Intelligent monitoring management method and system for sodium hypochlorite production process

Abstract

The embodiment of the invention provides an intelligent monitoring management method and system for a sodium hypochlorite production process, and the method comprises the steps: scanning dye recovery water to obtain first scanning data, and matching a preset dye database to obtain a dye type and a first dye concentration; performing secondary scanning on the mixed solution to obtain second scanning data, and matching a preset dye database to obtain a dye type and a second dye concentration; obtaining the corrected dye concentration through a dye calculation model; electrolytic parameters are collected, and physical quantities are obtained through calculation; comparing the abnormal characteristics of each physical quantity through a threshold value; converting the abnormal feature into an abnormal probability; and finally executing hierarchical response. According to the method, the fuel recovery water data and the electrolysis parameters are intelligently monitored, the abnormal probability is accurately estimated in combination with the abnormal calculation model, and graded early warning is performed, so that the abnormity can be found in time, the stability, the safety and the management efficiency of the sodium hypochlorite production process are improved, and intelligent control and efficient operation and maintenance of the production process are realized.

Description

Technical Field

[0001] This specification relates to the field of intelligent production management technology in the chemical industry, and in particular to an intelligent monitoring and management method and system for sodium hypochlorite production process. Background Technology

[0002] Sodium hypochlorite (NaClO), with its significant cost advantages and strong oxidizing properties, occupies an irreplaceable position in the pretreatment stage of the textile industry. It is one of the most widely used bleaching agents in key processes such as desizing, scouring, and bleaching. It can efficiently decompose impurities, natural pigments, and sizing agents on textiles, laying a good foundation for subsequent dyeing and printing processes, directly affecting the final quality and production efficiency of textiles. Therefore, it has long been favored by textile mills.

[0003] However, in current production practices, textile mills often choose recycled water as a raw material for sodium hypochlorite production in order to achieve water resource recycling and reduce production costs. While this recycled water saves resources to some extent, its composition is extremely complex, containing large amounts of residual azo and anthraquinone dyes. These dye molecules have stable chemical structures and are difficult to completely decompose during electrolysis, easily accumulating within the electrolytic cell. Simultaneously, the recycled water also contains metal ions such as calcium and magnesium, which react with substances produced during electrolysis to form insoluble precipitates that adhere to the electrode surface or the inside of the cell. Furthermore, suspended particles in the water continuously deposit as electrolysis progresses, further exacerbating the deterioration of the internal environment of the electrolytic cell.

[0004] These factors combined directly led to an abnormal increase in the voltage of the electrolytic cell. This abnormal voltage increase not only significantly increases energy consumption, causing production costs to soar, but also reduces the efficiency of the electrolytic reaction, prolonging the sodium hypochlorite production cycle and severely impacting the overall production efficiency of the textile mill. More seriously, prolonged voltage anomalies can damage the electrodes and other components of the electrolytic cell, shortening the equipment's lifespan, increasing maintenance and replacement costs, and imposing a heavy economic burden on the company.

[0005] Existing technologies have significant shortcomings in monitoring these production anomalies. Most textile mills still rely on manual inspections, requiring staff to periodically check production equipment. This method is not only labor-intensive and time-consuming but also makes it difficult to monitor equipment operation in real time, often resulting in significant delays in detection after anomalies occur. Even when some companies employ single-parameter monitoring technology, it can only monitor one indicator, failing to comprehensively and accurately reflect the overall operation of the electrolytic cell. Due to the complexity of the production process, changes in a single parameter often cannot directly pinpoint the root cause of the problem, leading to low accuracy in anomaly identification and easily causing misjudgments or missed diagnoses. This, in turn, prevents timely and effective implementation of corrective measures, further amplifying the negative impact of the anomalies. Summary of the Invention

[0006] This specification provides one or more embodiments of an intelligent monitoring and management method for a sodium hypochlorite production process. The method includes: in the dye recovery water addition stage, scanning the dye recovery water to obtain first scan data; matching the first scan data with a preset dye database to obtain a dye type and a first dye concentration; the scan data including the absorbance and transmittance of the dye recovery water; in the brine addition stage, performing a second scan on the mixture to obtain second scan data; matching the second scan data with a preset dye database to obtain a dye type and a second dye concentration; and obtaining a corrected dye concentration based on the first dye concentration and the second dye concentration using a dye calculation model.

