Water filtering and impurity removing device for hydroenergy hydrogen production

Abstract

The invention discloses a water filtering and impurity removing device for hydroenergy hydrogen production, which belongs to the technical field of water treatment and comprises a pretreatment efficiency analysis module, a membrane system efficiency analysis module, a water quality risk analysis module, a water quality-performance analysis module and a water inlet pressure optimization module. And correspondingly outputting a pretreatment efficiency coefficient, a membrane system operation efficiency coefficient, a water quality risk coefficient and a water quality-performance adaptation degree according to the modules. Finally, the water inlet pressure of the RO membrane is adaptively adjusted based on the pressure optimization model, and a closed-loop intelligent control system is formed. Accurate sensing and optimal control of the water treatment process are achieved, the stability of the water quality of hydrogen production water is effectively guaranteed, the operation efficiency of the system is improved, and the service life of equipment is prolonged.

Description

Technical Field

[0001] This invention belongs to the field of water treatment technology, and in particular relates to a water body filtration and impurity removal device for water-based hydrogen production. Background Technology

[0002] Hydrogen production from water, particularly the production of high-purity hydrogen through water electrolysis, is a key pathway for developing the green energy industry. This technology places extremely stringent requirements on the purity of the raw water. Impurities such as suspended solids, dissolved ions, organic matter, and trace gases in the water can severely poison the electrolyzer catalyst and clog the proton exchange membrane, leading to a sharp drop in hydrogen production efficiency and a shortened equipment lifespan. Therefore, a highly efficient and stable water filtration and impurity removal device is a prerequisite for the reliable operation of a hydropower hydrogen production system.

[0003] Currently, most existing water treatment systems employ a conventional combined process of "pretreatment + reverse osmosis (RO) + fine treatment," producing pure water through multi-stage filtration combined with RO membranes. However, these existing technologies largely rely on preset, fixed process parameters. Key parameters such as the operating pressure of the RO system are typically set based on experience or simply controlled by PID, lacking a comprehensive and forward-looking assessment of the overall system's treatment efficiency, membrane fouling status, and influent water quality risks. When influent water quality fluctuates or membrane module performance gradually declines, the system cannot adaptively adjust to its optimal operating state, leading to problems such as unstable product water quality, premature membrane fouling, or excessive energy consumption. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a water body filtration and impurity removal device for water-based hydrogen production, thus solving the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a water body filtration and impurity removal device for hydropower hydrogen production, comprising:

[0006] The pretreatment efficiency analysis module constructs a pretreatment efficiency model based on influent turbidity (RO membrane inlet point), oxidation-reduction potential (RO membrane inlet point), and pressure difference A (pressure difference between multi-media filter and activated carbon filter), and outputs the pretreatment efficiency coefficient.

[0007] The membrane system performance analysis module constructs a membrane system operation performance model based on the pressure difference B (the inlet and outlet pressure difference between the RO membrane module and the EDI unit), the RO membrane concentrate discharge pressure, the RO permeate flow rate, and the inlet water temperature (RO membrane inlet point), and outputs the membrane system operation performance coefficient.

[0008] The water quality risk analysis module constructs a water quality risk model based on liquid pH value (RO membrane inlet), silica concentration (RO membrane inlet), and dissolved oxygen concentration (final product outlet), and outputs water quality risk coefficients.

[0009] The water quality-performance analysis module constructs a water quality-performance adaptation model based on the water quality risk coefficient and the resistivity (final product outlet) and total organic carbon concentration (final product outlet) under the membrane system operating efficiency coefficient, and outputs the water quality-performance adaptation degree.

[0010] The inlet water pressure optimization module constructs a pressure optimization model based on water quality-performance compatibility, pretreatment efficiency coefficient, and current RO membrane inlet water pressure, and outputs the target RO membrane inlet water pressure.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] A further technical solution: The pressure optimization model is expressed as follows:

[0013]

[0014] in, Indicates the target RO membrane inlet pressure. Indicates the minimum permissible RO membrane inlet pressure. Indicates the maximum permissible RO membrane inlet pressure. This indicates the current RO membrane inlet pressure. Indicates the target water quality-performance compatibility. This represents the preprocessing efficiency coefficient.

[0015] Further technical solution: The working content of the water quality-performance analysis module includes:

[0016] The resistivity (final water outlet) and total organic carbon concentration (final water outlet) are subjected to maximum-minimum normalization to obtain the resistivity index and organic carbon concentration index.

