A method and system for optimizing energy consumption of a water purification system

Abstract

This invention discloses a method and system for optimizing energy consumption in a water purification system, relating to the field of intelligent control technology for water treatment processes. The method includes multi-source hydraulic and water quality collaborative data acquisition, physical consistency energy consumption benchmark modeling, water quality load-driven recovery rate safety domain construction, and a three-objective trade-off energy consumption optimization decision-making process. Combining the impact of water quality load changes on scaling risk, pollution trends, and energy consumption growth, a recovery rate safety domain is dynamically constructed, limiting the feasible adjustment range of the recovery rate while meeting operational safety and energy consumption constraints. Under the constraints of the recovery rate safety domain, an adaptive energy consumption optimization control strategy is generated by comprehensively balancing energy consumption, water quality deviation, and operational stability objectives. This method enables refined assessment and optimization decision-making of water purification system energy consumption under fluctuating water quality conditions, effectively improving the system's energy efficiency, safety, and stability.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for water treatment processes, specifically to a method and system for optimizing energy consumption in a water purification system. Background Technology

[0002] A method and system for optimizing energy consumption in a water purification system refers to an intelligent operation control method and its implementation system that performs multi-objective trade-off optimization by collaboratively collecting and analyzing hydraulic parameters and water quality parameters during the operation of the water purification system. With the widespread application of membrane water purification technologies such as reverse osmosis and ultrafiltration in industrial water, municipal water supply and reclaimed water treatment, the energy consumption problem of water purification systems is becoming increasingly prominent while ensuring water quality meets standards.

[0003] The energy consumption of existing water purification systems is mainly concentrated in the operation of pump sets and the hydraulic transportation process. The energy consumption per unit of produced water is directly affected by a combination of factors such as the quality of the influent, hydraulic conditions, and the setting of operating parameters. This method and system can effectively cope with the impact of fluctuations in the quality of raw water on energy consumption and operational risks. Under the premise of ensuring the quality of the effluent and the safe and stable operation of the system, it can achieve a reasonable reduction in the energy consumption per unit of produced water in the water purification system, and provide reliable technical support for the energy-saving operation and refined management of the water purification system.

[0004] However, existing energy consumption optimization methods for water purification systems generally rely solely on a single hydraulic parameter or empirical setting for energy consumption control, ignoring the impact of water quality fluctuations on system pressure difference, recovery rate feasibility, and actual energy consumption levels. This can easily lead to energy consumption optimization failure or increased operational risks under changing water quality conditions.

[0005] In existing energy consumption benchmark modeling methods, there are technical problems such as the use of statistical regression or historical average energy consumption as reference benchmarks, which do not fully consider the physical influence mechanism of pressure difference, flow distribution, water temperature and viscosity changes and water quality load on energy consumption. This results in the lack of physical consistency of energy consumption benchmarks and difficulty in reflecting the theoretically achievable energy consumption level under different operating conditions.

[0006] Existing methods for constructing constraint domains often rely solely on design experience or a single scaling index to limit the recovery rate range, failing to simultaneously characterize the coupling relationship between changes in water quality load and the trend of energy consumption growth. This can easily lead to situations where the recovery rate is set too high, resulting in a sharp increase in energy consumption or the accumulation of operational risks. Summary of the Invention

[0007] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method and system for optimizing energy consumption in a water purification system. The technical solution adopted by this invention is as follows: This invention provides a method for optimizing energy consumption in a water purification system, which includes the following steps:

[0008] Step S1: Multi-source hydraulic and water quality collaborative data acquisition;

[0009] Step S2: Physically consistent energy consumption baseline modeling;

[0010] Step S3: Construction of the recovery rate safety domain driven by water quality load;

[0011] Step S4: Three-objective trade-off energy consumption optimization decision.

[0012] Further, in step S1, the multi-source hydraulic and water quality collaborative data acquisition is used to construct a basic operating data set for the energy consumption optimization of the water purification system. Specifically, it involves synchronously collecting hydraulic parameters and water quality parameters that are directly related to energy consumption during the operation of the water purification system, and performing timestamp alignment, outlier removal, and smoothing on data from different sources to obtain a multi-source hydraulic and water quality collaborative state dataset that characterizes the current operating condition of the water purification system.

[0013] The hydraulic parameters include inlet pressure, product water pressure, concentrate pressure, inlet flow rate, product water flow rate, concentrate flow rate, circulation return flow rate, pump operating frequency, and power parameters.

[0014] The water quality parameters include influent conductivity, dissolved solids content, turbidity, water temperature, and water quality load parameters related to scaling or pollution risk.

[0015] The water quality load parameters include the water quality load index, scaling risk characterization quantity, and pollution trend reference quantity.

[0016] Furthermore, in step S2, the physical consistency energy consumption benchmark modeling is used to construct a reference benchmark for the unit water production energy consumption of the water purification system under the current hydraulic conditions and water quality load constraints. Specifically, based on the multi-source hydraulic and water quality coordinated state data obtained in step S1, the pumping energy consumption, pressure difference loss and energy consumption related to water production in the water purification system are modeled and analyzed. Combined with the influence relationship between flow rate, pressure and temperature on fluid transport power consumption, a unit water production energy consumption calculation model that conforms to the hydraulic and energy conservation relationship is constructed.

[0017] During the modeling process, a correction factor related to water quality load is introduced to improve the calculation model, which is used to reflect the impact of water quality changes on system pressure difference and flux demand. Based on this, a theoretically achievable energy consumption reference level under the current operating conditions is formed. The energy consumption deviation of the actual operating energy consumption relative to the energy consumption reference level is calculated to obtain physical consistency energy consumption benchmark data characterizing the deviation of the system operating state from the ideal energy consumption level.

[0018] The physical consistency energy consumption benchmark modeling specifically adopts the water quality load-corrected physical consistency energy consumption benchmark modeling method, including the following steps:

[0019] Step S21: Construct the measured unit water production energy consumption, which is used to construct the measured unit water production energy consumption index of the water purification system under the current operating conditions. Specifically, based on the real-time power data of the pump group and the corresponding water production flow data in the multi-source hydraulic and water quality coordinated state dataset obtained in Step S1, the water production volume is normalized to calculate the power consumption of the pump group, and the measured energy consumption data representing the unit water production energy consumption under the current operating conditions is obtained.

[0020] Step S22: Construction of equivalent hydraulic pressure difference and hydraulic loss term. This is used to construct the equivalent hydraulic pressure difference and hydraulic loss term that reflect the overall hydraulic transport resistance characteristics of the water purification system. Specifically, based on the influent pressure, product water pressure and concentrate pressure data in the multi-source hydraulic and water quality coordinated state dataset, the pressure difference between the product water path and the concentrate path is weighted and combined to form the equivalent hydraulic pressure difference that characterizes the overall hydraulic resistance level of the system. Based on this, a hydraulic loss term is constructed for energy consumption benchmark calculation.

[0021] Step S23: Temperature viscosity correction construction, used to correct the impact of water temperature changes on fluid transport power consumption and hydraulic loss. Specifically, based on the water temperature data in the multi-source hydraulic and water quality coordinated state dataset, a fluid viscosity correction factor corresponding to the water temperature is introduced to correct the equivalent hydraulic pressure difference and hydraulic loss terms obtained in step S22, and a temperature viscosity correction result reflecting the actual hydraulic transport characteristics under the current water temperature conditions is obtained.

[0022] Step S24: Construction of water quality load correction factor, used to characterize the impact of water quality changes on system pressure difference demand and energy consumption level and to correct the energy consumption benchmark. Specifically, based on the water quality load index, scaling risk characterization quantity and pollution trend reference quantity in the multi-source hydraulic and water quality coordinated state dataset, the water quality load related characteristics are normalized and combined to construct a water quality load correction factor that reflects the degree of impact of water quality condition changes on system energy consumption.

