A chemical oxygen demand detection system for intermediate synthesis

Abstract

A chemical oxygen demand detection system for intermediate synthesis relates to the field of wastewater treatment detection technology. The system monitors the oxidation efficiency of nitro groups through an oxidation assessment module, and establishes an electron transfer enhancement model based on density functional theory and electron transfer theory in combination with a masking analysis module to evaluate signal masking. A signal correction module is used to perform multi-region synchronous acquisition, and COD signal correction is achieved through spatial uniformity and kinetic consistency verification. A dissipation identification module is used to extract the front propagation band and divide the effect interval to formulate a partitioned COD signal compensation strategy. An oxidation-spectrum collaborative model is constructed through a collaborative optimization module to achieve real-time COD value inversion and process parameter feedback control. The present invention can improve the accuracy of nitro group concentration monitoring, identify dissipation effects and perform partitioned compensation, optimize process parameters in combination with real-time feedback, and ensure COD detection accuracy and wastewater treatment efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of wastewater treatment detection, and in particular to a chemical oxygen demand detection system for intermediate synthesis. Background Art

[0002] In the field of intermediate synthesis, compounds containing nitro groups, such as nitrobenzene and nitrophenol, are key intermediates. Their production wastewater has complex composition and high chemical oxygen demand (COD) values, posing special challenges to COD detection in wastewater treatment. Existing detection technologies face the following technical bottlenecks when dealing with this type of wastewater:

[0003] The intermediate products such as nitroso and azo generated by the oxidation of nitro groups produce overlapping absorption with the COD characteristic signal in the 260nm ultraviolet region. However, traditional spectral detection does not combine electronic structure parameters and does not quantify the absorbance interference intensity through electron transfer theory, resulting in COD detection errors caused by signal masking.

[0004] Existing technologies often use suspended particulate matter in intermediate synthesis wastewater, which can easily cause particle deposition in the inlet area and fluid stagnation in the dead volume area of ​​the microfluidic chip reaction channel, resulting in differences in signal masking values ​​in different areas (such as the inlet area and the middle reaction area). Traditional systems do not perform collaborative analysis of signals in independent areas such as the eddy current area and the outlet area, and are unable to locate local interference sources.

[0005] To this end, the present invention provides a chemical oxygen demand detection system for intermediate synthesis. Summary of the Invention

[0006] The object of the present invention is to provide a chemical oxygen demand detection system for intermediate synthesis to solve at least one of the above-mentioned problems in the prior art.

[0007] A chemical oxygen demand detection system for intermediate synthesis includes the following modules:

[0008] Oxidation Assessment Module: During the treatment of intermediate synthesis wastewater, the oxidation efficiency of the nitro group of the target product in the oxidation reactor is monitored to assess whether the oxidation efficiency meets the requirements of chemical oxygen demand detection;

[0009] Masking analysis module: If not, an electron transfer enhancement model is established based on the molecular structure and oxidation potential parameters of the nitro group to obtain the signal masking value in the spectrum detection stage and evaluate whether the absorbance of the nitro group masks the characteristic signal;

[0010] Signal correction module: If the characteristic signal is masked, the spectral signal in the microfluidic chip is collected synchronously in multiple regions to diagnose whether the spectral signal distribution is uniform; if the spectral signal distribution is uniform, the spectral compensation amount is obtained to complete the COD signal correction; if the spectral signal distribution is uneven, a regional detection signal is generated;

[0011] Dissipation identification module: Based on the regional detection signal, the spectral signal of all regions is tested for effects, the spatial propagation characteristics of the dissipation effect between regions are identified, and a partition compensation strategy is formulated based on the spatial propagation characteristics.

[0012] As a further technical solution of the present invention: the signal masking value in the spectrum detection stage is obtained by:

[0013] Obtain the absorbance A of the nitro group at an ultraviolet wavelength of 260 nm 260 , the linear correlation calculation is performed on the absorbance of the nitro group at 260nm in the ultraviolet region and the characteristic wavelength;

[0014] If a linear correlation is present, the interference intensity value can be obtained by numerical analysis based on the electron transfer rate output by the electron transfer enhancement model;

[0015] The interference intensity value and absorbance are analyzed for net value and characteristic value respectively to obtain characteristic absorbance ratio and interference net value ratio;

[0016] The signal masking value is obtained by multiplying the characteristic absorbance ratio and the net interference value ratio.

[0017] As a further technical solution of the present invention: the method of performing net value and characteristic analysis on the interference intensity value and absorbance is as follows:

[0018] Calculate the difference between the net interference intensity value and the interference intensity value to obtain the net interference value ratio;

[0019] The absorbance of the nitro group at an ultraviolet wavelength of 260 nm is A 260 The characteristic absorbance ratio is obtained by ratio processing with the absorbance at the characteristic wavelength.

