Method and device for evaluating application benefit of underground brackish water resources in northwest inland river basin

Abstract

This invention discloses a method, apparatus, computer equipment, and computer-readable storage medium for evaluating the application benefits of groundwater saline water resources in the inland river basins of Northwest China. It relates to the technical field of groundwater saline water resource application benefit evaluation. The method includes: collecting water quality data such as groundwater salinity and turbidity; dividing desalination gradient intervals based on crop salt tolerance thresholds and CDI desalination characteristics; screening pretreatment processes; establishing a nonlinear model of crop yield and irrigation water salinity; determining the optimal sub-desalination target salinity based on CDI performance; calculating energy consumption per ton of water and comprehensive cost based on the dynamic correlation between CDI desalination capacity and energy consumption; and constructing an IRR model including planting revenue and desalination costs by incorporating agricultural time-of-use electricity pricing weighted processing; and quantifying the impact of parameters on IRR through sensitivity analysis. This method achieves a quantitative assessment of the benefits of Northwest China's saline water resources and CDI technology, balancing yield, cost, and ecological risks, and improving the feasibility and adaptability of saline water utilization.

Description

Technical Field

[0001] This invention relates to the interdisciplinary technical field of water resource development and utilization and techno-economic evaluation, and in particular to a method and apparatus for evaluating the application benefits of groundwater saline resources in inland river basins in Northwest China. Background Technology

[0002] As a core component of my country's arid and semi-arid regions, the Northwest Inland River Basin accounts for 35% of the country's total land area. Although it plays a core role in the production of high-quality and distinctive agricultural products, with cotton and seed corn production accounting for more than 80% of the national total, it is a strategic base for ensuring the country's agricultural product supply security. However, there is an extreme imbalance between the region's water resources endowment and production needs.

[0003] Among existing saline water desalination technologies, reverse osmosis (RO), nanofiltration (NF), electrodialysis (ED), and distillation-based thermal methods have been widely used in seawater desalination and urban water supply. However, these technologies generally suffer from high operating pressure, high energy consumption, high equipment investment and maintenance costs, and sensitivity to water quality fluctuations. Especially in typical scenarios like the Northwest inland region, where electricity prices are relatively high, power supply and maintenance conditions are limited, and water demand is mostly from small to medium-sized decentralized point sources, traditional desalination processes face significant challenges in terms of engineering applicability and economics. Current watershed water resources research largely focuses on the allocation and management of conventional freshwater resources, with a lack of systematic research on the utilization of underground saline water, especially a lack of a benefit evaluation system adapted to the characteristics of CDI technology. Existing evaluation methods are mostly for traditional desalination technologies such as reverse osmosis, and are largely based on water treatment processes or water quality improvement itself. Evaluation indicators are mainly based on technical parameters such as unit desalination capacity (SAC), desalination rate, energy consumption, and electrode cycle stability. Evaluation objects are mostly focused on fields such as industrial wastewater treatment and drinking water purification, with limited coupling with the agricultural irrigation water scenario in arid areas. They have not fully considered the complexity of underground saline water quality in the Northwest inland, the unique advantages of CDI technology, and the regional agricultural-ecological synergy constraints. Summary of the Invention

[0004] The main objective of this invention is to provide a method for evaluating the benefits of groundwater saline water resources in inland river basins in Northwest China.

[0005] Another objective of this invention is to propose a device for evaluating the benefits of underground saline water resources in the Northwest Inland River Basin.

[0006] The third objective of this invention is to provide an electronic device.

[0007] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for evaluating the utilization benefits of groundwater saline water resources in the Northwest Inland River Basin, comprising:

[0009] S1, collect groundwater saline water quality data, including salt concentration, turbidity, hardness and ion concentration, and divide the target desalination gradient range according to the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology. S2. Based on the divided target desalination gradient interval, calculate the crop yield loss and planting income change corresponding to different salinities in the interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. S3, based on the dynamic correlation between the unit desalination capacity, capacity retention rate and energy consumption of the CDI system, uses integral operation to eliminate the influence of salt concentration fluctuation on energy consumption calculation, and calculates the comprehensive desalination cost within the determined optimal sub-desalination target salinity corresponding to the target desalination gradient. S4. Combining the agricultural time-of-use electricity pricing policy and the water storage capacity of the CDI system, the time-of-use electricity price is weighted and processed. The resulting planting income and the calculated comprehensive desalination cost are integrated to construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

[0010] Optionally, collect groundwater quality data, including salt concentration, turbidity, hardness, and ion concentration, and define target desalination gradient ranges based on the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology, including: S11 employs a combination of grid-based sampling and denser sampling in irrigated areas. Conductivity, turbidity, and pH are measured in real-time using portable instruments. Samples are collected, sealed, and sent for testing to detect substances including Na. + Cl - Ca² + Mg² + Ion concentration and hardness; S12, based on the salt tolerance threshold of irrigated crops and the efficient desalination characteristics of CDI technology, divides the desalination target gradient into mild desalination, moderate desalination and mild desalination, and selects suitable pretreatment processes based on turbidity.

[0011] Optionally, based on the defined target desalination gradient intervals, calculate the crop yield loss and planting income changes corresponding to different salinities within the intervals, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system, including: S21, using a nonlinear relationship model to calculate relative crop yield:

[0012] Where x is the salt concentration of the irrigation water; S22, based on the dynamic balance between irrigation water salt concentration and crop yield, determines the optimal sub-desalination target salinity by adjusting the target salt concentration range and controlling relative yield loss.

[0013] Optionally, based on the dynamic correlation between unit desalination rate, capacity retention rate, and energy consumption in the CDI system, integral calculations are used to eliminate the impact of salt concentration fluctuations on energy consumption calculations. For the target desalination gradient corresponding to the determined optimal sub-desalination target salinity, the comprehensive desalination cost within that gradient is calculated, including: S31, eliminate the influence of salt concentration fluctuations through integral calculation, and calculate the average stable unit desalination amount of electrode material in one cycle within the target desalination gradient; S32, substitute the average stable unit desalination amount ΔRAC into the fitting formula of desalination amount and energy consumption, and combine it with the SAC formula to calculate the energy consumption per ton of water within the target desalination gradient.

[0014] Optionally, by combining agricultural time-of-use electricity pricing policies and the water storage capacity of the CDI system, the time-of-use electricity price is weighted, and the resulting planting revenue is integrated with the calculated comprehensive desalination cost to construct an internal rate of return (IRR) calculation model that includes planting revenue, comprehensive desalination cost, and electricity price fluctuations. Sensitivity analysis is then used to quantify the impact of key parameter fluctuations on the IRR, including: S41, using a weighted formula to calculate the comprehensive electricity price:

[0015] in For the first Various electricity pricing scenarios, Weighting of electricity consumption duration for the corresponding time period; S42 quantifies the impact of key parameter fluctuations on IRR through sensitivity analysis, including initial salinity of groundwater, target sub-desalination salinity, electrode material cost and lifespan, time-of-use electricity pricing structure, and setting boundary values ​​for fluctuations in crop price and yield parameters.

[0016] S5, Calculate the soil salinization mitigation benefit by quantifying the difference in soil salt accumulation rates between irrigation with saline water and irrigation with desalinated water, using the following formula:

[0017] Where A is the irrigated area. This represents the rate of salt accumulation in soil irrigated with saline water. The rate of salt accumulation in the irrigated soil after desalination, where K is the unit cost of soil improvement.

[0018] To achieve the above objectives, a second aspect of the present invention provides a device for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin, comprising: The water quality data acquisition and gradient division module is used to collect water quality data of groundwater, including salt concentration, turbidity, hardness and ion concentration, and to divide the target desalination gradient range according to the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology. The nonlinear relationship modeling and sub-desalination target determination module is used to calculate crop yield loss and planting income changes corresponding to different salinities within the divided target desalination gradient interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. The dynamic correlation integral and cost calculation module is used to calculate the comprehensive desalination cost within the target desalination gradient corresponding to the optimal sub-desalination target salinity. It is based on the dynamic correlation between the unit desalination amount, capacity retention rate and energy consumption of the CDI system. It uses integral calculation to eliminate the impact of salt concentration fluctuation on energy consumption calculation. For the target desalination gradient corresponding to the determined optimal sub-desalination target salinity, it calculates the comprehensive desalination cost within the gradient. The electricity price weighting and IRR model construction module is used to combine agricultural time-of-use electricity pricing policies and the water storage capacity of the CDI system to weight the time-of-use electricity price, integrate the obtained planting income with the calculated comprehensive desalination cost, and construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

[0019] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0020] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing the method for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin as described in the first aspect embodiment.

[0021] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin as described in the first aspect embodiment.

