Method, medium and system for energy efficiency optimization of a capacitive deionization system

Abstract

The application relates to an energy efficiency optimization method, medium and system of a capacitive deionization system, which is suitable for energy-saving operation regulation and control of the capacitive deionization system. The method firstly constructs a multi-physical field simulation model coupling electric field, fluid and mass transfer, simulates an electric loss distribution curve in a deionization process, and determines preliminary adsorption time length parameters according to the electric loss distribution curve. Then, the adsorption time length parameters and preliminary desorption time length parameters that are optimal in energy efficiency are calculated and corrected by comprehensively considering factors such as auxiliary system power consumption and pumping energy consumption, so that adaptive adjustment of system cycle operation parameters under different working conditions is realized. Finally, under the premise of ensuring electrode full regeneration, the water inflow speed and desorption time length parameters in the desorption stage are adjusted based on the simulation model, the water recovery rate and energy recovery efficiency of the system are improved, and the overall energy consumption is reduced. The application realizes intelligent operation control of the electric adsorption process under the condition of multiple working conditions, and has high engineering adaptability and popularization value.

Description

Technical Field

[0001] This invention relates to water treatment technology, specifically to an energy efficiency optimization method, medium, and system for a capacitor deionization system. Background Technology

[0002] Capacitive deionization (CDI) is a highly efficient separation method for desalinating water by using an applied electric field to drive ion migration and electro-adsorption through porous electrodes. Its basic working principle is that, under applied voltage, cations and anions in the solution migrate to the surface of electrodes of opposite polarity and are temporarily stored in the formed electrical double layer (EDL), thereby removing dissolved salts from the water.

[0003] In the typical adsorption stage of a capacitive deionization system, as the operating time continues, the ion concentration in the water gradually decreases, the interfacial mass transfer rate declines, and the electrode potential continuously approaches saturation, resulting in a significant slowdown in the ion removal rate per unit time. If the adsorption state is maintained at this point, not only will the deionization effect diminish marginally, but it will also cause additional energy consumption, such as the continuous output of the pumping system and the routine consumption of auxiliary systems. Therefore, to improve energy efficiency and charge utilization, existing conventional strategies often choose to switch to the desorption stage before the electrode is fully saturated, thereby avoiding energy waste caused by inefficient adsorption. However, the industry currently lacks a quantifiable and universal control mechanism to accurately determine this "early switching" timing. In actual operation, it often relies on manual experience to set fixed time, voltage, or current thresholds. This approach is difficult to adapt to the dynamic changes under different water quality conditions and operating parameters, limiting further optimization of system operating efficiency. Therefore, it is necessary to introduce a data-driven method based on simulation prediction or online monitoring. By analyzing the evolution trend of key parameters (such as deionization rate, current density, and electrode potential), a universal and adaptive switching criterion can be constructed to achieve dynamic identification and efficient switching control at the end of the adsorption stage.

[0004] In the desorption stage of a capacitive deionization (CDI) system, ions adsorbed by the electrodes are typically released by short-circuiting, reversing, or applying a negative voltage, resulting in concentrated water. Simultaneously, a DC / DC energy conversion module can recover some of the electrical energy released by the electrodes, which can be used for pre-charging in the next adsorption cycle or fed back to the energy storage device. The energy recovery efficiency and charge utilization efficiency in this process are influenced by multiple factors, with desorption duration and influent flow rate being the key variables. Previous studies have indicated that appropriately reducing the flow rate during the desorption stage helps increase the concentrated water concentration, enhance electrode discharge stability, and significantly improve the system's water return rate (the ratio of permeate to influent) and energy recovery efficiency. However, improper flow rate control can also lead to new problems: too low a flow rate may result in incomplete ion release and insufficient electrode regeneration, thus affecting subsequent adsorption performance; too high a flow rate leads to excessively rapid concentrate dilution, decreased energy recovery efficiency, and premature termination of electrode regeneration, wasting energy output from auxiliary and pumping systems. Therefore, accurately setting the flow rate adjustment rhythm and control range during the desorption stage, tailored to different operating conditions, has become a key challenge for improving the overall performance of CDI systems.

[0005] In summary, existing capacitive deionization systems face challenges in both adsorption and desorption stages, including reliance on experience for setting operating parameters and limited control methods. This makes it difficult to simultaneously optimize both energy utilization efficiency and water recovery rate. The lack of a method to comprehensively analyze ion migration, electrode potential changes, and energy recovery characteristics under various operating conditions, and to adaptively optimize key operating parameters (such as switching timing and influent flow rate), has become a significant bottleneck restricting further performance improvements in this type of system. Therefore, there is an urgent need for an operating parameter optimization technology that can simultaneously reduce energy consumption and improve water recovery rate, achieving synergistic optimization of the entire adsorption and desorption process and driving the development of capacitive deionization systems towards higher efficiency and greater intelligence. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide an energy efficiency optimization method, medium and system for a capacitor deionization system, which addresses the above-mentioned problems in the prior art. By constructing a prediction and energy dissipation model based on multiphysics simulation, the dynamic evolution of key parameters is quantitatively evaluated. Combined with adaptive parameter optimization, an adjustable adsorption endpoint determination mechanism and desorption flow rate control strategy are established to improve the water recovery rate and energy recovery efficiency and reduce the system's operating energy consumption.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] An energy efficiency optimization method for a capacitor deionization system includes the following steps:

[0009] A finite element simulation model coupling electric field, flow field and mass transfer field is constructed to simultaneously simulate the dynamic evolution of electrode potential, current density, fluid flow and ion concentration, resulting in a multiphysics simulation model. Field operating parameters are input into the multiphysics simulation model for adsorption stage calculations to obtain the electrical loss distribution curve during the deionization process. The initial adsorption time is determined based on the electrical loss distribution curve.

[0010] The time difference between the optimal adsorption time and the initial adsorption time is obtained by solving the energy efficiency evaluation formula that takes into account the energy input of the pumping system and other auxiliary equipment. The optimal adsorption time is calculated based on the time difference and the initial adsorption time, and the initial desorption time is set as the optimal adsorption time.