[0020] As a further technical solution of the present invention: the electron transfer enhancement model is constructed as follows:

[0021] Get the molecular structure parameters of the nitro group;

[0022] Calculate oxidation potential based on molecular structure parameters;

[0023] The electron transfer rate was modeled based on the oxidation potential to obtain an electron transfer enhancement model;

[0024] The electron transfer enhancement model is verified to determine the confidence level of the electron transfer enhancement model.

[0025] As a further technical solution of the present invention: the method for diagnosing whether the spectral signal distribution is uniform is:

[0026] If the characteristic signal is masked, the reaction channel of the microfluidic chip is obtained and the reaction channel is divided into multiple independent detection areas, and a transparent detection window is set in each area;

[0027] Obtain the signal masking value and electron transfer rate in the corresponding transparent detection window in each independent detection area, and construct a regional parameter group including the signal masking value and the electron transfer rate;

[0028] The spatial uniformity and dynamic consistency of the regional parameter group are verified. If all regional detection pairs in the regional parameter group meet the spatial uniformity and dynamic consistency, the diagnostic spectral signal is evenly distributed.

[0029] As a further technical solution of the present invention: the method of verifying the spatial uniformity and dynamic consistency of the regional parameter group is:

[0030] Construct region detection pairs, obtain spatial uniformity values ​​of region detection pairs and determine the uniformity of regional spatial distribution;

[0031] The spatial uniformity value is obtained as follows:

[0032] Get any two independent detection areas to form an area detection pair;

[0033] Obtain the signal masking values ​​of the regional detection pairs within multiple monitoring cycles, calculate the Pearson correlation coefficient of the signal masking values ​​in the regional detection pairs, and obtain the spatial uniformity value;

[0034] Kinetic consistency verification was performed based on the electron transfer model.

[0035] As a further technical solution of the present invention: the method for verifying the kinetic consistency based on the electron transfer model is:

[0036] Obtaining the electron transfer rates of two independent detection regions of a regional detection pair over multiple monitoring cycles, and calculating the mean and standard deviation of the electron transfer rates of the independent detection regions over multiple monitoring cycles;

[0037] The dynamic stability value is obtained by performing ratio processing on the mean and standard deviation of the electron transfer rate of the independent detection area in multiple monitoring cycles;

[0038] Calculate the difference ratio of the dynamic stability values ​​of two independent detection areas within the regional detection pair to obtain the stability difference ratio;

[0039] Based on the stable difference ratio, the dynamic consistency of the region detection pair is verified.

[0040] As a further technical solution of the present invention, the method for identifying the spatial propagation characteristics of the dissipation effect between the regions is:

[0041] Obtain the stable difference ratio and spatial uniformity of the monitoring pairs in adjacent areas, and calculate the attenuation gradient of the stable difference ratio and spatial uniformity of the monitoring pairs in adjacent areas;

[0042] Based on the attenuation gradient of the stable difference ratio and spatial uniformity, the front propagation zone of the dispersion effect is proposed;

[0043] The frontier propagation belt is used as the spatial propagation feature;

[0044] Obtain the spectral curve of the spectral signal, extract the peak position shift of the spectral curve, and perform a shift test to determine whether it is a real dissipation effect;

[0045] If there is a true dissipation effect, the effect interval of the front propagation zone is identified based on the attenuation gradient of the stable difference ratio and the spatial uniformity;

[0046] A partitioned COD signal compensation strategy is formulated based on the effect interval.

[0047] A chemical oxygen demand detection system for intermediate synthesis, further comprising:

[0048] Collaborative optimization module: Build an oxidation-spectrum collaborative optimization model to output real-time COD value and feed it back to the synthesis process control system.

[0049] As a further technical solution of the present invention: the method of constructing the oxidation-spectrum collaborative optimization model is:

[0050] Data fusion and oxidation-spectroscopy coupling model establishment;

[0051] Parameter adaptive update and COD inversion calculation;

[0052] COD parameter feedback control.

[0053] Beneficial effects of the present invention:

[0054] 1. High-performance liquid chromatography is used to collect nitro group concentration, which helps reduce matrix interference and thus reduce the deviation rate of oxidation efficiency calculation. By dynamically comparing the deviation of nitro group concentration, the oxidation efficiency is evaluated to see whether it meets the COD detection requirements. When the oxidation efficiency deviates from the preset range, the oxidation potential is calculated, and a quantitative model of oxidation potential and electron transfer rate is established to characterize the oxidation reaction kinetics. The signal masking value is calculated to quantify the interference intensity in the spectral detection stage, providing a theoretical basis for signal correction.