[0007] Electrolysis parameters are collected by sensors, including the voltage, current, electrode impedance, and membrane pressure difference of the electrolytic cell. Based on the scan data and the electrolysis parameters, the dye concentration change rate, voltage-current ratio shift, membrane pressure difference increase rate, and chlorate generation are calculated. Abnormal characteristics of each physical quantity are compared using thresholds. The abnormal characteristics are converted into abnormal probabilities using a logistic regression model. A graded response is executed based on the abnormal probabilities.

[0008] In some embodiments, the dye concentration change rate The calculation formula is:

[0009] ;

[0010] in Let be the dye concentration at time t. for The dye concentration at any given time;

[0011] The voltage-current ratio offset The calculation formula is:

[0012] ;

[0013] in This is the actual voltage of the electrolytic cell. This represents the actual current in the electrolytic cell. The electrolytic cell voltage under reference conditions. The current of the electrolytic cell under reference conditions;

[0014] The membrane pressure difference growth rate The calculation formula is:

[0015] ;

[0016] in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is:

[0017] ;

[0018] in The actual membrane pressure difference at time t. This is a temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor;

[0019] The chlorate formation acceleration The calculation formula is:

[0020] ;

[0021] in For the rate of chlorate formation, This is a preset time interval;

[0022] The chlorate formation rate The calculation formula is:

[0023] ;

[0024] in Let be the chlorate concentration at time t. for The chlorate concentration at time.

[0025] In some embodiments, the dye concentration change rate, the voltage-current ratio offset, the membrane pressure difference increase, and the chlorate generation are compared with preset thresholds to determine abnormal characteristics, including: marking the dye concentration change rate as abnormal in response to the dye concentration change rate not being within a first preset range; marking the voltage-current ratio offset as abnormal in response to the voltage-current ratio offset not being within a second preset range; marking the membrane pressure difference increase as abnormal in response to the membrane pressure difference increase being greater than a first preset threshold and triggering a membrane fouling warning; and marking the chlorate generation as abnormal and the side reaction as abnormal in response to the chlorate generation being greater than a second preset threshold.

[0026] In some embodiments, the logistic regression model includes: using the abnormal features as independent variables, training the model with historical fault data, outputting the abnormal probability, and the model solving for regression coefficients using the maximum likelihood estimation method to reflect the influence weight of each abnormal feature on the abnormal situation.

[0027] In some embodiments, performing a tiered response based on the anomaly probability includes:

[0028] In response to the abnormal probability being less than a first probability parameter, a low-risk response is executed: automatic fine-tuning of electrolysis parameters, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or, suppressing side reactions based on the amount of chlorate generated;

[0029] In response to the abnormal probability being greater than or equal to the first probability parameter and less than the second probability parameter, a medium-risk response is executed: triggering an audible and visual warning and initiating an auxiliary processing mechanism, including: turning on the electrolytic cell cleaning device and switching to a backup ion exchange membrane;

[0030] In response to the anomaly probability being greater than or equal to the second probability parameter, a high-risk response is executed: the power supply is cut off, the emergency emission procedure is initiated, and the exhaust gas treatment system is activated.

[0031] This specification provides one or more embodiments of an intelligent monitoring and management system for a sodium hypochlorite production process, the system comprising:

[0032] The data acquisition module is used to scan the dye recovery water to obtain first scan data during the dye recovery water addition process, and to match the first scan data with a preset dye database to obtain the dye type and first dye concentration. The scan data includes the absorbance and transmittance of the dye recovery water. During the brine addition process, the mixed solution is scanned a second time to obtain second scan data, and the second scan data is matched with a preset dye database to obtain the dye type and second dye concentration. Based on the first and second dye concentrations, a corrected dye concentration is obtained through a dye calculation model. Electrolysis parameters are collected in real time by sensors, and the electrolysis parameters include at least the voltage, current, electrode impedance, and membrane voltage difference of the electrolyzer.

[0033] The anomaly calculation module, based on the scanning parameters and the electrolysis parameters, calculates the dye concentration change rate, voltage-current ratio offset, membrane pressure difference increase rate, and chlorate generation amount; obtains anomaly features through threshold comparison; and then converts the anomaly features into anomaly probabilities through a logistic regression model.

[0034] The graded response module is used to execute graded responses based on anomaly indices.