[0011] The influent flow rate during the desorption stage is set to be equal to the influent flow rate during the adsorption stage in the field operating parameters. The influent flow rate during the desorption stage and the initial desorption duration are input into the multiphysics simulation model to calculate the desorption stage and obtain the desorption duration. If the desorption duration is less than the initial desorption duration, the influent flow rate during the desorption stage is adjusted to improve the return water rate. The influent flow rate during the desorption stage is then input into the multiphysics simulation model again to calculate the desorption stage and obtain a new desorption duration. This process continues until the iteration stop condition is met. The net desorption energy corresponding to the desorption duration of each iteration is calculated. The influent flow rate and desorption duration corresponding to the minimum net desorption energy are selected as the desorption parameter combination with the optimal energy efficiency.

[0012] Furthermore, before performing the adsorption stage calculations using the multiphysics simulation model, the step of calculating the boundary conditions of the multiphysics simulation model is also included. Specifically, this includes: constructing a Randle equivalent circuit suitable for electroadsorption conditions, performing electrochemical impedance spectroscopy tests on the electrodes of the capacitive deionization system to obtain corresponding impedance data, fitting the impedance data to the Randle equivalent circuit to invert and obtain the Randle equivalent circuit parameters, which are then used as the boundary conditions of the multiphysics simulation model.

[0013] Furthermore, the Randles equivalent circuit includes a series resistor. Electrolyte solution resistance Equivalent double-layer capacitance and polarization resistance The equivalent double-layer capacitance With polarization resistance Parallel connection forms an interface reaction module, which is then combined with a series resistor. Electrolyte solution resistance They are connected in series to form a complete adsorption circuit channel, and connected to an external voltage applied across the electrodes. Together they form an electroadsorption circuit.

[0014] Furthermore, the electrical loss distribution curve includes a time-series curve of the total system input power and a time-series curve of the polarization loss power. The initial adsorption time is the point in time when the total system input power first equals the polarization loss power. The mathematical expression for the total system input power is as follows:

[0015]

[0016] in, It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in the capacitive deionization system;

[0017] The mathematical expression for the polarization loss power is as follows:

[0018]

[0019] in, It is composed of equivalent double-layer capacitance The adsorption current formed It is a polarization resistor.

[0020] Furthermore, the electrical loss distribution curve also includes a time-series curve of the series ohmic loss power, the mathematical expression of which is as follows:

[0021]

[0022] in, It is a series resistor. It is the resistance of the electrolyte solution.

[0023] Furthermore, the mathematical expression of the energy efficiency assessment formula is as follows:

[0024]

[0025] in, It is the time difference to be solved. It is the initial adsorption time. This is the optimal adsorption time. The effective double-layer capacitance is derived from the Randle equivalent circuit of a capacitor deionization system. The adsorption current formed It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in a capacitor deionization system. It is the series resistance of the Randle equivalent circuit. It is the electrolyte solution resistance of the Randle equivalent circuit. This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. It is a time infinitesimal element.

[0026] Furthermore, when adjusting the influent flow rate during the desorption stage, the influent flow rate during the previous iteration is multiplied by a specified rate reduction coefficient.

[0027] Furthermore, the desorption time is the electrolyte concentration in the effluent from the device, simulated and output by the multiphysics simulation model. The time point at which the electrode completes effective regeneration in the curve that changes over time, and the iteration stopping condition specifically, is that the desorption time is equal to the initial desorption time, or the influent flow rate in the desorption stage of the last iteration is less than the specified flow rate, and the electrode has not completed effective regeneration within the time range of the initial desorption time.

[0028] Furthermore, the specific time point at which the electrode completes effective regeneration is determined by the electrolyte concentration. Compared with the initial raw water concentration in the on-site operating parameters The relative difference between them is less than a specified proportion of the time points.

[0029] Furthermore, the mathematical expression for the net desorption energy is as follows:

[0030]

[0031] in, This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. It is the desorption duration. This is the optimal adsorption time. For time infinitesimal elements, This refers to the power recovered by the DC-DC converter during the desorption phase, expressed mathematically as follows:

[0032]

[0033] in, It refers to the external voltage of the capacitive deionization system during the desorption stage. It is the external current of the capacitive deionization system during the desorption stage.

[0034] The present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy efficiency optimization method for the capacitor deionization system.

[0035] The present invention also proposes an energy efficiency optimization system for a capacitor deionization system, comprising a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which is executed by the processor to implement the steps of the energy efficiency optimization method for the capacitor deionization system described in any one of the present invention.

[0036] Compared with the prior art, the advantages of the present invention are as follows:

[0037] This invention constructs a finite element simulation model coupling the electric field, flow field, and mass transfer field. Through simulation, it accurately captures the dynamic distribution curve of electrical loss during the adsorption stage, thereby precisely identifying the initial adsorption duration. Simultaneously, the energy efficiency evaluation formula comprehensively considers the energy consumption of the pumping system and auxiliary equipment, rather than only considering electrode power consumption, achieving end-to-end energy efficiency optimization from the electrode to the auxiliary system. The determined optimal adsorption duration significantly reduces the energy consumption per unit of produced water.

[0038] This invention sets the initial desorption time to be equal to the optimal adsorption time and aims to improve the water return rate. It adaptively adjusts the desorption influent flow rate and finally selects the parameter combination to ensure the lowest net energy consumption in the desorption stage, thus achieving the dual goals of reducing operating energy consumption and improving the water return rate. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the basic flow of the control method according to an embodiment of the present invention.

[0040] Figure 2 This is the Randle equivalent circuit for electroadsorption conditions in this embodiment of the invention.

[0041] Figure 3 The results are the calculated outlet concentrations from the adsorption stage simulation model in this embodiment of the invention.

[0042] Figure 4 The results show the energy consumption distribution of various types in the simulation model of the adsorption stage in the embodiments of the present invention.

[0043] Figure 5 The overall energy efficiency evaluation index for the system adsorption stage operation in this embodiment of the invention. curve.

[0044] Figure 6 The results show the calculated outlet concentration of the simulation model for different inlet flow rates during the desorption stage of the system in this embodiment of the invention.

[0045] Figure 7 This represents the energy recovery power achieved under different inlet flow rates during the system desorption stage in this embodiment of the invention. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0047] To address the common problems in existing capacitive deionization (CDI) systems, such as reliance on empirical judgment and the potential for improvement in operational efficiency and water recovery rate during the adsorption and desorption stages, this embodiment proposes an energy efficiency optimization method, medium, and system for capacitive deionization systems. This method constructs a multi-physics coupled numerical simulation model to quantitatively predict electrode potential, ion concentration evolution, and energy dissipation behavior, enabling precise control of adsorption termination timing and desorption flow rate, and establishing an adjustable and scalable operational control method.