[0055] 2. The reaction channel of the microfluidic chip is divided into independent detection areas. The Pearson correlation coefficient of the signal masking value and the dynamic stability value of the electron transfer rate are analyzed to realize the diagnosis of the uniformity of the spectral signal distribution. The spectral compensation amount of the uniform area is calculated based on the spatial uniformity value and the mean of the signal masking value, which is beneficial to reduce the COD detection deviation caused by the difference in the distribution of regional interfering substances.

[0056] 3. By calculating the attenuation gradient of the stable difference ratio and spatial uniformity of adjacent regions, combined with confidence interval testing of spectral peak shifts, the leading propagation band of the dissipation effect is located and the effect interval is divided. Differentiated compensation strategies are implemented for different intervals based on gradient characteristics, ensuring that the deviation between the compensated spectrum and the HPLC data is within a controllable range. Simultaneously, by integrating multiple data sources such as oxidation efficiency and compensated spectra, an oxidation-spectroscopy coupling model is constructed, and recursive least squares is used to update parameters online, enabling real-time inversion of COD values. Process parameters are adjusted based on the deviation between the inversion results and the target values, forming a closed-loop detection-modeling-control system that facilitates the coordinated operation of COD detection and wastewater treatment processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is a module diagram of a chemical oxygen demand detection system for intermediate synthesis provided by the present invention;

[0059] Figure 2 This is a flow chart for diagnosing whether the spectral signal distribution is uniform, provided by the present invention;

[0060] Figure 3 The present invention provides a flow chart of a method for detecting chemical oxygen demand in the synthesis of an intermediate. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0062] Example 1

[0063] like Figure 1 As shown, a chemical oxygen demand detection system for intermediate synthesis includes the following modules:

[0064] Oxidation Assessment Module: During the treatment of intermediate synthesis wastewater, the oxidation efficiency of the nitro group of the target product in the oxidation reactor is monitored to assess whether the oxidation efficiency meets the requirements of chemical oxygen demand detection;

[0065] The method for monitoring the nitro group oxidation efficiency of the target product in the oxidation reactor is as follows:

[0066] Preferably, when high performance liquid chromatography (HLPC) is used to obtain the nitro group concentration during the monitoring period, the wastewater sample to be treated is first collected before the wastewater oxidation reaction is started, filtered through a 0.22 μm filter membrane, and separated using a C18 reverse phase chromatography column. Methanol-water (e.g., 60:40, volume ratio) is used as the mobile phase, with a flow rate of 1.0 mL / min and a column temperature of 30°C. At an ultraviolet wavelength of 270 nm, a linear standard curve is established using standard substances such as nitrobenzene (0.1-10 mg / L). The initial concentration of the nitro group is calculated based on the peak area of ​​the sample nitro group. The reaction solution is collected at a fixed monitoring time (e.g., 30 minutes or 1 hour of reaction), and is also filtered and injected into HPLC, maintaining the same chromatographic conditions. The real-time peak area is substituted into the standard curve to obtain the nitro group concentration at the current moment.

[0067] High performance liquid chromatography can reduce interference by using chromatographic separation, which is beneficial to improving the accuracy of nitro group concentration monitoring;

[0068] Obtaining the initial concentration of the nitro group and the concentration of the nitro group at a fixed monitoring time, calculating the deviation ratio between the initial concentration of the nitro group and the concentration of the nitro group at the current time, and obtaining the nitro group oxidation efficiency;

[0069] comparing the oxidation efficiency of the nitro group with a preset range value, and continuously monitoring the oxidation efficiency of the nitro group if the oxidation efficiency of the nitro group is within the preset range value;

[0070] If the oxidation efficiency of the nitro group is not within the preset range, it is determined that the oxidation efficiency does not meet the detection requirements of the chemical oxygen demand.

[0071] Masking analysis module: If not, an electron transfer enhancement model is established based on the molecular structure and oxidation potential parameters of the nitro group to obtain the signal masking value in the spectrum detection stage and evaluate whether the absorbance of the nitro group masks the characteristic signal;

[0072] Among them, based on the molecular structure and oxidation potential parameters of the nitro group, the electron transfer enhancement model is established as follows:

[0073] S201. Obtain the molecular structure parameters of the nitro group by density functional theory;

[0074] Preferably, the nitro group (such as nitrobenzene, nitrophenol) is subjected to geometric structure optimization and frequency analysis; a stable configuration is obtained by minimizing energy, and geometric parameters such as bond length, bond angle, and dihedral angle, as well as electronic structure parameters such as molecular orbital energy level, charge distribution, and dipole moment are obtained;

[0075] Geometric parameters and electronic structure parameters are the basis for subsequent oxidation potential calculations, directly reflecting the electron delocalization ability and steric hindrance characteristics of the molecule;

[0076] S202, calculating oxidation potential based on molecular structure parameters;