[0017] A water quality-performance fit model is constructed based on the resistivity index and organic carbon concentration index under the water quality risk coefficient, membrane system operating efficiency coefficient, and the water quality-performance fit degree is obtained. The water quality-performance fit model is expressed as follows:

[0018]

[0019] in, Indicates water quality-performance compatibility. Indicates the water quality risk coefficient. Indicates the operating efficiency coefficient of the membrane system. Represents the resistivity index. Indicates the organic carbon concentration index. This indicates the weight of resistivity in the fit calculation. The higher the value, the better the water quality and system performance.

[0020] Further technical solution: The working content of the water quality risk analysis module includes:

[0021] The silica concentration and dissolved oxygen concentration were subjected to maximum-min normalization to obtain the silica concentration index and dissolved oxygen concentration index.

[0022] The pH deviation index is obtained by comparing the absolute difference between the influent pH value and the optimal pH value with the allowable deviation from the optimal pH value.

[0023] A water quality risk model is constructed based on the silica concentration index, dissolved oxygen concentration index, and pH deviation index to obtain the water quality risk coefficient. The water quality risk model is expressed as follows:

[0024]

[0025] in, Indicates the water quality risk coefficient. This indicates that the pH value deviates from the index. Indicates the silica concentration index. The dissolved oxygen concentration index, the Furthermore, the higher the value, the lower the risk of the membrane system.

[0026] Further technical solution: The working content of the membrane system performance analysis module includes:

[0027] The RO permeate flow rate is subjected to maximum-min normalization to obtain the permeate flow rate index.

[0028] The pressure difference B is normalized by the maximum-minimum process to obtain the pressure difference factor B, and the pressure difference index B is obtained by taking the complement of the pressure difference factor B (pressure difference index B = 1 - pressure difference factor B).

[0029] The absolute difference between the RO concentrate discharge pressure and the standard RO concentrate discharge pressure is compared with the allowable deviation from the standard RO concentrate discharge pressure to obtain the concentrate discharge pressure deviation factor.

[0030] The absolute difference between the inlet water temperature and the standard inlet water temperature is compared with the allowable deviation from the standard inlet water temperature to obtain the inlet water temperature deviation factor.

[0031] The concentrate discharge pressure deviation factor and the influent temperature deviation factor are subjected to maximum-minimum normalization to obtain the concentrate discharge pressure deviation index and the influent temperature deviation index.

[0032] A membrane system operating efficiency model is constructed based on the pressure difference index B, product water flow rate index, concentrate discharge pressure deviation index, and influent temperature deviation index, outputting the membrane system operating efficiency coefficient. The membrane system operating efficiency model is expressed as follows:

[0033]

[0034] in, Indicates the operating efficiency coefficient of the membrane system. Indicates the differential pressure index B. Water production flow index This indicates that the concentrated wastewater discharge pressure deviates from the index. The index indicates the deviation of the inlet water temperature. The higher the value, the better the membrane system performs.

[0035] Further technical solution: The preprocessing performance analysis module's working content includes:

[0036] Obtain the influent turbidity (RO membrane inlet point), oxidation-reduction potential (RO membrane inlet point), and pressure difference A (pressure difference between the multi-media filter and the activated carbon filter);

[0037] The influent turbidity is normalized by maximum-min to obtain the influent turbidity factor, and the complement of the influent turbidity factor is taken to obtain the influent turbidity index (influent turbidity index = 1 - influent turbidity factor).

[0038] The pressure difference A is normalized by the maximum-minimum process to obtain the pressure difference factor A, and the pressure difference index A is obtained by taking the complement of the pressure difference factor A (pressure difference index A = 1 - pressure difference factor A).

[0039] Importing redox potential into the formula In the process, the redox potential index is obtained. Indicates redox potential. This represents the redox potential sensitivity coefficient. Indicates the reference potential;

[0040] A pretreatment efficiency model is constructed based on the influent turbidity index, pressure difference index A, and redox potential index, and the pretreatment efficiency coefficient is obtained. The pretreatment efficiency model is expressed as follows:

[0041]

[0042] in, Indicates the preprocessing efficiency coefficient. Indicates the turbidity index of the influent. Indicates the differential pressure index A. Indicates the redox potential index. Represents the weight coefficient and The The larger the value, the better the preprocessing effect.

[0043] This invention provides a water body filtration and impurity removal device for water-based hydrogen production, which has the following advantages compared with the prior art:

[0044] 1. This invention achieves accurate perception and status assessment of the entire water treatment process by constructing a multi-level evaluation model that includes pretreatment efficiency, membrane system operation efficiency, water quality risk, and water quality-performance compatibility, thereby upgrading the control strategy from experience-driven to data and model-driven.