[0023] Step S25: Constructing the theoretically achievable energy consumption reference level, used to construct the theoretically achievable energy consumption reference level of the water purification system under the current hydraulic conditions, water temperature conditions and water quality load constraints. Specifically, the equivalent hydraulic pressure difference, hydraulic loss term obtained in step S22, temperature viscosity correction result obtained in step S23 and water quality load correction factor obtained in step S24 are comprehensively calculated, and combined with the system flow distribution relationship and pump group equivalent efficiency factor, to obtain unit water production energy consumption reference level data that conforms to the hydraulic and energy conservation relationship.

[0024] Step S26: Energy consumption offset calculation, used to quantify the degree of deviation of the current operating state of the water purification system from the theoretical energy consumption benchmark. Specifically, the difference between the measured energy consumption data obtained in step S21 and the theoretically achievable unit water production energy consumption reference level data obtained in step S25 is calculated to obtain the energy consumption offset that characterizes the deviation of the system operating state from the ideal energy consumption level, which is used as a component of the physical consistency energy consumption benchmark data.

[0025] Furthermore, in step S3, the water quality load-driven recovery rate safety domain is constructed to limit the feasible adjustment range of the water purification system recovery rate under the premise of ensuring water quality safety and operational stability. Specifically, based on the multi-source hydraulic and water quality coordinated state dataset and physical consistency energy consumption benchmark data, the influence relationship of recovery rate changes on system concentration ratio, transmembrane pressure difference and unit water production energy consumption is analyzed, and the operational risk trend introduced by the recovery rate increase under different water quality load conditions is identified.

[0026] Based on the analysis results of operational risk trends, a constraint relationship model between recovery rate and water quality load is constructed to determine the range of recovery rate values ​​under the current water quality conditions, thereby obtaining a safe recovery rate domain to guide energy consumption optimization decisions.

[0027] The construction of the water quality load-driven recovery rate safety domain specifically includes the following steps:

[0028] Step S31: Constructing a candidate set of recovery rates, which is used to construct a candidate set of recovery rates for the analysis of the recovery rate safety domain. Specifically, based on the influent flow rate and product flow rate in the multi-source hydraulic and water quality coordinated state dataset obtained in step S1, the current recovery rate of the water purification system is calculated, and based on the current recovery rate or the design recovery rate, multiple candidate values ​​of recovery rate are generated according to a preset step size to form a candidate set of recovery rates for subsequent risk and energy consumption analysis.

[0029] Step S32: Concentration ratio mapping model construction, which is used to establish the mapping relationship between the recovery rate change and the system concentration ratio. Specifically, based on the correspondence between the recovery rate and the influent flow rate and the product flow rate, a calculation model from recovery rate to concentration ratio is constructed, and each candidate recovery rate value obtained in step S31 is mapped to the corresponding concentration ratio, which is used to characterize the change in the degree of system water quality concentration caused by the increase in recovery rate.

[0030] Step S33: Constructing the operational risk potential function driven by water quality load, which is used to characterize the operational risk trend introduced by the increase in recovery rate under different water quality load conditions. Specifically, based on the water quality load index, scaling risk characterization quantity and pollution trend reference quantity obtained in step S1, and combined with the concentration ratio obtained in step S32, the water quality load parameters and concentration ratio are combined and calculated to construct an operational risk potential function that reflects the degree of influence of recovery rate changes on scaling risk and operational instability. Based on the preset risk threshold, the recovery rate risk constraint boundary is obtained.

[0031] Step S34: Construction of the energy consumption feasible domain coupled with the energy consumption benchmark, which is used to limit the energy consumption feasible range of the recovery rate under the energy consumption benchmark constraint. Specifically, based on the unit water production energy consumption reference level data and energy consumption offset obtained in step S2, combined with the change trend of the concentration ratio obtained in step S32, the unit water production energy consumption change under different recovery rate candidate values ​​is evaluated, a constraint relationship model between recovery rate and energy consumption change is constructed, and the recovery rate value range that satisfies the energy consumption growth not exceeding the preset threshold is determined as the recovery rate energy consumption constraint boundary.

[0032] Step S35: Solve the intersection of the two boundaries to determine the safe domain of recovery rate by comprehensively considering the operational risk constraints and energy consumption constraints. Specifically, perform an intersection operation on the recovery rate risk constraint boundary obtained in step S33 and the recovery rate energy consumption constraint boundary obtained in step S34 to determine the range of recovery rate values ​​that simultaneously meet operational safety and energy consumption controllability under the current water quality load conditions and energy consumption benchmark constraints, thus obtaining the safe domain of recovery rate used to guide energy consumption optimization decisions.

[0033] Furthermore, in step S4, the three-objective trade-off energy consumption optimization decision is used to comprehensively balance the energy consumption level, water quality compliance requirements, and operational stability of the water purification system under the constraint of the recovery rate safety domain, and generate an energy consumption optimization control strategy to guide the operation of the water purification system. Specifically, the adjustable control variables of the water purification system are jointly analyzed using the physical consistency energy consumption benchmark data obtained in step S2, the recovery rate safety domain constructed in step S3, and the real-time water quality compliance requirements as constraints.

[0034] The adjustable control variables include the pump unit operating frequency, the system recovery rate setpoint, and adjustment parameters related to flow distribution;

[0035] By constructing a three-objective function that simultaneously reflects the energy consumption per unit of water production, the degree of water quality deviation, and the trend of energy consumption offset, and by jointly optimizing the three-objective function under the constraint of the recovery rate safety domain, the combination of control variable values ​​that satisfies the minimization of energy consumption and does not exceed the constraints of water quality and operational stability is determined, and the energy consumption optimization decision results used to guide the operation of the water purification system are obtained.

[0036] The three-objective energy consumption optimization decision-making process specifically includes the following steps:

[0037] Step S41: Construction of computable metrics for the three objective functions, which is used to transform the energy consumption level, water quality compliance requirements, and operational stability of the water purification system into three types of objective functions that can be used for optimization decisions. Specifically, based on the unit water production energy consumption reference level and energy consumption offset data obtained in step S2, an energy consumption objective function reflecting the unit water production energy consumption level and a stability objective function reflecting the degree of deviation of the operational state from the ideal energy consumption benchmark are constructed. Combined with the real-time water quality compliance requirements and water quality risk characterization, a water quality objective function reflecting the degree of water quality deviation is constructed, resulting in the three objective functions used for energy consumption optimization decisions.

[0038] Step S42: The three-objective joint optimization decision output is used to determine the control strategy that meets the comprehensive trade-off requirements of the three objectives under the constraint of the recovery rate safety domain. Specifically, it involves constructing an optimization model with the pump group operating frequency, recovery rate setpoint, and flow distribution related adjustment parameters as independent variables and the three objective functions as the objective vector. Within the value space of the independent variables defined by the recovery rate safety domain constructed in step S3, the solution that minimizes the weighted sum of the three objective functions or satisfies Pareto optimality is solved, and the combination of control variable values ​​corresponding to the solution is output as the energy consumption optimization decision result of the water purification system.

[0039] When jointly optimizing the three objective functions, a benchmark offset gating mechanism is used to determine the weight of each objective function. Specifically, based on the energy consumption offset obtained in step S2, the weight coefficients of the energy consumption objective function and the stability objective function are dynamically adjusted. When the energy consumption offset exceeds the preset offset switching threshold, the weight of the stability objective function is increased through the S-shaped gating function, and vice versa.

[0040] This invention provides an energy consumption optimization system for a water purification system, comprising a data acquisition module, a baseline modeling module, a security domain construction module, and an optimization decision module;

[0041] The data acquisition module is used for multi-source hydraulic and water quality collaborative data acquisition. Through multi-source hydraulic and water quality collaborative data acquisition, a multi-source hydraulic and water quality collaborative state dataset is obtained, and the multi-source hydraulic and water quality collaborative state dataset is sent to the benchmark modeling module and the security domain construction module.