[0077] Preferably, based on the stable configuration obtained by S1, the oxidation potential is deduced by calculating the ionization energy (IE) during the monitoring period in combination with the thermodynamic cycle method;

[0078] For example, for the oxidation process The gas phase ionization energy IE is calculated by density functional theory (DFT). Ionization energy refers to the energy generated by losing one electron in the gaseous state. The energy required for the ion to absorb;

[0079] Here, M represents neutral gaseous atoms or molecules, which are reactants in the ionization process; It is a gaseous positive ion formed after M loses one electron. It is the product of the ionization process. - For electronics;

[0080] Use a solvation model (such as PCM) to correct for solvent effects, using the formula: Obtaining oxidation potential ;

[0081] Where n is the number of electron transfers, F is the Faraday constant, is the reference potential of the standard hydrogen electrode;

[0082] S203, modeling the electron transfer rate based on the oxidation potential to obtain an electron transfer enhancement model;

[0083] Preferably, the Marcus electron transfer theory is used to calculate the oxidation potential and electron transfer rate constant association;

[0084] For example, using the Marcus equation: Get the electron transfer rate ke;

[0085] in, is the redox potential difference between the reactants and products, is the reorganization energy, V is the electronic coupling matrix element, is the reduced Planck constant, is the Boltzmann constant, T is the absolute temperature, and π is the circumference of a circle;

[0086] S204, verifying the electron transfer enhancement model and determining the confidence level of the electron transfer enhancement model;

[0087] Preferably, the oxidation potential of the nitro compound is measured by cyclic voltammetry (CV), and the deviation rate between the measured oxidation potential of the nitro compound and the S2 oxidation potential is calculated. If the deviation rate is ≤5%, it meets the requirements;

[0088] The current-time curve of the oxidation reaction was monitored by UV-visible spectroscopy or electrochemical workstation to obtain the experimental electron transfer rate Kse, which was compared with the value predicted by the S3 model to calculate the coefficient of determination. The coefficient of determination was >0.9.

[0089] Among them, the method for predicting the signal interference intensity in the spectral detection stage and evaluating whether the absorbance of the nitro group masks the characteristic signal is:

[0090] By formula: Obtain the absorbance A of the nitro group at an ultraviolet wavelength of 260 nm 260 ;

[0091] in, is the cuvette optical path length, are the absorbance at 260 nm and the residual concentration of nitro groups, respectively;

[0092] Perform linear correlation calculation on the absorbance of the nitro group at 260nm in the ultraviolet region and the characteristic wavelength of 600nm. If a linear correlation is shown, the interference intensity value is obtained. ;

[0093] Preferably, by the formula: Get the interference intensity value ;

[0094] in, is the preset mapping coefficient, is the optical path, is the reaction time;

[0095] The net interference intensity value is calculated by double wavelength subtraction method and reference wavelength. ;

[0096] Preferably, the reference wavelength is 580 nm;

[0097] Calculate the difference between the net interference intensity value and the interference intensity value to obtain the net interference value ratio;

[0098] Obtain the absorbance at the characteristic wavelength of 600nm and the absorbance A of the nitro group at the ultraviolet wavelength of 260nm 260 ;

[0099] The absorbance of the nitro group at an ultraviolet wavelength of 260 nm is A 260 The characteristic absorbance ratio is obtained by ratio processing with the absorbance at the characteristic wavelength of 600 nm;

[0100] The signal masking value is obtained by multiplying the characteristic absorbance ratio and the interference net value ratio;

[0101] It can be understood that the signal masking value can quantify the interference intensity of the nitro group absorbance on the COD characteristic signal. The signal masking value is the ratio of the nitro group absorbance in the 260nm ultraviolet region (A260) to the absorbance of the COD characteristic wavelength (600nm), combined with the interference net value ratio calculated by the dual-wavelength subtraction method, to quantitatively characterize the degree of overlapping absorption of the COD characteristic signal when the nitro group is not completely oxidized. The numerical value of the signal masking value directly reflects the degree of masking of the 600nm characteristic signal by the nitro group absorbance. For example, when the signal masking value is greater than 1, it indicates that the characteristic signal is significantly masked due to the interference of the 260nm absorbance, and the COD detection value will deviate from the true value.

[0102] The signal masking value can correlate the oxidation reaction process with the electron transfer efficiency. The calculation of the signal masking value is based on the electron transfer enhancement model, which reveals the fundamental reason why the oxidation efficiency does not meet the COD detection requirements.

[0103] Based on the signal masking value, characteristic signal masking determination is performed;

[0104] Exemplarily, the method for determining whether the characteristic signal is masked is as follows:

[0105] If the signal mask value is greater than 1, it is determined that the characteristic signal is masked;

[0106] If the signal mask value is ≤1, further verification is performed in combination with the linear correlation coefficient of absorbance at 260 nm and 600 nm. If the correlation coefficient is higher than 0.7, the change of the signal mask value is continuously monitored.

