Online electrolyte monitoring system and method

Abstract

The invention belongs to the technical field of electrolyte production and detection, and particularly relates to an electrolyte online monitoring system and method. The on-line monitoring system comprises an optical probe, a near-infrared analyzer and a work station which are in electric signal connection; the optical probe is mounted in a bypass pipeline of the electrolyte preparation kettle; and the work station is respectively connected with the DCS or the upper computer and the remote PC device. The on-line monitoring method comprises the following steps: establishing a corresponding model of each index of the electrolyte and a near infrared spectrum signal, and storing the corresponding model to a work station; near-infrared spectrum signals of the electrolyte are collected through the optical probe and the near-infrared analyzer and transmitted to the work station, corresponding models of corresponding indexes are called to obtain prediction results, the prediction results are transmitted to the DCS or the upper computer to obtain monitoring data, and the monitoring data are synchronously transmitted to the remote PC device. The online monitoring system and the online monitoring method are high in detection efficiency, can realize full-process management and control, and can timely discover abnormal parameters in the kettle.

Description

Technical Field

[0001] This invention belongs to the field of electrolyte production and testing technology, specifically relating to an online electrolyte monitoring system and method. Background Technology

[0002] In the production process of electrolytes, it is generally necessary to test indicators such as moisture, acidity, color, density, conductivity, and composition to determine whether the product meets the standards. Current testing methods typically involve sampling and then performing tests using standard methods. For example, moisture content is tested using a coulometric moisture analyzer with coulometric reagent (the syringe is placed on a balance and tare; if there is residual liquid on the instrument's sealing gasket, it is wiped dry with a paper towel; once the instrument drift value (20ug / min) stabilizes, the "START" button is pressed, and the sample (1g±0.2g) is quickly injected using a syringe; the syringe is placed on the balance, and the injection volume is recorded; the injection mass is entered into the instrument, and "ENTER" is pressed to confirm. The instrument then enters the sample measurement state, and the test ends when the titration rate in the titration cup reaches 15ug / min; after the sample measurement is completed, the instrument automatically stops, the results are recorded, and the test is repeated twice, with a relative error of <20%). Acidity was tested using a potentiometric titration method (50 ml of ethanol was added to the titration vessel, a magnetic stir bar was placed in the vessel, and the vessel was then mounted on the instrument; 5–8 g (accurate to 0.001 g) of sample was weighed using the subtraction method and injected into the vessel; the sample weight was entered, and the "start" button was pressed. Titration was then initiated using a potassium hydroxide-ethanol standard solution (approximately 0.01 mol / L); after a sudden change in pH, titration continued until the pH reached above 10 and the titration curve became flat, indicating the test was complete; the "stop" button was then pressed to stop the titration; the instrument displayed the acidity result and the titration volume). Density was tested using a densitometer (deionized water was placed in a 25°C constant temperature water bath for 15 minutes until the temperature reached 25°C ± 0.3°C; the densitometer was used to measure the density of the deionized water at 25°C. The standard density of deionized water at 25.0°C was 0.9971 g / cm³). 3If the deviation is within ±0.0003, it can be used for normal measurement. Place the sample in a 25℃ constant temperature water bath for 15 minutes until its temperature is 25℃±0.3℃. Remove the sample from the water bath, open the lid, press down the injection pump as much as possible, immerse the injection tube into the sample, and slowly release the pump rod. The sample will be sucked into the U-shaped measuring tube. Press down the injection pump again to discharge the waste liquid. Repeat this operation with the sample at least 3 times. After aspirating the sample, ensure that there are no air bubbles in the U-shaped oscillating tube, and then read the density data of the sample at 25℃. Conductivity was tested using a conductivity meter (the Mettler standard solution was placed in a 25°C constant temperature water bath for 15 minutes until the standard solution temperature was 25°C ± 0.3°C; the electrode was placed in the conductivity calibration standard solution, and the instrument automatically started calibration by clicking "Calibration Information". The electrode was gently shaken with the standard solution for a while until the electrode temperature was 25°C and the instrument reading stabilized. Then, the "Manual Termination" button was pressed to end the instrument calibration. The electrode was removed, cleaned with anhydrous ethanol, and then rinsed with deionized water. The sample was placed in a 25°C constant temperature water bath for 15 minutes until the sample temperature was 25°C ± 0.3°C. The electrode was placed in the sample, with the groove of the electrode completely submerged below the liquid surface. The sample was gently shaken with the electrode for a while until the sample temperature and state stabilized. The "READ" button was pressed to start the measurement. The data was recorded when the instrument measurement data stabilized). Colorimetry was tested using a colorimetric method (standard solutions with concentrations of 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 Haen standard colorimetric gradients were prepared using purchased certified platinum-cobalt colorimetric solutions; the sample was poured into a 100ml colorimetric tube, and compared visually along the axis of the colorimetric tube in a standard light source box with the platinum-cobalt standard solution; the solution whose color was closest to the color was used as the result). The electrolyte components, including organic and salt components, were tested using gas chromatography and ion chromatography (the sample to be tested was placed in a clean sample bottle, 1.0-1.5ml, and the bottle cap was closed; the sample bottle was placed on the instrument's sample tray, and the sample position was recorded; after the instrument was powered on, the online workstation was turned on, and GC was started; after the instrument analysis was completed, the spectrum was processed and analyzed; the processed concentration results were entered into the component calculation table to calculate the final result, and the organic component results were recorded. (0.5±0.05)g of sample was accurately weighed into a 150mL opaque liquid.) In a clear PE bottle, weigh approximately 100g of ice water and record the mass of the sample and water. Place the sample, filtered through a 0.22μm filter, onto the instrument's sample tray and record the sample position. Turn on the IC workstation, select the desired method, and open it. Click the sequence interface, input the necessary information and weighing amount for each sample, and click the start button. The instrument will automatically inject the sample for analysis and detection. After the instrument analysis is complete, open the database to process and analyze the spectrum. Input the processed concentration results into the component calculation table to calculate the final result and record the salt component results.However, the above-mentioned tests require a wide variety of equipment and operating procedures, involve significant human and material costs, and pose safety risks during the sampling process. Summary of the Invention

