An Adaptive Backwashing Control Method for Large-Scale Filter Stations Based on PLC

Abstract

This invention discloses a PLC-based adaptive backwashing control method for large-scale filter stations, belonging to the field of industrial automation control technology. The method includes real-time acquisition of operating data from each filter unit; performing moving average filtering on the operating data to obtain preprocessed data; constructing a normalized clogging index calculation model for each filter unit based on the preprocessed data; comparing the clogging index of each filter unit with a dynamic trigger threshold; when the clogging index exceeds the dynamic trigger threshold, adding the corresponding filter unit to the backwashing queue; and performing backwashing operations on the filter units in the backwashing queue. This invention achieves accurate judgment of backwashing timing, balanced scheduling of hydraulic load, and refined management of energy consumption, significantly improving the operational stability and efficiency of the filter station.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to an adaptive backwashing control method for large-scale filter stations based on PLC. Background Technology

[0002] In modern water treatment engineering, large-scale filtration stations serve as core facilities for ensuring the quality of water supply, undertaking the crucial functions of removing suspended solids and reducing turbidity. A filtration station consists of multiple parallel filter units, each trapping impurities in the water through a filter media layer. However, as the filtration process continues, pollutants gradually accumulate in the filter media layer, leading to problems such as increased filtration resistance, decreased water production, and fluctuations in effluent turbidity. To restore filtration capacity, periodic backwashing operations must be performed on the filter units.

[0003] Currently, timed backwashing mechanisms are triggered only at fixed time intervals, completely ignoring the dynamic changes in influent water quality. This leads to excessive water consumption under low turbidity conditions and an inability to respond promptly in high turbidity abrupt changes, significantly increasing the risk of effluent exceeding standards. While fixed differential pressure triggering introduces process parameters, it relies solely on a single differential pressure indicator for decision-making, failing to organically integrate multi-dimensional information such as the filter's internal contaminant interception capacity, real-time effluent water quality, and historical cumulative filtration effects. This makes it difficult for the system to accurately assess the filter's true state under complex influent conditions. Manual intervention is limited by the operator's experience and real-time monitoring capabilities, resulting in delayed responses and significant judgment biases, making it difficult to meet the refined management needs of large-scale filtration stations. Current filtration stations only activate when parameters reach fixed thresholds, lacking the ability to quantitatively predict clogging trends. When multiple filter units simultaneously approach critical states due to the same influent load, concentrated backwashing triggers will cause a precipitous drop in the entire station's water supply capacity, seriously threatening the stability of the pipeline pressure. Therefore, improvements are urgently needed. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an adaptive backwashing control method for large-scale filter stations based on PLC, which aims to solve the technical problems of the above-mentioned technical evaluation system having a single dimension and lacking timeliness, as well as lacking the ability to quantitatively predict clogging trends.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an adaptive backwashing control method for a large-scale filtration station based on PLC, comprising the following steps: Real-time acquisition of operating data from each filter unit; processing of the operating data by moving average filtering to obtain pre-processed data, including influent turbidity, effluent turbidity, real-time filtration flow rate, and inlet / outlet pressure difference of the filter unit. Normalized clogging index for each filter unit is constructed based on preprocessed data. The calculation model and formula are as follows:

[0006] in, For the first Each filter unit is in Congestion index at any given time; , and These are the pressure difference weighting coefficient, water quality weighting coefficient, and pollution interception weighting coefficient, respectively, and they satisfy the following conditions: + + =1; For the first Each filter unit is in Real-time pressure difference at any given moment; The maximum allowable differential pressure threshold; For the first Each filter unit is in Turbidity of the effluent at any given time; This is the warning value for effluent turbidity; This refers to the time point at which the last backwashing of this filter unit ended; For the middle and The integral time variable between them; For the first Each filter unit is in Real-time filtering of traffic at all times; For the first Each filter unit is in Real-time turbidity of incoming water at any given moment; The clogging index of each filter unit is compared with the dynamic trigger threshold. When the clogging index exceeds the dynamic trigger threshold, the corresponding filter unit is added to the backwash queue. Perform backwashing operations on the filter units that are recorded in the backwash queue.

[0007] Preferably, the method for determining whether to include a filter unit in the backwash queue further includes: Select the current time and before The congestion index corresponding to each sampling time constitutes the sample set; The slope of the current clogging change is obtained by performing a first-order linear regression on the clogging index sequence of each historical filter unit using the least squares method. ; like Greater than 0, according to the formula The predicted time to reach the danger threshold was calculated. ,in The set mandatory backwashing hazard threshold and the predicted time If the timeframe is less than the preset safety lead time, the triggering condition is determined to be met, and the corresponding filter unit is added to the backwash queue in advance. like Greater than 0 or predicted time If the timeframe exceeds the preset safety lead time, the corresponding filter unit will not be included in the backwash queue.

[0008] Preferably, the dynamic trigger threshold The calculation is as follows:

[0009] in, The preset basic trigger threshold; This represents the proportion of currently running filter units to the total number of units. This is the load correction function, varying with the operating filter unit ratio. Increase and decrease; The influent turbidity change rate is calculated using the following formula: , for Turbidity of the incoming water at any given time. for Turbidity of the incoming water at any given time. The sampling period is The water quality trend correction function is the rate of change of influent turbidity. Increases and decreases.

[0010] Preferably, the weighting coefficient is dynamically adjusted based on the backwashing effect of the filter unit in the previous step, including: If the pressure drop of the filter unit after the last backwash did not reach the preset target pressure drop, then in the next backwash cycle, the pressure drop will be increased. And reduce proportionally and The update will be performed, and the updated version will be... , , The sum is still 1; If the pressure difference of the filter unit after the last backwash reaches the preset target pressure difference, then maintain the current value. , , The value remains unchanged.

[0011] Preferably, before performing backwashing operations on the filter units recorded in the backwashing queue, the method further includes determining the number of filter units that can simultaneously perform backwashing operations based on the overall station hydraulic balance, including: Real-time acquisition of total water inflow for the entire station Total outflow demand And calculate the current hydraulic margin. ; Based on the rated flow rate required for a single backwash filter unit Calculate the maximum number of filter beds that can be backwashed simultaneously. ,in [] indicates the floor function.

[0012] Preferably, in the backwash queue, when multiple filter units are simultaneously awaiting backwashing, they are sorted according to the priority score P of each filter unit. The formula for calculating the priority score P is as follows:

[0013] in, Indicates the urgency of the congestion; For continuous running time, The preset maximum continuous running time, The time interval since the last backwash. The preset backwash interval period; , , These are preset weighting coefficients.