[0042] The benchmark modeling module is used for physical consistency energy consumption benchmark modeling. Through physical consistency energy consumption benchmark modeling, physical consistency energy consumption benchmark data is obtained, and the physical consistency energy consumption benchmark data is sent to the security domain construction module and the optimization decision module.

[0043] The safety domain construction module is used to construct a recovery rate safety domain driven by water quality load. Through the construction of the recovery rate safety domain driven by water quality load, the recovery rate safety domain is obtained and sent to the optimization decision module.

[0044] The optimization decision module is used for energy consumption optimization decision-making with three objectives in mind, and obtains the energy consumption optimization decision result through the energy consumption optimization decision-making with three objectives in mind.

[0045] The beneficial effects achieved by the present invention using the above solution are as follows:

[0046] (1) In view of the technical problems that existing water purification system energy consumption optimization methods generally rely solely on a single hydraulic parameter or experience setting for energy consumption control, ignoring the impact of water quality fluctuations on system pressure difference, recovery rate feasibility and actual energy consumption level, and are prone to energy consumption optimization failure or increased operational risks under water quality change conditions, this solution creatively adopts a comprehensive water purification system energy consumption optimization method that combines energy consumption benchmark modeling, recovery rate safety domain construction and three-objective trade-off energy consumption optimization decision-making. This realizes systematic constraints and collaborative decision-making on the system energy consumption optimization process under different water quality load conditions, so that the energy consumption optimization results simultaneously meet the requirements of water quality safety, stable operation and energy saving and consumption reduction, and avoid the problems of scaling, pollution or system instability caused by only pursuing energy consumption reduction;

[0047] (2) In view of the technical problem that existing energy consumption benchmark modeling methods often use statistical regression or historical average energy consumption as reference benchmarks, without fully considering the physical influence mechanism of pressure difference, flow distribution, water temperature and viscosity changes and water quality load on energy consumption, resulting in a lack of physical consistency of energy consumption benchmarks and difficulty in reflecting the theoretically achievable energy consumption level under different operating conditions, this scheme creatively adopts a physical consistency energy consumption benchmark modeling method with water quality load correction. By unifying the modeling of equivalent hydraulic pressure difference, hydraulic loss, water temperature and viscosity correction and water quality load correction factors, a unit water production energy consumption reference level that conforms to the relationship between hydraulics and energy conservation is constructed. Furthermore, energy consumption offset is introduced to quantify the actual operating state, realizing the adaptive correction of energy consumption benchmark under water quality fluctuations and operating condition changes, and improving the engineering credibility of energy consumption evaluation and optimization decision-making.

[0048] (3) In view of the technical problem that existing constraint domain construction methods usually limit the recovery rate range based only on design experience or a single scaling index, and fail to simultaneously characterize the coupling relationship between water quality load change and energy consumption growth trend, which easily leads to the situation that the recovery rate is set too high, resulting in a sharp increase in energy consumption or accumulation of operational risks, this solution creatively adopts a recovery rate safety domain construction method that combines water quality load and energy consumption benchmark. By constructing an operational risk potential function driven by water quality load and introducing energy consumption benchmark constraints, the recovery rate risk boundary and energy consumption feasible boundary are doubly defined and their intersection is found. This enables the dynamic determination of the feasible adjustment range of recovery rate under different water quality conditions, thereby ensuring operational safety while avoiding the problem of energy consumption runaway caused by blindly increasing the recovery rate.

[0049] (4) In view of the technical problem that existing target-balancing energy consumption optimization decision-making methods generally use a single energy consumption index or use operational stability and water quality safety as fixed threshold conditions for constraint, it is difficult to comprehensively characterize the relationship between energy consumption, water quality compliance and operational stability when the system operating state changes, which leads to insufficient adaptability of the optimization strategy when deviating from the ideal operating conditions. This solution creatively constructs an energy consumption optimization decision-making method based on a three-objective function, which introduces the unit water production energy consumption, water quality deviation degree and energy consumption offset reflecting the change of system operating state into the same optimization framework. Under the constraint of the recovery rate safety domain, the three types of objectives are jointly optimized, so as to effectively reduce the energy consumption level while ensuring water quality compliance and operational stability, and make the obtained optimization control strategy more in line with the actual operating needs of the water purification system under complex operating conditions. Attached Figure Description

[0050] Figure 1 A flowchart illustrating an energy consumption optimization method for a water purification system provided by the present invention;

[0051] Figure 2 This is a schematic diagram of an energy consumption optimization system for a water purification system provided by the present invention;

[0052] Figure 3 A flowchart illustrating the process of modeling the physical consistency energy consumption baseline for step S2;

[0053] Figure 4 A schematic diagram of the process for constructing the recovery rate safety domain driven by water quality load in step S3.

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0055] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0056] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0057] Example 1, see Figure 1 The present invention provides a method for optimizing energy consumption in a water purification system, the method comprising the following steps:

[0058] Step S1: Multi-source hydraulic and water quality collaborative data acquisition;

[0059] Step S2: Physically consistent energy consumption baseline modeling;

[0060] Step S3: Construction of the recovery rate safety domain driven by water quality load;

[0061] Step S4: Three-objective trade-off energy consumption optimization decision.

[0062] By performing the above operations, this solution addresses the technical problem in existing water purification system energy consumption optimization methods that generally rely solely on a single hydraulic parameter or empirical setting for energy consumption control, ignoring the impact of water quality fluctuations on system pressure difference, recovery rate feasibility, and actual energy consumption levels. This often leads to energy consumption optimization failure or increased operational risks under varying water quality conditions. This solution creatively adopts a comprehensive water purification system energy consumption optimization method that combines energy consumption benchmark modeling, recovery rate safety domain construction, and three-objective trade-off energy consumption optimization decision-making. This achieves systematic constraints and collaborative decision-making for the system's energy consumption optimization process under different water quality load conditions, ensuring that the energy consumption optimization results simultaneously meet the requirements of water quality safety, operational stability, and energy saving. This avoids problems such as scaling, pollution, or system instability caused by solely pursuing energy consumption reduction.

[0063] Example 2, see Figure 1 and Figure 2This embodiment is based on the above embodiment. In step S1, the multi-source hydraulic and water quality collaborative data acquisition is used to construct a basic operating data set for the energy consumption optimization of the water purification system. Specifically, it involves synchronously collecting hydraulic parameters and water quality parameters that are directly related to energy consumption during the operation of the water purification system, and performing timestamp alignment, outlier removal, and smoothing on data from different sources to obtain a multi-source hydraulic and water quality collaborative state dataset that characterizes the current operating condition of the water purification system.

[0064] The hydraulic parameters include inlet pressure, product water pressure, concentrate pressure, inlet flow rate, product water flow rate, concentrate flow rate, circulation return flow rate, pump operating frequency, and power parameters.

[0065] The water quality parameters include influent conductivity, dissolved solids content, turbidity, water temperature, and water quality load parameters related to scaling or pollution risk.

[0066] The water quality load parameters include the water quality load index, scaling risk characterization quantity, and pollution trend reference quantity;

[0067] Preferably, Table 1 is a specific example table of the hydraulic parameters. As part of this embodiment, Table 1 provides specific examples of the hydraulic parameters used for multi-source hydraulic and water quality collaborative data acquisition.

[0068] In this embodiment, hydraulic parameters are mainly used to reflect the hydraulic state of the water purification system under different operating conditions and its direct impact on energy consumption. Specifically, the inlet pressure, product water pressure, and concentrate pressure are used to characterize the pressure levels on the inlet side, product water side, and concentrate discharge side of the system, respectively. Through joint analysis of these pressure parameters, the overall pressure differential distribution of the system under the current operating conditions can be obtained, thus providing a basis for calculating the energy consumption per unit of product water.