[0107] It is understandable that the role of determining whether the absorbance of the nitro group masks the characteristic signal is:

[0108] Function 1: By calculating the linear correlation and net interference ratio between the absorbance of the nitro group in the ultraviolet region at 260nm and the COD characteristic wavelength (600nm), the degree of masking of the characteristic signal by the nitro group is quantified, the distortion of the COD test value caused by the overlap of absorbance is reduced, and the test results reflect the oxidation state of organic matter in the wastewater;

[0109] Function 2: Determine whether the characteristic signal is masked based on the signal masking value, provide a quantitative basis for subsequent spectral signal correction, and enable the COD signal compensation coefficient of each area in the microfluidic chip to be dynamically adjusted according to the masking degree, thereby improving detection accuracy;

[0110] Function three: Combined with the electron transfer enhancement model (established based on the molecular structure of the nitro group and the oxidation potential parameters), the absorbance masking degree is correlated with the oxidation reaction kinetics to assist in determining whether the oxidation reaction process meets the detection requirements. If the masking value is abnormal, it can be fed back to the oxidation assessment module to guide the adjustment of the oxidant dosage or reaction time.

[0111] Example 2

[0112] like Figure 1 As shown, a chemical oxygen demand detection system for intermediate synthesis includes the following modules:

[0113] Signal correction module: If the characteristic signal is masked, the spectral signal in the microfluidic chip is collected synchronously in multiple regions to diagnose whether the spectral signal distribution is uniform; if the spectral signal distribution is uniform, the spectral compensation amount is obtained to complete the COD signal correction; if the spectral signal distribution is uneven, a regional detection signal is generated;

[0114] Among them, the diagnosis method for whether the spectral signal distribution is uniform is:

[0115] If the characteristic signal is masked, the reaction channel of the microfluidic chip is obtained and the reaction channel is divided into multiple independent detection areas, and a transparent detection window is set in each area;

[0116] For example, the reaction channel is divided into an inlet area, a middle reaction area, an outlet area, a dead volume area, and a vortex area;

[0117] Obtain the signal masking value and electron transfer rate in the corresponding transparent detection window in each independent detection area, and construct a regional parameter group including the signal masking value and the electron transfer rate;

[0118] Verify the spatial uniformity and dynamic consistency of the regional parameter group to diagnose whether the spectral signal distribution is uniform;

[0119] Among them, the method of verifying the spatial uniformity and dynamic consistency of the regional parameter group is as follows:

[0120] S301: Construct a region detection pair, obtain a spatial uniformity value of the region detection pair, and determine the uniformity of the regional spatial distribution;

[0121] Get any two independent detection areas to form an area detection pair;

[0122] Obtain the signal masking values ​​of the regional detection pairs within multiple monitoring cycles, calculate the Pearson correlation coefficient of the signal masking values ​​in the regional detection pairs, and obtain the spatial uniformity value;

[0123] It should be explained that the spatial uniformity value is a quantitative indicator obtained by calculating the Pearson correlation coefficient of the signal masking values ​​of any two independent detection areas in the reaction channel of the microfluidic chip. Its physical meaning is to characterize the spatial consistency of the spectral signal masking characteristics of different regions. The higher the correlation coefficient, the stronger the linear correlation of the signal masking values ​​in each region, and the better the uniformity of the spatial distribution of interference factors such as the absorbance of the nitro group. On the contrary, it reflects significant differences in the interference distribution between regions (for example, particulate matter deposition in the inlet area leads to a high local masking value), providing a key basis for the diagnosis of uneven spectral signal distribution.

[0124] Preferably, multiple monitoring cycles means that the number of monitoring cycles is greater than 5;

[0125] For example, the method of determining the spatial distribution uniformity based on the spatial uniformity value is as follows: calculating the signal masking value S of any two regions i and j i With the signal mask value S j Pearson correlation coefficient :

[0126] Among them, i, j are the numbers of independent detection areas, S is the signal masking value, S i Indicates the signal masking value of the independent detection area numbered i;

[0127] like , then the regional signal masking characteristics of the regional detection pairs are independent and unevenly distributed;

[0128] like , then the signal characteristics of the regional detection pairs are similar and the distribution tends to be uniform;

[0129] S302, verify the kinetic consistency based on the electron transfer model;

[0130] Obtaining the electron transfer rates of two independent detection regions of a regional detection pair over multiple monitoring cycles, and calculating the mean and standard deviation of the electron transfer rates of the independent detection regions over multiple monitoring cycles;

[0131] The dynamic stability value is obtained by performing ratio processing on the mean and standard deviation of the electron transfer rate of the independent detection area in multiple monitoring cycles;