[0048] like Figure 1 As shown, the method in this embodiment includes the following steps:

[0049] S1) A multiphysics simulation model is established based on the on-site operating parameters to calculate and output the electrical loss distribution curve during the deionization process, thereby obtaining the initially set adsorption stage duration. Specifically, a finite element simulation model coupling the electric field, flow field, and mass transfer field is constructed to simultaneously simulate the dynamic evolution of electrode potential, current density, fluid flow, and ion concentration, resulting in a multiphysics simulation model. The on-site operating parameters are input into the multiphysics simulation model for adsorption stage calculations to obtain the electrical loss distribution curve during the deionization process. The initial adsorption duration is then determined based on the electrical loss distribution curve.

[0050] S2) Taking into account factors such as auxiliary system power and pumping energy consumption, a comprehensive calculation is performed to achieve the optimal adsorption time and initial desorption time for energy efficiency. Specifically, the energy efficiency evaluation formula considering the energy input of the pumping system and other auxiliary equipment is solved to obtain the time difference between the optimal adsorption time and the initial adsorption time. Based on the time difference and the initial adsorption time, the optimal adsorption time is calculated, and the initial desorption time is set as the optimal adsorption time.

[0051] S3) Under the premise of ensuring sufficient electrode regeneration, the influent flow rate and desorption duration in the desorption stage are adjusted using a simulation model to improve the water return rate, enhance energy recovery efficiency, and reduce the overall energy consumption of the system. Specifically, the influent flow rate in the desorption stage is set to be equal to the influent flow rate in the adsorption stage in the field operating parameters. The influent flow rate in the desorption stage and the initial desorption duration are input into the multiphysics simulation model to calculate the desorption stage and obtain the desorption duration. If the desorption duration is less than the initial desorption duration, the influent flow rate in the desorption stage is adjusted to improve the water return rate. The influent flow rate in the desorption stage is then input into the multiphysics simulation model again to calculate the desorption stage and obtain a new desorption duration. This process continues until the iteration stop condition is met. The net desorption energy corresponding to the desorption duration in each iteration is calculated. The influent flow rate and desorption duration corresponding to the minimum net desorption energy are selected as the desorption parameter combination with the optimal energy efficiency.

[0052] Through the above steps, this embodiment constructs a prediction and energy dissipation model based on multiphysics simulation, quantitatively evaluates the dynamic evolution of key parameters, and establishes an adjustable adsorption endpoint determination mechanism and desorption flow rate control strategy by combining adaptive parameter optimization, thereby achieving the effect of improving water recovery rate and energy recovery efficiency and reducing system operating energy consumption.

[0053] The following is a detailed explanation of each step.

[0054] In step S1 of this embodiment, the multiphysics simulation model is a finite element simulation model that couples the electric field, flow field, and mass transfer field, capable of simultaneously simulating the dynamic evolution of electrode potential, current density, fluid flow, and ion concentration. This model drives ion migration through the electric field, regulates flow velocity distribution through the flow field, and characterizes concentration changes through the mass transfer field. Furthermore, based on constructing an equivalent circuit model to describe the electrochemical behavior of the electrodes, it can further decouple the time-varying energy loss of each circuit element. Under coupled solution, the model can output key parameters such as electrode reaction behavior, power input, energy loss distribution, and effluent concentration curves during the adsorption process, providing accurate basis for the optimized control of adsorption time and desorption flow rate, and achieving energy efficiency improvement and quantitative optimization of operating parameters.

[0055] In this embodiment, the multiphysics simulation model adopts a three-dimensional parallel plate structure, dividing the CDI unit into three regions: an upper electrode layer, a porous spacer layer (flow channel), and a lower electrode layer. Taking this embodiment as an example, the electrode spacing in the simulation model is set to 0.68 mm, and a single electrode is 100 mm long and 10 mm wide. The electrode-related structural parameters include: electrode porosity of 0.72, electrode solid volume fraction of 0.84, and electrolyte volume fraction of 0.5. When considering the MCDI operating condition, a corresponding ion exchange membrane boundary (cation exchange membrane or anion exchange membrane) can be set at the contact interface between the cathode / anode and the porous spacer layer (flow channel), and membrane parameters such as membrane thickness, membrane conductivity, membrane ion diffusion coefficient, membrane fixed charge concentration, membrane porosity, membrane selectivity coefficient for anions / cations, and membrane / electrode interface contact resistance can be defined to characterize the selective permeation behavior of the membrane layer for ion migration.

[0056] Optionally, in the electric field calculation sub-model of the multiphysics simulation model, the current distribution calculation module is used to calculate the potential and current density distribution in the electrodes and electrolyte, taking into account the influence of electrode dynamics. The current density in the electrolyte satisfies Ohm's law, and the potential field is solved based on the set conductivity. Potential boundary conditions are applied to the outer surface of the electrodes. In this embodiment, the potentials of the cathode and anode are set to 0V and 0.6V, respectively. Specifically, a positive voltage is applied during the adsorption stage to drive ions to migrate to the electrode surface and complete electroadsorption. During the desorption stage, the two electrodes are short-circuited, i.e., the potentials of both the cathode and anode are set to 0V, thereby realizing the conversion of the CDI device from adsorption mode to desorption mode.

[0057] Optionally, in the flow field calculation sub-model of the multiphysics simulation model, the laminar Brinkman equation is used to describe the fluid flow in the porous electrode and spacer layer channels of the CDI, treating the fluid as incompressible and neglecting the inertial term. In this embodiment, a normal inflow velocity boundary condition is applied at the inlet boundary of the porous spacer layer, and the normal inflow velocity is used as the flow rate control variable input; a pressure boundary condition is applied at the outlet boundary of the porous spacer layer, and the static pressure is set to 0, thereby calculating the velocity field distribution and realizing the simulation and control of the flow rate conditions of the capacitor deionization system at different operating stages.