[0022] The embodiments of the present invention have the following beneficial effects: By clarifying the economic value, ecological impact, and agricultural contribution of the technology application, this study reveals the synergistic relationship between saline water desalination, resource utilization, and ecological protection. This provides decision-making support for the scientific development of groundwater resources in the basin, the rational promotion of CDI technology, and the optimal allocation of water resources. Ultimately, it aims to improve the efficiency of water resource utilization and resilience in the basin, and ensure the sustainable development of regional agriculture and ecological security. Attached Figure Description

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a method for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin, provided as an embodiment of the present invention; Figure 2 A technical roadmap for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin, provided by an embodiment of the present invention; Figure 3 A correlation fitting diagram of relative cotton yield and irrigation water salt concentration is provided for an embodiment of the present invention; Figure 4 This is a structural diagram of a device for evaluating the application benefits of underground saline water resources in the Northwest Inland River Basin, provided as an embodiment of the present invention. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] The following describes, with reference to the accompanying drawings, a method and apparatus for evaluating the application benefits of groundwater saline water resources in the Northwest Inland River Basin according to embodiments of the present invention.

[0027] Example 1 This invention provides a method for evaluating the benefits of groundwater saline water resource utilization in inland river basins in Northwest China. Figure 1 This is a flowchart illustrating a method for evaluating the application benefits of groundwater saline water resources in inland river basins in Northwest China, provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: S1 collects groundwater quality data, including salt concentration, turbidity, hardness, and ion concentration, and divides the target desalination gradient range based on the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology.

[0028] Specifically, collecting groundwater quality data is a fundamental step in the entire evaluation system for saline water desalination and irrigation benefits. This step utilizes a systematic sampling strategy and multi-parameter detection methods to obtain data including salt concentration, turbidity, hardness, and major ions (such as...). , , , Key water quality indicators, including concentration, provide precise input for subsequent CDI system design, pretreatment process selection, and irrigation benefit assessment.

[0029] In this embodiment, the sampling points are arranged using a combination of grid-based and denser irrigated area methods to ensure spatial representativeness and data continuity in shallow groundwater distribution areas. Before sampling, the wells must be thoroughly flushed to eliminate interference from well wall residues on water quality testing. On-site testing uses a portable conductivity meter to measure conductivity, and the salt concentration (usually expressed as a standard conversion formula) is derived. (Indicated). Turbidity was measured in NTU units, and pH was used as an auxiliary parameter to assess water quality stability. Collected water samples were sealed and sent to the laboratory for analysis using ion chromatography or atomic absorption spectrometry to obtain... , , , The concentration data of major ions are typically controlled within ±5%.

[0030] In this application, salt concentration is the core basis for dividing the target desalination gradient range. Based on the salt tolerance thresholds of major irrigated crops (such as cotton and maize) in the Northwest Inland River Basin (suitable range is...),... ,Exceed (Hourly output decreased significantly), combined with CDI technology in The high-efficiency desalination characteristics at different salt concentrations allow for the division of desalination targets into three gradient ranges: light, moderate, and deep. Turbidity, as a key parameter for selecting pretreatment processes, is categorized as follows: less than 5 NTU requires no pretreatment; 5–20 NTU requires simple filtration; and greater than 20 NTU requires flocculation followed by filtration. Hardness is used to determine whether softening treatment is necessary, and is typically measured using... Equivalents indicate that if the concentration exceeds 200 mg / L, pretreatment measures should be considered.

[0031] In this embodiment, the step is applicable to agricultural irrigation areas in inland river basins in Northwest China, particularly in typical arid areas such as the Shiyang River basin, where groundwater quality is complex, salinity is high, and irrigation demand is strong. By collecting data and dividing the target desalination gradient, input parameters can be provided for the design of the CDI system, while also laying the foundation for subsequent economic analysis and ecological benefit assessment.

[0032] In this application, this step enables the accurate identification and classification of underground saline water resources, ensuring that the CDI system possesses good desalination performance and economic efficiency within the target salt concentration range. Simultaneously, by adapting pretreatment processes to different turbidity and hardness conditions, the stability of system operation and the reliability of effluent quality are improved, providing a scientific basis for subsequent irrigation benefit assessments.

[0033] Furthermore, S1 includes: S11 employs a combination of grid-based sampling and denser sampling in irrigated areas. Conductivity, turbidity, and pH are measured in real-time using portable instruments. Samples are collected, sealed, and sent for testing to detect substances including Na. + Cl - Ca² + Mg² + Ion concentration and hardness.

[0034] Specifically, this invention employs a combination of grid-based sampling and denser sampling in irrigation areas to ensure comprehensive and systematic collection and analysis of the water quality characteristics of groundwater saline resources. Based on spatial statistics principles, this method establishes a regular grid in saline water distribution areas and combines it with high-density sampling in agricultural irrigation areas to achieve precise matching between the spatial heterogeneity of water quality parameters and irrigation needs.

[0035] In the specific implementation of this application, the layout of sampling points is first based on regional hydrogeological maps and groundwater monitoring data, constructing a system... A grid system with a fixed spacing is used to cover the entire saline water distribution area. In agricultural irrigation areas, especially in areas where major crops such as cotton and corn are grown, the grid is further densified, typically using... The spacing was designed to capture localized water quality changes in irrigation sources. Each sampling point required thorough flushing of the well before sampling to eliminate interference from residual water on the well walls and to ensure the representativeness of the collected samples.

[0036] On-site testing employed a portable water quality analyzer to measure conductivity (EC), turbidity (NTU), and pH in real time. Conductivity was used to calculate the total dissolved solids (TDS) concentration of the saline water, turbidity was used to determine the need for pretreatment (such as filtration or flocculation), and pH was used to assess the corrosiveness of the water to the electrode materials and the stability of the CDI system. Collected water samples were then sealed and sent to a CMA-certified third-party laboratory for further analysis, with a focus on key parameters. , , , The concentration of major ions and total hardness (in terms of...) (Calculation) to fully understand the chemical composition characteristics of saline water.

[0037] This step plays a fundamental and supporting role in the entire technical solution. The collected water quality data is a key input parameter for subsequent CDI system selection, desalination target setting, pretreatment process selection, and energy consumption and cost accounting. For example, based on the salt tolerance threshold of irrigated crops (e.g., the suitable salt concentration for cotton is...),... ), combined with CDI technology in The efficient desalination characteristics at varying salt concentrations allow for the scientific division of desalination gradients, thereby optimizing system operating parameters and improving overall economic and ecological benefits. Furthermore, turbidity and hardness data directly influence the configuration of pretreatment processes, serving as crucial indicators for achieving stable system operation and reducing maintenance costs.

[0038] In summary, this step, through scientific site selection and multi-parameter testing, provides high-precision and highly representative basic data support for the subsequent performance evaluation and benefit analysis of the saline water desalination system. It is a key starting point for realizing the four-level benefit evaluation system of "water source-technology-farmland-watershed".

[0039] S12, based on the salt tolerance threshold of irrigated crops and the efficient desalination characteristics of CDI technology, divides the desalination target gradient into mild desalination, moderate desalination and mild desalination, and selects suitable pretreatment processes based on turbidity.

[0040] Specifically, this invention collects water quality parameters of underground saline water and combines crop salt tolerance thresholds with the desalination performance of CDI technology to achieve gradient division of desalination targets and adaptive selection of pretreatment processes, thereby providing accurate data support for subsequent system design and benefit evaluation.

[0041] In this embodiment, the first step involves setting up sampling points in the target area, using a combination of grid-based sampling and denser sampling in irrigated areas to ensure representativeness and spatial coverage. Before sampling, the wells must be thoroughly flushed to obtain raw saline water samples. Preliminary on-site testing is performed using a portable conductivity meter, turbidity meter, and pH meter. The conductivity is converted to salt concentration (g·L⁻¹) using empirical formulas or standard conversion tables.- ¹), turbidity is used to determine whether pretreatment is required. Further laboratory testing of Na... + Cl - Ca² + Mg² + The concentrations of major ions and total hardness were used to assess water quality complexity. This was based on the crop salt tolerance threshold (2–5 g / L). - ¹ represents the suitable range, exceeding 5 g·L - ¹Significant decrease in yield) and CDI technology at 1~10 g·L - ¹The high-efficiency desalination characteristics at different salt concentrations allow for the division of desalination targets into three gradients: light, moderate, and deep, corresponding to different initial and target salt concentration ranges. Simultaneously, suitable pretreatment processes are selected based on turbidity indices (<5 NTU requires no pretreatment, 5–20 NTU uses simple filtration, and >20 NTU uses flocculation plus filtration) to ensure that the water quality entering the CDI system meets its operational requirements.