[0035] In some embodiments, the anomaly calculation module further includes:

[0036] The rate of change of dye concentration The calculation formula is:

[0037] ;

[0038] in Let be the dye concentration at time t. for The dye concentration at any given time;

[0039] The voltage-current ratio offset The calculation formula is:

[0040] ;

[0041] in This is the actual voltage of the electrolytic cell. This represents the actual current in the electrolytic cell. The electrolytic cell voltage under reference conditions. The current of the electrolytic cell under reference conditions;

[0042] The membrane pressure difference growth rate The calculation formula is:

[0043] ;

[0044] in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is:

[0045] ;

[0046] in The actual membrane pressure difference at time t. This is a temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor;

[0047] The chlorate formation acceleration The calculation formula is:

[0048] ;

[0049] in For the rate of chlorate formation, This is a preset time interval;

[0050] The chlorate formation rate The calculation formula is:

[0051] ;

[0052] in Let be the chlorate concentration at time t. for The chlorate concentration at time.

[0053] In some embodiments, the anomaly calculation module is further configured to:

[0054] In response to the dye concentration change rate not being within a first preset range, an abnormal dye concentration change rate is marked; in response to the voltage-current ratio offset not being within a second preset range, an abnormal voltage-current ratio offset is marked; in response to the membrane pressure difference increase being greater than a first preset threshold, an abnormal membrane pressure difference increase is marked and a membrane fouling warning is triggered; in response to the chlorate generation being greater than a second preset threshold, an abnormal chlorate generation is marked and an abnormal side reaction is marked.

[0055] In some embodiments, the logistic regression model includes: using the abnormal features as independent variables, training the model with historical fault data, outputting the abnormal probability, and the model solving for regression coefficients using the maximum likelihood estimation method to reflect the influence weight of each abnormal feature on the abnormal situation.

[0056] In some embodiments, the hierarchical response module further includes:

[0057] A low-risk response unit is used to automatically fine-tune the electrolysis parameters when the abnormal probability is less than a first probability parameter, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or suppressing side reactions based on the amount of chlorate generated;

[0058] The medium-risk response unit is used to trigger an audible and visual warning and start an auxiliary processing mechanism when the abnormal probability is greater than or equal to the first probability parameter and less than the second probability parameter, including: turning on the electrolytic cell cleaning device and switching the backup ion membrane.

[0059] The high-risk response unit is used to cut off the power supply, initiate the emergency emission procedure, and simultaneously start the exhaust gas treatment system when the abnormal probability is greater than or equal to the second probability parameter.

[0060] Beneficial effects:

[0061] By calculating the probability of anomalies, abnormal problems caused by excessive dye concentration can be effectively suppressed, significantly reducing the probability of equipment damage and safety hazards.

[0062] By integrating multiple indicators to determine the probability of anomalies, this method is more accurate than the traditional single threshold method, thus reducing the false alarm rate. Attached Figure Description

[0063] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0064] Figure 1 This is an exemplary flowchart of an intelligent monitoring and management method for sodium hypochlorite production process according to some embodiments of this specification;

[0065] Figure 2 This is an exemplary block diagram of an intelligent monitoring and management system for sodium hypochlorite production process, as shown in some embodiments of this specification. Detailed Implementation

[0066] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0067] It should be understood that the terms "system," "unit," and / or "module" used herein are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0068] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0069] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0070] Before providing examples, it is necessary to describe the application scenarios of this invention. This invention considers the complex operating conditions in textile factories. To save costs, most textile factories choose dye-recycled water as the raw material for sodium hypochlorite production. However, due to the special components in the dye-recycled water (such as indigo dye), insoluble polymers are generated during anodic oxidation, covering the electrode surface. Simultaneously, sulfate ions from the dye combine with calcium ions, causing blockage of the ion-exchange membrane. Under these circumstances, the electrolytic cell voltage may gradually increase, current efficiency may decrease, and the effective chlorine content of the produced sodium hypochlorite may be unstable.

[0071] The conventional method is to manually shut down and clean the equipment, which is time-consuming, results in huge production losses, and has high maintenance costs for the ion exchange membrane.

[0072] Example 1: As Figure 1 As shown in the figure, this embodiment provides an intelligent monitoring and management method for sodium hypochlorite production process, the method comprising:

[0073] In the dye recovery water addition stage, the dye recovery water is scanned to obtain first scan data. Based on the first scan data, a preset dye database is matched to obtain the dye type and first dye concentration. The scan data includes the absorbance and transmittance of the dye recovery water. In the brine addition stage, the mixture is scanned a second time to obtain second scan data. Based on the second scan data, a preset dye database is matched to obtain the dye type and second dye concentration. Based on the first dye concentration and the second dye concentration, a corrected dye concentration is obtained through a dye calculation model.