[0058] Optionally, the Nernst–Planck model is used in the mass transfer calculation sub-model of the multiphysics simulation model to simulate the diffusion, convection, and electromigration processes of ions, and numerically coupled with the above-mentioned electric field sub-model to reflect ion migration driven by the electric field. A rare matter transport module is enabled in the cathode and anode regions, treating the electrodes as porous media. Combined with parameters such as electrode porosity, the simulation of convection and transport processes within the porous electrode and the porous medium is achieved. Furthermore, the double-layer current in the Randle equivalent circuit is coupled as the porous electrode reaction, thus obtaining the evolution of the ion concentration field within the flow channel and electrode over time. In the simulation, the influent salt concentration is input as the input parameter of the mass field at the inlet boundary of the spacer layer flow channel, and the instantaneous ion concentration calculated at the outlet section of the flow channel is used as the output result of the mass field to characterize the desalination effect under this condition. Furthermore, by integrating the outlet concentration and flow rate over time, performance indicators such as salt adsorption and desalination rate can be calculated, enabling quantitative identification and evaluation of the adsorption and desorption process performance.

[0059] In step S1 of this embodiment, to accurately model the electrode electrochemical behavior in the multiphysics simulation model and simulate the ion adsorption and desorption during CDI operation, a Randle equivalent circuit structure is used to fit and describe the CDI electrode interface process. Before performing the adsorption stage calculation using the multiphysics simulation model, the step of calculating the boundary conditions of the multiphysics simulation model is also included, specifically:

[0060] First, construct the Randles equivalent circuit suitable for electroadsorption conditions, such as... Figure 2 As shown, the equivalent circuit consists of the following key components: series resistor. Ohmic loss characterizing the internal conductive path of the electrode; electrolyte solution resistance. The dynamic response reflecting the change in electrolyte conductivity with solution concentration; equivalent double-layer capacitance. Used to simulate the temporary storage behavior of ions in the electric double layer at the electrode interface; polarization resistance This characterizes the kinetic resistance of the electrochemical reaction at the electrode interface. In the above circuit structure, the equivalent double-layer capacitance... With polarization resistance Parallel connection forms an interface reaction module, which is then combined with a series resistor. Electrolyte solution resistance They are connected in series to form a complete adsorption circuit channel, and connected to an external voltage applied across the electrodes. Together they form an electro-adsorption circuit. In the equivalent circuit, under an external voltage... The current generated in the main circuit under excitation is defined as the total system current. Due to the equivalent double-layer capacitance The adsorption current formed is defined as .

[0061] Then, electrochemical impedance spectroscopy (EIS) was performed on the electrodes of the CDI-capacitor deionization system to extract impedance data from their Nyquist plots or Bode plots.

[0062] Finally, by combining equivalent circuit fitting, the impedance data is fitted to the Randles equivalent circuit using circuit fitting software, and the parameter values ​​of the above components are obtained as the Randles equivalent circuit parameters applicable to electroadsorption conditions, including series resistance. Equivalent double-layer capacitance Polarization resistance and the solution resistance as a function of electrolyte concentration Among them, the solution resistance varies with electrolyte concentration. The parameters are automatically calculated by the multiphysics model based on the electrolyte solution concentration. These parameters are embedded into the multiphysics model as input boundary values ​​to achieve dynamic simulation of electrode adsorption and desorption behavior under actual operating conditions, providing a quantifiable and reproducible basis for subsequent optimization of key operating parameters such as adsorption time and desorption flow rate.

[0063] In this embodiment, an exemplary method is to perform EIS electrochemical analysis on a certain electrode material and then fit it to a circuit using circuit fitting software. Figure 2 In the Randles equivalent circuit constructed in the paper, a set of equivalent parameters is obtained: series resistance (Ohms), polarization resistance Equivalent double-layer capacitance Among them, the resistance of the electrolyte solution The finite element software automatically updates the calculations based on the electrolyte solution concentration.

[0064] In step S1 of this embodiment, the on-site operating parameters input to the multiphysics simulation model include the initial electrolyte concentration of the raw water to be treated. The fluid flow rate at the inlet of the single electrode pair in the capacitive deionization system during the adsorption stage And the adsorption voltage applied across the single pair of electrodes in the capacitive deionization system. The above-mentioned variable parameters are set based on the raw water quality and on-site operating conditions, and are operating parameters that can be actually adjusted on-site. In actual operation, a constant voltage adsorption mode is generally adopted, i.e., the adsorption voltage... This value is usually set to a constant. Meanwhile, in order to realize the energy consumption estimation of the actual system from the simulation results, the energy consumption results of a single pair of electrodes in the simulation model are enlarged according to the ratio of the actual electrode to the simulated electrode in terms of area, so as to obtain the estimated electroadsorption energy consumption under the whole machine scale, so as to ensure the accurate mapping of the simulation model to the actual desalination conditions.

[0065] For example, the field operating parameters input into the multiphysics simulation model include the initial electrolyte concentration of the raw water to be treated. (millimols per liter), fluid flow rate at the inlet of a single pair of electrodes in the adsorption stage capacitive deionization system (ml per minute), and the adsorption voltage applied across a single pair of electrodes in the capacitive deionization system. (Volt), a constant voltage adsorption mode is used during the adsorption stage.

[0066] In step S1 of this embodiment, the multiphysics simulation model's operation during the adsorption stage refers to performing finite element analysis under set boundary conditions to simulate the coupling process of the electric field, flow field, and mass transfer field in the capacitive deionization system. The termination criterion for the operation during the adsorption stage is the electrolyte concentration in the effluent from the equipment. Compared with the initial raw water concentration If the relative difference between the two is less than 5%, the adsorption process is considered to have reached equilibrium, and the multiphysics model calculation stops.

[0067] For example, such as Figure 3 The figure shows the calculated outlet concentration results of the simulation model during the adsorption stage, indicating the electrolyte concentration in the effluent from the equipment. Compared with the initial raw water concentration The relative difference between the values ​​is less than 5%, indicating that the adsorption process has reached equilibrium. In this embodiment, the time for the adsorption stage to reach saturation equilibrium is... (seconds) Export concentration reaches The multiphysics model calculation has stopped.

[0068] During the above-mentioned adsorption stage calculations, based on the Randle equivalent circuit parameters and the boundary conditions of the capacitor deionization system, the simulation model can perform component-by-component calculations of the energy loss caused by each resistive element, thereby achieving a quantitative evaluation of the electric drive efficiency and outputting the power loss distribution curve of the complete deionization process, as follows:

[0069] The voltage applied to a single pair of electrodes in the capacitive deionization system Total current of a single pair of electrodes The input power constituted is defined as the total input power of the system, and its calculation expression is:

[0070]

[0071] in, It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in a capacitor deionization system.