[0042] In this application, the crop salt tolerance threshold is set at 2~5 g·L. - ¹, The CDI technology has an efficient desalination range of 1~10 g·L. - ¹. The specific desalination target gradient is: mild desalination (6~8 g·L⁻¹). - ¹→3.5~4g·L - ¹) Moderate desalination (4~6 g·L) - ¹→2.5~3g·L - ¹), Deep desalination (2~4 g·L) - ¹→1.5~2g·L - ¹). The turbidity classification criteria are as follows: less than 5 NTU requires no pretreatment; 5~20 NTU requires simple filtration (such as sand filtration or microfiltration); and greater than 20 NTU requires flocculation and filtration processes (such as PAC flocculation + quartz sand filtration) to remove suspended solids and colloids, prevent clogging of CDI electrode channels, and avoid affecting desalination efficiency.

[0043] In this embodiment, this step is applicable to the development and utilization of saline groundwater resources in inland river basins in Northwest China, particularly in arid areas with strong agricultural irrigation needs, complex water quality conditions, and limited power supply. By precisely defining the desalination gradient, the irrigation needs of different crops can be adapted, such as cotton and corn, which have significantly different salt tolerances, thereby optimizing the operating parameters and return on investment of the CDI system. Simultaneously, the appropriate selection of pretreatment processes can effectively reduce system operation and maintenance costs, and improve overall processing efficiency and equipment lifespan.

[0044] In this application, this step scientifically divides the desalination target range, enabling the CDI system to operate within its optimal performance range, thereby improving desalination efficiency and reducing energy consumption. Simultaneously, selecting the pretreatment process based on turbidity indicators effectively removes suspended impurities, reduces contamination and wear on electrode materials, and extends system lifespan. This step provides key input parameters for subsequent economic analysis, energy consumption calculation, and comprehensive benefit evaluation. It is a fundamental link in realizing the four-level benefit evaluation system of "water source – technology – farmland – watershed," and has significant engineering practical value and technical guidance significance.

[0045] S2. Based on the defined target desalination gradient interval, calculate the crop yield loss and planting income changes corresponding to different salinities within the interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system.

[0046] Specifically, this step aims to establish a nonlinear relationship model between relative crop yield and irrigation water salinity. Based on the target desalination gradient range, it calculates the crop yield loss and changes in planting income corresponding to different salinities. Finally, by combining the desalination performance parameters of the CDI system, the optimal sub-desalination target salinity is determined. This step is the core link in the entire saline water desalination benefit evaluation system. Its technical implementation is based on the influence of agricultural irrigation water salinity on crop yield, combined with electrochemical desalination performance and economic accounting models, to achieve a quantitative mapping from water quality input to agricultural output.

[0047] In this embodiment, the present invention employs a nonlinear regression method, based on experimental data on cotton irrigation in arid Northwest China from Web of Science and CNKI, to construct a fitting relationship between irrigation water salt concentration and relative crop yield. The specific formula is as follows:

[0048] in, This indicates the relative yield of cotton (based on freshwater irrigation yield). The salt concentration of irrigation water (g·L) - ¹). This model in It has a high goodness of fit within the range ( This can accurately reflect the trend of the impact of irrigation water salinity on crop yield. When the salt concentration exceeds... At that time, the relative yield decreased significantly, so this range was set as the target range for sub-desalination suitable for irrigation.

[0049] In this application, the key parameters involved in this step include: the initial salt concentration of the irrigation water. Target salt concentration Theoretical maximum yield of crops relative output Crop unit price Planting area Water demand per unit, etc.

[0050] In this embodiment, this step is applicable to the assessment of irrigation utilization of groundwater with different salinity in inland river basins in Northwest China. Multiple target desalination gradients (e.g., 3.5~4 g·L⁻¹) are set. - ¹、2.5~3 g·L - ¹、1.5~2 g·L - ¹), combined with the desalination performance of the CDI system (such as unit desalination amount SAC, capacity retention rate, energy consumption, etc.), crop yield loss and profit changes under different desalination targets can be evaluated, thus providing a scientific basis for the design of agricultural irrigation systems.

[0051] In this application, this step realizes a nonlinear mapping between irrigation water salinity and crop yield, providing a key input for subsequent economic feasibility analysis (such as IRR calculation). By quantifying the changes in agricultural returns under different desalination targets, the optimal desalination range that maximizes comprehensive benefits while ensuring crop yield can be effectively identified, thereby enhancing the application value and promotion feasibility of the CDI system in agricultural irrigation.

[0052] Furthermore, S2 includes: S21, using a nonlinear relationship model to calculate relative crop yield: Where x is the salt concentration of the irrigation water; Specifically, this invention employs a nonlinear relationship model to calculate the relative yield of crops, wherein... This indicates the salt concentration of irrigation water (g·L). - ¹), This represents the relative yield based on freshwater irrigation yield. The model is constructed using extensive empirical data from Web of Science and CNKI (China National Knowledge Infrastructure) on brackish water irrigation of cotton in Northwest China's inland river basins. It employs nonlinear regression analysis and exhibits high goodness of fit, accurately reflecting the influence of irrigation water salinity on cotton yield.

[0053] In this application, the model employs a modified form of the logistic function, adjusting parameters to fit the crop yield response curve. The constants 9022.017 and 9021.038 in the model are used to calibrate the yield baseline and response amplitude, ensuring... exist When the value approaches 1, it represents the maximum yield under freshwater irrigation. Parameter The half-inhibition point (EC) represents the salt concentration. 50This means that when the salt concentration of irrigation water reaches this value, crop yield drops to 50% of maximum yield. (Index term) This determines the steepness of the yield response curve, reflecting the crop's sensitivity to changes in salt concentration.

[0054] In this embodiment of the application, the model is applicable to irrigation water salt concentrations at... Within this range, crop yield decline is relatively small, meeting the tolerance requirements of agricultural irrigation for water quality. However, when the salt concentration exceeds... When the model predicts a significant decrease in relative yield, it suggests the need for sub-desalination through a CDI system to maintain crop yield.

[0055] This step plays a crucial role in the entire technical solution. By quantifying the nonlinear relationship between irrigation water salinity and crop yield, it provides a scientific basis for setting desalination targets for the subsequent CDI system, achieving an optimal balance between the degree of irrigation water desalination and crop yield loss. Simultaneously, by combining crop market prices and planting area, the economic benefits under different irrigation water salinity levels can be further evaluated, providing data support for the synergistic optimization of water resource utilization and agricultural production.

[0056] S22, based on the dynamic balance between irrigation water salt concentration and crop yield, determines the optimal sub-desalination target salinity by adjusting the target salt concentration range and controlling relative yield loss.

[0057] Specifically, this step, based on the dynamic balance between irrigation water salinity and crop yield, achieves synergistic optimization by setting a reasonable sub-desalination target salinity concentration, minimizing crop yield loss and optimizing irrigation water economics. Its core technology lies in establishing a nonlinear functional relationship between irrigation water salinity and relative crop yield, and combining this with agricultural economic parameters to determine a salinity range of 2–5 g·L⁻¹. - ¹The optimal irrigation water salinity threshold within the salt concentration range, which controls relative yield loss to within 12%, provides a scientific basis for setting desalination targets for the CDI system.

[0058] In this application, the present invention uses empirical data fitting to construct a nonlinear functional relationship between irrigation water salt concentration (X) and relative cotton yield, the specific formula of which is:

[0059] This formula, based on extensive research data on brackish water irrigation of cotton in arid Northwest China from Web of Science and CNKI, exhibits a high goodness of fit (R² = 0.933), accurately reflecting the influence of irrigation water salt concentration on cotton yield. In practical operation, the target salt concentration is set at 2–5 g·L⁻¹. -The range ¹ ensures that the relative yield loss does not exceed 12%, thereby significantly reducing the desalination energy consumption and operating costs of the CDI system while maintaining a basically stable crop yield.

[0060] In this application, the key parameters for this step include the initial salt concentration of the irrigation water. Target salt concentration Maximum crop yield and relative output .in, and The value range needs to be matched with the characteristics of groundwater salinity and the crop salt tolerance threshold. The value is 6016.62 kg·ha - ¹, This is calculated using the formula described above. In addition, crop prices ( The price is 18.33 yuan / kg. - ¹, used to calculate changes in planting returns, thereby assessing the economic feasibility of sub-desalination treatment.

[0061] In this embodiment of the application, this step is applicable to low-salinity groundwater (e.g., CO = 6 g·L⁻¹) in inland river basins in Northwest China. - ¹) agricultural irrigation scenarios, especially for crops with high salt tolerance such as cotton and corn. By setting reasonable... This method can effectively avoid the high energy consumption and low economic returns caused by excessive desalination, while also avoiding soil salinization caused by excessive salinity, thus achieving "precise desalination" and "maximum benefits" for agricultural irrigation water.

[0062] The technical advantage of this step lies in controlling crop yield loss within an acceptable range (≤12%) by scientifically setting the target salinity for sub-desalination, while significantly improving the economic feasibility of the CDI system. In the case study, when Set to 3 g·L - ¹time, The IRR of 11.98% indicates that the desalination of irrigation water has a good return on investment within this salinity range, providing key input parameters for subsequent equipment selection, energy consumption calculation and economic evaluation.