[0045] 2. This invention uses the comprehensive indicator of "water quality-performance compatibility" to provide real-time feedback on whether the final produced water (resistivity, total organic carbon) meets the requirements for hydrogen production. It also dynamically adjusts the RO inlet pressure through a pressure optimization model, ensuring the continuous stability and compliance of the produced water quality from the result point.

[0046] 3. This invention can identify pretreatment unit blockage, membrane fouling trends and water quality risks at an early stage, and by optimizing pressure settings, it can ensure the quality of produced water while avoiding the RO membrane from operating under excessively high or low pressure, effectively slowing down the rate of membrane fouling, reducing system energy consumption, and significantly extending the service life of core equipment.

[0047] 4. This invention uses a water quality risk model to quantitatively assess key potential risk factors such as pH, silica, and dissolved oxygen, and issue early warnings so that operators can take preventive measures to prevent scaling, chemical pollution, and other problems, thereby improving the stability and safety of the system operation. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0051] Please see Figure 1 According to one embodiment of the present invention, a water body filtration and impurity removal device for hydrogen production from water includes:

[0052] The pretreatment efficiency analysis module constructs a pretreatment efficiency model based on influent turbidity (RO membrane inlet point), oxidation-reduction potential (RO membrane inlet point), and pressure difference A (pressure difference between multi-media filter and activated carbon filter), and outputs the pretreatment efficiency coefficient.

[0053] The membrane system performance analysis module constructs a membrane system operation performance model based on the pressure difference B (the inlet and outlet pressure difference between the RO membrane module and the EDI unit), the RO membrane concentrate discharge pressure, the RO permeate flow rate, and the inlet water temperature (RO membrane inlet point), and outputs the membrane system operation performance coefficient.

[0054] The water quality risk analysis module constructs a water quality risk model based on liquid pH value (RO membrane inlet), silica concentration (RO membrane inlet), and dissolved oxygen concentration (final product outlet), and outputs water quality risk coefficients.

[0055] The water quality-performance analysis module constructs a water quality-performance adaptation model based on the water quality risk coefficient and the resistivity (final product outlet) and total organic carbon concentration (final product outlet) under the membrane system operating efficiency coefficient, and outputs the water quality-performance adaptation degree.

[0056] The inlet water pressure optimization module constructs a pressure optimization model based on water quality-performance compatibility, pretreatment efficiency coefficient, and current RO membrane inlet water pressure, and outputs the target RO membrane inlet water pressure.

[0057] This invention can monitor key water quality and operating parameters in real time, forming a closed-loop feedback to adaptively adjust the RO membrane feed water pressure. When the feed water quality fluctuates or the membrane module performance deteriorates, it automatically optimizes to the best operating state, thereby effectively preventing problems such as unstable product water quality, premature membrane module fouling, and excessive energy consumption, and ensuring that the stringent requirements for the purity of raw water in the hydrohydrogen production process are continuously met.

[0058] Preferably, the preprocessing performance analysis module includes the following functions:

[0059] Obtain the influent turbidity (RO membrane inlet point), oxidation-reduction potential (RO membrane inlet point), and pressure difference A (pressure difference between the multi-media filter and the activated carbon filter);

[0060] The influent turbidity is normalized by maximum-min to obtain the influent turbidity factor, and the complement of the influent turbidity factor is taken to obtain the influent turbidity index (influent turbidity index = 1 - influent turbidity factor).

[0061] The pressure difference A is normalized by the maximum-minimum process to obtain the pressure difference factor A, and the pressure difference index A is obtained by taking the complement of the pressure difference factor A (pressure difference index A = 1 - pressure difference factor A).

[0062] Importing redox potential into the formula In the process, the redox potential index is obtained. Indicates redox potential. This represents the redox potential sensitivity coefficient. Indicates the reference potential;

[0063] A pretreatment efficiency model is constructed based on the influent turbidity index, pressure difference index A, and redox potential index, and the pretreatment efficiency coefficient is obtained. The pretreatment efficiency model is expressed as follows:

[0064]

[0065] in, Indicates the preprocessing efficiency coefficient. Indicates the turbidity index of the influent. Indicates the differential pressure index A. Indicates the redox potential index. Represents the weight coefficient and The The larger the value, the better the preprocessing effect.