[0072] The equivalent circuit of Randle consists of series resistors and solution resistance related to electrolyte concentration The resulting power loss is classified as series ohmic power loss (Rs Leak Power), and its calculation formula is:

[0073]

[0074] in, It is a series resistor. It is the resistance of the electrolyte solution.

[0075] Polarizing resistors in the equivalent circuit The resulting power loss is defined as polarization loss power (Rct LeakPower). This energy loss can be considered as parasitic power consumption caused by non-ideal electrochemical behavior during double-layer adsorption, and its calculation formula is as follows:

[0076]

[0077] in Indicating the double-layer capacitance in the equivalent circuit The resulting adsorption current, this value can be extracted from the physics module of the finite element method software. This refers to the polarization resistance. Through the above power component calculations, the time-series curves of Total Power, Rs Leak Power, and Rct Leak Power can be dynamically output at each time step of the adsorption process, thereby constructing a complete electrical loss distribution curve for evaluating the energy consumption structure and efficiency performance of the electroadsorption system under specific operating conditions.

[0078] During the adsorption stage, different types of power losses exhibit distinct evolutionary trends. Total Power (system input power), determined by the applied voltage and total system current, peaks in the early stages of adsorption, then rapidly decays as the current decreases, stabilizing to near zero watts (W) in the later stages of adsorption. Rs Leak Power (series ohmic power loss) also accounts for a major proportion of energy consumption in the initial stages of adsorption, rapidly decreasing and stabilizing as the concentration field stabilizes and the current decreases. In contrast, Rct Leak Power (polarization power loss, i.e., interfacial power loss caused by polarization resistance) continuously increases with adsorption progress, approaching a stable value in the later stages of adsorption. This loss primarily reflects the trend of increased charge dissipation under conditions of intensified concentration polarization and slower interfacial mass transfer, particularly pronounced during high-voltage operation.

[0079] In this embodiment, based on the above power distribution trend, a preliminary adsorption time is proposed. The definition method is as follows. This time point corresponds to the point at which Total Power first equals Rct Leak Power, that is, when the system input power is exactly equal to the power loss caused by interfacial polarization. Its physical significance is that when Total Power equals Rct Leak Power, the total input energy of the system is insufficient to cover the electrochemical losses caused by concentration polarization and other effects, and the input charge can no longer drive the effective ion adsorption. The adsorption process then enters an inefficient or even energy-inverted stage. Therefore, this critical time point is taken as the initial adsorption time. This allows for the reasonable identification of adsorption energy efficiency boundaries, providing an important reference for subsequent adsorption termination judgment and energy consumption optimization.

[0080] For example, a simulation model is used to calculate the energy loss caused by different resistive elements, thereby achieving a quantitative assessment of the electric drive efficiency. Figure 4 The figure shows the energy consumption distribution during the adsorption stage. Based on this distribution trend, the time point when Total Power first equals Rct Leak Power is determined as the initial adsorption duration. In practical applications of capacitive deionization (CDI) systems, to balance desalination performance and energy efficiency, the adsorption phase time is typically set to approximately two-thirds of the time required for the electrode to reach adsorption saturation. This embodiment incorporates the initial adsorption time... The calculation and analysis results show that the obtained optimal adsorption time is also within the time range set by the experience, which further confirms the applicability and scientific nature of the empirical strategy in the actual system.

[0081] The aforementioned initial adsorption time This is based on the relative relationship between Total Power and power loss distribution during electroadsorption, and its core lies in identifying the critical time point when the input power no longer covers the interfacial electrochemical loss. However, in actual equipment operation, the total energy consumption of the capacitive deionization system includes not only electroadsorption power but also the energy input of the pumping system and other auxiliary equipment. Therefore, further derivation of the optimal adsorption time for comprehensive energy efficiency is necessary. Therefore, it is necessary to introduce an analysis of pumping energy consumption and auxiliary system energy consumption.

[0082] Pumping systems are a significant energy source during CDI implementation. Their power consumption is influenced by flow control strategies and pump motor frequency regulation, determining the inlet velocity and operating pressure. In most practical applications, pumping power is primarily controlled by fluid velocity and is related to system piping conditions. For typical industrial fluid systems where fluid properties (density, viscosity) and pipe geometry (length, diameter, roughness) remain constant, the relationship between pumping power and inlet velocity can be determined by the flow state.

[0083] When Reynolds number When turbulent friction dominates, according to the cubic law of turbulence, the pressure drop increases with the square of the flow velocity, and the pumping power... With inlet flow rate Approximately cubic relationship:

[0084]

[0085] When Reynolds number In the laminar flow region, according to the laminar flow formula (Hagen–Poiseuille), the pressure drop is approximately proportional to the flow velocity, and the pumping power is approximately proportional to the square of the flow velocity. With inlet flow rate The relationship is approximately quadratic:

[0086]

[0087] When Reynolds number In the transition region, as an engineering approximation, linear weighted interpolation or power-law interpolation based on Re can also be used to smoothly transition between quadratic and cubic power-law methods. For example, a simple linear weighted method is as follows:

[0088]

[0089] Then pumping power With inlet flow rate The relationship is:

[0090]

[0091] in, For reference flow rate The pumping power was measured, and this value can be obtained by testing the inlet flow rate of a single pair of electrodes and the power of the motor in a set of actual capacitive deionization systems.

[0092] Meanwhile, the number of pumping motors in the capacitor deionization system This will also directly affect the total pumping power (if the flow rates of the pumping motors are inconsistent, the power can be calculated separately for each pumping electrode and then summed). Therefore, the total pumping power of the capacitor deionization system... It can be represented as:

[0093]

[0094] Besides the electrical energy consumption during the electro-adsorption process and the hydraulic energy consumption of the pumping system, the capacitive deionization (CDI) system also includes several auxiliary systems in actual operation, such as power management and control modules (e.g., PLC controllers, sensors, and communication units), energy feedback units (e.g., DC / DC converters), and necessary circulating water treatment or dosing systems. These auxiliary systems operate continuously during the CDI unit's operation, and although their individual power consumption is relatively small, their cumulative energy consumption is not negligible. To achieve a comprehensive evaluation and optimization of system energy efficiency, the power consumption of these auxiliary systems is simplified to a constant value in the model. And considered as related to adsorption time A constant power input term exists synchronously; this value can be obtained directly when the CDI field device is in standby mode. This setting helps to comprehensively cover the actual energy consumption structure of the capacitor deionization system in energy efficiency calculations, thereby more accurately deriving the optimal adsorption time and desorption strategy for overall energy efficiency.