[0063] S3, based on the dynamic correlation between the unit desalination capacity, capacity retention rate and energy consumption of the CDI system, uses integral calculation to eliminate the influence of salt concentration fluctuations on energy consumption calculation, and calculates the comprehensive desalination cost within the determined optimal sub-desalination target salinity corresponding to the target desalination gradient.

[0064] Specifically, this invention eliminates the impact of salt concentration fluctuations on energy consumption calculations through integral calculations, thereby achieving accurate calculations of energy consumption per ton of water and overall cost within the target desalination gradient. This step is a key component in constructing the economic evaluation model for the CDI system, and its technical implementation is based on the dynamic correlation between the desalination performance and energy consumption of electrode materials at different salt concentrations.

[0065] In this application, the desalination performance and energy consumption of the CDI system are not constant, but rather exhibit a non-linear relationship with changes in salt concentration. Therefore, to accurately evaluate the desalination performance at a specific desalination gradient (e.g., from the initial salt concentration), To the target salt concentration The average energy consumption of the internal system needs to be considered in relation to salt concentration and unit desalination capacity (SAC), capacity retention rate (…). The fitting function of the CDI system is integrated over this interval. Specifically, the CDI system is experimentally measured in... Desalination performance data within a salt concentration range were used to fit the relationship between salt concentration and maximum unit desalination capacity. ) and capacity retention rate ( The linear relationships are as follows:

[0066]

[0067] Furthermore, the above function is... to Integrate within the interval to calculate the average stable unit desalination rate per unit mass of electrode material per cycle within that gradient. The calculation formula is as follows:

[0068] Through numerical integration, we can obtain This parameter reflects the average desalination capacity and cycle stability of the CDI system electrode materials within the target desalination range, and is the basis for subsequent energy consumption calculations.

[0069] In this embodiment of the application, the key parameters involved in this step include: initial salt concentration. Target salt concentration Total mass of electrode materials Treatment water volume Unit desalination amount Capacity retention rate The fitting coefficient between desalination capacity and energy consumption , All parameters are derived from fitting experimental data and have clear physical meaning and repeatability.

[0070] In this application, this step is applicable to the design and economic evaluation of CDI sub-desalination treatment systems for groundwater in inland river basins in Northwest China. Through integral calculations, energy consumption errors caused by salt concentration fluctuations can be effectively eliminated, providing a scientific basis for equipment selection, operation strategy optimization, and return on investment analysis under different desalination gradients.

[0071] In this embodiment, this step, through the combination of mathematical modeling and experimental data, achieves dynamic averaging of the energy consumption of the CDI system during actual operation, improving the accuracy and representativeness of energy consumption per ton of water. Combined with subsequent electricity price weighting and IRR calculation, it can further support the economic feasibility analysis of the equipment, providing quantitative decision support for the sustainable development and utilization of saline water resources in the basin.

[0072] Furthermore, S3 includes: S31 eliminates the influence of salt concentration fluctuations through integral calculation and calculates the average stable unit desalination amount of the electrode material in one cycle within the target desalination gradient.

[0073] Specifically, by eliminating the influence of salt concentration fluctuations through integral calculations, the average stable unit desalination capacity of the electrode material in one cycle within the target desalination gradient is calculated. This step is a key step in achieving accurate energy consumption calculation for the CDI system in this invention. Based on experimental data of the desalination performance of electrode materials at different salt concentrations, this step constructs a relationship between salt concentration and maximum unit desalination capacity (…). ) and stable capacity retention rate ( The linear fitting relationship of the target desalination range is obtained, and the desalination performance is averaged through integral calculation, thereby eliminating the impact of performance fluctuations caused by dynamic changes in salt concentration and improving the accuracy of energy consumption and cost accounting.

[0074] In this application, this step first involves fitting the salt concentration based on experimental data. With maximum unit desalination Linear relationships, for example and salt concentration With stable capacity retention Linear relationships, for example Subsequently, at the preset initial salt concentration... With target salt concentration Between these two functions, we perform integration operations on each of them, that is:

[0075] The integral operation is essentially a continuous assessment of desalination performance within the target desalination range. By taking the average value of the function within this range, it can more realistically reflect the desalination capacity and stability of the electrode material as the salt concentration changes during actual operation. In this step, the length of the integral range... Usually set at Within a certain range, to match the water quality requirements for agricultural irrigation.

[0076] Furthermore, the calculation results of this step This will serve as the core input parameter for subsequent energy consumption per ton of water calculations, used to evaluate the desalination efficiency and economics of the CDI system under different salt concentration gradients. This method effectively avoids misjudgments of energy consumption caused by local salt concentration fluctuations, thus providing a scientific basis for equipment selection, operational strategy optimization, and return on investment analysis. Technically, this step integrates electrochemical performance modeling and mathematical integral analysis, demonstrating the innovation of this invention in establishing a quantitative bridge between process performance and economic evaluation.

[0077] S32, substitute the average stable unit desalination amount ΔRAC into the fitting formula of desalination amount and energy consumption, and combine it with the SAC formula to calculate the energy consumption per ton of water within the target desalination gradient.

[0078] Specifically, the energy consumption per ton of water is calculated based on the fitting relationship between the desalination performance and energy consumption of the CDI system, combined with the average stable unit desalination capacity. The quantitative analysis was conducted. The core of this step lies in fitting a linear relationship formula between desalination capacity and energy consumption using experimental data. ,in The amount of salt removed per unit (mg·g) - ¹), This represents the energy consumption per unit mass of electrode material per cycle (×10). -5 kWh·g - ¹). This formula was obtained through 45 cycles of adsorption-desorption experiments, exhibiting high fitting accuracy and is suitable for... The salt concentration range.

[0079] In practice, the first step is to determine the target desalination gradient (e.g., from the initial salt concentration). Reduce to target salt concentration ), calculate the average stable unit desalination rate per unit mass of electrode material per cycle within this interval. This value is obtained by integrating the fitting function of salt concentration, maximum unit desalination, and capacity retention rate over the target interval and taking the mean value. The calculation formula is as follows:

[0080] in and These represent the linear fitting relationships between salt concentration and maximum unit desalination capacity and capacity retention rate, respectively. In this invention, these relationships are obtained through fitting experimental data. and Substitute the values ​​into the integral to obtain the final result. .

[0081] Furthermore, Substitute into the fitting formula for desalination rate and energy consumption The average steady-state energy consumption per unit mass of electrode material in a single cycle can be calculated. Based on this, combined with the SAC formula... Ultimately, the energy consumption per ton of water is determined by the following formula:

[0082] Calculate the average energy consumption per ton of water within the target desalination gradient (kWh·t). - ¹). This step plays a crucial role in the technical solution, providing basic energy consumption parameters for subsequent economic feasibility analysis (such as IRR calculation), ensuring the comparability and scientific validity of energy consumption assessments of the CDI system under different water quality conditions, thereby supporting techno-economic decision-making in agricultural irrigation scenarios.

[0083] S4. Combining the agricultural time-of-use electricity pricing policy and the water storage capacity of the CDI system, the time-of-use electricity price is weighted and processed. The resulting planting income and the calculated comprehensive desalination cost are integrated to construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

[0084] Specifically, in step five, the present invention combines agricultural time-of-use electricity pricing policy with the water storage capacity of equipment to weight the electricity price, constructs an internal rate of return (IRR) calculation model that includes planting income, desalination costs and electricity price fluctuations, and further quantifies the impact of key parameter fluctuations on IRR through sensitivity analysis, thereby achieving a scientific assessment of the economic feasibility of the CDI saline water desalination system in agricultural irrigation.

[0085] In this embodiment, the present invention first divides a 24-hour day into three periods: peak, flat, and off-peak, based on the local power grid's time-of-use pricing policy (such as the Gansu Province 2025 Agricultural Production Electricity Price List), and obtains the electricity value for each period. Because the CDI system is modular and has low energy consumption, and its accompanying freshwater tank has sufficient storage capacity, the system can operate and store treated freshwater at any time, thus its electricity consumption is not limited by time of day. To accurately reflect the electricity cost throughout the equipment's entire lifecycle, a weighted average of the electricity price is applied, using the following formula:

[0086] in, This represents the weighted average electricity price (yuan / kWh). Let the value of electricity be the value of electricity under the i-th electricity price scenario. Assign weights to the duration of electricity consumption in this scenario. This refers to the number of electricity pricing scenarios. For example, under the electricity pricing structure in Gansu Province, if equipment operates for 7 hours during peak hours, 11 hours during normal hours, and 6 hours during off-peak hours, then the weighted average electricity price is... Yuan / kWh.