[0014] Preferably, during the period when the filter units in the backwash queue are queuing for backwashing, the process further includes: If the clogging index of all filter units in the backwash queue is greater than or equal to the dynamic trigger threshold but does not exceed the danger threshold. If the priority score is too high, then backwashing will be initiated sequentially, ensuring that the number of filter units performing backwashing simultaneously does not exceed [a certain threshold]. ; If there are in the backwash queue If a filter unit exceeds the danger threshold, it will be forcibly assigned the highest priority.

[0015] Preferably, performing backwashing operations on filter units recorded in the backwashing queue further includes adaptive control of the backwashing intensity, including: During the backwashing process, the turbidity of the backwash wastewater in the backwash wastewater pipeline is monitored in real time, and the slope of the change in wastewater turbidity is calculated. If the absolute value of the slope of change is less than the preset convergence threshold and the turbidity of the discharged wastewater is lower than the target cleanliness threshold, then the backwashing process is terminated. If the backwashing time reaches the preset maximum time limit and the turbidity of the discharged wastewater is greater than or equal to the target cleaning threshold, the backwashing will be stopped and an abnormal maintenance alarm for the filter unit will be triggered.

[0016] Preferably, during the backwashing process of the filter unit, the frequency of the backwash water pump is controlled by a frequency converter, including: If the turbidity of the discharged wastewater does not decrease after the preset backwash start time, the inverter output frequency will be gradually increased according to the preset increment. If the inverter output frequency reaches the upper limit and the turbidity of the discharged wastewater does not decrease significantly, then stop after reaching the maximum backwash time and record the abnormal energy consumption of the backwash pump. If the turbidity of the discharged wastewater has decreased after the preset backwash start time and the rate of decrease is within the preset normal range, then the current inverter output frequency will be maintained.

[0017] Preferably, the functions of each filter unit are periodically activated. The operational data from each backwash cycle is used to optimize the clogging index through an iterative search algorithm. Weight parameters in the calculation model , , ,include: Establish a comprehensive evaluation function for the entire station's operation. The calculation formula is as follows:

[0018] in, This is the system performance evaluation index for the weighted parameters under their current values; This refers to the total number of historical backwashing cycles. For periodic index variables; For the first Total water production of the filter unit within each cycle; For the first The amount of self-consumption water consumed during the backwashing operation within each cycle; For the first The total electrical energy consumed by the backwash pump within each cycle; This is the weighting factor for converting electrical energy into water consumption; exist , , Under the given conditions, calculate the evaluation function. right , , The partial derivatives are calculated, and the weight parameters are updated along the gradient direction where the partial derivatives increase, in order to obtain the optimal weight combination that maximizes the system performance evaluation index.

[0019] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. By constructing a normalized clogging index model, which organically integrates pressure difference changes, instantaneous water quality fluctuations, and cumulative interception volume, and combined with the least squares linear regression prediction mechanism, the system can quantify clogging trends and predict the time to reach a dangerous state, transforming passive response into proactive prevention. This effectively avoids the risk of exceeding standards caused by sudden changes in influent water quality, ensuring the high efficiency of filtration and the stability of effluent water quality.

[0020] 2. By calculating the maximum number of filter tanks that can be backwashed simultaneously in real time, a precipitous drop in water supply capacity caused by simultaneous backwashing of multiple units is prevented. Combined with priority scoring and mandatory intervention based on danger thresholds, the backwash queue is scientifically ordered, which not only balances the contradiction between production water supply and self-consumption water, but also greatly reduces the impact of backwashing operations on pipeline pressure. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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.

[0022] Figure 1 A flowchart of an adaptive backwashing control method for a large-scale filter station based on PLC is shown.

[0023] Figure 2 A flowchart is shown showing a method for determining whether to include filter units in the backwash queue based on predicted time.

[0024] Figure 3 A flowchart is shown to determine the number of filter units that can perform backwashing operations simultaneously based on the overall station hydraulic balance.

[0025] Figure 4 A flowchart of an adaptive control method for backwash intensity is shown.

[0026] Figure 5 A flowchart is shown showing a method for controlling the frequency of the backwash water pump by a frequency converter during the backwashing process of the filter unit. Detailed Implementation

[0027] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Traditional water treatment systems rely solely on single indicators, such as timed cycles or fixed differential pressure thresholds, to trigger backwashing operations. This fails to integrate and normalize multi-dimensional operational data, including differential pressure changes, effluent turbidity fluctuations, and cumulative contaminant interception. This singular evaluation mechanism leads to a delayed response to sudden changes in influent water quality, making it impossible to dynamically optimize backwashing timing while ensuring effluent quality. Furthermore, the lack of linear prediction of clogging trends means that when multiple filter units approach clogging due to uniform influent load distribution, concurrent flushing events can easily occur, causing a sharp drop in the overall water supply capacity. In addition, the absence of hydraulic balance regulation means that backwashing operations may compete with production water supply for flow, affecting pipeline pressure stability; fixed backwashing intensity and duration prevent real-time adjustment based on effluent quality, leading to increased self-consumption of water and energy; and rigid system parameters prevent closed-loop optimization based on historical operational data, resulting in decreased control efficiency over long-term operation.

[0030] In this regard, refer to Figure 1 An adaptive backwashing control method for large-scale filtration stations based on PLC is proposed, including the following steps: S1: Real-time acquisition of operating data from each filter unit; processing of the operating data by moving average filtering to obtain preprocessed data. S2, Construct the normalized clogging index for each filter unit based on the preprocessed data. Computational model; S3, compare the clogging index of each filter unit with the dynamic trigger threshold. When the clogging index exceeds the dynamic trigger threshold, the corresponding filter unit is recorded in the backwash queue. S4, perform backwashing operation on the filter units recorded in the backwash queue.

[0031] In step S1, to accurately grasp the operating status of each filter unit, it is necessary to collect its operating data in real time. The operating data includes the influent turbidity, effluent turbidity, real-time filtration flow rate, and inlet / outlet pressure difference of the filter unit. This data collection can be achieved by installing sensors in the influent and effluent pipelines of the filter unit, inside the filter unit, and on related equipment. For example, a turbidity meter can be used to measure the influent and effluent turbidity, a flow meter to measure the filtration flow rate, and a differential pressure transmitter to measure the inlet / outlet pressure difference. The collected raw data may contain noise or instantaneous fluctuations. To improve the reliability and stability of the data, this operating data needs to be processed by moving average filtering. Moving average filtering can be implemented through programming in a PLC or host computer system. For example, a fixed-length time window can be set, and the average value of the data within that window can be calculated as the preprocessed data for the current moment.