[0003] To address the shortcomings and deficiencies of the existing technologies, the primary objective of this invention is to provide an online electrolyte monitoring system. This system aims to solve the problems of cumbersome offline electrolyte sampling and testing procedures, low detection efficiency, inability to achieve full-process control, and inability to promptly detect abnormal parameters within the electrolyte preparation vessel.

[0004] Another objective of this invention is to provide a method for online monitoring of electrolytes.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] An online electrolyte monitoring system includes an optical probe, a near-infrared analyzer, and a workstation connected by electrical signals; the optical probe is installed in a bypass pipe of the electrolyte preparation vessel; the workstation is connected to a DCS or host computer and a remote PC device.

[0007] Furthermore, the optical probe is sealed and fixedly connected to the bypass pipe via a flange.

[0008] Furthermore, the optical probe is installed at a 45° angle to the flow direction of the electrolyte in the bypass pipe.

[0009] An online monitoring method for electrolytes, characterized by comprising the following steps:

[0010] (1) Establish a model for the correspondence between various electrolyte parameters and near-infrared spectral signals, and save it to the workstation;

[0011] (2) The above-mentioned online electrolyte monitoring system is used to monitor the electrolyte online. The near-infrared spectral signal of the electrolyte is collected by an optical probe and a near-infrared analyzer. The signal is transmitted to the workstation to call the corresponding model of the corresponding index to obtain the prediction result. The prediction result is transmitted to the DCS or host computer to obtain the monitoring data and is simultaneously transmitted to the remote PC device.

[0012] Furthermore, the indicator includes moisture content; the method for establishing the correspondence model between moisture content and near-infrared spectral signal is as follows:

[0013] Electrolyte samples with a true moisture content range of 9–60 ppm were used. The samples included at least four different moisture content gradients, with at least 10 samples for each moisture content. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0014] Furthermore, the index includes chromaticity; the method for establishing the correspondence model between chromaticity and near-infrared spectral signals is as follows:

[0015] Electrolyte samples with true colorimetric values ​​ranging from 5 to 60 ppm were used. The samples included at least four different colorimetric gradients, with at least ten samples for each colorimetric gradient. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0016] Furthermore, the indicator includes acidity; the method for establishing the correspondence model between acidity and near-infrared spectral signals is as follows:

[0017] Electrolyte samples with an actual acidity range of 10–250 ppm were used. The samples included at least four different acidity gradients, with at least 10 samples for each acidity level. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0018] Furthermore, the index includes electrical conductivity; the method for establishing the correspondence model between electrical conductivity and near-infrared spectral signals is as follows:

[0019] Electrolyte samples with a true conductivity range of 7–17 mS / cm were used. The samples included at least four different conductivity gradients, with at least ten samples for each conductivity gradient. Samples were collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0020] Furthermore, the index includes density; the method for establishing the correspondence model between density and near-infrared spectral signal is as follows:

[0021] Electrolyte samples with a true density range of 1.1–1.3 g / ml were used. The samples contained at least four different density gradients, with at least ten samples for each density. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0022] Furthermore, the indicators include component composition; the method for establishing the correspondence model between the component composition and the near-infrared spectral signal is as follows:

[0023] Electrolyte samples with different compositions were used, with each composition containing at least four different proportions, and at least ten samples for each proportion. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] (1) The online monitoring system and method of the present invention can eliminate offline sampling and testing, reduce the manpower and material costs caused by sampling and testing, solve the problems of cumbersome offline sampling and testing steps and low detection efficiency of existing electrolytes, which cannot achieve full-process control and cannot detect abnormal parameters in electrolyte preparation kettle in a timely manner, and can realize the digital anti-mistake and error prevention function of electrolyte and improve the safety of production.

[0026] (2) The online monitoring system and method of the present invention can test the electrolyte moisture, acidity, density, conductivity, color and composition. The operation steps are simple, the monitoring data is easy to read, and all indicators can be read at the same time, which has the advantages of being simple and fast.

[0027] (3) The online monitoring system of the present invention uses a simple optical probe, which only requires connecting the probe with the blind plate to the pipe flange, and has the advantage of easy installation. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the structure of an online electrolyte monitoring system according to the present invention.

[0029] Figure 2 This is a comprehensive response spectrum of moisture content, color, acidity, conductivity, density, and component content used for modeling in this embodiment of the invention.

[0030] Figure 3 This is a model diagram showing the actual and predicted values ​​of moisture content established in the embodiments of the present invention.

[0031] Figure 4 This is a model diagram of the actual and predicted chromaticity values ​​established in the embodiments of the present invention.

[0032] Figure 5 This is a model diagram showing the actual and predicted acidity values ​​established in the embodiments of the present invention.

[0033] Figure 6 This is a model diagram showing the actual and predicted values ​​of conductivity established in the embodiments of the present invention.

[0034] Figure 7 This is a model diagram of the actual and predicted density values ​​established in the embodiments of the present invention.

[0035] Figure 8 This is a model diagram showing the actual and expected values ​​of component content established in the embodiments of the present invention. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0037] Example 1

[0038] A schematic diagram of an online electrolyte monitoring system is shown below. Figure 1 As shown, the system includes an optical probe 1, a near-infrared analyzer 2, and a workstation 3, all connected by electrical signals. The optical probe 1 is installed inside a bypass pipe 4 of the electrolyte preparation vessel. The workstation 3 is connected to a DCS or host computer 5 and a remote PC device 6. The optical probe 1 is sealed and fixed to the bypass pipe 4 via a flange 7. The optical probe 1 is installed at a 45° angle to the flow direction of the electrolyte in the bypass pipe 4 to avoid the influence of air bubbles generated when the electrolyte passes through the bypass pipe.

[0039] The method for online monitoring of electrolyte using the above-mentioned online monitoring system includes the following steps:

[0040] (1) Establish a model that corresponds to each index of the electrolyte and the spectral signal, and save it to the workstation; the specific steps are as follows:

[0041] 1. Moisture Content: After sampling the electrolyte, it was left for different times (0 min, 20 min, 25 min, 30 min, 1 h, 2 h, 12 h) and then transferred to a glove box (ensuring the water content in the glove box was less than 5 ppm). Samples were then extracted using a syringe and tested using the laboratory coulometric method. The obtained data (true values) were entered into the workstation as accurate sub-fit curves. Electrolytes with moisture contents of 9 ppm, 29 ppm, 30 ppm, 32 ppm, 38 ppm, 44 ppm, and 60 ppm were obtained. Ten samples were taken from each electrolyte with a moisture content of 9 ppm, 29 ppm, 30 ppm, 32 ppm, 38 ppm, 44 ppm, and 60 ppm. Near-infrared spectral signals of each electrolyte sample were collected using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. The predicted value is calculated based on the response results. A model is built based on the actual and predicted values, and the results are as follows. Figure 3 As shown, the offset value is 0.049, the slope is 0.998, and the correlation coefficient is 0.9993. After curve fitting, the method is saved. Before testing, the method is called and 25 sets of data are continuously tested and compared with laboratory data. The maximum deviation is 17.5%, which is within the standard allowable deviation range of ≤20%.