[0087] In this application, actual electricity price data and operating time distribution are used for weighted calculation to ensure that the electricity price parameters closely reflect the actual operating environment. In addition, water storage capacity, as one of the key parameters, must ensure the continuity of irrigation water after the equipment operates during off-peak periods. Typically, the freshwater tank capacity is required to be no less than 80% of the equipment's maximum daily output to meet the temporal demand for irrigation water.

[0088] In this embodiment, this step is applicable to the economic evaluation of CDI equipment in agricultural irrigation systems in the Northwest Inland River Basin, especially in areas where the power grid implements time-of-use pricing policies and agricultural electricity costs fluctuate significantly. By introducing weighted pricing, misjudgments of IRR caused by a single price assumption can be avoided, improving the model's accuracy in fitting actual operating costs.

[0089] In this application, the IRR model constructed in this step not only considers crop planting revenue and desalination costs, but also effectively reflects the impact of dynamic changes in electricity costs on the project's economics through electricity price weighting. Furthermore, through sensitivity analysis, the sensitivity of key parameters such as electricity price fluctuations, electrode lifespan, and crop prices to IRR can be identified, providing a quantitative basis for equipment selection, operational strategy optimization, and investment decisions, significantly improving the accuracy of economic assessment and the scientific nature of decision-making for saline water desalination projects in the agricultural sector.

[0090] The method for evaluating the application benefits of underground saline water resources in this invention provides a systematic method for evaluating the application benefits of underground saline water resources and capacitive deionization (CDI) technology adapted to the inland river basins of Northwest China. It realizes multi-dimensional quantitative evaluation from water quality characteristics to agricultural irrigation benefits, from equipment energy consumption to economic feasibility, and effectively supports the scientific development of saline water resources and the rational promotion of CDI technology.

[0091] Furthermore, S4 includes: S41, using a weighted formula to calculate the comprehensive electricity price: ;in For the first Various electricity pricing scenarios, Weighting of electricity consumption duration for the corresponding time period; Specifically, the weighted formula used is a time-weighted average of electricity prices for agricultural production to more accurately reflect the electricity costs of the CDI system during its actual operating cycle. This step plays a crucial role in this invention and is one of the fundamental steps in achieving economic feasibility evaluation.

[0092] In this application, the formula is based on the peak-valley time-of-use pricing policy implemented by the power grid in the Northwest Inland River Basin. It divides a 24-hour day into three pricing periods: peak, flat, and off-peak, and assigns weights based on the duration of electricity consumption. Specifically, the peak period price is 0.6714 yuan / kWh for 7 hours; the flat period price is 0.4489 yuan / kWh for 11 hours; and the off-peak price is 0.2265 yuan / kWh for 6 hours. Because the CDI system is modular and can operate continuously, and its accompanying freshwater tank provides water storage, the system's operation is not dependent on specific time periods. Therefore, a weighted average of the electricity price is necessary to eliminate the interference of price fluctuations during different time periods on cost accounting. This step involves summing the products of the electricity price for each time period and the corresponding operating time, and then dividing by the total duration of 24 hours to obtain the comprehensive electricity price. .

[0093] In this embodiment of the application, the key parameters involved in this step include electricity price. Weighting of electricity usage time The electricity price data comes from the Gansu Province 2025 Electricity Price Table for Agricultural Production, specifically the price standard for voltage levels below 1 kV. (Weight) The peak-valley time-of-use electricity price is determined according to the criteria outlined in the "Notice of the Gansu Provincial Development and Reform Commission on Matters Concerning the Optimization and Adjustment of Peak-Valley Time-of-Use Electricity Pricing Policies for Industrial and Commercial Users." The comprehensive electricity price calculated using this formula is 0.4582 yuan / kWh, providing a key input parameter for subsequent analysis of energy consumption per ton of water and economic viability.

[0094] In this application, this step applies to all agricultural irrigation projects using CDI technology for underground saline water desalination, especially in regions with complex electricity pricing policies and significant fluctuations in electricity load, such as the Northwest Inland River Basin. By calculating weighted electricity prices, economic misjudgments caused by single-price assumptions can be avoided, improving the accuracy of cost accounting and the scientific basis of engineering decisions.

[0095] The technical advantage of this step lies in its ability to dynamically and accurately calculate the operating costs of the CDI system by introducing a time-weighted electricity price mechanism, thereby improving the reliability and applicability of the overall benefit evaluation model. Its innovation lies in combining grid electricity pricing policies with the operational characteristics of the water resource treatment system to construct an electricity cost assessment method that conforms to regional realities, laying a solid foundation for subsequent calculations of economic indicators such as IRR.

[0096] S42 quantifies the impact of key parameter fluctuations on IRR through sensitivity analysis, including initial salinity of groundwater, target sub-desalination salinity, electrode material cost and lifespan, time-of-use electricity pricing structure, and setting boundary values ​​for fluctuations in crop price and yield parameters.

[0097] Specifically, this invention employs single-factor sensitivity analysis to quantitatively assess the impact of fluctuations in key parameters on the internal rate of return (IRR). The core of this step lies in identifying and evaluating which parameters significantly influence the economic feasibility of the CDI system when applied to groundwater irrigation in the Northwest Inland River Basin, thereby providing a scientific basis for engineering decisions.

[0098] In this application, sensitivity analysis uses a fixed set of other parameters as baseline values, changing only one sensitive parameter to its fluctuation boundary value, and then recalculating the IRR value using the IRR calculation formula to assess the impact of this parameter change on the project's economics. Specifically, sensitive parameters include, but are not limited to, initial salinity of groundwater, target sub-desalination salinity, electrode material cost and lifespan, time-of-use electricity pricing structure, and crop prices and yields. For example, the fluctuation boundary of the initial salinity can be set as follows: The target sub-desalination salinity is set based on the crop's salt tolerance threshold. The cost fluctuation range of electrode materials is set at ±20%, referencing market quotations and historical data, while the lifespan fluctuation is set at ±15% based on experimental data. The fluctuation of the time-of-use electricity pricing structure is determined by adjusting the weighting of different electricity price scenarios according to local grid policies, such as ±10% for peak electricity prices and ±15% for off-peak electricity prices. The fluctuation boundaries of crop price and yield parameters are set based on market data from the past five years, such as ±15% for cotton prices and ±10% for yield.

[0099] By setting the above parameters as boundary values ​​and substituting them into the IRR calculation formula:

[0100]

[0101] The IRR values ​​under different parameter variations can be further calculated. This step quantifies the magnitude of change by comparing the baseline IRR with the IRR after parameter fluctuations, thereby assessing the sensitivity of each parameter. For example, if a 20% increase in electrode material cost leads to a 3.5% decrease in IRR, it indicates that this parameter is highly sensitive to the project's economics.

[0102] This step plays a crucial role in the overall technical solution. Its technical value lies in revealing the dependence of the CDI system on key variables in practical applications, providing data support for project design, equipment selection, and operational strategy optimization. Through sensitivity analysis, parameters with the greatest impact on IRR can be identified, allowing for priority control or optimization during project implementation, thereby enhancing the project's resilience and economic feasibility. Furthermore, this step provides policymakers and investors with clear decision-making support, facilitating the large-scale application of CDI technology in the development of saline groundwater in arid regions.

[0103] The method for evaluating the application benefits of underground saline water resources in this invention provides a systematic method for evaluating the application benefits of underground saline water resources and capacitive deionization (CDI) technology adapted to the inland river basins of Northwest China. It realizes multi-dimensional quantitative evaluation from water quality characteristics to agricultural irrigation benefits, from equipment energy consumption to economic feasibility, and effectively supports the scientific development of saline water resources and the rational promotion of CDI technology.

[0104] S5, Calculate the soil salinization mitigation benefit by quantifying the difference in soil salt accumulation rates between irrigation with saline water and irrigation with desalinated water, using the following formula: Where A is the irrigated area, This represents the rate of salt accumulation in soil irrigated with saline water. The rate of salt accumulation in the irrigated soil after desalination, where K is the unit cost of soil improvement.

[0105] Specifically, the formula for calculating the benefits of mitigating soil salinization. This is a key quantitative method in the ecological benefit assessment dimension of this invention, used to evaluate the effect of the CDI system on the inhibition of soil salinization trend and its corresponding economic value after the sub-desalination treatment of groundwater.

[0106] In this embodiment, the formula is based on a comparative analysis of soil salinity accumulation rates. It calculates the difference in salinity accumulation per unit area and per unit time between the original saline irrigation and the irrigation after desalination, multiplies this difference by the unit cost of salinity remediation, and thus derives the economic benefits of soil improvement. Specifically, This represents the annual accumulation rate of soil salinity under untreated saline irrigation conditions, expressed in t / ha·year. This represents the annual rate of soil salinity accumulation caused by irrigation water after desalination treatment via a CDI system, expressed in units of the same value. The difference between the two values ​​is... This reflects the reduced salt input due to lower irrigation water salinity, thereby reducing the risk of soil salinization. A represents the irrigated area (ha), and K represents the unit cost of salt remediation (yuan / t), which is usually estimated based on the average cost of regional soil improvement projects. For example, in the inland river basins of Northwest China, K is often taken as 150 yuan / t.