[0066] Among them, the complement of the index refers to obtaining a new index by subtracting the normalized value from 1. It can be understood as converting a negative index into a positive index. For example, for turbidity, the smaller the value, the better the effect. Taking the complement to make the index larger indicates a better effect. The goal is to make all indices positively correlated with the preprocessing effect. The sigmoid function is an S-shaped curve function that can be expressed as a logistic function. This is achieved by addressing the nonlinear relationship between redox potential and pretreatment effect, through adjusting the sensitivity coefficient. (Values ​​can be assigned through expert experience or calibrated experimentally) and reference potential To adapt to different water quality conditions; the pretreatment efficiency model refers to a mathematical model based on a weighted combination of multiple exponentials, which can be implemented using a linear combination method. This is achieved by comprehensively evaluating the overall performance of the preprocessing unit, and by constraining the weight coefficients to 1 to ensure that the performance coefficients are within the range of [0,1].

[0067] Specifically, the proposed solution first obtains the influent turbidity at the RO membrane inlet point, the redox potential at the RO membrane inlet point, and the pressure difference A between the multi-media filter and the activated carbon filter as basic parameters. These parameters comprehensively reflect the physical clogging status, oxidative risk, and filtration efficiency of the pretreatment unit. Then, the influent turbidity is subjected to maximum-minimum normalization and the complement is taken to obtain the influent turbidity index; the pressure difference A is subjected to maximum-minimum normalization and the complement is taken to obtain the pressure difference index A, ensuring a positive correlation between these indices and the pretreatment effect. The redox potential is processed using the Sigmoid function to obtain the redox potential index, capturing its nonlinear influence. Finally, a pretreatment efficiency model is constructed based on a weighted linear combination of the influent turbidity index, pressure difference index A, and redox potential index, outputting a pretreatment efficiency coefficient. This coefficient is strictly limited to the [0,1] interval, with a larger value indicating a better pretreatment effect. This approach comprehensively balances the contributions of each parameter, avoiding evaluation bias caused by a single parameter, and enabling the efficiency coefficient to dynamically adapt to water quality fluctuations, providing a precise basis for influent pressure optimization.

[0068] In one specific implementation, the influent turbidity (RO membrane inlet point), oxidation-reduction potential (RO membrane inlet point), and pressure difference A can be obtained by a turbidity sensor, an oxidation-reduction potential sensor, and a pressure sensor, respectively.

[0069] Through the above technical solution, this application achieves accurate quantitative evaluation of pretreatment efficiency, enabling the system to adaptively adjust the inlet pressure based on real-time water quality status, effectively cope with water quality fluctuations, and improve the stability and reliability of the water filtration and impurity removal device.

[0070] Preferably, the working content of the membrane system performance analysis module includes:

[0071] The RO permeate flow rate is subjected to maximum-min normalization to obtain the permeate flow rate index.

[0072] The pressure difference B is normalized by the maximum-minimum process to obtain the pressure difference factor B, and the pressure difference index B is obtained by taking the complement of the pressure difference factor B (pressure difference index B = 1 - pressure difference factor B).

[0073] The absolute difference between the RO concentrate discharge pressure and the standard RO concentrate discharge pressure is compared with the allowable deviation from the standard RO concentrate discharge pressure to obtain the concentrate discharge pressure deviation factor.

[0074] The absolute difference between the inlet water temperature and the standard inlet water temperature is compared with the allowable deviation from the standard inlet water temperature to obtain the inlet water temperature deviation factor.

[0075] The concentrate discharge pressure deviation factor and the influent temperature deviation factor are subjected to maximum-minimum normalization to obtain the concentrate discharge pressure deviation index and the influent temperature deviation index.

[0076] A membrane system operating efficiency model is constructed based on the pressure difference index B, product water flow rate index, concentrate discharge pressure deviation index, and influent temperature deviation index, outputting the membrane system operating efficiency coefficient. The membrane system operating efficiency model is expressed as follows:

[0077]

[0078] in, Indicates the operating efficiency coefficient of the membrane system. Indicates the differential pressure index B. Water production flow index This indicates that the concentrated wastewater discharge pressure deviates from the index. The index indicates the deviation of the inlet water temperature. The higher the value, the better the membrane system performs.

[0079] Among them, the complement processing of the differential pressure index B refers to converting the normalized differential pressure factor B into an indicator positively correlated with the membrane system's operating performance through complement calculation. It can be numerically converted based on the characteristic that an increase in differential pressure B indicates aggravated membrane fouling, with the aim of making differential pressure data directly reflect the contribution of membrane fouling degree to system performance. The concentrate discharge pressure deviation factor is a technical means of quantifying the degree of pressure anomaly by the ratio of the absolute difference to the allowable deviation range. It can be achieved by comparing real-time monitoring data with a preset threshold, with the aim of converting pressure deviation assessment into an adaptive relative indicator. The feed water temperature deviation factor is a parameter that is standardized by utilizing the sensitivity of membrane performance to temperature fluctuations. It can be calculated based on the relative deviation between temperature sensor data and standard values, with the aim of keeping the temperature impact assessment consistent with the actual operating state of the system.