[0074] An online flow tank is installed at the outlet of the main recycled water pipe, and the tank integrates a dual-wavelength LED light source and a silicon photodiode detector; the dual-wavelength LED light source includes: an ultraviolet wavelength for capturing dye skeletons containing benzene rings or conjugated systems; and a visible wavelength for capturing azo and anthraquinone chromogenic groups.

[0075] The preset dye database contains data on various commonly used dyes. This dye data refers to the normalized absorbance and transmittance vectors for each scan, along with the corresponding dye type and concentration for each absorbance and transmittance vector. Matching the scanned data with the preset dye data allows for the determination of the corresponding dye type and concentration.

[0076] The dye calculation model calculates the corrected dye concentration using a weighted method, where the weighting coefficients can be set empirically.

[0077] Electrolysis parameters are collected by sensors, including the voltage, current, electrode impedance, and membrane pressure difference of the electrolytic cell.

[0078] Electrolysis parameters are collected by sensors, including the voltage, current, electrode impedance, and membrane pressure difference of the electrolytic cell.

[0079] The sensors include a Hall voltage sensor, a Hall current sensor, an impedance analyzer, and a nuclear membrane pressure transmitter.

[0080] Hall voltage sensors are used to monitor the electrolytic cell voltage, and Hall current sensors are used to monitor the electrolytic cell current.

[0081] An impedance analyzer is installed between the anode and cathode of the electrolytic cell to collect electrode impedance.

[0082] Diaphragm pressure transmitters are installed on both sides of the ion exchange membrane to output the membrane pressure difference in real time.

[0083] Based on the scan data and the electrolysis parameters, the dye concentration change rate, voltage-current ratio shift, membrane pressure difference increase rate, and chlorate generation amount are calculated; the abnormal characteristics of each physical quantity are compared by threshold comparison; and the abnormal characteristics are converted into abnormal probabilities by a logistic regression model.

[0084] The rate of change of dye concentration The calculation formula is:

[0085] ;

[0086] in Let be the dye concentration at time t. for The dye concentration at any given time;

[0087] The voltage-current ratio offset The calculation formula is:

[0088] ;

[0089] in This is the actual voltage of the electrolytic cell. This represents the actual current in the electrolytic cell. The electrolytic cell voltage under reference conditions. The current of the electrolytic cell under reference conditions;

[0090] The membrane pressure difference growth rate The calculation formula is:

[0091] ;

[0092] in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is:

[0093] ;

[0094] in The actual membrane pressure difference at time t. This is a temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor;

[0095] The chlorate formation acceleration The calculation formula is:

[0096] ;

[0097] in For the rate of chlorate formation, This is a preset time interval;

[0098] The chlorate formation rate The calculation formula is:

[0099] ;

[0100] in Let be the chlorate concentration at time t. for The chlorate concentration at time.

[0101] The rate of change of dye concentration, the offset of the voltage-current ratio, the increase in membrane pressure difference, and the amount of chlorate formation are compared with preset thresholds to determine abnormal characteristics, including:

[0102] In response to the dye concentration change rate not being within a first preset range, an abnormal dye concentration change rate is marked.

[0103] The first preset range is determined based on a large amount of historical production data and process requirements. Its range is typically the reasonable fluctuation range of dye concentration under normal production conditions. Preferably, the first preset range is... .

[0104] When the rate of change of dye concentration calculated by the formula is not within the first preset range, the system will mark it as an abnormal rate of change of dye concentration. If the rate of change of dye concentration is greater than... This could mean that the replenishment rate of the dye recovery water is too fast, or that the preceding treatment steps failed to effectively reduce the dye concentration. Excessive dye entering the electrolysis system increases the electrolysis burden, affecting the efficiency and quality of sodium hypochlorite production. If the rate of change in dye concentration is less than... If the reaction rate is abnormally accelerated, it may be due to abnormal electrolysis parameters, and further investigation is needed to determine the cause.

[0105] In response to the voltage-current ratio offset being outside the second preset range, an abnormal voltage-current ratio offset is marked.

[0106] The second preset range represents the normal operating state of the electrolyzer under baseline conditions. Preferably, the second preset range is... .

[0107] If the calculated voltage-current ratio offset is not within the second preset range, the voltage-current ratio offset is marked as abnormal. If the voltage-current ratio offset is greater than... This indicates that the actual voltage-current ratio is higher than the reference value, possibly due to increased resistance caused by electrode contamination or aging of the ion-exchange membrane within the electrolyzer, requiring a higher voltage for the same current and increasing energy consumption. If the voltage-current ratio deviation is less than... If the problem is not resolved, it could be due to issues such as electrode short circuits or abnormal current measurements, which can affect the stability of the electrolysis reaction.