[0107] In this application, and The acquisition of this data relies on long-term field trials and model simulations. For example, in the case of this invention, the soil salt accumulation rate under saline irrigation was... After treatment by the CDI system, the salinity of the irrigation water decreased, and the corresponding rate of soil salt accumulation decreased to [a lower level]. The irrigated area A is set to 10 ha, and K is 150 yuan / t. Substituting these values ​​into the formula, we can obtain... This means that the cost of soil salinization control in this area can be reduced by 2,250 yuan per year through desalination.

[0108] In this application, the formula is applicable to the assessment of agricultural irrigation systems in arid and semi-arid regions such as the Northwest Inland River Basin, particularly in scenarios where low-energy, modular desalination technologies such as CDI are used for saline water treatment. This formula can provide agricultural managers with a quantitative basis for soil improvement benefits, assisting them in making scientific decisions regarding water resource allocation and ecological protection.

[0109] In this embodiment, this step incorporates the cost of soil salinization remediation into the evaluation system for saline water desalination benefits, thus extending the value from "water quality improvement" to "soil ecological restoration." Its innovation lies in unifying the modeling of the ecological benefits of agricultural irrigation and soil improvement, providing more convincing economic and ecological support for the promotion of the CDI system in arid regions.

[0110] The method for evaluating the benefits of underground saline water resource utilization in this invention introduces a step for calculating the benefits of soil salinization mitigation. Based on a quantitative model of irrigation area, differences in soil salt accumulation rate, and improvement costs, it further evaluates the long-term improvement effect of CDI desalination technology on the agricultural soil environment. Thus, based on the original water quality-irrigation-energy consumption-economic evaluation system, it enhances the comprehensive consideration of ecological sustainability and improves the scientificity and practicality of saline water utilization benefit evaluation.

[0111] Example 2 This invention relates to a system for evaluating the benefits of underground saline water resources in inland river basins in Northwest China. Figure 2 This is a technical roadmap for evaluating the application benefits of groundwater saline water resources in inland river basins in Northwest China, provided as an embodiment of the present invention. Figure 2 As shown, the system includes: Sampling points were deployed in designated areas using a grid-based approach combined with denser sampling in irrigated areas, in shallow groundwater distribution zones. Before sampling, wells were flushed to obtain raw water. Conductivity (converted to salt concentration), turbidity, and pH were measured on-site using portable instruments. After collection, samples were sealed and sent to the laboratory for focused Na+ analysis. + Cl - Ca² + Mg² + The main ion concentrations and hardness were measured. The collected data directly served as the core input for subsequent steps: based on the salt tolerance thresholds (2~5 g·L⁻¹) of major irrigated crops (such as cotton and corn) in the Northwest Inland River Basin. - ¹ represents the suitable range, exceeding 5 g·L - ¹Crop yield decreased significantly), combined with CDI technology at 1~10 g·L -¹Highly efficient desalination characteristics at different salt concentrations, dividing the target desalination gradient into: mild desalination (initial salt concentration 6~8 g·L⁻¹) - ¹To the target salt concentration of 3.5~4 g·L - ¹) Moderate desalination (initial salt concentration 4~6 g·L) - ¹To the target salt concentration of 2.5~3 g·L - ¹) Mild desalination (initial salt concentration 2~4 g·L) - ¹To the target salt concentration of 1.5~2 g L - ¹); Based on the water quality requirements for irrigation water, the appropriate pretreatment processes are selected according to the range of turbidity: less than 5 NTU (no pretreatment required), 5~20 NTU (simple filtration pretreatment), and greater than 20 NTU (flocculation and filtration pretreatment). This provides accurate and comprehensive basic data support for parameter optimization, full-process cost accounting, and comprehensive benefit evaluation of the saline water desalination system.

[0112] The above data serves as direct input for subsequent steps: determining the target desalination gradient range based on salt concentration; selecting appropriate pretreatment processes and operating parameters by combining turbidity and hardness; and providing basic data support for CDI system design, operating parameter optimization, cost accounting, and benefit evaluation.

[0113] By fitting the relationship between irrigation water salinity and crop yield, a portion of crop yield can be sacrificed in exchange for lower desalination costs to maximize overall benefits. Taking cotton as an example, this invention organizes relevant research data, comprehensively considering different irrigation systems and regional differences, and calculates that the average maximum cotton yield Y0 using freshwater irrigation in these studies is 6016.62 kg·ha. -1 The average irrigation water volume is 4586.47 m³. 3 ·ha -1 To balance desalination costs and cotton yield, this invention determines the optimal sub-desalination target concentration using a cotton relative yield fitting formula. This formula is derived from relevant research on cotton irrigation with brackish water of different salinity concentrations in arid Northwest China, found on Web of Science and CNKI (China National Knowledge Infrastructure). The fitting formula (R... 2 =0.933) Figure 3 As shown:

[0114] Among them, Y rel Let be the relative yield of cotton (based on freshwater irrigation yield), and x be the salt concentration of the irrigation water. From the fitting results, it can be observed that when using 2~5 g·L... -1 The relative yield decrease is smaller when irrigated with saline water, but once the irrigation water concentration exceeds 5 g·L... -1The relative yield will decrease significantly. Therefore, to ensure cotton yield, the salt concentration of irrigation water should not exceed 5 g·L⁻¹. -1 .

[0115] According to data released by the China Cotton Association in 2025, the average selling price of Xinjiang cotton was 18.33 yuan / kg. -1 Based on this, the sales revenue per hectare of cotton and the desalination raw material and energy consumption costs of the CDI system were calculated under different initial salt concentrations and salinity brackish water irrigation. Then, the comprehensive benefits of CDI system sub-desalination of cotton under different salinity brackish water irrigation compared to direct irrigation of cotton with initial salinity brackish water were calculated.

[0116] The core of the CDI system is the desalination module. This invention quantifies the adsorption capacity of the desalination module using a unit desalination capacity (SAC) formula. Combined with the characteristics of groundwater saline water quality and irrigation needs in the target watershed, a suitable CDI system is determined for adaptation, ensuring stable desalination performance, low energy consumption, and economic viability commensurate with agricultural benefit calculations within the target salt concentration range. The SAC calculation formula is as follows:

[0117] Where C0 is the initial concentration of brackish water (g·L) -1 ), C t The concentration of saline water at time t during desalination (g·L) -1 ), V s The volume of water to be treated (L) is given, and m is the total mass of the electrode materials (g).

[0118] This method is applicable to the calculation of energy consumption per ton of water for various CDI systems. Its core principle is to achieve unified accounting for different CDI types by quantifying the correlation between desalination performance and energy consumption. In actual brackish water desalination processes, the salt solution concentration continuously changes as desalination progresses. To further investigate the relationship between the desalination performance and energy consumption of CDI systems and the salt solution concentration, a range of 0.5–6 g·L⁻¹ was used. -1 Salt solutions with a salt concentration, every 0.5 g·L -1 A gradient is set, and each gradient is subjected to 45 cycles of adsorption-desorption experiments under preset operating conditions. The current during desalination of the CDI system is recorded in real time using a current analyzer. The maximum unit desalination capacity of the CDI system during the first cycle and its corresponding energy consumption and stable capacity retention rate after 45 cycles are calculated.

[0119] Under preset operating conditions, three key correlations were fitted through experiments: the first group was the salt solution concentration (x, g·L). -1 ) and the maximum unit desalination capacity of the CDI system (y1, mg·g) -1 The linear fitting relationship of )

[0120] Where a and b reflect the effect of salt concentration on desalination capacity; The second group is the salt solution concentration (x, g·L). - ¹) Linear fit relationship with the stable capacity retention rate (y2, %) of the CDI system:

[0121] Where c and d reflect the cyclic adsorption stability of the electrode; The third group is the desalination capacity of the CDI system (x, mg·g). - ¹) and unit cycle energy consumption (y3, ×10 -5 kWh·g - ¹) Linear fitting relationship:

[0122] Where e and f reflect the coupling relationship between desalination capacity and energy consumption.

