimum total energy consumption as the core of optimization and combining multiple process and equipment constraints. The hierarchical collaborative energy-saving control module is used to dynamically control the cooling air volume by adopting hierarchical collaborative logic of rapid damper fine adjustment, fan frequency conversion fine adjustment, and fan group group optimization, and to perform iterative optimization based on multi-dimensional data. The operating condition switching and adjustment module is used to monitor the start-up, shutdown, speed increase, speed decrease, and specification change of the production line in real time, predict changes in cooling load, and pre-adjust the fan frequency, damper opening, and fan grouping.

[0053] In this embodiment, the real-time data acquisition module pre-records basic process parameters of the production line and deploys multiple types of sensors to collect real-time data on production, equipment, environment, and production line operation, outputting multi-dimensional data. The cooling load calculation module presets a load identification model, classifies four production conditions based on the multi-dimensional data, and calculates the real-time cooling load and target heat dissipation power under each condition. The four production conditions include preheating, steady-state production, speed-reduction standby, and shutdown buffer conditions. The multi-parameter collaborative optimization module uses minimum total energy consumption as its core optimization principle, combining multiple process and equipment constraints. The optimal matching control parameters for fan grouping, operating frequency, and damper opening are obtained through iterative solutions. The hierarchical collaborative energy-saving control module adopts a hierarchical collaborative logic of rapid damper fine-tuning, fan frequency conversion fine-tuning, and fan group group optimization to dynamically control the cooling air volume and perform iterative optimization based on multi-dimensional data. The operating condition switching adjustment module monitors the start-up, shutdown, speed increase, speed decrease, and specification change of the production line in real time, predicts changes in cooling load, and pre-adjusts the fan frequency, damper opening, and fan grouping. Through the above methods, it is possible to achieve multi-parameter collaborative control that adapts to all production conditions of galvanized steel sheets, thereby adapting to the production needs of the production line under changing operating conditions.

[0054] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0055] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0056] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the energy-saving control method for the fan cooling system in the galvanized steel sheet production process as described above. Figure 8 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which is part of an energy-saving control system for a fan cooling system in the production process of galvanized steel sheets, according to an embodiment of the present invention. (Except for...) Figure 8In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0057] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the energy-saving control method for the fan cooling system in the galvanized steel sheet production process described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0058] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0059] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An energy-saving control method for a fan cooling system during the production of galvanized steel sheets, characterized in that, Includes the following steps: Pre-record basic process parameters of the production line and deploy multiple types of sensors to collect production, equipment, environment, and production line operation data in real time, and output multi-dimensional data; A pre-defined load identification model is used to classify four types of production conditions based on multi-dimensional data, and to calculate the real-time cooling load and target heat dissipation power under each condition. The four types of production conditions include heating and preheating conditions, steady-state production conditions, speed reduction and standby conditions, and shutdown buffer conditions. With minimizing total energy consumption as the core optimization, and combining multiple process and equipment constraints, the optimal matching control parameters for fan grouping, operating frequency, and damper opening are iteratively solved. It adopts a hierarchical collaborative logic of rapid damper fine-tuning, variable frequency fan fine-tuning, and fan group group optimization to dynamically control the cooling air volume, and relies on multi-dimensional data for iterative optimization; Real-time monitoring of production line start-up, shutdown, speed increase, speed decrease, and specification change; prediction of cooling load changes; and pre-adjustment of fan frequency, damper opening, and fan grouping.

2. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 1, characterized in that, In the process of pre-recording basic process parameters of the production line, deploying various types of sensors to collect real-time data on production, equipment, environment, and production line operation, and outputting multi-dimensional data: Enter the basic process parameters of the galvanized steel sheet production line to complete the initial parameter configuration; the basic process parameters include steel sheet specification parameters, production process parameters, and temperature control thresholds. Temperature sensors, wind speed sensors, equipment load acquisition modules, and environmental monitoring modules are installed in the cooling section of the galvanizing production line to build a comprehensive data acquisition hardware system. During the operation of the production line, real-time data on steel plate temperature, air duct temperature, fan operating load, ambient temperature and humidity, and production line feed rate are captured, summarized and integrated to form multi-dimensional data, and then output.

3. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 1, characterized in that, Based on a pre-defined load identification model, four production conditions are classified according to multi-dimensional data, and the real-time cooling load and target heat dissipation power under each condition are calculated. The four production conditions include preheating, steady-state production, speed-reducing standby, and shutdown buffering. Build and pre-set a cooling load identification model adapted to the galvanizing production scenario, and bind the judgment criteria and load calculation logic corresponding to various working conditions; Based on multi-dimensional data and judgment criteria, the production line operation status is divided into four types of working conditions: heating and preheating, steady-state production, speed reduction and standby, and shutdown buffer. For each of the various production conditions that have been defined, the corresponding heat dissipation logic is matched based on the cooling load identification model to calculate the current real-time cooling load requirements of the production line.

4. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 3, characterized in that, After calculating the real-time cooling load requirements of the production line based on the cooling load identification model and matching the corresponding heat dissipation logic for the various production conditions that have been defined: Based on the target heat dissipation power range matched to the real-time cooling load, the basic heat dissipation control standards of the cooling system under each operating condition are defined.

5. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 1, characterized in that, In the process of iteratively solving for the optimal matching control parameters of fan grouping, operating frequency, and damper opening, with the minimum total energy consumption as the core optimization and in combination with multiple process and equipment constraints: The core optimization objective of minimizing the overall energy consumption of the cooling system is used as the optimization benchmark for all parameter adjustments. In conjunction with steel plate cooling process standards, wind speed uniformity requirements, fan rated operating range, and damper adjustment limits, operating constraint rules are set. Within the constraints, for the current real-time cooling load, a multi-dimensional parameter iterative comparison is performed on the fan operation combination mode, the operating frequency of a single fan, and the opening and closing degree of the cooling damper.

6. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 5, characterized in that, Within the constraints, after performing multi-dimensional parameter iterative comparisons of the fan operation combination, single fan operating frequency, and cooling damper opening degree for the current real-time cooling load: Based on the comparison results, the parameter combination that is suitable for the current working conditions, meets the process requirements, and has the lowest energy consumption is selected, and the optimal matching control parameters for fan grouping, operating frequency, and damper opening are output.

7. The energy-saving control method for the fan cooling system during the galvanized steel sheet production process as described in claim 1, characterized in that, The process involves a hierarchical collaborative logic that employs rapid damper fine-tuning, variable frequency fan tuning, and optimized fan group grouping to dynamically control cooling airflow and iteratively optimize based on multi-dimensional data: A hierarchical collaborative control logic is constructed, and the damper fine-tuning is set as a rapid response level. For small fluctuations in steel plate temperature, the cooling air volume is quickly adapted by adjusting the damper opening. Set the fan frequency converter as the control level. When the damper opening is adjusted to the upper or lower limit threshold, the fan operating frequency is finely adjusted. To optimize the control level, the number of operating fans is dynamically increased or decreased based on the fan operating status and real-time cooling load, thereby optimizing the overall fan group grouping mode.

8. The energy-saving control method for the fan cooling system in the galvanized steel sheet production process as described in claim 7, characterized in that, After setting up the fan group grouping to optimize the control level, dynamically increasing or decreasing the number of operating fans based on the fan operating status and real-time cooling load, and optimizing the overall fan group grouping mode: The hierarchical regulation logic and control parameters are dynamically and iteratively optimized based on multi-dimensional data.

9. The energy-saving control method for the fan cooling system in the galvanized steel sheet production process as described in claim 1, characterized in that, In the process of real-time monitoring of production line start-up, shutdown, speed increase, speed decrease, and specification change, predicting changes in cooling load, and pre-adjusting fan frequency, damper opening, and fan grouping: Real-time monitoring of production line operation status switching signals; among which, the switching scenarios of operation status switching signals include equipment start-up and shutdown, production rate increase / decrease, and steel plate specification change; Based on the switching scenarios of operating conditions and combined with the relationship between historical operating data and operating load, the trend of increase or decrease in cooling load can be predicted.

10. The energy-saving control method for the fan cooling system in the galvanized steel sheet production process as described in claim 9, characterized in that, After predicting the trend of cooling load increase or decrease based on the switching scenarios and the relationship between historical operating data and operating load: Based on the predicted load change trend, adjust the fan operating frequency and cooling damper opening, simultaneously optimize the fan group operation formation, and pre-adjust cooling parameters during the switching of operating conditions.

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