[0108] In response to the membrane pressure difference growth rate exceeding a first preset threshold, an abnormal membrane pressure difference growth rate is marked, and a membrane fouling warning is triggered.

[0109] The first preset threshold is determined based on the performance parameters of the ion exchange membrane and the changes in membrane pressure difference during normal use. During normal operation, the membrane pressure difference increases slowly, but if the rate of increase exceeds a certain limit, it indicates that the membrane may be fouled. Preferably, the first preset threshold is... .

[0110] When the rate of increase in membrane pressure difference exceeds a first preset threshold, it is marked as an abnormal increase in membrane pressure difference and a membrane fouling warning is triggered. An excessively rapid increase in membrane pressure difference is usually due to the deposition of dye molecules, suspended particles, etc., on the membrane surface, leading to decreased membrane permeability and increased resistance to liquid permeation. If not addressed promptly, this will further exacerbate membrane fouling, reduce electrolysis efficiency, and even damage the ion exchange membrane.

[0111] In response to the chlorate generation exceeding a second preset threshold, an abnormal chlorate generation is marked, and an abnormal side reaction is marked.

[0112] The second preset threshold is determined based on the product quality standards and production process requirements of sodium hypochlorite. Chlorate is a byproduct of the electrolysis reaction, and excessive formation of it will affect the purity and bleaching effect of sodium hypochlorite. Preferably, the second preset threshold is... .

[0113] The logistic regression model includes: using the anomalous features as independent variables, training the model with historical fault data, and outputting the anomalous probability. The model solves for the regression coefficients using the maximum likelihood estimation method, reflecting the influence weight of each anomalous feature on the anomalous situation. The anomalous features are represented by binary variables, for example, 1 represents an anomalous situation, and 0 represents an anomalous situation.

[0114] Historical failure data includes various combinations of abnormal features that occurred in past production, as well as the results of whether or not a failure occurred.

[0115] A tiered response is executed based on the stated anomaly probability.

[0116] In response to the anomaly probability being less than a first probability parameter, a low-risk response is executed: automatic fine-tuning of electrolysis parameters, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or suppressing side reactions based on the amount of chlorate generated; the first probability parameter is 30%.

[0117] In response to the abnormal probability being greater than or equal to the first probability parameter and less than the second probability parameter, a medium-risk response is executed: triggering an audible and visual warning and initiating an auxiliary processing mechanism, including: turning on the electrolytic cell cleaning device and switching to a backup ion exchange membrane; the second probability parameter is 70%.

[0118] In response to the anomaly probability being greater than or equal to the second probability parameter, a high-risk response is executed: the power supply is cut off, the emergency emission procedure is initiated, and the exhaust gas treatment system is activated.

[0119] Example 2, as Figure 2 As shown in the figure, this embodiment provides an intelligent monitoring and management system for sodium hypochlorite production process, the system comprising:

[0120] The data acquisition module is used to scan the dye-recycled water to obtain first scan data during the dye-recycled water addition stage, and to match the first scan data with a preset dye database to obtain the dye type and first dye concentration. The scan data includes the absorbance and transmittance of the dye-recycled water. During the brine addition stage, the mixed solution is scanned a second time to obtain second scan data, and the dye type and second dye concentration are matched with a preset dye database to obtain the second scan data. Based on the first dye concentration and the second dye concentration, a corrected dye concentration is obtained through a dye calculation model.

[0121] An online flow tank is installed at the outlet of the main recycled water pipe, and the tank integrates a dual-wavelength LED light source and a silicon photodiode detector; the dual-wavelength LED light source includes: ultraviolet wavelength... Used to capture dye skeletons containing benzene rings or conjugated systems; visible wavelength It is used to capture azo and anthraquinone chromogenic groups.

[0122] The preset dye database contains data on various commonly used dyes. This dye data refers to the normalized absorbance and transmittance vectors for each scan, along with the corresponding dye type and concentration for each absorbance and transmittance vector. Matching the scanned data with the preset dye data allows for the determination of the corresponding dye type and concentration.

[0123] The dye calculation model calculates the corrected dye concentration using a weighted method, where the weighting coefficients can be set empirically.