[0123] To eliminate the impact of salt concentration fluctuations within the target desalination range on energy consumption calculations, the fitting function of the above salt concentration with the maximum unit desalination capacity and capacity retention rate is used at a preset initial salt concentration (C0, g·L). - ¹) to the target salt concentration (C t g·L - ¹) Perform integral operations within the interval, take the interval mean of the fitted function, and multiply them to obtain the average stable unit desalination capacity of the electrode material in one cycle within the desalination gradient. mg·g - ¹), the calculation formula is as follows:

[0124] The calculated average stable unit desalination rate per cycle within the desalination gradient. Substituting these values ​​into the fitting formula for desalination rate and energy consumption, we obtain the average steady-state energy consumption E of the HCDI system per unit mass of electrode material per cycle within the corresponding desalination gradient. 单位循环 The formula for calculating energy consumption per ton of water is:

[0125] Because the electricity used for agricultural production in the arid Northwest region is subject to time-of-use pricing (based on the local power grid's agricultural electricity price list), the price fluctuates throughout the day depending on user demand. However, the equipment in this invention operates regardless of time of day, and its accompanying freshwater tank has ample storage capacity. Therefore, a weighted average of the electricity price is necessary to accurately reflect the actual electricity cost throughout the equipment's entire lifecycle, avoiding misjudgments of economic viability caused by relying on a single price calculation.

[0126]

[0127] Among them, P 用电 This represents the weighted average electricity price (yuan / kWh); P i Let ω be the electricity price in the i-th scenario. i is the weight of electricity usage duration for the i-th scenario; n is the number of electricity price scenarios.

[0128] This paper estimates the cost of desalination based on the desalination performance of the HCDI system. The desalination cost mainly includes the raw material cost of replacing electrode materials and energy consumption cost. The economic feasibility evaluation index of this invention is calculated using the Internal Rate of Return (IRR). The Internal Rate of Return is the discount rate that makes the net present value (NPV) of the equipment zero over its life cycle, reflecting the actual profitability of the equipment investment. The calculation formula is as follows:

[0129]

[0130] Where NPV represents the net present value of the project, which is the sum of the present values ​​of cash flows over the equipment's lifespan; n represents the calculation year; N represents the equipment's lifespan; i is the rate of return; Y is the theoretical maximum yield of the crop (kg / ha); Y rel P represents the relative yield of the crop; A represents the crop planting area (ha); P represents the relative yield of the crop. 作物 For crop market prices; CF n I represents the net cash flow in year t; 总 CF0 represents the initial total investment (RMB), which is the total fixed asset investment at the start of the project in year 0, with no income or other expenditures; V is the annual water production capacity of the equipment (tons).

[0131] When NPV is 0, the value of 'i' in this equation is the IRR. Since this equation is of high order and has no analytical solution, a trial-and-error method combined with linear interpolation is used to solve it. (Two discount rates are selected for trial calculations to obtain a positive and a negative net present value to lock in the range of the internal rate of return. Then, the discount rate that makes the net present value zero within this range is estimated through linear approximation. If the accuracy is insufficient, the range is narrowed and the trial calculations are repeated until an approximate internal rate of return is obtained.) The IRR value can be approximated at this point. A higher IRR value indicates higher economic feasibility of the project.

[0132] A single-factor sensitivity analysis method is employed, where other parameters are fixed as baseline values, and only a single sensitive parameter is changed to its fluctuation boundary value. This value is then substituted into the benefit evaluation formula of this invention (including the energy consumption per ton of water formula, comprehensive cost formula, and IRR calculation formula) to calculate the internal rate of return (IRR) under the fluctuation of each parameter. By comparing the change in IRR before and after parameter fluctuation, the degree of sensitivity is quantified. Sensitivity analysis parameters may include, but are not limited to: initial salinity and water quality of groundwater; target sub-desalination salinity; electrode material cost and lifespan; time-of-use electricity pricing structure and weighted electricity price level; and crop price and yield parameters. The setting of the fluctuation boundary value of the sensitive parameter in the sensitivity analysis must adhere to the core principles of conforming to the actual scenario of the Northwest Inland River Basin, covering extreme risks throughout the project's entire life cycle, and matching the characteristics of CDI technology with engineering feasibility, ensuring that the boundary values ​​are scientifically reasonable and provide effective support for engineering decisions. For example, the initial salinity boundary is set with reference to the natural distribution range of 1~10 g / L of shallow brackish water in the basin, and the crop price boundary is set based on the actual market fluctuation range of the past 5 years, avoiding extreme values ​​that are detached from reality.

[0133] To address issues such as uneven spatial and temporal distribution of water resources, supply-demand imbalance, and reduced crop yields in river basins, saline water supply can enhance the water resource security capacity of the basin and reflect the social public value of water resource security. Its benefits can be quantified using the following formula:

[0134]

[0135]

[0136] Among them, V 需求 The total annual irrigation water demand of the basin (m³) 3 ); V 淡化 The annual volume of saline water after sub-desalination treatment (m³) 3 B is obtained by multiplying the equipment's water production flow rate and annual effective operating hours; g Social benefits for water resource protection (yuan); P 缺水损失 The social loss value per unit of water shortage (yuan / m³).

[0137] Meanwhile, long-term irrigation with high-salinity water can lead to soil salinization. To achieve efficient utilization of saline water resources while protecting the ecosystem, desalination of saline water can effectively reduce the risk of soil salinization. The following formula can quantify the soil improvement benefits of annual desalination: B e :

[0138] Where A is the irrigated area (ha); S0 is the original salt accumulation rate of saline irrigated soil (t / ha·year); S accK represents the rate of salt accumulation in the irrigated soil after desalination (t / ha·year); K represents the unit cost of soil improvement (yuan / t).

[0139] This case study evaluates the application benefits of a CoHCF / CNT (cobalt iron cyanide / carbon nanotube) electrode HCDI system in groundwater irrigation in the Shiyang River basin. It assumes an initial groundwater salt concentration of C0 = 6 g·L⁻¹. -1 The turbidity is 8 NTU and the hardness is 280 mg / L (calculated as CaCO3), which meets the applicable conditions of "no pretreatment or softening required" mentioned above.

[0140] Assuming the crop is cotton, the planting area is A = 10 ha, and the maximum yield with freshwater irrigation is Y = 6016.62 kg·ha. -1 According to data released by the China Cotton Association in 2025, the average selling price of Xinjiang cotton was 18.33 yuan / kg. -1 The initial total investment of the selected HCDI system is I = 5 million yuan, and the equipment life cycle is N = 10 years. It is assumed that the annual water production of the equipment exactly covers the total annual irrigation water demand of cotton.

[0141] Based on the crop relative yield formula, a target salt concentration C is set to balance yield and desalination costs. t 3 g·L -1 Substituting into the formula, we can obtain Y. rel =0.88, corresponding to the actual cotton yield Y = Y0 × Y rel =6016.62 × 0.88 ≈ 5270.56kg·ha -1 The water requirement per unit yield is 0.87 t·kg. -1 .

[0142] Energy consumption per ton of water Based on the dynamic changes in the desalination performance of the CoHCF / CNT electrode HCDI system under different salt solutions and measured data, the correlation between the salt solution concentration and the maximum unit desalination capacity of the CDI system was fitted as follows:

[0143] The correlation between salt solution concentration and the capacity retention rate of the CDI system was fitted, and the relationship between salt solution concentration (x) and stable capacity retention rate (y) was obtained as follows:

[0144] The relationship between the desalination rate and energy consumption of the CDI system was fitted, and the relationship between the desalination rate (x) and energy consumption (y) is as follows:

[0145] The linear coefficients of these correlations are all close to 1. This indicates that the optimal concentration of these correlations is within the range of 0.5–6 g·L⁻¹. -1 The maximum unit desalination capacity of the electrode in the salt solution exhibits a good linear relationship with salt concentration, electrode stable capacity retention rate with salt concentration, and electrode material desalination capacity with energy consumption. Substituting these values, we can obtain the average stable unit desalination capacity (mg·g) per unit mass of electrode material per cycle within this desalination gradient. - ¹):

[0146] Will Substituting these values ​​into the fitting relationship between the desalination capacity and energy consumption of the CDI system, we can obtain the average stable energy consumption per unit mass of electrode material per cycle for desalination of brackish water with different salt concentration gradients (×10⁻⁵ kWh·g). - ¹):

[0147] Substituting the average stable energy consumption per unit mass of electrode material in a single cycle into the formula for calculating energy consumption per ton of water, we get:

[0148] Electricity cost calculation Table 1 shows the electricity price table for agricultural production in Gansu Province in 2025. The equipment uses 220V mains power, a voltage level of less than 1 kV. Considering that the equipment operates without time-of-use distinctions and the freshwater tank can continuously store water, the electricity price is weighted. According to the "Notice of the Gansu Provincial Development and Reform Commission on Optimizing and Adjusting Peak-Valley Time-of-Use Electricity Pricing Policies for Industrial and Commercial Users," the peak-valley time-of-use electricity pricing is divided as shown in Table 2. The electricity price is calculated by taking the weighted average value based on the time period.

[0149] The subsequent calculation uses an electricity price of 0.458 yuan per kWh. -1 .