[0032] In step S2, based on the obtained preprocessed data, the normalized clogging index of each filter unit is constructed. The calculation model, which reflects the degree of filter clogging, is formulated as follows:

[0033] in, Indicates the first Each filter unit is in The congestion index at any given time is a weighted sum of three main components: the first component is the real-time differential pressure. With the maximum allowable differential pressure threshold The ratio reflects the physical resistance of the filter media layer; the second part is the effluent turbidity. With effluent turbidity warning value The ratio reflects the effluent quality of the filter bed; the third part is the cumulative intercepted pollutant volume, calculated based on the time point of the last backwash. up to the current moment Real-time filtering of traffic With real-time influent turbidity The integral of the product reflects the total amount of impurities cumulatively trapped by the filter, i.e., the amount of contaminants trapped. (Pressure differential weighting coefficient) Water quality weighting coefficient And the weighting coefficient of pollution interception This is used to adjust the relative importance of these three components in the congestion index, and their sum satisfies... These weighting coefficients can be set based on experience or preset values.

[0034] In step S3, the clogging index calculated for each filter unit is compared with the dynamic trigger threshold. The dynamic trigger threshold is a preset value based on current operating conditions and can be adjusted according to factors such as the overall load of the filtration station and changes in influent water quality. When the clogging index of a filter unit exceeds the dynamic trigger threshold, it indicates that the clogging level of that filter unit has reached a point requiring backwashing. At this time, the corresponding filter unit will be added to a backwashing queue.

[0035] In step S4, backwashing is performed on the filter units added to the backwash queue. Once a filter unit is added to the backwash queue, the system selects one or more filter units from the queue and initiates backwashing according to preset scheduling logic. The backwashing operation typically includes a series of steps such as closing the inlet valve, emptying the filter, starting the backwash pump or blower, performing water flushing or combined air-water flushing, and draining the water. The intensity and duration of backwashing can be controlled according to preset fixed parameters; for example, the backwash pump can be set to operate at a fixed frequency for a preset duration.

[0036] As a specific implementation method, suppose a large filtration station contains multiple parallel filter units, such as filter unit A, filter unit B, and filter unit C. First, the PLC system collects real-time operating data from filter units A, B, and C, including influent turbidity, effluent turbidity, real-time filtration flow rate, and inlet / outlet pressure difference. For example, data is collected every few minutes. This raw operating data is first processed by moving average filtering to eliminate sensor noise and instantaneous fluctuations, resulting in smooth pre-processed data. For example, the pre-processed value for the current moment is obtained by averaging data from the past 10 sampling points.

[0037] Next, based on this preprocessed data, the system will construct and calculate the normalized clogging index for each filter unit. Taking filter unit A as an example, its clogging index... It will be based on the real-time pressure difference effluent turbidity And the accumulated amount of contaminants intercepted since the last backwash is calculated comprehensively. Assuming the current moment... Real-time pressure difference of filter unit A for Maximum permissible pressure difference for ; Effluent turbidity for effluent turbidity warning value for Furthermore, since the last backwash, the cumulative amount of contaminants intercepted has reached a certain level. This is achieved through preset weighting coefficients. , , (For example, , , The clogging index of filter unit A was calculated. .

[0038] Subsequently, the system will calculate the clogging index of filter unit A. Compare with the current dynamic trigger threshold. This dynamic trigger threshold may be adjusted based on the overall operating load of the filtration station or the trend of the influent water quality. For example, when the influent turbidity is high, the dynamic trigger threshold may be appropriately lowered to initiate backwashing earlier, ensuring the quality of the effluent. If the calculated... The current dynamic trigger threshold has been exceeded, for example, for The dynamic trigger threshold is If filter unit A is added to the backwash queue, the system will perform a backwash operation on it at an appropriate time according to the scheduling logic in the queue. For example, if there is only filter unit A in the queue, the system will immediately start its backwash process. The backwash operation will be carried out according to the preset process and parameters, such as flushing at a fixed flow rate and duration, until completion.

[0039] In summary, by introducing a multi-dimensional normalized clogging index and a dynamically triggered queue management mechanism, the intelligence level and responsiveness of backwashing control in large-scale filtration plants are significantly improved. This solution can more comprehensively assess the operating status of the filter bed and more flexibly determine the timing of backwashing, thereby optimizing the overall operating efficiency of the filtration plant while ensuring effluent quality.

[0040] According to several embodiments of the present invention, in step S4, reference is made to... Figure 2 Furthermore, a prediction time-based approach was proposed. The method for determining whether a filter unit should be added to the backwash queue includes: Select the current time and before The congestion index corresponding to each sampling time constitutes the sample set; The slope of the current clogging change is obtained by performing a first-order linear regression on the clogging index sequence of each historical filter unit using the least squares method. ; like According to the formula The predicted time to reach the danger threshold was calculated. ,in The set mandatory backwashing hazard threshold and the predicted time If the timeframe is less than the preset safety lead time, the triggering condition is determined to be met, and the corresponding filter unit is added to the backwash queue in advance. like Or predict time If the timeframe exceeds the preset safety lead time, the corresponding filter unit will not be included in the backwash queue.

[0041] Specifically, the current moment is selected periodically. and before The congestion index corresponding to each sampling time point forms a time series sample set. Based on this sample set, a first-order linear regression analysis is performed on the historical congestion index sequence using the least squares method to calculate the slope of the congestion change at the current time point. The slope It can quantify the upward or downward trend of the congestion index. When an upward trend in the congestion index is detected (i.e., When this happens, the system will further consider the difference between the current clogging index and the preset forced backflushing danger threshold, combined with the slope. The system calculates the predicted time when filter clogging will reach a dangerous threshold. This predicted time is then compared to a preset safety lead time. If the predicted time is less than the preset safety lead time, it indicates that the filter unit is at risk of reaching a dangerous state in the short term. In this case, the system determines that the triggering condition has been met and adds the corresponding filter unit to the backwash queue in advance.

[0042] As a specific implementation method, assume that the PLC system of a certain filtration station operates every [time period missing]. The system collects operational data from the filter unit every minute and calculates the clogging index. To predict clogging trends, the system can select the most recent... Each sampling time (i.e., the current time) and before The congestion index (at each sampling time) is used as the sample set. For example, this congestion index data can be stored in the PLC's internal data register or a connected storage module. When it is necessary to calculate the slope of the congestion change... At this time, the PLC's arithmetic module will call the preset least squares algorithm to perform this... A first-order linear regression was performed on the congestion index data points to obtain a slope. Value. For example, if the calculated Value This indicates that the congestion index increases per hour. Meanwhile, the system has a preset forced backwashing danger threshold. for The preset safety lead time is Hours. If the current filter unit's clogging index... for Then the predicted time to reach the danger threshold is calculated. Hours. Due to The time is greater than the preset safety lead time. If the current clogging index is low, the filter unit will not be added to the backwash queue in advance. Conversely, if the current clogging index is high... for ,and Value ,but Hours. Due to The hour is less than the preset safety lead time At this time, the PLC will immediately determine that the triggering condition is met and add the ID of the filter unit to the backwash queue so that the scheduling system can plan its backwashing operation in advance.