[0124] Electrolysis parameters are collected in real time by sensors, including at least the voltage, current, electrode impedance, and membrane pressure difference of the electrolytic cell.

[0125] The sensors include a Hall voltage sensor, a Hall current sensor, an impedance analyzer, and a nuclear membrane pressure transmitter.

[0126] Hall voltage sensors are used to monitor the electrolytic cell voltage, and Hall current sensors are used to monitor the electrolytic cell current.

[0127] An impedance analyzer is installed between the anode and cathode of the electrolytic cell to collect electrode impedance.

[0128] Diaphragm pressure transmitters are installed on both sides of the ion exchange membrane to output the membrane pressure difference in real time.

[0129] The anomaly calculation module, based on the scanning parameters and the electrolysis parameters, calculates the dye concentration change rate, voltage-current ratio offset, membrane pressure difference increase rate, and chlorate generation amount; obtains anomaly features through threshold comparison; and then converts the anomaly features into anomaly probabilities through a logistic regression model.

[0130] The membrane pressure difference growth rate The calculation formula is:

[0131] ;

[0132] in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is:

[0133] ;

[0134] in The actual membrane pressure difference at time t. This is a temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor;

[0135] The chlorate formation acceleration The calculation formula is:

[0136] ;

[0137] in For the rate of chlorate formation, This is a preset time interval;

[0138] The chlorate formation rate The calculation formula is:

[0139] ;

[0140] in Let be the chlorate concentration at time t. for The chlorate concentration at time.

[0141] In some embodiments, the anomaly calculation module is further configured to:

[0142] In response to the dye concentration change rate not being within a first preset range, an abnormal dye concentration change rate is marked.

[0143] The first preset range is determined based on a large amount of historical production data and process requirements. Its range is typically the reasonable fluctuation range of dye concentration under normal production conditions. Preferably, the first preset range is... .

[0144] When the rate of change of dye concentration calculated by the formula is not within the first preset range, the system will mark it as an abnormal rate of change of dye concentration. If the rate of change of dye concentration is greater than... This could mean that the replenishment rate of the dye recovery water is too fast, or that the preceding treatment steps failed to effectively reduce the dye concentration. Excessive dye entering the electrolysis system increases the electrolysis burden, affecting the efficiency and quality of sodium hypochlorite production. If the rate of change in dye concentration is less than... If the reaction rate is abnormally accelerated, it may be due to abnormal electrolysis parameters, and further investigation is needed to determine the cause.

[0145] In response to the voltage-current ratio offset being outside the second preset range, an abnormal voltage-current ratio offset is marked.

[0146] The second preset range represents the normal operating state of the electrolyzer under baseline conditions. Preferably, the second preset range is... .

[0147] If the calculated voltage-current ratio offset is not within the second preset range, the voltage-current ratio offset is marked as abnormal. If the voltage-current ratio offset is greater than... This indicates that the actual voltage-current ratio is higher than the reference value, possibly due to increased resistance caused by electrode contamination or aging of the ion-exchange membrane within the electrolyzer, requiring a higher voltage for the same current and increasing energy consumption. If the voltage-current ratio deviation is less than... If the problem is not resolved, it could be due to issues such as electrode short circuits or abnormal current measurements, which can affect the stability of the electrolysis reaction.

[0148] In response to the membrane pressure difference growth rate exceeding a first preset threshold, an abnormal membrane pressure difference growth rate is marked, and a membrane fouling warning is triggered.

[0149] The first preset threshold is determined based on the performance parameters of the ion exchange membrane and the changes in membrane pressure difference during normal use. During normal operation, the membrane pressure difference increases slowly, but if the rate of increase exceeds a certain limit, it indicates that the membrane may be fouled. Preferably, the first preset threshold is... .

[0150] When the rate of increase in membrane pressure difference exceeds a first preset threshold, it is marked as an abnormal increase in membrane pressure difference and a membrane fouling warning is triggered. An excessively rapid increase in membrane pressure difference is usually due to the deposition of dye molecules, suspended particles, etc., on the membrane surface, leading to decreased membrane permeability and increased resistance to liquid permeation. If not addressed promptly, this will further exacerbate membrane fouling, reduce electrolysis efficiency, and even damage the ion exchange membrane.

[0151] In response to the chlorate generation exceeding a second preset threshold, an abnormal chlorate generation is marked, and an abnormal side reaction is marked.

[0152] The second preset threshold is determined based on the product quality standards and production process requirements of sodium hypochlorite. Chlorate is a byproduct of the electrolysis reaction, and excessive formation of it will affect the purity and bleaching effect of sodium hypochlorite. Preferably, the second preset threshold is... .