[0095] Therefore, in step S2 of this embodiment, the adsorption time with optimal energy efficiency is... It is obtained through the following calculation method:

[0096] First, set its duration relative to the initial adsorption time. The time difference between them is ,Right now:

[0097]

[0098] Among them, the initial adsorption time The determination is based on the relationship between power input and internal distributed losses during the electroadsorption process. This process typically takes a long time and does not fully consider other power input factors during system operation. Therefore, it is more conservative than the optimal solution for overall energy efficiency. In actual calculations The initial value can be chosen to be greater than 5 seconds to ensure smooth optimization calculations. To further optimize energy efficiency, it is necessary to... The value is based on a comprehensive analysis of multiple system energy consumption factors, including: the utilization efficiency of charge required per unit desalination in the adsorption stage (i.e., adsorption charge efficiency, using double-layer adsorption current). The system calculates the adsorbed charge, the power consumption of the pumping system at a set flow rate, and the constant energy consumption of the auxiliary system during the operating cycle. Based on these multiple factors, a total energy efficiency evaluation index for the system's adsorption operation is defined. Then, by optimizing the calculation, the desired result can be obtained. Adsorption time at maximum This is the optimal adsorption time recommended in the final control strategy. The specific energy efficiency evaluation formula is:

[0099]

[0100] In the above formula It is the time difference to be solved. This refers to the initial adsorption time. This is the optimal adsorption time. The effective double-layer capacitance is derived from the Randle equivalent circuit of a capacitor deionization system. The adsorption current formed It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in a capacitor deionization system. It is the series resistance of the Randle equivalent circuit. It is the electrolyte solution resistance of the Randle equivalent circuit. This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. dt is a time infinitesimal element, representing an infinitesimally small time interval that approaches infinity. It is used for integration operations on continuous-time signals.

[0101] in, , , These are all time-varying parameters, which can output relevant data under time-varying conditions from a pre-calculated multiphysics finite element model. When During the growth process, The value will first increase and then decrease, because this is due to the later stage of the adsorption phase. The current used in actual desalination double-layer adsorption current The branch is gradually decreasing, and coupled with the non-ideal power of pumping power and auxiliary system power, it will inevitably lead to... Find the optimal adsorption time for energy efficiency under multiple factors at a certain value. ,Right now The maximum value corresponding to .

[0102] For example, in this embodiment, the pumping power resulting from the energy consumption caused by the pumping system is: With Reynolds number The power was calculated using the turbulent cubic law, and a small industrial pump with a rated power of 2kW was used for system water delivery. The reference fluid velocity at the single electrode inlet of the capacitor deionization system was... At that time, reference pumping power The number of pumping motors in a capacitor deionization system Under these conditions, the total pumping power of the system is: ,in The inlet flow rate value is the actual set value for a single electrode pair in the capacitive deionization system during the adsorption stage. This embodiment defines the actual set inlet flow rate value for a single electrode pair in the capacitive deionization system during the adsorption stage. .

[0103] In a typical industrial electroadsorption system, the auxiliary system includes sub-modules such as sensing, control, and data acquisition. Its overall power consumption typically accounts for 5–15% of the total system power. To achieve comprehensive evaluation and optimization of system energy efficiency, this type of auxiliary power is simplified to a constant value in the model. .

[0104] Adsorption time for optimal energy efficiency It is obtained through the following calculation method:

[0105] Set its duration relative to the initial adsorption time The time difference between them is ,Right now:

[0106]

[0107] In energy efficiency assessment formula Substitute the known constants into the input, including the applied voltage. Series resistor Pumping power Auxiliary system power and initial adsorption time And so on, combined with time-varying parameters that can be extracted from the simulation model, including the total current. Electrolyte solution resistance Double-layer current Furthermore, in this embodiment, the actual CDI device is composed of multiple pairs of electrodes connected in parallel, and the size of a single electrode is [missing information]. The CDI device consists of 500 electrode pairs. It is scaled up according to the ratio of the actual electrodes to the simulated electrodes in terms of area. Therefore, in this embodiment, the energy consumption for electroadsorption needs to be multiplied by the corresponding energy consumption factor. .

[0108] The formula for calculation after substitution is as follows:

[0109]

[0110] Calculate the overall energy efficiency evaluation index of the system adsorption operation. Curves Figure 5 As shown, obtain The maximum value corresponds to the time difference value. The corresponding optimal adsorption time for energy efficiency In this embodiment, under unoptimized operating conditions, the time required for the capacitive deionization system to reach complete electrode saturation from the start of the adsorption phase is approximately 1820 s. However, in the later stages of this cycle, the system mainly maintains inefficient current input and a slow deionization rate, resulting in a significant decrease in desalination efficiency per unit energy consumption and affecting overall energy efficiency. Based on the optimization calculations performed using the simulation model described in this application, combined with the quantitative analysis results of electrical energy consumption during electroadsorption and the dynamic changes in mass transfer rate in the later stages, it was determined that adjusting the adsorption time to approximately 980 s can effectively avoid the continuous input of electrically driven energy in the inefficient stage, thereby improving the overall energy utilization efficiency of the adsorption phase.

[0111] In step S2 of this embodiment, the initial desorption time is... Set to equal the determined adsorption time In practical engineering applications, conventional methods typically set the duration of the adsorption and desorption stages to be equal. If the influent flow rate remains constant, the corresponding system return rate is 50%. Based on this conventional process setting, this invention proposes an approach to optimize and adjust the parameters of the desorption stage to achieve higher energy efficiency.