[0069] Influent flow rate, product flow rate, and concentrate flow rate are used to characterize the water distribution relationship within the system, especially the ratio between product flow rate and influent flow rate. This can be used to calculate the system recovery rate and further for energy consumption normalization analysis. Circulation return flow rate is used to reflect the hydraulic regulation state within the system, and its changes usually have an indirect impact on pump load and power demand.

[0070] Furthermore, the pump unit operating frequency and pump unit operating power parameters directly reflect the actual operating status of the pump unit. Among them, the pump unit operating frequency, as a key control variable, has a clear correspondence with the pump shaft power, while the pump unit operating power constitutes the direct source for calculating the total energy consumption of the system. By synchronously collecting and uniformly modeling the above hydraulic parameters, a reliable data foundation can be provided for subsequent energy consumption benchmark construction and optimization decisions.

[0071] Table 1. Example table of specific hydraulic parameters;

[0072]

[0073] More preferably, Table 2 is a table of specific examples of the water quality parameters. As a further explanation of this embodiment, Table 2 gives specific examples of water quality parameters that are indirectly related to energy consumption.

[0074] In this embodiment, water quality parameters are used to characterize the raw water quality conditions and their impact on system operating resistance and energy consumption. Specifically, influent conductivity and dissolved solids content reflect the overall level of dissolved ions in the raw water. These parameters are prone to causing changes in system pressure differential when water quality fluctuates, thus affecting the energy consumption per unit of produced water. Turbidity reflects the content of suspended particulate matter in the raw water; when turbidity increases, the system is more likely to experience increased resistance or heavier operating burden.

[0075] Water temperature parameters are used to reflect changes in the physical properties of fluids. These changes affect fluid viscosity and hydraulic transport efficiency. Therefore, introducing water temperature parameters into energy consumption analysis helps improve the physical consistency of energy consumption modeling.

[0076] Furthermore, the water quality load parameters related to scaling or pollution risk are not single sensor output values, but rather a comprehensive measure based on multiple water quality parameters, used to reflect the overall level of system operating load under current water quality conditions. By introducing these water quality parameters, the bias caused by energy consumption optimization based solely on hydraulic parameters can be avoided, making energy consumption analysis and optimization decisions more consistent with actual operating conditions.

[0077] Table 2. Example table of specific water quality parameters;

[0078]

[0079] As a further optimization of this embodiment, Table 3 is an example table of the combination of water quality load parameters. As shown in the table, the water quality load parameters are generated by a combination calculation method and are used to comprehensively characterize multiple water quality factors.

[0080] Among them, the water quality load index is obtained by weighting the parameters of influent conductivity, dissolved solids content, turbidity and water temperature, and is used to reflect the comprehensive impact of current water quality conditions on system operating resistance and energy consumption growth trend.

[0081] Specifically, the water quality load index can be calculated by weighting and combining multiple water quality parameters. The calculation formula is as follows:

[0082] ;

[0083] In the formula, QL is the water quality load index, w1 is the weight of the influent conductivity term, EC is the influent conductivity, and EC is the influent conductivity. refThis is the reference value for the conductivity of the influent, w2 is the weight of the dissolved solids content item, and TDS is the dissolved solids content. ref This is the reference value for dissolved solids content, w3 is the weight of the turbidity term, and NTU is the influent turbidity. ref This is the reference value for influent turbidity, w4 is the weight of the water temperature term, and T is the influent temperature. ref It is the reference value for the inlet water temperature, and all weights are greater than zero and sum to 1;

[0084] The scaling risk characterization quantity is calculated by correlating the dissolved solids content after water temperature correction with the system recovery rate parameter. This characterization quantity can be used to depict the trend of scaling risk with water quality under different recovery rate settings, and provide a quantitative input basis for the subsequent construction of the recovery rate safety domain.

[0085] Specifically, the scaling risk characterization metric can be calculated by correlating the dissolved solids content (corrected for water temperature) with the system recovery rate, using the following formula:

[0086] ;

[0087] In the formula, SR is a measure of scaling risk. This is the water temperature correction factor, used to characterize the degree of influence of water temperature changes on the scaling tendency of dissolved substances. It can take a positive or negative value, depending on the dominant scaling component of the current water quality. For example, when calcium carbonate scaling is dominant, a positive value is taken, indicating that high temperature exacerbates the scaling risk. R is the current recovery rate of the system, defined as the ratio of product water flow rate to influent water flow rate. ref This is a baseline value for recovery rate;

[0088] The pollution trend reference quantity is calculated by combining the time change rate of turbidity parameters with the continuous operating time of the system, and is used to identify potential operational deterioration trends caused by changes in raw water quality.

[0089] Specifically, the pollution trend reference value can be calculated based on a combination of the turbidity change rate and the continuous operating time of the system, using the following formula:

[0090] ;

[0091] In the formula, PT is the pollution trend reference value, and t run It is the continuous running time of the system since the last flush or cleaning, t is the reference value of the continuous running time, and t is the time index;

[0092] In discrete sampled control systems, the above differential term can be approximated in difference form:

[0093] ;

[0094] In the formula, NTU t This is the turbidity value collected at the current moment. It is the turbidity value collected at the previous moment. It is the time interval between two consecutive turbidity samples;

[0095] By introducing the above-mentioned combination of water quality load parameters, a comprehensive characterization of water quality complexity and its impact on energy consumption can be achieved without adding additional sensors.

[0096] Table 3. Examples of combinations of water quality load parameters;

[0097]

[0098] Example 3, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S2, the physical consistency energy consumption benchmark modeling is used to construct a reference benchmark for the unit water production energy consumption of the water purification system under the current hydraulic conditions and water quality load constraints. Specifically, based on the multi-source hydraulic and water quality coordinated state data obtained in step S1, the pumping energy consumption, pressure difference loss and energy consumption related to water production in the water purification system are modeled and analyzed. Combined with the influence relationship between flow rate, pressure and temperature on fluid transport power consumption, a unit water production energy consumption calculation model that conforms to the hydraulic and energy conservation relationship is constructed.

[0099] During the modeling process, a correction factor related to water quality load is introduced to improve the calculation model, which is used to reflect the impact of water quality changes on system pressure difference and flux demand. Based on this, a theoretically achievable energy consumption reference level under the current operating conditions is formed. The energy consumption deviation of the actual operating energy consumption relative to the energy consumption reference level is calculated to obtain physical consistency energy consumption benchmark data characterizing the deviation of the system operating state from the ideal energy consumption level.

[0100] The physical consistency energy consumption benchmark modeling specifically adopts the water quality load-corrected physical consistency energy consumption benchmark modeling method, including the following steps:

[0101] Step S21: Construct the measured unit water production energy consumption, which is used to construct the measured unit water production energy consumption index of the water purification system under the current operating conditions. Specifically, based on the real-time power data of the pump group and the corresponding water production flow data in the multi-source hydraulic and water quality coordinated state dataset obtained in Step S1, the water production volume is normalized to calculate the power consumption of the pump group, and the measured energy consumption data representing the unit water production energy consumption under the current operating conditions is obtained.

[0102] Step S22: Construction of equivalent hydraulic pressure difference and hydraulic loss term. This is used to construct the equivalent hydraulic pressure difference and hydraulic loss term that reflect the overall hydraulic transport resistance characteristics of the water purification system. Specifically, based on the influent pressure, product water pressure and concentrate pressure data in the multi-source hydraulic and water quality coordinated state dataset, the pressure difference between the product water path and the concentrate path is weighted and combined to form the equivalent hydraulic pressure difference that characterizes the overall hydraulic resistance level of the system. Based on this, a hydraulic loss term is constructed for energy consumption benchmark calculation.