[0132] Calculate the difference ratio of the dynamic stability values ​​of two independent detection areas within the regional detection pair to obtain the stability difference ratio;

[0133] Preferably, multiple monitoring cycles means that the number of monitoring cycles is greater than 5;

[0134] Based on the stable difference ratio, the dynamic consistency of the regional detection pairs is verified;

[0135] It can be understood that the stability difference ratio represents the consistency of the oxidation reaction kinetic characteristics in different regions. The smaller the ratio, the smaller the stability difference of the electron transfer rate between regions, and the higher the uniformity of the mass transfer efficiency and the reaction environment. Conversely, it reflects the significant differences in kinetic characteristics between regions (such as the different fluctuation amplitudes of the electron transfer rate in the strong effect region and the weak effect region), providing a kinetic judgment basis for identifying the spatial propagation characteristics of the dissipation effect (such as the parameter mutation of the front propagation zone).

[0136] If the stable difference ratio of the regional detection pair meets the preset difference stability interval, the regional detection pair is considered to meet the dynamic consistency, otherwise it does not meet the requirements;

[0137] like Figure 2 As shown, if all regional detection pairs in the regional parameter group meet the requirements of spatial uniformity and dynamic consistency, the diagnostic spectral signal is evenly distributed;

[0138] If all regional detection pairs in the regional parameter group do not all meet the spatial uniformity and dynamic consistency, the spectral signal distribution is diagnosed to be uneven, there is a potential dissipation effect, and a regional detection signal is generated;

[0139] If the spectral signal is evenly distributed, the spectral compensation amount is obtained and the COD signal correction is completed as follows:

[0140] Through the equation: Obtain the spectrum compensation coefficient K;

[0141] in, 、 are the mean of the spatial uniformity value and the mean of the signal masking value of all area detections respectively;

[0142] By formula: Get spectral compensation ;

[0143] It should be explained that the measurement of chemical oxygen demand (COD) depends on the electron transfer rate of the redox reaction. The reaction environment in the area with uneven characteristic signals is different, which leads to deviations in the electron transfer rate and causes errors in the chemical oxygen demand.

[0144] When the spectral signal is uneven, the distribution of interfering substances (such as turbidity, color, or non-specific absorbents) in different areas of the reaction channel varies significantly, resulting in the local masking of characteristic absorbance signals (such as the absorption of organic matter at a wavelength of 260nm). For example, if there is particulate matter deposition in the inlet area, its signal masking value is higher than that in the middle reaction area, causing the overall COD signal to be overestimated or underestimated.

[0145] It can be understood that the function of judging whether the spectral signal distribution is uniform is:

[0146] Function 1: Through the collaborative analysis of multi-region signal masking values ​​and electron transfer rates, the spatial distribution consistency of spectral signals in the reaction channel of the microfluidic chip is determined, providing a decision-making basis for spectral compensation strategy. If the signal distribution is uniform, a unified compensation amount can be calculated based on the mean value of the parameters in the entire region to avoid compensation deviation caused by regional differences;

[0147] Function 2: When the spectral signal distribution is uneven, a detection signal containing the characteristics of each region is generated to provide raw data support for the dissipation recognition module. By locating the signal mutation area (such as the parameter difference between the entrance area and the eddy current area), the spatial propagation characteristics of the dissipation effect are improved;

[0148] Function 3: Combined with the dual verification of spatial uniformity and kinetic consistency, it quantifies the impact of the distribution differences of interfering substances in the reaction channel on the COD signal, establishes a systematic overestimation or underestimation of the detection value caused by local masking (such as abnormal absorbance caused by fluid stagnation in the dead volume area), and ensures the reliability of the COD test results.

[0149] Dissipation identification module: Based on the regional detection signal, the spectral signal of all regions is tested for effect, the spatial propagation characteristics of the dissipation effect between regions are identified, and a partition compensation strategy is formulated based on the spatial propagation characteristics;

[0150] Among them, the effect test is performed on the spectral signals of all regions, and the spatial propagation characteristics of the dissipation effect between regions are identified as follows:

[0151] Obtain the stable difference ratio and spatial uniformity of the monitoring pairs in adjacent areas, and calculate the attenuation gradient of the stable difference ratio and spatial uniformity of the monitoring pairs in adjacent areas;

[0152] It needs to be explained that the gradient of the stability difference ratio between adjacent regions is positive and increasing, indicating that the dissipation effect propagates from the high stability region to the low stability region;

[0153] Based on the attenuation gradient of the stable difference ratio and spatial uniformity, the front propagation zone of the dispersion effect is proposed;

[0154] Preferably, when the dissipation effect propagates, the stable difference ratio and spatial uniformity value of the front region will undergo a sudden change. By setting the gradient threshold, the area with the most significant parameter changes is located to obtain the front propagation belt;