[0042] 2. Colorimetry: A small amount of pigment was added to the electrolyte to produce different colors. Laboratory colorimetric methods were used for testing, and the obtained data (true values) were entered into the workstation as accurate sub-fit curves. Electrolytes with colorimetry of 5 hazen, 15 hazen, 30 hazen, and 55 hazen were obtained. Ten samples were taken from each colorimetry electrolyte. Near-infrared spectral signals of each electrolyte sample were collected using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. The predicted value is calculated based on the response results. A model is built based on the actual and predicted values, and the results are as follows. Figure 4 As shown, the offset value is 0.218, the slope is 0.965, and the correlation coefficient is 0.9973. After curve fitting, the method is saved. Before testing, the method is called and 25 sets of data are continuously tested and compared with laboratory data. The maximum deviation is 2 hazen, which is within the standard allowable deviation range of ≤5 hazen.

[0043] 3. Acidity: After sampling the electrolyte, it was left for different times (0 min, 20 min, 25 min, 30 min, 1 h, 2 h, 12 h, 24 h, 48 h) and then transferred to a glove box (ensuring the water content in the glove box was less than 5 ppm). Samples were then extracted using a syringe and tested using laboratory acid-base titration. The obtained data (true values) were entered into the workstation as accurate sub-fit curves. Electrolytes with acidities of 10 ppm, 20 ppm, 100 ppm, 120 ppm, 180 ppm, 200 ppm, 240 ppm, and 250 ppm were obtained. Ten samples were taken from each acidity level. Near-infrared spectral signals of each electrolyte sample were collected using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. The predicted value is calculated based on the response results. A model is built based on the actual and predicted values, and the results are as follows. Figure 5 As shown, the offset value is 0.231, the slope is 0.995, and the correlation coefficient is 0.9973. After curve fitting, the method is saved. Before testing, the method is called and 25 sets of data are continuously tested and compared with laboratory data. The maximum deviation is 14.5%, which is within the standard allowable deviation range of ≤20%.

[0044] 4. Conductivity: Prepare samples with different conductivity levels and test them using a laboratory conductivity meter. Enter the obtained data (true values) into the workstation as accurate sub-fit curves. Electrolytes with conductivity levels of 14.7 ms / cm, 14.9 ms / cm, 15 ms / cm, 15.1 ms / cm, 15.2 ms / cm, 15.6 ms / cm, 16.4 ms / cm, and 16.5 ms / cm were obtained. Ten samples were taken for each conductivity level. Near-infrared spectral signals of each electrolyte sample were acquired using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. The predicted value is calculated based on the response results. A model is built based on the actual and predicted values, and the results are as follows. Figure 6 As shown, the offset value is 0.466, the slope is 0.969, and the correlation coefficient is 0.9896. After curve fitting, the method is saved. Before testing, the method is called and 25 sets of data are continuously tested and compared with laboratory data. The maximum deviation is 0.15ms / cm, which is within the standard allowable deviation range of ≤0.2ms / cm.

[0045] 5. Density: Prepare samples of different densities and test them using a laboratory densitometer. Enter the obtained data (true values) into the workstation as accurate sub-fit curves. Electrolytes with densities of 1.147 g / ml, 1.149 g / ml, 1.159 g / ml, 1.180 g / ml, and 1.207 g / ml were obtained. Ten samples were taken for each density electrolyte. Near-infrared spectral signals of each electrolyte sample were acquired using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. The predicted value is calculated based on the response results. A model is built based on the actual and predicted values, and the results are as follows. Figure 7 As shown, the offset value is 0.002, the slope is 0.999, and the correlation coefficient is 0.9995. After curve fitting, the method is saved. Before testing, the method is called and 25 sets of data are tested continuously and compared with laboratory data. The maximum deviation is 0.0015 g / ml, which is within the standard allowable deviation range of ≤0.003.

[0046] 6. Composition: Spectra of different electrolyte brands were collected. Different brands of electrolytes (different concentrations of ethyl acetate (MES)) were tested using laboratory gas chromatography. The obtained data (true values) were entered into the workstation as accurate sub-fit curves. Electrolytes with MES contents of 37.5%, 39.5%, 42%, 46%, 48%, 57%, and 58.5% were obtained. Ten samples were taken from each electrolyte content. Near-infrared spectral signals of each electrolyte sample were acquired using an optical probe and a near-infrared analyzer to establish a model. The spectra were in the range of 4000-10000 cm⁻¹. -1 The comprehensive response was obtained within the range, and its comprehensive response spectrum is shown below. Figure 2 As shown. Predicted values ​​are calculated based on the response results. Models are built based on the actual and predicted values, with different models for different components. The content model for component MES (ethyl acetate) is shown below. Figure 8 As shown, the offset value is 0.051, the slope is 0.999, and the correlation coefficient is 0.9995.