[0043] The above technical solution effectively solves the lag problem caused by relying solely on the current clogging index in traditional backwashing control. By introducing a clogging trend prediction mechanism, the system can identify filter units with rapidly rising clogging indices in advance and pre-select them for backwashing before they reach the danger threshold. This makes backwashing operations more timely and proactive, avoiding problems such as a sudden drop in filtration efficiency, deterioration of effluent quality, or filter overload caused by rapid clogging development. This improves the stability and reliability of the filtration station, thereby extending the effective filtration cycle of the filter and reducing operational risks.

[0044] According to several embodiments of the present invention, a dynamic triggering threshold is further proposed. The dynamic trigger threshold is calculated as follows: , in, The preset basic trigger threshold; This represents the proportion of currently running filter units to the total number of units. This is the load correction function, varying with the operating filter unit ratio. Increase and decrease; The change rate of influent turbidity, where, Calculated using the following formula: , for Turbidity of the incoming water at any given time. for Turbidity of the incoming water at any given time. The sampling period is The water quality trend correction function is the rate of change of influent turbidity. Increases and decreases.

[0045] Specifically, when the operating load of the filtration station increases, This will decrease, thereby lowering the dynamic trigger threshold. This means that under high system load conditions, the filter unit will be triggered for backwashing at a relatively low clogging index to ensure the filtration station can continuously and stably provide the required water volume and avoid affecting the overall water production capacity due to excessive filter clogging. Simultaneously, when the rate of change in influent turbidity increases, indicating that the raw water quality is rapidly deteriorating, This will decrease, further reducing the dynamic trigger threshold.

[0046] As one specific implementation method, the calculation of the dynamic trigger threshold can be performed periodically by the PLC system. For example, the preset basic trigger threshold can be set as follows: Load correction function It can be designed as a linear function, for example ,in The range of values ​​is arrive .when for hour, ;when for hour, Water quality trend correction function It can be designed as a piecewise linear function, for example, when Less than hour, ;when exist arrive In between, ;when Greater than hour, The PLC system samples every preset period of time. (For example (Every few minutes) collect the operating data of each filter unit and calculate the influent turbidity change rate. Then, calculate based on the number of currently running filter units and the total number of units. Finally, substitute these parameters into the formula. In this process, the real-time dynamic trigger threshold is obtained. .

[0047] Through the above technical solution, the dynamic trigger threshold can be adjusted in real time according to the actual operating load of the filtration station and the changing trend of the influent water quality. When the system load is high or the influent water quality deteriorates rapidly, the dynamic trigger threshold will be lowered accordingly, so that the filter unit is triggered for backwashing when the degree of clogging is relatively low, thereby avoiding the risk of excessive clogging of the filter leading to a decline in effluent water quality or insufficient filtration flow. Conversely, when the system load is low or the influent water quality is stable, the dynamic trigger threshold can be appropriately increased to extend the operating cycle of the filter, reduce unnecessary backwashing times, thereby saving backwashing water and energy consumption, significantly improving the overall operational stability and economy of the filtration station, and effectively solving the problem of inaccurate backwashing timing due to changes in operating conditions.

[0048] According to several embodiments of the present invention, a method for dynamically adjusting the weighting coefficient based on the previous backwashing effect of the filter unit is further proposed, including: If the pressure drop of the filter unit after the last backwash did not reach the preset target pressure drop, then in the next backwash cycle, the pressure drop will be increased. And reduce proportionally and Update, and the updated , , Still satisfied and for ; If the pressure difference of the filter unit after the last backwash reaches the preset target pressure difference, then maintain the current value. , , The value remains unchanged.

[0049] Specifically, after each backwash operation of the filter unit, the system evaluates the effectiveness of the backwash, primarily by monitoring whether the pressure drop after backwashing reaches the preset target pressure drop. If the backwash effect is poor, i.e., the pressure drop fails to reach the preset target, it indicates that the current clogging index calculation model may be insufficiently sensitive to pressure drop, or that the clogging characteristics of the filter have changed, necessitating an emphasis on the role of pressure drop in clogging assessment. Therefore, in the next backwash cycle, the system will increase the pressure drop weighting coefficient. And correspondingly reduce the water quality weighting coefficient proportionally. And the weighting coefficient of pollution interception To ensure that the sum of the three weighting coefficients remains the same. This adjustment reduces the congestion index. The computational model pays closer attention to changes in differential pressure, thus more accurately reflecting the actual clogging status of the filter and prompting the system to trigger backwashing more promptly when differential pressure issues are prominent. Conversely, if the backwashing effect is good, meaning the differential pressure decrease reaches the preset target differential pressure, it indicates that the current weighting coefficient configuration is reasonable and can effectively guide the backwashing operation; therefore, maintaining the current configuration is acceptable. , , The value remains unchanged.

[0050] As a specific implementation method, assuming that before a backwashing operation, the weighting coefficients in the clogging index calculation model of a certain filter unit are as follows: , , After the backwashing operation, the system monitored a pressure drop of [value missing] for this filter unit. The preset target pressure difference is .because Not achieved This indicates that the backwashing effect did not fully meet expectations. At this point, the control system will adjust the weighting coefficients in the next backwashing cycle according to preset adjustment logic. For example, it can... Increase to make it In order to maintain , , The sum of them is The remaining The allocation will be made in the original proportion. and If the original ratio is Then the new and They will be respectively and Thus, in subsequent congestion indices... In the calculations, differential pressure will have a greater weight, making the system more sensitive to changes in differential pressure. Conversely, if after another backwash, the differential pressure drop of this filter unit reaches [a certain value], [the system will be more sensitive to these changes]. It exceeded the preset target pressure difference. This indicates that the backwashing was effective, the current weighting coefficient configuration is valid, and the system will maintain [the desired result]. , , The value remains unchanged, and the current control strategy will continue to be used.