[0155] The frontier propagation belt is used as the spatial propagation feature;

[0156] Obtain the spectral curve of the spectral signal, extract the peak position shift of the spectral curve, and perform a shift test to determine whether it is a real dissipation effect;

[0157] If there is a true dissipation effect, the effect interval of the front propagation zone is identified based on the attenuation gradient of the stable difference ratio and the spatial uniformity;

[0158] It can be understood that the spectral signal is baseline corrected and denoised, the characteristic peak position is extracted by the second-order derivative method, the peak position difference between the current spectrum and the reference spectrum is calculated, and a confidence interval for peak position fluctuation is established based on historical data or blank samples (such as mean ± 3 times the standard deviation). If the offset exceeds this interval, it is determined to be a true dissipation effect;

[0159] Confirm the existence of a real effect, calculate the attenuation gradient of the stable difference ratio and spatial uniformity of adjacent areas, divide the continuous area where the absolute value of the gradient exceeds the dynamic threshold into a frontier propagation belt, and then divide the frontier belt and its adjacent areas into strong effect areas and weak effect areas based on the relationship between the gradient attenuation rate and spatial position to complete the identification of the effect interval;

[0160] Formulate a zoned COD signal compensation strategy based on the effect interval;

[0161] Preferably, firstly, the corresponding compensation model is matched according to the gradient characteristics of the effect interval, the gradient inversion gain compensation is applied to the strong effect area, the signal attenuation ratio is calculated by the stable difference ratio, and the spectral compensation coefficient is corrected in combination with the spatial uniformity value;

[0162] Linear interpolation smooth compensation is used in the weak effect area to generate a smooth compensation curve based on the parameter gradient of the adjacent area; the spectral deformation pattern is determined by the peak position offset, and then differentiated compensation is applied to each area in the frontier band according to the gradient direction (such as a compensation coefficient of 1.5 in the center of the frontier and 1.2 at the edge). Finally, the strategy is optimized by verifying the deviation rate between the compensated spectrum and the high-performance liquid chromatography data (deviation ≤ 5%).

[0163] Example 3

[0164] like Figure 1 As shown, a chemical oxygen demand detection system for intermediate synthesis includes the following modules:

[0165] Collaborative optimization module: Build an oxidation-spectrum collaborative optimization model to output real-time COD values ​​and feed them back to the synthesis process control system;

[0166] Among them, the method of constructing the oxidation-spectroscopy collaborative optimization model is:

[0167] S501, data fusion and oxidation-spectroscopy coupling model establishment;

[0168] Preferably, based on the nitro group oxidation efficiency, the corrected spectral signal and the partition compensation parameter, the dimension difference is eliminated by standardization processing, abnormal data are eliminated, and a unified data matrix is ​​established;

[0169] The Michaelis-Menten equation was used to describe the oxidation rate of the nitro group, and the mapping relationship between absorbance, signal masking value and COD was established in combination with partial least squares regression.

[0170] S502, parameter adaptive update and COD inversion calculation;

[0171] Preferably, each time new nitro group concentration data is collected, the weight of historical data is adjusted by the forgetting factor, the gain matrix is ​​calculated, and the model coefficients are iteratively optimized to adapt to fluctuations in wastewater quality;

[0172] Input the current spectral signal and oxidation efficiency data, calculate the real-time COD value through the oxidation-spectrum coupling model, and compare it with the high-performance liquid chromatography (HPLC) offline detection results. If the deviation is greater than 5%, the model self-correction is triggered;

[0173] S503, COD parameter feedback control;

[0174] It can be understood that based on the deviation between the real-time COD value and the target value, the adjustment parameters such as the oxidant dosage and reaction time are adjusted, zoning control is implemented for the strong and weak effect areas, and the control parameters are optimized through the genetic algorithm to balance the COD removal rate and energy consumption.

[0175] Example 4

[0176] like Figure 3 As shown, a method for detecting chemical oxygen demand of an intermediate synthesis comprises the following steps:

[0177] S1. During the treatment of intermediate synthesis wastewater, monitor the oxidation efficiency of the nitro group of the target product in the oxidation reactor and evaluate whether the oxidation efficiency meets the detection requirements of chemical oxygen demand;

[0178] S2. If not, establish an electron transfer enhancement model based on the molecular structure and oxidation potential parameters of the nitro group, obtain the signal masking value in the spectrum detection stage, and evaluate whether the absorbance of the nitro group masks the characteristic signal;

[0179] S3. If the characteristic signal is masked, the spectral signal in the microfluidic chip is synchronously collected in multiple regions to diagnose whether the spectral signal distribution is uniform; if the spectral signal distribution is uniform, the spectral compensation amount is obtained to complete the COD signal correction; if the spectral signal distribution is uneven, a regional detection signal is generated;

[0180] S4. Based on the regional detection signals, the spectral signals of all regions are tested for effects, the spatial propagation characteristics of the dissipation effects between regions are identified, and a partition compensation strategy is formulated based on the spatial propagation characteristics;

[0181] S5. Construct an oxidation-spectrum collaborative optimization model to output real-time COD value and feed it back to the synthesis process control system.