[0047] 7. After the model is established, if a new grade appears, it is necessary to collect no less than 10 batches of data to expand the model and improve the linearity of the model in order to achieve data accuracy. After curve fitting, save the method, call the method before testing, and continuously test 25 sets of data to compare with laboratory data. The allowable deviation range is different for different contents of each item, and the deviation results need to be within the standard allowable range.

[0048] (2) The above-mentioned online electrolyte monitoring system is used to monitor the electrolyte online. The near-infrared spectral signal of the electrolyte is collected by an optical probe and a near-infrared analyzer. The signal is transmitted to the workstation to call the corresponding model of the corresponding index to obtain the prediction result. The prediction result is transmitted to the DCS or host computer to obtain the monitoring data and is simultaneously transmitted to the remote PC device.

[0049] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. An online electrolyte monitoring system, characterized in that, It includes an optical probe connected by electrical signals, a near-infrared analyzer, and a workstation; the optical probe is installed in a bypass pipe of the electrolyte preparation vessel; the workstation is connected to a DCS or host computer and a remote PC device.

2. The online electrolyte monitoring system according to claim 1, characterized in that, The optical probe is sealed and fixedly connected to the bypass pipe via a flange.

3. The online electrolyte monitoring system according to claim 1, characterized in that, The optical probe is installed at a 45° angle to the flow direction of the electrolyte in the bypass pipe.

4. A method for online monitoring of electrolyte, characterized in that, Includes the following steps: (1) Establish a model for the correspondence between various electrolyte parameters and near-infrared spectral signals, and save it to the workstation; (2) The above-mentioned online electrolyte monitoring system is used to monitor the electrolyte online. The near-infrared spectral signal of the electrolyte is collected by an optical probe and a near-infrared analyzer. The signal is transmitted to the workstation to call the corresponding model of the corresponding index to obtain the prediction result. The prediction result is transmitted to the DCS or host computer to obtain the monitoring data and is simultaneously transmitted to the remote PC device.

5. The method for online monitoring of electrolyte according to claim 4, characterized in that, The indicator includes moisture content; the method for establishing the correspondence between moisture content and near-infrared spectral signals is as follows: Electrolyte samples with a true moisture content range of 9–60 ppm were used. The samples included at least four different moisture content gradients, with at least 10 samples for each moisture content. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

6. The method for online monitoring of electrolyte according to claim 4, characterized in that, The indicators include chromaticity; the method for establishing the correspondence model between chromaticity and near-infrared spectral signals is as follows: Electrolyte samples with true colorimetric values ​​ranging from 5 to 60 ppm were used. The samples included at least four different colorimetric gradients, with at least ten samples for each colorimetric gradient. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

7. The method for online monitoring of electrolyte according to claim 4, characterized in that, The indicators include acidity; the method for establishing the correspondence between acidity and near-infrared spectral signals is as follows: Electrolyte samples with an actual acidity range of 10–250 ppm were used. The samples included at least four different acidity gradients, with at least 10 samples for each acidity level. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

8. The method for online monitoring of electrolyte according to claim 4, characterized in that, The index includes electrical conductivity; the method for establishing the correspondence model between electrical conductivity and near-infrared spectral signals is as follows: Electrolyte samples with a true conductivity range of 7–17 mS / cm were used. The samples included at least four different conductivity gradients, with at least ten samples for each conductivity gradient. Samples were collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

9. The method for online monitoring of electrolyte according to claim 4, characterized in that, The index includes density; the method for establishing the correspondence model between density and near-infrared spectral signals is as follows: Electrolyte samples with a true density range of 1.1–1.3 g / ml were used. The samples contained at least four different density gradients, with at least ten samples for each density. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

10. The method for online monitoring of electrolyte according to claim 4, characterized in that, The indicators include component composition; the method for establishing the correspondence between the component composition and the near-infrared spectral signal is as follows: Electrolyte samples with different compositions were used, with each composition containing at least four different proportions, and at least ten samples for each proportion. Each sample was collected at a depth of 4000–10000 cm⁻¹. -1 Near-infrared spectra within the wavelength range are used to calculate predicted values ​​based on the comprehensive response results of near-infrared spectra, and models are established based on the actual and predicted values.

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