[0103] Preferably, the formula for calculating the equivalent hydraulic pressure difference is:

[0104] ;

[0105] In the formula, It is the equivalent hydraulic pressure difference. This is the path weight, with a preferred default value of 0.7. It's the inlet water pressure. It is the pressure on the concentrate side. It is the pressure on the product water side;

[0106] The formula for calculating the hydraulic loss term is as follows:

[0107] ;

[0108] In the formula, It is a hydraulic loss item. It is the inflow rate. It is the water production flow rate. This is a unit conversion factor;

[0109] Step S23: Temperature viscosity correction construction, used to correct the impact of water temperature changes on fluid transport power consumption and hydraulic loss. Specifically, based on the water temperature data in the multi-source hydraulic and water quality coordinated state dataset, a fluid viscosity correction factor corresponding to the water temperature is introduced to correct the equivalent hydraulic pressure difference and hydraulic loss terms obtained in step S22, and a temperature viscosity correction result reflecting the actual hydraulic transport characteristics under the current water temperature conditions is obtained.

[0110] Preferably, the formula for calculating the fluid viscosity correction factor corresponding to water temperature is:

[0111] ;

[0112] In the formula, It is a fluid viscosity correction factor corresponding to water temperature. It is the dynamic viscosity of water at a given temperature. It is the temperature correction index;

[0113] Step S24: Construction of water quality load correction factor, used to characterize the impact of water quality changes on system pressure difference demand and energy consumption level and to correct the energy consumption benchmark. Specifically, based on the water quality load index, scaling risk characterization quantity and pollution trend reference quantity in the multi-source hydraulic and water quality coordinated state dataset, the water quality load related characteristics are normalized and combined to construct a water quality load correction factor that reflects the degree of impact of water quality condition changes on system energy consumption.

[0114] The formula for calculating the water quality load correction factor is as follows:

[0115] ;

[0116] In the formula, It is a water quality load correction factor. It is the osmotic pressure influence coefficient. It is a normalized water quality load index. It is the scaling resistance influence coefficient. It is a normalized measure of scaling risk. It is the pollution trend correction factor. It is a normalized reference value for pollution trends;

[0117] Step S25: Constructing the theoretically achievable energy consumption reference level, used to construct the theoretically achievable energy consumption reference level of the water purification system under the current hydraulic conditions, water temperature conditions and water quality load constraints. Specifically, the equivalent hydraulic pressure difference, hydraulic loss term obtained in step S22, temperature viscosity correction result obtained in step S23 and water quality load correction factor obtained in step S24 are comprehensively calculated, and combined with the system flow distribution relationship and pump group equivalent efficiency factor, to obtain unit water production energy consumption reference level data that conforms to the hydraulic and energy conservation relationship.

[0118] The formula for calculating the reference level data of energy consumption per unit of water production is as follows:

[0119] ;

[0120] In the formula, This is reference level data for energy consumption per unit of water production. It is the equivalent efficiency factor, and the preferred calculation method is: ,in, It is a reference efficiency. It is a frequency efficiency curve function. It is the pump operating frequency, f ref It is a reference frequency;

[0121] Step S26: Energy consumption offset calculation, used to quantify the degree of deviation of the current operating state of the water purification system from the theoretical energy consumption benchmark. Specifically, the difference between the measured energy consumption data obtained in step S21 and the theoretically achievable unit water production energy consumption reference level data obtained in step S25 is calculated to obtain the energy consumption offset that characterizes the deviation of the system operating state from the ideal energy consumption level, which is used as a component of the physical consistency energy consumption benchmark data.

[0122] The physical consistency energy consumption benchmark data includes energy consumption offset, unit water production energy consumption reference level data, equivalent hydraulic pressure difference, water quality load correction factor and equivalent efficiency factor.

[0123] The formula for calculating the energy consumption offset is:

[0124] ;

[0125] In the formula, It is the energy consumption deviation that characterizes the system's operating state from the ideal energy consumption level. These are actual measured energy consumption data.

[0126] By performing the above operations, this solution addresses the technical problem in existing energy consumption benchmark modeling methods, which often use statistical regression or historical average energy consumption as reference benchmarks without fully considering the physical impact mechanisms of pressure difference, flow distribution, water temperature and viscosity changes, and water quality load on energy consumption. This results in a lack of physical consistency in the energy consumption benchmark and an inability to reflect the theoretically achievable energy consumption level under different operating conditions. This solution creatively adopts a physical consistency energy consumption benchmark modeling method based on water quality load correction. By uniformly modeling equivalent hydraulic pressure difference, hydraulic loss, water temperature and viscosity correction, and water quality load correction factors, a reference level for unit water production energy consumption that conforms to the relationship between hydraulics and energy conservation is constructed. Furthermore, an energy consumption offset is introduced to quantitatively evaluate the actual operating state, achieving adaptive correction of the energy consumption benchmark under water quality fluctuations and changes in operating conditions, thereby improving the engineering credibility of energy consumption evaluation and optimization decisions.

[0127] Example 4, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S3, the water quality load-driven recovery rate safety domain is constructed to limit the feasible adjustment range of the water purification system recovery rate under the premise of ensuring water quality safety and stable operation. Specifically, based on the multi-source hydraulic and water quality coordinated state dataset and physical consistency energy consumption benchmark data, the influence relationship of recovery rate change on system concentration ratio, transmembrane pressure difference and unit water production energy consumption is analyzed, and the operational risk trend introduced by the recovery rate increase under different water quality load conditions is identified.

[0128] Based on the analysis results of operational risk trends, a constraint relationship model between recovery rate and water quality load is constructed to determine the range of recovery rate values ​​under the current water quality conditions, thereby obtaining a safe recovery rate domain to guide energy consumption optimization decisions.

[0129] The construction of the water quality load-driven recovery rate safety domain specifically includes the following steps:

[0130] Step S31: Constructing a candidate set of recovery rates, which is used to construct a candidate set of recovery rates for the analysis of the recovery rate safety domain. Specifically, based on the influent flow rate and product flow rate in the multi-source hydraulic and water quality coordinated state dataset obtained in step S1, the current recovery rate of the water purification system is calculated, and based on the current recovery rate or the design recovery rate, multiple candidate values ​​of recovery rate are generated according to a preset step size to form a candidate set of recovery rates for subsequent risk and energy consumption analysis.

[0131] Step S32: Concentration ratio mapping model construction, which is used to establish the mapping relationship between the recovery rate change and the system concentration ratio. Specifically, based on the correspondence between the recovery rate and the influent flow rate and the product flow rate, a calculation model from recovery rate to concentration ratio is constructed, and each candidate recovery rate value obtained in step S31 is mapped to the corresponding concentration ratio, which is used to characterize the change in the degree of system water quality concentration caused by the increase in recovery rate.

[0132] The formula for calculating the concentration ratio is:

[0133] ;

[0134] In the formula, CF is the theoretical concentration ratio corresponding to the recovery rate R. The concentration ratio is a theoretically calculated value used to describe the water concentration trend caused by the change in recovery rate. Its relative change relationship is used for risk assessment and is not used to directly replace the online measurement value. R is a candidate value for the recovery rate.

[0135] Step S33: Constructing the operational risk potential function driven by water quality load, which is used to characterize the operational risk trend introduced by the increase in recovery rate under different water quality load conditions. Specifically, based on the water quality load index, scaling risk characterization quantity and pollution trend reference quantity obtained in step S1, and combined with the concentration ratio obtained in step S32, the water quality load parameters and concentration ratio are combined and calculated to construct an operational risk potential function that reflects the degree of influence of recovery rate changes on scaling risk and operational instability. Based on the preset risk threshold, the recovery rate risk constraint boundary is obtained.

[0136] The formula for calculating the operational risk potential function is as follows:

[0137] ;

[0138] In the formula, This is the operational risk potential function, where a1 is the weight corresponding to the overall water quality load, a2 is the weight corresponding to the scaling risk, and a3 is the weight corresponding to particulate pollution. It is a nonlinear index of concentration risk corresponding to the overall water quality load. It is a nonlinear index of concentration risk corresponding to scaling risk; preferably, the weights a1, a2, a... 3可 The relative weights of different water quality load factors are determined through regression fitting or sensitivity analysis based on historical stable operating data and are used to reflect the degree of contribution of different water quality load factors to operational risk.