[0112] In step S3 of this embodiment, under the premise of ensuring sufficient electrode regeneration, the parameters of the desorption stage are adjusted with the help of a simulation model. First, the influent flow rate of the desorption stage is set. equal to the influent flow rate during the adsorption stage The desorption time is the initial value. The simulation was conducted under the specified conditions, and the electrolyte concentration of the water discharged from the equipment was output. A curve showing how the electrolyte concentration changes over time. Compared with the initial raw water concentration When the relative difference between the two values ​​is less than 5%, the electrode is considered to have completed effective regeneration. The corresponding time at this point is defined as follows: Based on the simulation results, there are three possible scenarios:

[0113] The first scenario is: when Less than The fact that the desorption process is redundant in the later stages indicates that excessive time and energy consumption do not bring any additional benefits. The overall energy efficiency of the system can be improved by shortening the desorption time. It also indicates that there may be room for optimization in the desorption stage to improve the energy efficiency and water return rate. In this case, further optimization tests can be conducted.

[0114] The second scenario is: when equal This indicates that the system is operating reasonably under the current parameter configuration, and the desorption process is sufficient and without obvious redundancy;

[0115] The third scenario is: if in The target was never reached within the specified range. ≈ If the desorption termination criterion is not met, it means that the desorption process has not achieved sufficient regeneration. This situation is uncommon, as the desorption stage is generally faster than the adsorption stage and rarely occurs in engineering.

[0116] In the second and third cases, no further optimization testing is required. Therefore, the specific conditions for stopping the iteration are that the desorption time is equal to the initial desorption time, or the influent flow rate in the desorption stage of the last iteration is less than the specified flow rate, and the electrode has not been effectively regenerated within the time range of the initial desorption time.

[0117] when Less than At that time, by reducing the influent flow rate during the desorption stage To test and improve the recirculation rate of the CDI equipment, the influent flow rate during the desorption phase of the previous iteration is multiplied by a specified rate reduction coefficient, which is set to [value missing]. ,Right now ,in Can be selected first The simulation was performed again using the parameters after reducing the flow rate to obtain the electrolyte concentration in the effluent from the equipment. Compared with the initial raw water concentration The time parameter when the relative difference between them is less than 5% is , and judge again Is it less than .like Still smaller Then multiply by the deceleration factor again. Reduce the influent flow rate during the desorption stage And conduct simulations to determine the influent flow rate during the desorption stage. When the flow rate is reduced to a certain level, The target was never reached within the specified range. ≈ This means that the effect of complete electrode regeneration cannot be achieved, and the reduction of the influent flow rate during the desorption stage should be stopped. Simulation.

[0118] After the iteration is completed, in step S3 of this embodiment, the influent flow rate during the desorption stage of each iteration is obtained. The relevant parameters under simulation are calculated using the following formula for net energy desorption. The calculations are then used to select the optimal desorption parameters:

[0119]

[0120] in, This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. With pump flow rate Related to auxiliary system power All of these represent the power consumed by the system during the desorption phase. It is the desorption duration. This is the optimal adsorption time. This refers to the power recovered by the DC-DC converter during the desorption phase, expressed mathematically as follows:

[0121]

[0122] in, It refers to the external voltage of the capacitive deionization system during the desorption stage. It is the external current of the capacitive deionization system during the desorption stage, and its time-varying value can be obtained from the parameter curve in the simulation model.

[0123] By calculating the influent flow rate at different desorption stages and desorption duration Net desorption energy below ,when When the value is minimized, it indicates that, considering pumping power, auxiliary system power consumption, and DC-DC energy recovery conditions, the selected parameters not only meet the basic requirements for electrode regeneration but also achieve optimal energy efficiency matching in the desorption stage while reducing the desorption influent flow rate and increasing the return water rate. Therefore, the value should be minimized. The influent flow rate during the desorption stage is valuable. and desorption duration This is the optimal combination of desorption parameters for energy efficiency.

[0124] For example, in the constructed simulation model, the running time of the adsorption stage is set to... The process lasts 980 seconds, after which it switches to the desorption phase. The initial desorption duration is set as follows. Consistent with the adsorption time, i.e. A preliminary assessment of mass transfer behavior under symmetrical periodic conditions was conducted using a 980s timer. The influent flow rate during the desorption phase was set. equal to the influent flow rate during the adsorption stage ,Right now Under the premise of ensuring sufficient electrode regeneration, a simulation model is used to adjust the parameters of the desorption stage, setting the electrolyte concentration in the effluent from the equipment. Compared with the initial raw water concentration When the relative difference between the two values ​​is less than 5%, the electrode is considered to have completed effective regeneration, and the corresponding time is defined as the desorption time. .

[0125] Figure 6 shows the simulation model results of the outlet concentration for different inlet flow velocities during the desorption stage in this embodiment. The calculation is performed using a decrease coefficient of... In the case of influent flow rate during the desorption phase They are respectively: Under the simulation prediction, the effective regeneration adsorption time of the electrode is obtained. They are respectively: In the corresponding The flow rate at The range is insufficient to achieve complete electrode regeneration, and there is no corresponding adsorption time. Therefore, further measures to reduce the inlet flow rate are no longer being considered. The simulation results, and the selection of parameters for the subsequent desorption stage are also taken into consideration. Less than The situation.

[0126] In the above Influent flow rate during desorption stage Corresponding pumping power The values ​​are as follows: Set the external voltage of the CDI device during the desorption phase. External current of CDI device during desorption phase The curve is obtained from the simulation model, and the auxiliary system power... Substitute these data into the net energy of desorption. The calculation formula was used, and Figure 7 shows the energy recovery power results achieved by the DC-DC converter under different inlet flow rates during the desorption stage in this embodiment. The results show that the change in inlet flow rate has little impact on the instantaneous power output during the energy recovery process, and the overall energy recovery performance remains stable. This conclusion is consistent with existing related literature reports. It should be noted that although the instantaneous power does not change much under different desorption durations, the total recovered energy will still differ due to the different desorption durations.

[0127] The influent flow rate during the desorption stage was obtained from the above calculations. They are respectively: Net desorption energy below They correspond to:

[0128] : (kilojoules)

[0129] :

[0130] :

[0131] Calculate the influent flow rate at different desorption stages and desorption duration Net desorption energy below ,when When the value is at its minimum, it indicates that, considering pumping power, auxiliary system power consumption, and DC-DC energy recovery conditions, the selected parameters not only meet the basic requirements for electrode regeneration but also achieve optimal energy efficiency matching in the desorption stage while reducing the desorption influent flow rate and increasing the return water rate. Therefore, the selected parameters are... , These are the parameter values ​​during the desorption phase.