[0153] The logistic regression model includes: using the anomalous features as independent variables, training the model with historical fault data, and outputting the anomalous probability. The model solves for the regression coefficients using the maximum likelihood estimation method, reflecting the influence weight of each anomalous feature on the anomalous situation. The anomalous features are represented by binary variables, for example, 1 represents an anomalous situation, and 0 represents an anomalous situation.

[0154] Historical failure data includes various combinations of abnormal features that occurred in past production, as well as the results of whether or not a failure occurred.

[0155] The graded response module is used to execute graded responses based on anomaly indices.

[0156] In some embodiments, the hierarchical response module further includes:

[0157] A low-risk response unit is used to automatically fine-tune the electrolysis parameters when the abnormal probability is less than a first probability parameter, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or suppressing side reactions based on the amount of chlorate generated; the first probability parameter is 30%.

[0158] The medium-risk response unit is used to trigger an audible and visual warning and start an auxiliary processing mechanism when the abnormal probability is greater than or equal to a first probability parameter and less than a second probability parameter, including: turning on the electrolytic cell cleaning device and switching the backup ion membrane; the second probability parameter is 70%.

[0159] The high-risk response unit is used to cut off the power supply, initiate the emergency emission procedure, and simultaneously start the exhaust gas treatment system when the abnormal probability is greater than or equal to the second probability parameter.

[0160] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0161] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the flow and sequence of the laminar flow shield. Although various examples have been discussed in the foregoing disclosure of some embodiments that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments described in this specification. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0162] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0163] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for intelligent monitoring and management of sodium hypochlorite production process, characterized in that, The method includes: In the dye recovery water addition stage, the dye recovery water is scanned to obtain first scan data. Based on the first scan data, a preset dye database is matched to obtain the dye type and first dye concentration. The scan data includes the absorbance and transmittance of the dye recovery water. In the brine addition stage, the mixture is scanned a second time to obtain second scan data. Based on the second scan data, a preset dye database is matched to obtain the dye type and second dye concentration. Based on the first dye concentration and the second dye concentration, a corrected dye concentration is obtained through a dye calculation model. Electrolysis parameters are collected by sensors, including the voltage, current, electrode impedance, and membrane pressure difference of the electrolytic cell. Based on the scan data and the electrolysis parameters, the dye concentration change rate, voltage-current ratio shift, membrane pressure difference increase rate, and chlorate generation amount are calculated; the abnormal characteristics of each physical quantity are compared by threshold comparison; and the abnormal characteristics are converted into abnormal probabilities by a logistic regression model. A tiered response is executed based on the stated anomaly probability.

2. The method according to claim 1, characterized in that, The method further includes: The rate of change of dye concentration The calculation formula is: ； in Let be the dye concentration at time t. for The dye concentration at any given time; The voltage-current ratio offset The calculation formula is: ； in This is the actual voltage of the electrolytic cell. This represents the actual current in the electrolytic cell. The electrolytic cell voltage under reference conditions. The current of the electrolytic cell under reference conditions; The membrane pressure difference growth rate The calculation formula is: ； in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is: ； in The actual membrane pressure difference at time t. This is the temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor; ； in For the rate of chlorate formation, This is a preset time interval; The chlorate formation rate The calculation formula is: ； in Let be the chlorate concentration at time t. for The chlorate concentration at time.

3. The method according to claim 2, characterized in that, The method further includes: The rate of change of dye concentration, the offset of the voltage-current ratio, the increase in membrane pressure difference, and the amount of chlorate formation are compared with preset thresholds to determine abnormal characteristics, including: In response to the dye concentration change rate not being within a first preset range, an abnormal dye concentration change rate is marked. In response to the voltage-current ratio offset being outside the second preset range, an abnormal voltage-current ratio offset is marked. In response to the membrane pressure difference growth rate exceeding a first preset threshold, an abnormal membrane pressure difference growth rate is marked, and a membrane fouling warning is triggered. In response to the chlorate generation exceeding a second preset threshold, an abnormal chlorate generation is marked, and an abnormal side reaction is marked.

4. The method according to claim 1, characterized in that, The logistic regression model includes: The abnormal features are used as independent variables, and the model is trained using historical fault data to output the abnormal probability. The model solves the regression coefficients using the maximum likelihood estimation method, which reflects the influence weight of each abnormal feature on the abnormal situation.