[0051] The above technical solution allows for the dynamic adjustment of weighting coefficients in the clogging index calculation model based on the actual backwashing effect of the filter unit. This adaptive adjustment mechanism enables the clogging index calculation model to better reflect the actual clogging status and backwashing requirements of the filter unit under different operating conditions. When the backwashing effect is poor, the differential pressure weighting coefficient can be increased. This enhances the model's sensitivity to pressure differential changes, enabling the system to respond more promptly to clogging issues caused by pressure differentials, thus preventing the filter bed from being affected by excessive pressure differentials. Conversely, when the backwashing effect is good, keeping the weighting coefficients unchanged maintains a stable and effective control strategy. This not only improves the accuracy and timeliness of backwash triggering, effectively avoiding unnecessary or insufficient backwashing, but also helps optimize the overall operating efficiency of the filtration station, extend the filter bed's operating cycle, reduce energy consumption, and ultimately improve the economic benefits and stability of water treatment.

[0052] According to several embodiments of the present invention, with reference to Figure 3 Furthermore, it proposes that before performing backwashing operations on filter units recorded in the backwashing queue, the number of filter units allowed to perform backwashing operations simultaneously should be determined based on the overall station hydraulic balance, including: Real-time acquisition of total water inflow for the entire station Total outflow demand And calculate the current hydraulic margin. ; Based on the rated flow rate required for a single backwash filter unit Calculate the maximum number of filter beds that can be backwashed simultaneously. ,in This represents the floor function.

[0053] Specifically, by monitoring the total influent flow rate of the entire filtration station in real time. Total water outflow demand And calculate the current hydraulic margin. Given the rated flow rate required for backwashing a single filter unit. Since it is known, the system can determine the current hydraulic margin. The maximum number of filter units that can be backwashed simultaneously at any given time is calculated by rounding down, without affecting the normal water supply. .

[0054] As a specific embodiment, suppose a large filtration station is configured with multiple filter units. When the control system determines that multiple filter units require backwashing based on the clogging index of the filter units and has added them to the backwash queue, the system will first perform a hydraulic balance assessment before actually initiating the backwashing operation. For example, the current total influent flow rate can be obtained in real time through an electromagnetic flow meter installed on the main influent pipeline. The total demand for water output is obtained by monitoring the operating data of downstream water supply pumping stations or by measuring the flow meter on the main outlet pipe. Assuming the current situation... for , for The calculated hydraulic margin for The rated flow rate required for backwashing of a single filter unit in this filtration station is known. for At this point, the system will calculate the maximum number of filter beds that can be backwashed simultaneously. This means that even if there are more than [number] in the backwash queue... The system will only start a maximum of one filter unit at a time. Backwashing of each filter unit is performed to ensure that the remaining filter units can meet the requirements. To meet the water demand and maintain the hydraulic balance of the entire station.

[0055] The above technical solution effectively avoids the hydraulic imbalance caused by simultaneously backwashing too many filter units in a large filtration station. By monitoring the hydraulic conditions of the entire station in real time and dynamically calculating the number of filter units that can be backwashed simultaneously, the backwashing operation is ensured to be carried out without affecting normal water supply and system stability. This not only improves the operational reliability and water supply continuity of the filtration station but also optimizes the utilization of backwashing resources, making the backwashing process more intelligent and adaptive, thereby improving the overall operational efficiency of the entire filtration station.

[0056] According to several embodiments of the present invention, a further method is proposed to score filter units based on their priority when multiple filter units exist simultaneously in the backwash queue. Sort by numerical value and assign priority score The calculation formula is:

[0057] in, Indicates the urgency of the congestion; This refers to the continuous operating time of the filter unit since the last backwash. This is the maximum continuous operating time allowed for a filter unit, determined based on experience or design requirements. The time interval since the last backwash. The preset backwash interval period; , , These are preset weighting coefficients.

[0058] Specifically, for each filter unit in the backwash queue, the system will determine its current urgency of clogging. The ratio of continuous runtime to maximum continuous runtime And the complement of the ratio of the time interval since the last backwash to the preset backwash interval period. To calculate its priority score These three parameters comprehensively evaluate the backwashing requirements of the filter unit from multiple dimensions, including the current operating status, operating history, and maintenance cycle of the filter. Preset weighting coefficients are used to... , , By weighting and summing these three dimensions, the importance of different factors in prioritization decisions can be flexibly adjusted. For example, when the congestion situation is extremely urgent, it can be assigned... Higher weighting assigns higher priority to filter units with higher clogging indices. Priority scores are calculated for all filter units awaiting backwashing. Then, the system will sort these scores in descending order to form a clear backwashing execution sequence.

[0059] As a specific example, suppose a large filtration station has multiple filter units, where filter units A, B, C, and D are all in the backwashing queue. Based on the overall station's hydraulic balance calculations, the maximum number of filter units allowed to be backwashed simultaneously is determined. for To determine which two filter units should be backwashed first, the system will calculate a priority score for these four filter units. For example, preset weighting coefficients. It can be set to , It can be set to , It can be set to .

[0060] For filter unit A, the urgency of clogging Possibly (This indicates that the congestion index has exceeded the threshold) ), continuous runtime ratio Possibly The complement of the backwash interval ratio Possibly Then, the priority score for filter unit A is... For filter unit B, the urgency of its clogging... Possibly Continuous runtime ratio Possibly The complement of the backwash interval ratio Possibly Then the priority score for filter unit B. For filter unit C, the urgency of its clogging... Possibly Continuous runtime ratio Possibly The complement of the backwash interval ratio Possibly Then, the priority score for filter unit C is determined. For filter unit D, the urgency of its clogging Possibly Continuous runtime ratio Possibly The complement of the backwash interval ratio Possibly Then, the priority score for filter unit D is determined. .

[0061] Calculated priority score Sort in descending order. .because for The system will prioritize filter unit A and filter unit B (or D, if other rules break the tie, such as the first to join the queue takes precedence) for backwashing.

[0062] The above technical solution addresses the challenge of efficient scheduling and prioritization in large-scale filtration plants when multiple filter units require backwashing simultaneously, given the limited backwashing capacity of the system. This is achieved by introducing a comprehensive priority scoring system. This system enables a comprehensive assessment of the backwashing urgency of each filter unit, ensuring that the most critical units receive priority access to backwashing resources. This not only prevents excessive clogging, effluent quality deterioration, or increased operational risks caused by prolonged backwashing waits, but also optimizes the allocation of limited backwashing resources, improving the targeting and efficiency of backwashing operations. Furthermore, adjustable weighting coefficients allow for flexible adaptation to different operating strategies and environmental changes, further enhancing the overall adaptive control capabilities and operational stability of the filtration station, ensuring continuous compliance with effluent quality standards and economical operation.