[0139] The risk threshold is set based on long-term system operating experience and is denoted by [symbol]. Specifically, time periods during historical operation with no abnormal pressure differences and no water quality exceeding standards can be selected to calculate the corresponding... The 95th percentile value was used as the threshold.

[0140] The formula for calculating the recovery rate risk constraint boundary is:

[0141] ;

[0142] In the formula, It is the upper bound of risk constraints. It is the set of candidate values ​​for the recovery rate. It is a risk threshold;

[0143] Step S34: Construction of the energy consumption feasible domain coupled with the energy consumption benchmark, which is used to limit the energy consumption feasible range of the recovery rate under the energy consumption benchmark constraint. Specifically, based on the unit water production energy consumption reference level data and energy consumption offset obtained in step S2, combined with the change trend of the concentration ratio obtained in step S32, the unit water production energy consumption change under different recovery rate candidate values ​​is evaluated, a constraint relationship model between recovery rate and energy consumption change is constructed, and the recovery rate value range that satisfies the energy consumption growth not exceeding the preset threshold is determined as the recovery rate energy consumption constraint boundary.

[0144] The calculation formula for evaluating the change in unit water production energy consumption under different candidate recovery rate values ​​is as follows:

[0145] ;

[0146] In the formula, At the current time t, if the recovery rate is adjusted to R, the system evaluates the predicted energy consumption per unit of produced water. This is the reference level data for energy consumption per unit of produced water. b1 is the osmotic pressure energy consumption sensitivity coefficient, used to characterize the proportion of the impact of the increase in osmotic pressure caused by the increase in concentration on the total energy consumption. b2 is the energy consumption anomaly inheritance coefficient, used to determine the degree to which the current existing energy consumption deviation is inherited to the prediction model. This is the concentration factor increment, used to represent the increase in concentration resulting from the candidate recovery rate relative to the current recovery rate. It is the energy consumption offset;

[0147] More preferably, as a specific implementation of the prediction model, the system first determines the osmotic pressure energy consumption sensitivity coefficient b1 based on the real-time collected water temperature and dissolved solids content using the osmotic pressure principle to reflect the theoretical energy consumption increment brought about by the concentration increase; at the same time, it dynamically matches the energy consumption anomaly inheritance coefficient b2 according to the statistical characteristics of historical energy consumption deviations to assess the continued impact of the current equipment performance degradation on the future; finally, it traverses the recovery rate candidate set, substitutes the concentration ratio corresponding to each candidate value into the formula to calculate one by one, and generates a predicted energy consumption dataset that changes with the recovery rate for subsequent threshold determination.

[0148] The calculation formula for the constraint relationship model between the recovery rate and energy consumption change is as follows:

[0149] ;

[0150] In the formula, This is the energy consumption growth tolerance rate, used to represent the percentage increase in predicted energy consumption that is allowed to exceed the theoretical baseline in order to improve recycling rates. The overall threshold is a preset threshold, representing the upper limit of energy consumption allowed under the current operating conditions;

[0151] The formula for calculating the energy consumption constraint boundary of the recovery rate is:

[0152] ;

[0153] In the formula, It is the upper bound of energy consumption constraints;

[0154] Step S35: Solve the intersection of the two boundaries to determine the safe domain of recovery rate by combining the operational risk constraints and energy consumption constraints. Specifically, perform an intersection operation on the recovery rate risk constraint boundary obtained in step S33 and the recovery rate energy consumption constraint boundary obtained in step S34 to determine the range of recovery rate values ​​that simultaneously meet operational safety and energy consumption controllability under the current water quality load conditions and energy consumption benchmark constraints, and obtain the safe domain of recovery rate to guide energy consumption optimization decisions.

[0155] The formula for calculating the recovery rate safety range is:

[0156] ;

[0157] In the formula, It is the recycling rate safety domain. It is the lower bound of the safe range for recycling rate. This is the upper bound of the recovery rate safety region; wherein, the formula for calculating the upper bound of the recovery rate safety region is: The formula for calculating the lower bound of the recovery rate safety region is: , It is the minimum design recovery rate of the equipment, determined by the minimum flow rate of the pump or the minimum concentrate flow rate limit of the membrane module. It is the maximum allowable downward adjustment in a single adjustment, used to prevent drastic fluctuations in the recovery rate from causing hydraulic shock to the system;

[0158] As a further optimization of this embodiment, the recovery rate safety domain can be updated in real time with water quality load and energy consumption status to form a dynamic time-varying feasible domain.

[0159] By performing the above operations, this solution addresses the technical problem in existing constraint domain construction methods that typically limit the recovery rate range based solely on design experience or a single scaling index, failing to simultaneously characterize the coupling relationship between water quality load changes and energy consumption growth trends. This can easily lead to situations where the recovery rate is set too high, resulting in a sharp increase in energy consumption or the accumulation of operational risks. This solution creatively adopts a recovery rate safety domain construction method that combines water quality load and energy consumption benchmarks. By constructing an operational risk potential function driven by water quality load and introducing energy consumption benchmark constraints, the solution double-limits and finds the intersection of the recovery rate risk boundary and the energy consumption feasible boundary. This enables the dynamic determination of the feasible adjustment range of the recovery rate under different water quality conditions, thereby ensuring operational safety while avoiding the problem of energy consumption runaway caused by blindly increasing the recovery rate.

[0160] Example 5, see Figure 1 , Figure 2 This embodiment is based on the above embodiment. In step S4, the three-objective trade-off energy consumption optimization decision is used to comprehensively balance the energy consumption level, water quality compliance requirements and operational stability of the water purification system under the constraint of the recovery rate safety domain, and generate an energy consumption optimization control strategy to guide the operation of the water purification system. Specifically, the adjustable control variables of the water purification system are jointly analyzed using the physical consistency energy consumption benchmark data obtained in step S2, the recovery rate safety domain constructed in step S3 and the real-time water quality compliance requirements as constraints.

[0161] The adjustable control variables include the pump unit operating frequency, the system recovery rate setpoint, and adjustment parameters related to flow distribution;

[0162] By constructing a three-objective function that simultaneously reflects the energy consumption per unit of water production, the degree of water quality deviation, and the trend of energy consumption offset, and by jointly optimizing the three-objective function under the constraint of the recovery rate safety domain, the combination of control variable values ​​that satisfies the minimization of energy consumption and does not exceed the constraints of water quality and operational stability is determined, and the energy consumption optimization decision results used to guide the operation of the water purification system are obtained.

[0163] The three-objective energy consumption optimization decision-making process specifically includes the following steps:

[0164] Step S41: Construction of computable metrics for the three objective functions, which is used to transform the energy consumption level, water quality compliance requirements, and operational stability of the water purification system into three types of objective functions that can be used for optimization decisions. Specifically, based on the unit water production energy consumption reference level and energy consumption offset data obtained in step S2, an energy consumption objective function reflecting the unit water production energy consumption level and a stability objective function reflecting the degree of deviation of the operational state from the ideal energy consumption benchmark are constructed. Combined with the real-time water quality compliance requirements and water quality risk characterization, a water quality objective function reflecting the degree of water quality deviation is constructed, resulting in the three objective functions used for energy consumption optimization decisions.

[0165] The energy consumption objective function,

[0166] ;

[0167] In the formula, It is the energy consumption objective function value, and u is the control variable vector, which at least includes the pump operating frequency, recovery rate setpoint, and flow distribution parameters. It predicts the energy consumption per unit of water production;

[0168] The stability objective function,

[0169] ;

[0170] In the formula, It is the value of the stability objective function. It is the stability differential penalty coefficient, where, introduced It characterizes the degree of deviation of the current energy consumption state of the system from the physical consistency energy consumption benchmark. Its absolute value and rate of change are used to indirectly reflect the stability of the equipment's operating state.