5. The method according to claim 1, characterized in that, The step of performing a graded response based on the anomaly probability includes: In response to the abnormal probability being less than a first probability parameter, a low-risk response is executed: automatic fine-tuning of electrolysis parameters, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or, suppressing side reactions based on the amount of chlorate generated; In response to the abnormal probability being greater than or equal to the first probability parameter and less than the second probability parameter, a medium-risk response is executed: triggering an audible and visual warning and initiating an auxiliary processing mechanism, including: turning on the electrolytic cell cleaning device and switching to a backup ion exchange membrane; In response to the anomaly probability being greater than or equal to the second probability parameter, a high-risk response is executed: the power supply is cut off, the emergency emission procedure is initiated, and the exhaust gas treatment system is activated.

6. An intelligent monitoring and management system for sodium hypochlorite production process, characterized in that, The system includes: The data acquisition module is used to scan the dye recovery water to obtain first scan data during the dye recovery water addition process, and to match the first scan data with a preset dye database to obtain the dye type and first dye concentration. The scan data includes the absorbance and transmittance of the dye recovery water. During the brine addition process, the mixed solution is scanned a second time to obtain second scan data, and the second scan data is matched with a preset dye database to obtain the dye type and second dye concentration. Based on the first and second dye concentrations, a corrected dye concentration is obtained through a dye calculation model. Electrolysis parameters are collected in real time by sensors, and the electrolysis parameters include at least the voltage, current, electrode impedance, and membrane voltage difference of the electrolyzer. The anomaly calculation module, based on the scanning parameters and the electrolysis parameters, calculates the dye concentration change rate, voltage-current ratio offset, membrane pressure difference increase rate, and chlorate generation amount; obtains anomaly features through threshold comparison; and then converts the anomaly features into anomaly probabilities through a logistic regression model. The graded response module is used to execute graded responses based on the probability of an anomaly.

7. The system according to claim 6, characterized in that, The anomaly calculation module includes: The rate of change of dye concentration The calculation formula is: ； in Let be the dye concentration at time t. for The dye concentration at any given time; The voltage-current ratio offset The calculation formula is: ； in This is the actual voltage of the electrolytic cell. This represents the actual current in the electrolytic cell. The electrolytic cell voltage under reference conditions. The current of the electrolytic cell under reference conditions; The membrane pressure difference growth rate The calculation formula is: ； in The corrected membrane pressure difference, the corrected membrane pressure difference The calculation formula is: ； in The actual membrane pressure difference at time t. This is the temperature correction factor. Let t be the temperature of the electrolytic cell. It is the temperature under reference conditions. This is the dye concentration correction factor; ； in For the rate of chlorate formation, This is a preset time interval; The chlorate formation rate The calculation formula is: ； in Let be the chlorate concentration at time t. for The chlorate concentration at time.

8. The system according to claim 6, characterized in that, The anomaly calculation module is further configured as follows: In response to the dye concentration change rate not being within a first preset range, an abnormal dye concentration change rate is marked. In response to the voltage-current ratio offset being outside the second preset range, an abnormal voltage-current ratio offset is marked. In response to the membrane pressure difference growth rate exceeding a first preset threshold, an abnormal membrane pressure difference growth rate is marked, and a membrane fouling warning is triggered. In response to the chlorate generation exceeding a second preset threshold, an abnormal chlorate generation is marked, and an abnormal side reaction is marked.

9. The system according to claim 6, characterized in that, The logistic regression model further includes: The abnormal features are used as independent variables, and the model is trained using historical fault data to output the abnormal probability. The model solves the regression coefficients using the maximum likelihood estimation method, which reflects the influence weight of each abnormal feature on the abnormal situation.

10. The system according to claim 6, characterized in that, The hierarchical response module further includes: A low-risk response unit is used to automatically fine-tune the electrolysis parameters when the abnormal probability is less than a first probability parameter, including: dynamically adjusting the electrolysis current based on the rate of change of dye concentration, and / or suppressing side reactions based on the amount of chlorate generated; The medium-risk response unit is used to trigger an audible and visual warning and start an auxiliary processing mechanism when the abnormal probability is greater than or equal to the first probability parameter and less than the second probability parameter, including: turning on the electrolytic cell cleaning device and switching the backup ion membrane. The high-risk response unit is used to cut off the power supply, initiate the emergency emission procedure, and simultaneously start the exhaust gas treatment system when the abnormal probability is greater than or equal to the second probability parameter.

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