[0063] According to several embodiments of the present invention, during the period when filter units in the backwash queue are queuing for backwashing, the method further includes: If the clogging index of all filter units in the backwash queue is greater than or equal to the dynamic trigger threshold but does not exceed the danger threshold. If the priority score is too high, then backwashing will be initiated sequentially, ensuring that the number of filter units performing backwashing simultaneously does not exceed [a certain threshold]. ; If the blockage index in the backwash queue exceeds the danger threshold If a filter unit is selected, then that filter unit will be forcibly assigned the highest priority.

[0064] As a specific embodiment, assume a large filtration station has multiple filter units, and the system has calculated the clogging index and priority score of each filter unit based on real-time operating data. Simultaneously, based on the overall station hydraulic balance, the system has determined the maximum number of filters currently allowed to undergo backwashing simultaneously. When multiple filter units are added to the backwashing queue, such as filter units A, B, C, and D, the system first checks the clogging index of these filter units. If the clogging index of all filter units A, B, C, and D... If all values ​​are greater than or equal to the dynamic trigger threshold but do not exceed the danger threshold, the system will score them according to their respective priorities. Sort, for example, At this point, the system will initiate backwashing sequentially in this order, ensuring that the number of filter units backwashed simultaneously does not exceed [a certain number]. For example, if for If, during this period, the clogging index of filter unit B suddenly rises sharply and exceeds the danger threshold, the system will immediately force filter unit B to be given the highest priority, making it initiate backwashing before filter units A and C that are currently being backwashed, as well as other filter units waiting to be backwashed. This mechanism ensures a rapid response to emergencies.

[0065] By introducing an emergency scheduling mechanism based on whether the clogging index reaches a dangerous threshold, a rapid response and forced backwashing can be initiated when the filter unit reaches a critical clogging state. This effectively avoids serious consequences such as a sharp drop in filtration efficiency, deterioration of effluent quality, or even equipment damage that may result from excessive clogging of the filter unit. Simultaneously, in non-emergency situations, orderly scheduling can still be carried out based on priority scoring and hydraulic balance constraints, optimizing the allocation and utilization of backwashing resources and avoiding unnecessary system disturbances. This strategy, combining conventional priority scheduling and emergency intervention, significantly improves the intelligence, safety, and reliability of backwashing control in large-scale filtration plants, enabling them to maintain efficient and stable operation under complex and changing operating conditions.

[0066] According to several embodiments of the present invention, with reference to Figure 4 Furthermore, it proposes to perform backwashing operations on filter units recorded in the backwashing queue, and also includes adaptive control of backwashing intensity, specifically including: During the backwashing process, the turbidity of the backwash wastewater in the backwash wastewater pipeline is monitored in real time, and the slope of the change in wastewater turbidity is calculated. If the absolute value of the slope of change is less than the preset convergence threshold and the turbidity of the discharged wastewater is lower than the target cleanliness threshold, then the backwashing process is terminated. If the backwashing time reaches the preset maximum time limit and the turbidity of the discharged wastewater is greater than or equal to the target cleaning threshold, the backwashing will be stopped and an abnormal maintenance alarm for the filter unit will be triggered.

[0067] Specifically, during the backwashing operation, the system continuously monitors the turbidity of the wastewater in the backwash discharge pipeline in real time and calculates the slope of the turbidity change based on this real-time data. This real-time monitoring and calculation mechanism allows the system to dynamically evaluate the cleaning progress and effectiveness of the filter. When the absolute value of the slope of the turbidity change is less than a preset convergence threshold, it indicates that the cleaning efficiency of the filter has significantly decreased and the dirt removal rate has leveled off. Simultaneously, if the turbidity of the wastewater is also lower than the preset target cleaning threshold, it is determined that the filter has reached a sufficiently clean state. At this point, the system immediately terminates the backwashing process, avoiding unnecessary extension of backwashing time and effectively saving backwashing water and electricity. Furthermore, to address situations of poor backwashing effect or system malfunction, a protection mechanism is set up to prevent the backwashing time from reaching the preset maximum duration. If backwashing continues to the maximum duration limit, but the turbidity of the wastewater still does not reach the target cleaning threshold, the system will forcibly stop backwashing and trigger an abnormal maintenance alarm for that filter unit.

[0068] As a specific embodiment, in a large filtration station, when a filter unit (e.g., filter unit A) is determined to require backwashing and enters the execution phase, the PLC initiates its backwashing procedure. An online turbidity sensor is installed on the backwash wastewater discharge pipeline of filter unit A, which collects the turbidity of the discharged wastewater in real time. The data is then transmitted to the PLC. The PLC samples the data at a preset frequency (e.g., every...). The PLC program receives and records this turbidity data (in seconds). It maintains a historical turbidity data queue and calculates based on the data in the queue. The slope of change, for example, by analyzing the recent... The slope is estimated by performing linear regression on the turbidity values ​​of each sampling point. Assume a preset convergence threshold of... The target cleaning threshold is The maximum backwashing time is [limited to] Minutes. During the backwashing process, the PLC continuously monitors: if the calculated... The absolute value of the slope of change is less than And the current Value lower than The PLC will immediately issue a command to stop the backwash water pump and related valves of filter unit A, ending the backwashing process. On the other hand, if backwashing has already been ongoing... Minutes, but at this time The value is still higher than or equal to The PLC will forcibly stop the backwashing of filter unit A and display an alarm message on the operation interface that reads "Backwashing of filter unit A is abnormal, turbidity does not meet the standard," while also recording the abnormal event.

[0069] By introducing adaptive control of backwash intensity, the timing of backwash termination can be dynamically adjusted according to the actual cleanliness of the filter unit. This avoids the problems of over-backwashing or under-backwashing that may occur with traditional fixed-duration backwashing. When the filter reaches the cleanliness standard, the system can terminate backwashing in a timely manner, significantly saving backwashing water and electricity consumption and reducing operating costs. At the same time, by setting a maximum backwashing duration limit and combining it with abnormal alarms based on the turbidity of the discharged wastewater, potential problems in the filter or backwashing system can be detected in a timely manner, ensuring the cleaning effect of the filter and the stable operation of the system, and improving the overall operating efficiency and reliability of the filtration station.

[0070] According to several embodiments of the present invention, with reference to Figure 5 Furthermore, it proposes to control the frequency of the backwash water pump using a frequency converter during the backwashing process of the filter unit, so as to achieve adaptive adjustment of the backwashing intensity, including: If the turbidity of the discharged wastewater does not decrease after the preset backwash start time, the inverter output frequency will be gradually increased according to the preset increment. If the inverter output frequency reaches the upper limit and the turbidity of the discharged wastewater does not decrease significantly, then stop after reaching the maximum backwash time and record the abnormal energy consumption of the backwash pump. If the turbidity of the discharged wastewater has decreased after the preset backwash start time and the rate of decrease is within the preset normal range, then the current inverter output frequency will be maintained.