[0171] The water quality objective function

[0172] ;

[0173] In the formula, It is the objective function value for water quality. It is to predict the conductivity of the produced water. This is the limit for the conductivity of the produced water. It is to predict the turbidity of the produced water. It is the limit value for turbidity of the produced water;

[0174] More preferably, the above-mentioned water quality objective function adopts a dimensionless normalized superposition form to uniformly characterize the degree of exceedance of multiple water quality indicators, and its weight can be adjusted according to water quality management requirements;

[0175] Step S42: The three-objective joint optimization decision output is used to determine the control strategy that meets the comprehensive trade-off requirements of the three objectives under the constraint of the recovery rate safety domain. Specifically, it involves constructing an optimization model with the pump group operating frequency, recovery rate setpoint, and flow distribution related adjustment parameters as independent variables and the three objective functions as the objective vector. Within the value space of the independent variables defined by the recovery rate safety domain constructed in step S3, the solution that minimizes the weighted sum of the three objective functions or satisfies Pareto optimality is solved, and the combination of control variable values ​​corresponding to the solution is output as the energy consumption optimization decision result of the water purification system.

[0176] In a preferred embodiment of this invention, to achieve the three-objective joint optimization decision output described in step S42, it is necessary to specifically establish a quantitative mapping between control variables and objective functions, and determine the dynamic trade-offs among multiple objectives. Therefore, step S42 specifically includes the following steps:

[0177] Step S421: Construction of control variable prediction mapping, used to solve the problem of calculating the predicted values ​​required by the objective function in S41. Specifically, based on the multi-source hydraulic and water quality coordinated state dataset, a system prediction model is constructed with the control variable vector as input and the key performance indicators as output.

[0178] Preferably, the formula for predicting the energy consumption per unit of water production is as follows:

[0179] ;

[0180] In the formula, It predicts the energy consumption per unit of water production. It is a function of the pump set frequency and power characteristic curves. It is the osmotic pressure energy consumption sensitivity coefficient. It is about setting the recovery rate. This is the current concentration ratio, used to characterize the degree of water concentration under the actual operating conditions of the system at the current moment, and serves as a benchmark for calculating the osmotic pressure increment. It is the overall system efficiency. This is the system influent flow rate, which is multiplied by the set recovery rate to obtain the predicted product water flow rate, thereby normalizing the denominator of the unit product water energy consumption. The above prediction calculation formula is an approximate calculation model used for optimization decision-making, used to characterize the trend of osmotic pressure energy consumption increment caused by changes in recovery rate. Its parameters can be obtained by calibration based on historical data for specific systems.

[0181] The predicted conductivity of the produced water is based on a dissolution-diffusion model, and the calculation formula is as follows:

[0182] ;

[0183] In the formula, This is the predicted conductivity of the permeate, where B is the solute permeation coefficient of the membrane element. It is the average concentration on the concentrate side. It is the total membrane area; the solubility-diffusion model is used to reflect the relative trend of conductivity with recovery rate, and its parameter B can comprehensively characterize the membrane's effective permeation characteristics for dissolved ions;

[0184] The predicted permeate turbidity is based on a permeation model that considers influent turbidity and concentration ratio. The calculation formula is as follows:

[0185] ;

[0186] In the formula, It is to predict the turbidity of the produced water. This is the current measured turbidity of the influent. It sets the concentration ratio corresponding to the recovery rate, used to characterize the increase in suspended particulate matter concentration on the influent side due to the increased recovery rate. It is the standard turbidity rejection rate of the membrane module;

[0187] Step S422: Construction of benchmark offset gating dynamic weights, used to determine the weight coefficients when the three objective functions are weighted and summed, especially to resolve the contradiction between energy consumption and stability. Specifically, a benchmark offset gating mechanism is adopted, and the energy consumption target weights and stability target weights are dynamically adjusted based on the energy consumption offset calculated in step S2 to obtain dynamic weights.

[0188] Preferably, the dynamic adjustment of the energy consumption target weight and the stability target weight is achieved using an S-shaped gating function, and the calculation formula is as follows:

[0189] ;

[0190] ;

[0191] In the formula, It is the dynamic weight of the stability objective. This is the upper limit of the stability weight, with a default value of 0.8. k is an adjustment coefficient for sensitivity. It is the offset switching threshold. It is the dynamic weight of the energy consumption target. These are the static weights of water quality targets;

[0192] The dynamic weights of the stability target, the energy consumption target, and the water quality target all satisfy non-negativity constraints, and can be adjusted as necessary. Lower bound truncation is performed to ensure the effectiveness of weight optimization;

[0193] Step S423: Optimization within the constraint space to generate the final decision. Specifically, within the intersection space of the recovery rate safety domain and the equipment frequency constraint constructed in step S3, a set of candidate control strategies is discretized and generated. For each candidate strategy in the set, a comprehensive score is calculated by combining the three objective functions and the dynamic weights obtained in step S422. The solution that minimizes the weighted sum of the three objective functions is found, and the combination of control variable values ​​corresponding to this solution is output as the energy consumption optimization decision result of the water purification system.

[0194] The formula for calculating the comprehensive score is as follows:

[0195] ;

[0196] In the formula, It is a comprehensive score, where i is the index of the control variable vector;

[0197] By performing the above operations, this solution addresses the technical problem in existing target-balancing energy consumption optimization decision-making methods, which commonly use a single energy consumption index or constrain operational stability and water quality safety as fixed threshold conditions. This makes it difficult to comprehensively characterize the relationship between energy consumption, water quality compliance, and operational stability when the system's operating state changes, leading to insufficient adaptability of the optimization strategy when deviating from ideal operating conditions. This solution creatively constructs an energy consumption optimization decision-making method based on a three-objective function. It introduces unit water production energy consumption, water quality deviation degree, and energy consumption offset reflecting changes in system operating state into the same optimization framework. Under the constraint of the recovery rate safety domain, the three types of objectives are jointly optimized, thereby achieving an effective reduction in energy consumption while ensuring water quality compliance and operational stability. This makes the obtained optimized control strategy more in line with the actual operating needs of the water purification system under complex operating conditions.

[0198] Example 6, see Figure 1 and Figure 2 Based on the above embodiments, this embodiment provides a water purification system energy consumption optimization system, including a data acquisition module, a benchmark modeling module, a security domain construction module, and an optimization decision module;

[0199] The data acquisition module is used for multi-source hydraulic and water quality collaborative data acquisition. Through multi-source hydraulic and water quality collaborative data acquisition, a multi-source hydraulic and water quality collaborative state dataset is obtained, and the multi-source hydraulic and water quality collaborative state dataset is sent to the benchmark modeling module and the security domain construction module.

[0200] The benchmark modeling module is used for physical consistency energy consumption benchmark modeling. Through physical consistency energy consumption benchmark modeling, physical consistency energy consumption benchmark data is obtained, and the physical consistency energy consumption benchmark data is sent to the security domain construction module and the optimization decision module.

[0201] The safety domain construction module is used to construct a recovery rate safety domain driven by water quality load. Through the construction of the recovery rate safety domain driven by water quality load, the recovery rate safety domain is obtained and sent to the optimization decision module.

[0202] The optimization decision module is used for energy consumption optimization decision-making with three objectives in mind, and obtains the energy consumption optimization decision result through the energy consumption optimization decision-making with three objectives in mind.