[0132] Compare the desorption parameters (i.e., desorption inlet flow rate) under unoptimized conditions. Desorption time ), by using optimized parameter combinations ( , This not only reduced energy consumption, but also saved energy of: Furthermore, it significantly increased the system's water return rate from 50% to 69.8%, fully demonstrating the synergistic benefits of this strategy in optimizing energy efficiency and improving water resource utilization efficiency.

[0133] Furthermore, this embodiment also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy efficiency optimization method for the capacitor deionization system described in this embodiment.

[0134] Furthermore, this embodiment also proposes an energy efficiency optimization system for a capacitor deionization system, including a processor and a computer-readable storage medium. The computer-readable storage medium stores a computer program, which is executed by the processor to implement the steps of the energy efficiency optimization method for the capacitor deionization system described in this embodiment.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the energy efficiency of a capacitor deionization system, characterized in that, Includes the following steps: A finite element simulation model coupling electric field, flow field and mass transfer field is constructed to simultaneously simulate the dynamic evolution of electrode potential, current density, fluid flow and ion concentration, resulting in a multiphysics simulation model. Field operating parameters are input into the multiphysics simulation model for adsorption stage calculations to obtain the electrical loss distribution curve during the deionization process. The initial adsorption time is determined based on the electrical loss distribution curve. The time difference between the optimal adsorption time and the initial adsorption time is obtained by solving the energy efficiency evaluation formula that takes into account the energy input of the pumping system and other auxiliary equipment. The optimal adsorption time is calculated based on the time difference and the initial adsorption time, and the initial desorption time is set as the optimal adsorption time. The influent flow rate in the desorption stage is set to be equal to the influent flow rate in the adsorption stage in the field operating parameters. The influent flow rate in the desorption stage and the initial desorption time are input into the multiphysics simulation model to calculate the desorption stage and obtain the desorption time. If the desorption time is less than the initial desorption time, the influent flow rate in the desorption stage is adjusted to improve the return water rate. The influent flow rate in the desorption stage is input into the multiphysics simulation model again to calculate the desorption stage and obtain a new desorption time. This process continues until the iteration stop condition is met. The net desorption energy corresponding to the desorption time in each iteration is calculated. The influent flow rate and desorption time corresponding to the minimum net desorption energy are selected as the desorption parameter combination with the best energy efficiency. Before performing the adsorption stage calculations using a multiphysics simulation model, the step of calculating the boundary conditions of the multiphysics simulation model is also included. Specifically, this includes: constructing a Randle equivalent circuit suitable for electroadsorption conditions; performing electrochemical impedance spectroscopy on the electrodes of the capacitive deionization system to obtain corresponding impedance data; fitting the impedance data to the Randle equivalent circuit to invert and obtain the Randle equivalent circuit parameters, which are then used as the boundary conditions of the multiphysics simulation model. The Randle equivalent circuit includes a series resistor. Electrolyte solution resistance Equivalent double-layer capacitance and polarization resistance The equivalent double-layer capacitance With polarization resistance Parallel connection forms an interface reaction module, which is then combined with a series resistor. Electrolyte solution resistance They are connected in series to form a complete adsorption circuit channel, and connected to an external voltage applied across the electrodes. Together, they form an electro-adsorption circuit. The electrical loss distribution curve includes the time-series curve of the total system input power and the time-series curve of the polarization loss power. The initial adsorption time is the point at which the total system input power first equals the polarization loss power. The mathematical expression for the total system input power is as follows: in, It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in the capacitive deionization system; The mathematical expression for the polarization loss power is as follows: in, It is composed of equivalent double-layer capacitance The adsorption current formed It is a polarization resistor; The electrical loss distribution curve also includes a time-series curve of the series ohmic loss power, the mathematical expression of which is as follows: in, It is a series resistor. It is the resistance of the electrolyte solution.

2. The energy efficiency optimization method for the capacitor deionization system according to claim 1, characterized in that, The mathematical expression of the energy efficiency assessment formula is as follows: in, It is the time difference to be solved. This refers to the initial adsorption time. This is the optimal adsorption time. The effective double-layer capacitance is derived from the Randle equivalent circuit of a capacitor deionization system. The adsorption current formed It is the voltage applied to a single pair of electrodes in the capacitor deionization system, which is one of the field operating parameters. It is the total current of a single pair of electrodes in a capacitor deionization system. It is the series resistance of the Randle equivalent circuit. It is the electrolyte solution resistance of the Randle equivalent circuit. This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. It is a time infinitesimal element.

3. The energy efficiency optimization method for the capacitor deionization system according to claim 1, characterized in that, When adjusting the influent flow rate during the desorption stage, the influent flow rate during the previous iteration is multiplied by the specified reduction coefficient.

4. The energy efficiency optimization method for a capacitor deionization system according to claim 1, characterized in that, The desorption time is the electrolyte concentration in the effluent from the device, simulated by the multiphysics simulation model. The curve showing the change over time indicates the point at which the electrode achieves effective regeneration. Specifically, the point at which the electrolyte concentration is reached is the point at which effective regeneration is achieved. Compared with the initial raw water concentration in the on-site operating parameters The iteration stopping condition is that the relative difference between the two is less than a specified proportion of the time point. Specifically, the desorption time is equal to the initial desorption time, or the influent flow rate in the desorption stage of the last iteration is less than the specified flow rate, and the electrode has not completed effective regeneration within the time range of the initial desorption time.

5. The energy efficiency optimization method for a capacitor deionization system according to claim 1, characterized in that, The mathematical expression for the net desorption energy is as follows: in, This refers to the total pumping power of the capacitor deionization system. It is the auxiliary system power. It is the desorption duration. This is the optimal adsorption time. For time infinitesimal elements, This refers to the power recovered by the DC-DC converter during the desorption phase, expressed mathematically as follows: in, It refers to the external voltage of the capacitive deionization system during the desorption stage. It is the external current of the capacitive deionization system during the desorption stage.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the energy efficiency optimization method for the capacitor deionization system according to any one of claims 1 to 5.

7. An energy efficiency optimization system for a capacitor deionization system, characterized in that, The device includes a processor and a computer-readable storage medium storing a computer program, which is executed by the processor to implement the steps of the energy efficiency optimization method for the capacitor deionization system according to any one of claims 1 to 5.

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