[0071] Specifically, by combining backwash pump frequency control with real-time monitoring of wastewater turbidity, adaptive adjustment of backwash intensity is achieved. After backwashing is initiated, the system continuously monitors changes in wastewater turbidity. If the wastewater turbidity does not decrease within a preset time, indicating insufficient backwash intensity, the controller instructs the frequency converter to gradually increase the pump frequency to enhance the flushing effect. This dynamic enhancement mechanism ensures that the filter media is thoroughly flushed, effectively removing blockages. Conversely, if the wastewater turbidity shows a good downward trend and the rate of decrease is within the normal range, the current frequency is maintained, avoiding unnecessary energy consumption. Furthermore, when the backwash intensity reaches its upper limit but the effect is still unsatisfactory, the system will promptly stop and record the anomaly, avoiding ineffective prolonged operation and energy waste, and prompting for manual intervention.

[0072] As a specific embodiment, in a large filtration station, a PLC system is responsible for controlling the entire backwashing process. The backwash water pump is speed-regulated by a frequency converter. An online turbidity sensor is installed on the backwash wastewater discharge pipeline to monitor the turbidity of the discharge wastewater in real time. When a filter unit is determined to require backwashing, the PLC starts the backwash water pump, and the frequency converter operates at the initial frequency (e.g., The PLC starts timing and continuously receives data from the turbidity sensor. Data. If after backflush start Within seconds (preset time), The value did not decrease by more than The PLC will determine that the current intensity is insufficient and instruct the frequency converter to adjust the output frequency accordingly. The preset increment in seconds gradually increases. If After backwashing is started Within seconds Descending to And its descent slope is calculated as the rate of descent per minute. If the frequency is within the preset normal range, the PLC will maintain the inverter's current frequency unchanged. However, if the inverter's output frequency reaches a certain value during the gradual increase of the frequency, the PLC will not maintain this value. The upper limit, but Still higher And there was no significant decrease, and the backwashing time had reached the preset maximum time (e.g. (Minutes) The PLC will stop the backwash water pump and display an alarm message "Abnormal backwash water pump energy consumption" on the operator interface. At the same time, the event will be recorded, prompting maintenance personnel to check the filter tank or water pump.

[0073] The above technical solution allows for dynamic adjustment of the backwash pump's operating frequency based on real-time changes in the turbidity of the discharged wastewater during backwashing, thus achieving adaptive control of the backwash intensity. This effectively solves the problems of incomplete cleaning or energy waste caused by fixed or manually adjusted backwash intensity in traditional methods. Through refined control, it ensures that the filter media reaches optimal cleanliness in each backwash, extending the filter's operating cycle, reducing backwash water and electricity consumption, and promptly detecting and alerting to abnormal backwashing conditions, significantly improving the operational efficiency, stability, and economy of large-scale filtration stations.

[0074] In other implementations, a PLC-based adaptive backwashing control method for large-scale filtration stations is proposed. This method can periodically call the functions of each filter unit. The operational data from each backwash cycle is used to optimize the clogging index through an iterative search algorithm. Weight parameters in the calculation model , , This includes: establishing a comprehensive evaluation function for the entire site's operation. The calculation formula is as follows:

[0075] in, This is the system performance evaluation index for the weighted parameters under their current values; This refers to the total number of historical backwashing cycles. For periodic index variables; For the first Total water production of the filter unit within each cycle; For the first The amount of self-consumption water consumed during the backwashing operation within each cycle; For the first The total electrical energy consumed by the backwash pump within each cycle; This is the weighting factor for converting electricity into water consumption. , , satisfy Under the given conditions, calculate the evaluation function. right , , The partial derivatives are calculated, and the weight parameters are updated along the gradient direction where the partial derivatives increase, in order to obtain the optimal weight combination that maximizes the system performance evaluation index.

[0076] As a specific implementation method, in the PLC control system of a large-scale filtration station, a separate optimization module can be set up or the optimization algorithm can be integrated into the main control PLC. This module periodically (e.g., every time it completes...) (Every backwash cycle or every week) retrieves the operating data of each filter unit from the historical database. This data includes the total permeate volume of each filter unit during each backwash cycle. The amount of water consumed during backwashing and the total electrical energy consumed by the backwash pump After acquiring this data, the optimization module will use a preset comprehensive evaluation function for the entire site's operation. The formula is used to calculate the current weight parameters. , , The system performance evaluation index under For example, a weighting factor can be set for converting electricity into water consumption. For an empirical value, such as Cubic meters per kilowatt-hour. Subsequently, the optimization module uses the gradient ascent algorithm to update the weight parameters. Specifically, it calculates... right , , The partial derivatives are used to adjust the gradient direction along the gradient direction at a preset learning rate (step size). , , For example, if If it is positive, then It will increase the step size by a small amount; if it is negative, then... This will reduce the step size by one small step. At the same time, in order to satisfy... The constraints are set, and the system will adjust them after each update. , , Normalization is then performed. This iterative process continues until the change in the weight parameters is less than a very small threshold, or until the preset maximum number of iterations is reached. At this point, the system performance evaluation index is obtained. Maximize the optimal weight combination. For example, the final result might be... The optimal combination. These optimized weight parameters will then be loaded into the congestion index. In the computational model, this is used to guide subsequent backwashing decisions.

[0077] The above technical solution can solve the problem of weight parameters. , , The settings and adjustments may not fully reflect the long-term operational benefits and energy consumption of the filtration station, leading to a non-globally optimal backwashing strategy. This can be addressed by periodically accessing historical operational data and establishing a comprehensive evaluation function that considers water production, backwashing water consumption, and energy consumption for the entire station. The advantages and disadvantages of backflushing strategies are evaluated from a global perspective. The application of iterative search algorithms reduces the clogging index. Weight parameters in the calculation model , , This approach allows for adaptive optimization, resulting in the optimal weight combination that maximizes the system performance evaluation index. This means backwashing decisions are no longer based solely on local, instantaneous operating conditions, but rather on the long-term operational efficiency and economic goals of the filtration station. Therefore, this scheme significantly improves the adaptability and accuracy of the backwashing strategy, effectively balances water production efficiency and operating costs, reduces unnecessary backwashing frequency and energy consumption, and ultimately achieves overall optimization of the filtration station's operation and maximizes its economic benefits.