[0150] Table 1. Electricity Price List for Agricultural Production in Gansu Province, 2025 (Yuan)

[0151] Table 2. Peak-Valley Time-of-Use Electricity Pricing Standards in Gansu Province

[0152] Feasibility analysis The annual water production capacity of this equipment is:

[0153] The device's annual cash flow CF n for:

[0154] Let IRR be r, and let NPV be 0, then we have:

[0155] Solving the equation, we get r = approximately 11.98%, which is higher than the industry average internal rate of return, indicating that the project has good feasibility.

[0156] This case study assumes that the CDI sub-desalination equipment can fully meet the irrigation needs of a 10-ha cotton-growing area; therefore, the water resource security improvement rate for this cotton-growing area is 100%. Referring to the actual production situation in the arid Northwest region, the social loss value per unit of water shortage is assumed to be P. 缺水损失 If the cost is 2 yuan / m³, then the social benefits of water resource protection are:

[0157] Assuming the original salt accumulation rate S in the irrigated soil is saline... o For 2 t ha 1 Annual salt accumulation rate S in irrigated soil after desalination acc 0.5 t ha 1 If the annual cost of soil salinization remediation is 150 yuan / ton, then the benefits of soil improvement are:

[0158] Although the savings in soil salinization remediation costs may seem insignificant, soil improvement is crucial for the long-term sustainable use of planting areas.

[0159] Example 3 This invention provides an evaluation device for the application benefits of groundwater saline water resources in the Northwest Inland River Basin, such as... Figure 4 As shown, the device includes: The water quality data acquisition and gradient division module 100 is used to collect water quality data of groundwater, including salt concentration, turbidity, hardness and ion concentration, and to divide the target desalination gradient range according to the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology. The nonlinear relationship modeling and sub-desalination target determination module 200 is used to calculate the crop yield loss and planting income change corresponding to different salinities in the divided target desalination gradient interval based on the interval, establish a nonlinear relationship model between crop relative yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. The Dynamic Association Integration and Cost Calculation Module 300 is used to calculate the comprehensive desalination cost within the CDI system based on the dynamic relationship between unit desalination capacity, capacity retention rate and energy consumption. It uses integral calculation to eliminate the impact of salt concentration fluctuations on energy consumption calculation. For the target desalination gradient corresponding to the determined optimal sub-desalination target salinity, it calculates the comprehensive desalination cost within the gradient. The electricity price weighted and IRR model construction module 400 is used to combine the agricultural time-of-use electricity price policy and the water storage capacity of the CDI system to weight the time-of-use electricity price, integrate the obtained planting income with the calculated comprehensive desalination cost, and construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

[0160] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0161] Example 4 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0162] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0163] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0164] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0165] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0166] S2. Based on the divided target desalination gradient interval, calculate the crop yield loss and planting income change corresponding to different salinities in the interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. S3, based on the dynamic correlation between the unit desalination capacity, capacity retention rate and energy consumption of the CDI system, uses integral operation to eliminate the influence of salt concentration fluctuation on energy consumption calculation, and calculates the comprehensive desalination cost within the determined optimal sub-desalination target salinity corresponding to the target desalination gradient. S4. Combining the agricultural time-of-use electricity pricing policy and the water storage capacity of the CDI system, the time-of-use electricity price is weighted and processed. The resulting planting income and the calculated comprehensive desalination cost are integrated to construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

Claims

1. A method for evaluating the application benefit of underground saline water resources in the northwest inland river basin, characterized in that, include: S1, collect groundwater saline water quality data, including salt concentration, turbidity, hardness and ion concentration, and divide the target desalination gradient range according to the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology. S2. Based on the divided target desalination gradient interval, calculate the crop yield loss and planting income change corresponding to different salinities in the interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. S3, based on the dynamic correlation between the unit desalination capacity, capacity retention rate and energy consumption of the CDI system, uses integral operation to eliminate the influence of salt concentration fluctuation on energy consumption calculation, and calculates the comprehensive desalination cost within the determined optimal sub-desalination target salinity corresponding to the target desalination gradient. S4. Combining the agricultural time-of-use electricity pricing policy and the water storage capacity of the CDI system, the time-of-use electricity price is weighted and processed. The resulting planting income and the calculated comprehensive desalination cost are integrated to construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

2. The method of claim 1, wherein, The collected groundwater saline water quality data includes salt concentration, turbidity, hardness, and ion concentration. Based on the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology, the target desalination gradient range is defined. The data also includes: S11, the sampling points are arranged by combining the grid points with the irrigation area encryption, the conductivity, turbidity and pH value are determined in real time by the field portable instrument, and the samples are collected and sealed for detection to detect the ion concentration and hardness of Na + , Cl - , Ca² + , Mg² + ; S12, based on the salt tolerance threshold of irrigated crops and the efficient desalination characteristics of CDI technology, divides the desalination target gradient into mild desalination, moderate desalination and mild desalination, and selects suitable pretreatment processes based on turbidity.

3. The method of claim 1, wherein, The method, based on the defined target desalination gradient interval, calculates crop yield loss and planting income changes corresponding to different salinities within the interval, establishes a nonlinear relationship model between relative crop yield and irrigation water salinity, and determines the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. It also includes: S21, using a nonlinear relationship model to calculate relative crop yield: Where x is the salt concentration of the irrigation water; S22, based on the dynamic balance between irrigation water salt concentration and crop yield, determines the optimal sub-desalination target salinity by adjusting the target salt concentration range and controlling relative yield loss.

4. The method of claim 1, wherein, The dynamic correlation between unit desalination capacity, capacity retention rate, and energy consumption based on the CDI system is described. Integral calculations are used to eliminate the impact of salt concentration fluctuations on energy consumption calculations. For the target desalination gradient corresponding to the determined optimal sub-desalination target salinity, the comprehensive desalination cost within that gradient is calculated. This also includes: S31, eliminate the influence of salt concentration fluctuations through integral calculation, and calculate the average stable unit desalination amount of electrode material in one cycle within the target desalination gradient; S32, substitute the average stable unit desalination amount ΔRAC into the fitting formula of desalination amount and energy consumption, and combine it with the SAC formula to calculate the energy consumption per ton of water within the target desalination gradient.

5. The method of claim 1, wherein, The method combines agricultural time-of-use electricity pricing policies and the water storage capacity of the CDI system, weights the time-of-use electricity prices, integrates the resulting planting revenue with the calculated comprehensive desalination cost, and constructs an internal rate of return (IRR) calculation model that includes planting revenue, comprehensive desalination cost, and electricity price fluctuations. Sensitivity analysis is used to quantify the impact of key parameter fluctuations on the IRR. The method also includes: S41, using a weighted formula to calculate the comprehensive electricity price: wherein is a first electricity price scenario, is a power consumption duration weight for the corresponding time period; S42 quantifies the impact of key parameter fluctuations on IRR through sensitivity analysis, including initial salinity of groundwater, target sub-desalination salinity, electrode material cost and lifespan, time-of-use electricity pricing structure, and setting boundary values ​​for fluctuations in crop price and yield parameters.

6. The method of claim 1, wherein, Also includes: S5, Calculate the soil salinization mitigation benefit by quantifying the difference in soil salt accumulation rates between irrigation with saline water and irrigation with desalinated water, using the following formula: where A is the irrigated area, K is the cost of soil improvement unit, K is the cost of soil improvement unit, 7. A device for evaluating the application benefit of underground brackish water resources in the northwest inland river basin, characterized in that, include: The water quality data acquisition and gradient division module is used to collect water quality data of groundwater, including salt concentration, turbidity, hardness and ion concentration, and to divide the target desalination gradient range according to the salt tolerance threshold of irrigated crops and the desalination characteristics of capacitive deionization (CDI) technology. The nonlinear relationship modeling and sub-desalination target determination module is used to calculate crop yield loss and planting income changes corresponding to different salinities within the divided target desalination gradient interval, establish a nonlinear relationship model between relative crop yield and irrigation water salinity, and determine the optimal sub-desalination target salinity by combining the desalination performance parameters of the CDI system. The dynamic correlation integral and cost calculation module is used to calculate the comprehensive desalination cost within the target desalination gradient corresponding to the optimal sub-desalination target salinity. It is based on the dynamic correlation between the unit desalination amount, capacity retention rate and energy consumption of the CDI system. It uses integral calculation to eliminate the impact of salt concentration fluctuation on energy consumption calculation. For the target desalination gradient corresponding to the determined optimal sub-desalination target salinity, it calculates the comprehensive desalination cost within the gradient. The electricity price weighting and IRR model construction module is used to combine agricultural time-of-use electricity pricing policies and the water storage capacity of the CDI system to weight the time-of-use electricity price, integrate the obtained planting income with the calculated comprehensive desalination cost, and construct an internal rate of return (IRR) calculation model that includes planting income, comprehensive desalination cost, and electricity price fluctuations. Through sensitivity analysis, the impact of key parameter fluctuations on the IRR is quantified.

8. An electronic device, comprising: Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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