[0080] Specifically, the proposed solution normalizes the RO permeate flow rate into a permeate flow rate index, making the impact of flow rate changes on membrane flux quantifiable and compatible with other parameter dimensions. Simultaneously, it normalizes the pressure difference B and takes its complement to generate a pressure difference index B, ensuring a positive correlation between this index and operational performance, directly reflecting the contribution of pressure difference to efficiency. Furthermore, it ratios the absolute difference between the RO concentrate discharge pressure and the standard value to quantify the degree of pressure anomaly, laying the foundation for subsequent normalization. It also ratios the absolute difference between the influent temperature and the standard value to ensure that temperature deviation assessment is consistent with system status. Finally, it normalizes the pressure deviation factor and temperature deviation factor to obtain a deviation index. The (1 - P_c,ind) and (1 - T_ind) terms in the model ensure that a larger deviation index results in a smaller efficiency contribution, accurately reflecting the negative impact of deviation on operational performance. A multiplicative efficiency model is constructed based on these indices. By integrating the independent contributions of multiple dimensions, the efficiency coefficient reaches its peak when all parameters approach the optimized state, thereby dynamically capturing the real operating state of the membrane system.

[0081] In a preferred embodiment, the RO permeate flow rate is collected by an electromagnetic flow meter, the pressure difference B is monitored in real time by a pressure sensor installed between the RO membrane module and the EDI device, and the RO concentrate discharge pressure is obtained by a pressure sensor.

[0082] Through the above scheme, this application realizes the accurate quantitative evaluation of the operating efficiency of the membrane system, so that the water quality-performance compatibility calculation can truly reflect the membrane fouling state and the actual operating effect of the system. It effectively solves the problem of evaluation distortion caused by insufficient integration of multi-dimensional data in traditional methods, and significantly improves the adaptive optimization capability of water filtration and impurity removal devices to water quality fluctuations.

[0083] Preferably, the working content of the water quality risk analysis module includes:

[0084] The silica concentration and dissolved oxygen concentration were subjected to maximum-min normalization to obtain the silica concentration index and dissolved oxygen concentration index.

[0085] The pH deviation index is obtained by comparing the absolute difference between the influent pH value and the optimal pH value with the allowable deviation from the optimal pH value.

[0086] A water quality risk model is constructed based on the silica concentration index, dissolved oxygen concentration index, and pH deviation index to obtain the water quality risk coefficient. The water quality risk model is expressed as follows:

[0087]

[0088] in, Indicates the water quality risk coefficient. This indicates that the pH value deviates from the index. Indicates the silica concentration index. The dissolved oxygen concentration index, the Furthermore, the higher the value, the lower the risk of the membrane system.

[0089] Specifically, the silica concentration index refers to the technical characteristic of converting the original silica concentration into a dimensionless index through maximum-minimum normalization. It can be achieved by subtracting the historical minimum concentration from the measured concentration and then dividing by the concentration range value. The purpose is to eliminate the difference in concentration dimensions and quantify its contribution to the risk of the membrane system. The dissolved oxygen concentration index refers to the standardized parameter obtained by normalizing the dissolved oxygen concentration. It can be achieved by mapping the original concentration to the [0,1] interval through linear transformation. The purpose is to make the concentration parameters of different units comparable. The pH deviation index is a relative index that characterizes the degree of deviation between the actual pH value and the optimal value. It can be achieved by the quotient of the absolute difference and the allowable deviation threshold. The purpose is to capture the nonlinear risk characteristics of pH. The water quality risk model is a mathematical framework that integrates various normalized indices to output risk coefficients. It can be achieved by using an exponential decay function. The purpose is to reflect the cumulative effect of multi-parameter risks through nonlinear superposition.

[0090] Specifically, the water quality risk analysis module first performs maximum-minimum normalization on silica and dissolved oxygen concentrations, converting the original parameters with different dimensions and magnitudes into dimensionless exponents in the [0,1] range, thereby eliminating the masking effect of high concentration values ​​on the risk contribution of low concentration parameters. Simultaneously, considering the nonlinear risk characteristics of pH, a pH deviation index is generated that dynamically reflects the degree of deviation by calculating the ratio of the absolute difference between the actual pH value and the optimal pH value to the allowable deviation range. Based on this, the water quality risk model linearly superimposes the three normalized indices into an exponential function. As each index increases (risk increases), the exponential decay mechanism smoothly reduces the water quality risk coefficient, strictly limiting it to the (0,1] range. This ensures that a larger risk coefficient value indicates a lower risk to the membrane system. This design effectively avoids the evaluation distortion of the linear model under extreme values, enabling the risk quantification results to truly reflect the comprehensive threat of water quality fluctuations to the membrane system.