[0203] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0204] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0205] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for optimizing energy consumption in a water purification system, characterized in that: The method includes the following steps: Step S1: Multi-source hydraulic and water quality collaborative data acquisition. Simultaneously collect hydraulic parameters and water quality parameters that are directly related to energy consumption during the operation of the water purification system to obtain a multi-source hydraulic and water quality collaborative state dataset that characterizes the current operating condition of the water purification system. Step S2: Physically Consistent Energy Consumption Benchmark Modeling. Based on the multi-source hydraulic and water quality synergistic state data obtained in Step S1, the water purification system is modeled and analyzed to construct a unit water production energy consumption calculation model that conforms to the hydraulic and energy conservation relationship. During the modeling process, a correction factor related to water quality load is introduced to improve the calculation model, and the theoretically achievable energy consumption reference level is calculated. The energy consumption deviation of the actual operating energy consumption relative to the energy consumption reference level is calculated to obtain physically consistent energy consumption benchmark data. Step S3: Constructing the recovery rate safety domain driven by water quality load. Based on the multi-source hydraulic and water quality coordinated state dataset and the physical consistency energy consumption benchmark data, analyze the impact of recovery rate changes on the system concentration ratio, transmembrane pressure difference, and unit product water energy consumption, and identify the operational risk trends introduced by recovery rate improvement under different water quality load conditions; construct a constraint relationship model between recovery rate and water quality load to obtain the recovery rate safety domain used to guide energy consumption optimization decisions; Step S4: Three-objective trade-off energy consumption optimization decision. Using the physical consistency energy consumption benchmark data obtained in Step S2, the recovery rate safety domain constructed in Step S3, and the real-time water quality compliance requirements as constraints, the adjustable control variables of the water purification system are jointly analyzed. By constructing a three-objective function that reflects the changing trends of unit water production energy consumption, water quality deviation degree, and energy consumption offset, the three-objective function is jointly optimized under the constraint of the recovery rate safety domain to obtain the energy consumption optimization decision results used to guide the operation of the water purification system.

2. The energy consumption optimization method for a water purification system according to claim 1, characterized in that: In step S1, the hydraulic parameters include inlet pressure, product water pressure, concentrate pressure, inlet flow rate, product water flow rate, concentrate flow rate, circulation return flow rate, pump operating frequency, and power parameters. The water quality parameters include influent conductivity, dissolved solids content, turbidity, water temperature, and water quality load parameters related to scaling or pollution risk. The water quality load parameters include the water quality load index, scaling risk characterization quantity, and pollution trend reference quantity.

3. The energy consumption optimization method for a water purification system according to claim 2, characterized in that: The physical consistency energy consumption benchmark modeling method specifically adopts the physical consistency energy consumption benchmark modeling method with water quality load correction, including the following steps: Step S21: Constructing the measured unit water production energy consumption; Step S22: Constructing the equivalent hydraulic pressure difference and hydraulic loss terms; Step S23: Constructing the temperature and viscosity correction; Step S24: Constructing the water quality load correction factor; Step S25: Constructing the theoretically achievable energy consumption reference level; Step S26: Calculating the energy consumption offset.

4. The energy consumption optimization method for a water purification system according to claim 3, characterized in that: In step S24, based on the water quality load index, scaling risk characterization quantity, and pollution trend reference quantity in the multi-source hydraulic and water quality coordinated state dataset, the water quality load-related characteristics are normalized and combined to construct a water quality load correction factor that reflects the degree of influence of changes in water quality conditions on system energy consumption. In step S25, the equivalent hydraulic pressure difference, hydraulic loss term, temperature and viscosity correction results, and the water quality load correction factor obtained in step S24 are comprehensively calculated, and combined with the system flow distribution relationship and the pump group equivalent efficiency factor, to obtain reference level data of unit water production energy consumption that conforms to the hydraulic and energy conservation relationship.

5. The energy consumption optimization method for a water purification system according to claim 4, characterized in that: In step S3, the construction of the water quality load-driven recovery rate safety domain specifically includes the following steps: Step S31: Construction of the recovery rate candidate set; Step S32: Construction of the concentration ratio mapping model; Step S33: Construction of the water quality load-driven operation risk potential function; Step S34: Construction of the energy consumption feasible domain coupled with the energy consumption benchmark; Step S35: Solving the intersection of the two boundaries.

6. The energy consumption optimization method for a water purification system according to claim 5, characterized in that: In step S33, specifically, based on the water quality load index, scaling risk characterization quantity, and pollution trend reference quantity obtained in step S1, and combined with the concentration ratio, the water quality load parameters and concentration ratio are combined for calculation to construct an operational risk potential function reflecting the degree of influence of recovery rate changes on scaling risk and operational instability. Based on a preset risk threshold, the recovery rate risk constraint boundary is obtained. In step S34, specifically, based on the unit product water energy consumption reference level data and energy consumption offset obtained in step S2, and combined with the changing trend of the concentration ratio, the risk constraint boundary is determined for different candidate recovery rate values. The unit water production energy consumption change is evaluated, a constraint relationship model between recovery rate and energy consumption change is constructed, and the range of recovery rate values ​​that satisfy the energy consumption growth not exceeding a preset threshold is determined as the recovery rate energy consumption constraint boundary; in step S35, specifically, the intersection operation is performed on the recovery rate risk constraint boundary obtained in step S33 and the recovery rate energy consumption constraint boundary obtained in step S34 to determine the recovery rate value range that simultaneously satisfies the operational safety and energy consumption controllability under the current water quality load conditions and energy consumption benchmark constraints, and obtain the recovery rate safety domain used to guide energy consumption optimization decisions.

7. The energy consumption optimization method for a water purification system according to claim 6, characterized in that: In step S4, the adjustable control variables include the pump set operating frequency, the system recovery rate setpoint, and adjustment parameters related to flow distribution; The computable metric construction of the three objective functions for the three-objective trade-off energy consumption optimization decision is specifically as follows: based on the unit water production energy consumption reference level and energy consumption offset data obtained in step S2, an energy consumption objective function reflecting the unit water production energy consumption level and a stability objective function reflecting the degree of deviation of the operating state from the ideal energy consumption benchmark are constructed. Combined with real-time water quality compliance requirements and water quality risk characterization quantities, a water quality objective function reflecting the degree of water quality deviation is constructed, thus obtaining the three objective functions used for energy consumption optimization decision.

8. The energy consumption optimization method for a water purification system according to claim 7, characterized in that: In step S4, when jointly optimizing the three objective functions, a benchmark offset gating mechanism is used to determine the weights of each objective function; Specifically, based on the energy consumption offset obtained in step S2, the weight coefficients of the energy consumption objective function and the stability objective function are dynamically adjusted; when the energy consumption offset exceeds the preset offset switching threshold, the weight of the stability objective function is increased through the S-shaped gating function, and vice versa.

9. A water purification system energy consumption optimization system, used to implement the water purification system energy consumption optimization method as described in any one of claims 1-8, characterized in that: It includes a data acquisition module, a benchmark modeling module, a security domain construction module, and an optimization decision-making module.

10. The water purification system energy consumption optimization system according to claim 9, characterized in that: The data acquisition module is used for multi-source hydraulic and water quality collaborative data acquisition. Through multi-source hydraulic and water quality collaborative data acquisition, a multi-source hydraulic and water quality collaborative state dataset is obtained, and the multi-source hydraulic and water quality collaborative state dataset is sent to the benchmark modeling module and the security domain construction module. The benchmark modeling module is used for physical consistency energy consumption benchmark modeling. Through physical consistency energy consumption benchmark modeling, physical consistency energy consumption benchmark data is obtained, and the physical consistency energy consumption benchmark data is sent to the security domain construction module and the optimization decision module. The safety domain construction module is used to construct a recovery rate safety domain driven by water quality load. Through the construction of the recovery rate safety domain driven by water quality load, the recovery rate safety domain is obtained and sent to the optimization decision module. The optimization decision module is used for energy consumption optimization decision-making with three objectives in mind, and obtains the energy consumption optimization decision result through the energy consumption optimization decision-making with three objectives in mind.

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