[0078] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A PLC-based adaptive backwashing control method for large-scale filter stations, characterized in that, Includes the following steps: Real-time acquisition of operating data from each filter unit; processing of the operating data by moving average filtering to obtain pre-processed data, including influent turbidity, effluent turbidity, real-time filtration flow rate, and inlet / outlet pressure difference of the filter unit. Normalized clogging index for each filter unit is constructed based on preprocessed data. The calculation model and formula are as follows: in, For the first Each filter unit is in Congestion index at any given time; , and These are the pressure difference weighting coefficient, water quality weighting coefficient, and pollution interception weighting coefficient, respectively, and they satisfy the following conditions: + + =1; For the first Each filter unit is in Real-time pressure difference at any given moment; The maximum allowable differential pressure threshold; For the first Each filter unit is in Turbidity of the effluent at any given time; This is the warning value for effluent turbidity; This refers to the time point at which the last backwashing of this filter unit ended; For the middle and The integral time variable between them; For the first Each filter unit is in Real-time filtering of traffic at all times; For the first Each filter unit is in Real-time turbidity of incoming water at any given moment; The clogging index of each filter unit is compared with the dynamic trigger threshold. When the clogging index exceeds the dynamic trigger threshold, the corresponding filter unit is added to the backwash queue. Perform backwashing operations on the filter units that are recorded in the backwash queue.

2. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 1, characterized in that, The methods for determining whether to include a filter unit in the backwash queue also include: Select the current time and before The congestion index corresponding to each sampling time constitutes the sample set; The slope of the current clogging change is obtained by performing a first-order linear regression on the clogging index sequence of each historical filter unit using the least squares method. ; like Greater than 0, according to the formula The predicted time to reach the danger threshold was calculated. ,in The set mandatory backwashing hazard threshold and the predicted time If the timeframe is less than the preset safety lead time, the triggering condition is determined to be met, and the corresponding filter unit is added to the backwash queue in advance. like Greater than 0 or predicted time If the timeframe exceeds the preset safety lead time, the corresponding filter unit will not be included in the backwash queue.

3. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 1, characterized in that, The dynamic trigger threshold The calculation is as follows: in, The preset basic trigger threshold; This represents the proportion of currently running filter units to the total number of units. This is the load correction function, varying with the operating filter unit ratio. Increase and decrease; The influent turbidity change rate is calculated using the following formula: , for Turbidity of the incoming water at any given time. for Turbidity of the incoming water at any given time. The sampling period is The water quality trend correction function is the rate of change of influent turbidity. Increases and decreases.

4. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 2, characterized in that, The weighting coefficients are dynamically adjusted based on the backwashing effect of the filter unit in the previous cycle, including: If the pressure drop of the filter unit after the last backwash did not reach the preset target pressure drop, then in the next backwash cycle, the pressure drop will be increased. And reduce proportionally and Update, and the updated , , The sum is still 1; If the pressure difference of the filter unit after the last backwash reaches the preset target pressure difference, then maintain the current value. , , The value remains unchanged.

5. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 1, characterized in that, Before performing backwashing on filter units in the backwashing queue, the process also includes determining the number of filter units allowed to perform backwashing simultaneously based on the overall station hydraulic balance, including: Real-time acquisition of total water inflow for the entire station Total outflow demand And calculate the current hydraulic margin. ; Based on the rated flow rate required for a single backwash filter unit Calculate the maximum number of filter beds that can be backwashed simultaneously. ,in [] indicates the floor function.

6. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 5, characterized in that, In the backwash queue, when multiple filter units are simultaneously awaiting backwashing, they are sorted according to their priority score P. The formula for calculating the priority score P is as follows: in, Indicates the urgency of the congestion; For continuous running time, The preset maximum continuous running time, The time interval since the last backwash. This is the preset backwashing interval. , , These are preset weighting coefficients.

7. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 6, characterized in that, The period during which filter units in the backwash queue wait for backwashing also includes: If the clogging index of all filter units in the backwash queue is greater than or equal to the dynamic trigger threshold but does not exceed the danger threshold. If the priority score is too high, then backwashing will be initiated sequentially, ensuring that the number of filter units performing backwashing simultaneously does not exceed [a certain threshold]. ; If there are in the backwash queue If a filter unit exceeds the danger threshold, it will be forcibly assigned the highest priority.

8. A PLC-based adaptive backwashing control method for large-scale filter stations according to any one of claims 1 to 7, characterized in that, The backwashing operation is performed on the filter units recorded in the backwashing queue, and also includes adaptive control of the backwashing intensity, including: During the backwashing process, the turbidity of the backwash wastewater in the backwash wastewater pipeline is monitored in real time, and the slope of the change in wastewater turbidity is calculated. If the absolute value of the slope of change is less than the preset convergence threshold and the turbidity of the discharged wastewater is lower than the target cleanliness threshold, then the backwashing process is terminated. If the backwashing time reaches the preset maximum time limit and the turbidity of the discharged wastewater is greater than or equal to the target cleaning threshold, the backwashing will be stopped and an abnormal maintenance alarm for the filter unit will be triggered.

9. The adaptive backwashing control method for a large-scale filter station based on PLC according to claim 8, characterized in that, During the backwashing process of the filter unit, the frequency of the backwash water pump is controlled by a frequency converter, including: If the turbidity of the discharged wastewater does not decrease after the preset backwash start time, the inverter output frequency will be gradually increased according to the preset increment. If the inverter output frequency reaches the upper limit and the turbidity of the discharged wastewater does not decrease significantly, then stop after reaching the maximum backwashing time and record the abnormal energy consumption of the backwash water pump. If the turbidity of the discharged wastewater has decreased after the preset backwash start time and the rate of decrease is within the preset normal range, then the current inverter output frequency will be maintained.

10. A PLC-based adaptive backwashing control method for a large-scale filter station according to any one of claims 1 to 9, characterized in that, Periodically call each filter unit The operational data from each backwash cycle is used to optimize the clogging index through an iterative search algorithm. Weight parameters in the calculation model , , ,include: Establish a comprehensive evaluation function for the entire station's operation. The calculation formula is as follows: in, This is the system performance evaluation index for the weighted parameters under their current values; This refers to the total number of historical backwashing cycles. For the periodic index variable; For the first Total water production of the filter unit within each cycle; For the first The amount of self-consumption water consumed during the backwashing operation within each cycle; For the first The total electrical energy consumed by the backwash pump within each cycle; This is the weighting factor for converting electrical energy into water consumption. exist , , Under the given conditions, calculate the evaluation function. right , , The partial derivatives are calculated, and the weight parameters are updated along the gradient direction where the partial derivatives increase, in order to obtain the optimal weight combination that maximizes the system performance evaluation index.

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