[0091] As a specific implementation method, the silica concentration, dissolved oxygen concentration, and pH can be obtained using a silica sensor, a dissolved oxygen sensor, and a pH sensor.

[0092] Through the above technical solutions, this application achieves parameter standardization and dynamic risk quantification of silica concentration, dissolved oxygen concentration and pH value, so that the water quality risk coefficient can accurately reflect the actual risk level. In particular, it effectively eliminates the evaluation distortion caused by dimensional differences and nonlinear effects when the influent water quality fluctuates, thereby improving the reliability of the water quality-performance adaptation model and the overall adaptive optimization capability of the system.

[0093] Preferably, the working content of the water quality-performance analysis module includes:

[0094] The resistivity (final water outlet) and total organic carbon concentration (final water outlet) are subjected to maximum-minimum normalization to obtain the resistivity index and organic carbon concentration index.

[0095] A water quality-performance fit model is constructed based on the resistivity index and organic carbon concentration index under the water quality risk coefficient, membrane system operating efficiency coefficient, and the water quality-performance fit degree is obtained. The water quality-performance fit model is expressed as follows:

[0096]

[0097] in, Indicates water quality-performance compatibility. Indicates the water quality risk coefficient. Indicates the operating efficiency coefficient of the membrane system. Represents the resistivity index. Indicates the organic carbon concentration index. This indicates the weight of resistivity in the fit calculation. The higher the value, the better the water quality and system performance.

[0098] Specifically, max-min normalization refers to the technique of linearly transforming the original data to the [0,1] interval. Its purpose is to eliminate the dimensional differences and inconsistencies in the numerical ranges of resistivity and total organic carbon concentration, ensuring the comparability of different physical quantities in the model. Resistivity index refers to the normalized resistivity value, which can be understood as a dimensionless index reflecting water purity; a larger value indicates better water quality. Its purpose is to transform resistivity into a standardized input that fits the model, avoiding high-magnitude data dominating the calculation results. Organic carbon concentration index refers to the normalized resistivity value... Total organic carbon (TOC) concentration can be understood as a dimensionless indicator reflecting the degree of organic pollution; a smaller value indicates better water quality. The purpose is to transform TOC concentration into a standardized input that the adaptation model can handle, ensuring that the positive impact of low-concentration organic matter on water quality is reasonably reflected. The water quality-performance adaptation model is a mathematical model that comprehensively considers the water quality risk coefficient, membrane system operating efficiency coefficient, resistivity index, and organic carbon concentration index. It can use a hyperbolic tangent function to handle the resistivity index and an exponential decay function to handle the organic carbon concentration index, and uses adjustable weighting coefficients (the weight of resistivity in the adaptation calculation). The contribution of water quality and system performance can be dynamically allocated (either by assigning values ​​based on expert experience or by using the analytic hierarchy process). The purpose is to accurately quantify the degree of matching between water quality and system performance, and to avoid the shortcomings of linear models in capturing the nonlinear characteristics of indicators.

[0099] Specifically, the proposed solution first performs maximum-minimum normalization on the resistivity and total organic carbon concentration of the final water inlet, converting different physical quantities into the [0,1] interval to solve the data comparability problem. Then, it uses a hyperbolic tangent function to perform a nonlinear transformation on the resistivity exponent, effectively capturing the saturation characteristics between resistivity and water purity, and suppressing calculation bias in the high resistivity region. Simultaneously, it employs an exponential decay function to process the organic carbon concentration exponent, accurately reflecting the positive contribution of low-concentration organic matter to water quality. By dynamically adjusting the relative importance of resistivity and organic carbon through weighting coefficients, the model adapts to the priority of indicators under different water source conditions. Finally, it uses the water quality risk coefficient and membrane system operating efficiency coefficient as overall weighting factors, multiplying them with the weighted combined indicators to ensure that the water quality-performance fit is strictly limited to the [0,1] range and monotonically increases to reflect the direction of performance optimization, thereby providing an accurate decision-making basis for influent pressure optimization.

[0100] As a specific implementation method, resistivity data and total organic carbon concentration data of the final water outlet can be obtained in real time through sensors.

[0101] Through the above scheme, this application achieves precise quantification of water quality-performance compatibility, enabling the system to accurately assess the matching status between water quality and the performance of the electrolysis hydrogen production system. This effectively avoids the problem of inaccurate optimization of inlet water pressure caused by compatibility calculation deviation, thereby ensuring the stability and efficiency of the water-based hydrogen production process.

[0102] Preferably, the pressure optimization model is expressed as:

[0103]

[0104] in, Indicates the target RO membrane inlet pressure. This indicates the current RO membrane inlet pressure. Indicates the minimum permissible RO membrane inlet pressure. Indicates the maximum permissible RO membrane inlet pressure. This indicates the current RO membrane inlet pressure. Indicates the target water quality-performance compatibility. Indicates water quality-performance compatibility. This represents the preprocessing efficiency coefficient.

[0105] Specifically, the hyperbolic tangent function This refers to a nonlinear activation function with smooth transition characteristics. It can use a mathematical operation unit to calculate the adjustment amount of the fit deviation in real time, avoiding system oscillations caused by sudden pressure changes. Its purpose is to achieve stability in the pressure adjustment process; minimum allowable RO membrane inlet pressure. This refers to the lower limit threshold for safe system operation, which can be set based on the physical tolerance characteristics of the RO membrane module. Its purpose is to prevent a decrease in permeate efficiency caused by insufficient membrane flux under low pressure conditions; the maximum allowable RO membrane inlet pressure. This refers to the upper limit threshold for safe system operation, which can be determined according to the equipment manufacturer's specifications. Its purpose is to avoid membrane damage or energy waste caused by high pressure; pretreatment efficiency coefficient. It refers to a quantitative indicator that characterizes the effect of the pretreatment stage. It can be calculated in real time through the pretreatment performance analysis module. Its purpose is to dynamically adjust the pressure adjustment range to match the stability of pretreatment.

[0106] Specifically, the solution in this application addresses water quality-performance compatibility. With target value The difference is input into the hyperbolic tangent function to generate a smooth adjustment coefficient, effectively suppressing the nonlinear effects caused by water quality fluctuations; the pretreatment efficiency coefficient is utilized. The adjustment coefficients are dynamically weighted. When preprocessing efficiency is high, the pressure adjustment response is amplified to improve system efficiency; when preprocessing efficiency is low, over-adjustment is suppressed to avoid risks. Finally, the target pressure is constrained to a specific value using minimum and maximum value functions. and Within this range, the pressure is ensured to remain within the safe operating range of the equipment. The synergistic effect of these mechanisms closely links the pressure optimization process to water quality risk and system performance, achieving precise adaptive control of the inlet pressure.

[0107] Through the above solution, this application can dynamically and accurately adjust the RO membrane feed water pressure parameters when the feed water quality fluctuates or the membrane module performance deteriorates, effectively avoiding problems such as unstable product water quality, accelerated membrane fouling, and abnormally high system energy consumption, thereby ensuring the stable operation and efficient water production of the water body filtration and impurity removal device for hydropower hydrogen production.

[0108] 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 a process, method, article, or apparatus.

[0109] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A water-based hydrogen production device for filtering and removing impurities from a water body, characterized in that, include: The pretreatment efficiency analysis module constructs a pretreatment efficiency model based on influent turbidity, redox potential, and pressure difference A, and outputs the pretreatment efficiency coefficient. The membrane system performance analysis module constructs a membrane system operation performance model based on pressure difference B, RO membrane concentrate discharge pressure, RO permeate flow rate, and inlet water temperature, and outputs the membrane system operation performance coefficient. The water quality risk analysis module constructs a water quality risk model based on liquid pH value, silica concentration, and dissolved oxygen concentration, and outputs water quality risk coefficients. The water quality-performance analysis module constructs a water quality-performance adaptation model based on the water quality risk coefficient, resistivity under the membrane system operating efficiency coefficient, and total organic carbon concentration, and outputs the water quality-performance adaptation degree. The inlet water pressure optimization module constructs a pressure optimization model based on water quality-performance compatibility, pretreatment efficiency coefficient, and current RO membrane inlet water pressure, and outputs the target RO membrane inlet water pressure.

2. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 1, characterized in that, The pressure optimization model is expressed as follows: in, Indicates the target RO membrane inlet pressure. Indicates the minimum permissible RO membrane inlet pressure. Indicates the maximum permissible RO membrane inlet pressure. This indicates the current RO membrane inlet water pressure. Indicates the target water quality-performance compatibility. This represents the preprocessing efficiency coefficient.

3. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 2, characterized in that, The water quality-performance analysis module performs the following tasks: The resistivity and total organic carbon concentration were subjected to maximum-min normalization to obtain the resistivity index and the organic carbon concentration index. A water quality-performance fit model is constructed based on the resistivity index and organic carbon concentration index under the water quality risk coefficient, membrane system operating efficiency coefficient, and the water quality-performance fit degree is obtained. The water quality-performance fit model is expressed as follows: in, Indicates water quality-performance compatibility. Indicates the water quality risk coefficient. Indicates the operating efficiency coefficient of the membrane system. Represents the resistivity index. Indicates the organic carbon concentration index. This indicates the weight of resistivity in the fit calculation. The higher the value, the better the water quality and system performance.

4. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 3, characterized in that, The water quality risk analysis module's functions include: The silica concentration and dissolved oxygen concentration were subjected to maximum-min normalization to obtain the silica concentration index and dissolved oxygen concentration index. The pH deviation index is obtained by comparing the absolute difference between the influent pH value and the optimal pH value with the allowable deviation from the optimal pH value. A water quality risk model is constructed based on the silica concentration index, dissolved oxygen concentration index, and pH deviation index to obtain the water quality risk coefficient. The water quality risk model is expressed as follows: in, Indicates the water quality risk coefficient. This indicates that the pH value deviates from the index. Indicates the silica concentration index. The dissolved oxygen concentration index represents the dissolved oxygen concentration index. Furthermore, the higher the value, the lower the risk of the membrane system.

5. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 3, characterized in that, The working content of the membrane system performance analysis module includes: The pressure difference index B, the RO membrane concentrate discharge pressure, the RO permeate flow rate, and the inlet water temperature are obtained accordingly. A membrane system operating efficiency model is constructed based on the pressure difference index B, product water flow rate index, concentrate discharge pressure deviation index, and influent temperature deviation index, outputting the membrane system operating efficiency coefficient. The membrane system operating efficiency model is expressed as follows: in, Indicates the operating efficiency coefficient of the membrane system. Indicates the differential pressure index B. Water production flow index This indicates that the concentrated wastewater discharge pressure deviates from the index. The index indicates the deviation of the inlet water temperature. The higher the value, the better the membrane system performs.

6. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 3, characterized in that, The preprocessing performance analysis module's functions include: The influent turbidity index, pressure difference index A, and oxidation-reduction potential index are obtained based on the influent turbidity, oxidation-reduction potential, and pressure difference A. A pretreatment efficiency model is constructed based on the influent turbidity index, pressure difference index A, and redox potential index, and the pretreatment efficiency coefficient is obtained. The pretreatment efficiency model is expressed as follows: in, Indicates the pretreatment efficiency coefficient. Indicates the turbidity index of the influent. Indicates the differential pressure index A. Indicates the redox potential index. Represents the weight coefficient and The The larger the value, the better the preprocessing effect.

7. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 5, characterized in that, The steps for obtaining the pressure difference index B, permeate flow rate index, concentrate discharge pressure deviation index, and inlet water temperature deviation index based on the pressure difference B, RO membrane concentrate discharge pressure, RO permeate flow rate, and inlet water temperature are as follows: The RO permeate flow rate is subjected to maximum-min normalization to obtain the permeate flow rate index. The pressure difference B is obtained by performing maximum-minimum normalization on the pressure difference B, and the pressure difference index B is obtained by taking the complement of the pressure difference factor B. The absolute difference between the RO concentrate discharge pressure and the standard RO concentrate discharge pressure is compared with the allowable deviation from the standard RO concentrate discharge pressure to obtain the concentrate discharge pressure deviation factor. The absolute difference between the inlet water temperature and the standard inlet water temperature is compared with the allowable deviation from the standard inlet water temperature to obtain the inlet water temperature deviation factor. The concentrate discharge pressure deviation factor and the influent temperature deviation factor are subjected to maximum-minimum normalization to obtain the concentrate discharge pressure deviation index and the influent temperature deviation index.

8. The water body filtration and impurity removal device for hydropower hydrogen production according to claim 6, characterized in that, The steps for obtaining the influent turbidity index, pressure difference index A, and oxidation-reduction potential index based on influent turbidity, oxidation-reduction potential, and pressure difference A are as follows: Obtain the influent turbidity, oxidation-reduction potential, and pressure difference A; The influent turbidity is subjected to maximum-min normalization to obtain the influent turbidity factor, and the complement of the influent turbidity factor is taken to obtain the influent turbidity index. The pressure difference A is normalized by the maximum-minimum process to obtain the pressure difference factor A, and the pressure difference index A is obtained by taking the complement of the pressure difference factor A. Importing redox potential into the formula In the process, the redox potential index is obtained. Indicates redox potential. This represents the redox potential sensitivity coefficient. This represents the reference potential.

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