[0182] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A chemical oxygen demand detection system for intermediate synthesis, characterized in that: Includes the following modules: Oxidation Assessment Module: During the treatment of intermediate synthesis wastewater, the oxidation efficiency of the nitro group of the target product in the oxidation reactor is monitored to assess whether the oxidation efficiency meets the requirements of chemical oxygen demand detection; Masking analysis module: If not, an electron transfer enhancement model is established based on the molecular structure and oxidation potential parameters of the nitro group to obtain the signal masking value in the spectrum detection stage and evaluate whether the absorbance of the nitro group masks the characteristic signal; The signal masking value of the spectrum detection stage is obtained as follows: Obtain the absorbance A of the nitro group at an ultraviolet wavelength of 260 nm 260 , the linear correlation calculation is performed on the absorbance of the nitro group at 260nm in the ultraviolet region and the characteristic wavelength; If a linear correlation is present, the interference intensity value can be obtained by numerical analysis based on the electron transfer rate output by the electron transfer enhancement model; The interference intensity value and absorbance are analyzed for net value and characteristic value respectively to obtain characteristic absorbance ratio and interference net value ratio; The signal masking value is obtained by multiplying the characteristic absorbance ratio and the interference net value ratio; The interference intensity value and absorbance are respectively subjected to net value and characteristic analysis as follows: Calculate the difference between the net interference intensity value and the interference intensity value to obtain the net interference value ratio; The absorbance of the nitro group at an ultraviolet wavelength of 260 nm is A 260 The characteristic absorbance ratio is obtained by ratio processing with the absorbance of the characteristic wavelength; The electron transfer enhancement model is constructed as follows: Get the molecular structure parameters of the nitro group; Calculate oxidation potential based on molecular structure parameters; The electron transfer rate was modeled based on the oxidation potential to obtain an electron transfer enhancement model; Verify the electron transfer enhancement model and determine the confidence level of the electron transfer enhancement model; Signal correction module: If the characteristic signal is masked, the spectral signal in the microfluidic chip is collected synchronously in multiple regions to diagnose whether the spectral signal distribution is uniform; if the spectral signal distribution is uniform, the spectral compensation amount is obtained to complete the COD signal correction; if the spectral signal distribution is uneven, a regional detection signal is generated; Dissipation identification module: Based on the regional detection signal, the spectral signal of all regions is tested for effects, the spatial propagation characteristics of the dissipation effect between regions are identified, and a partition compensation strategy is formulated based on the spatial propagation characteristics.

2. The chemical oxygen demand detection system for intermediate synthesis according to claim 1, characterized in that: The method for diagnosing whether the spectral signal distribution is uniform is: If the characteristic signal is masked, the reaction channel of the microfluidic chip is obtained and the reaction channel is divided into multiple independent detection areas, and a transparent detection window is set in each area; Obtaining the signal masking value and the electron transfer rate in the corresponding transparent detection window in each independent detection area, and constructing a regional parameter group including the signal masking value and the electron transfer rate; The spatial uniformity and dynamic consistency of the regional parameter group are verified. If all regional detection pairs in the regional parameter group meet the spatial uniformity and dynamic consistency, the diagnostic spectral signal is evenly distributed.

3. The chemical oxygen demand detection system for intermediate synthesis according to claim 2, characterized in that: The spatial uniformity and dynamic consistency of the regional parameter set are verified as follows: Construct region detection pairs, obtain spatial uniformity values ​​of region detection pairs and determine the uniformity of regional spatial distribution; The spatial uniformity value is obtained as follows: Get any two independent detection areas to form an area detection pair; Obtain the signal masking values ​​of the regional detection pairs within multiple monitoring cycles, calculate the Pearson correlation coefficient of the signal masking values ​​in the regional detection pairs, and obtain the spatial uniformity value; Kinetic consistency verification was performed based on the electron transfer enhancement model.

4. The chemical oxygen demand detection system for intermediate synthesis according to claim 3, characterized in that: The method for verifying the kinetic consistency based on the electron transfer enhancement model is as follows: Obtaining the electron transfer rates of two independent detection regions of a regional detection pair over multiple monitoring cycles, and calculating the mean and standard deviation of the electron transfer rates of the independent detection regions over multiple monitoring cycles; The dynamic stability value is obtained by performing ratio processing on the mean and standard deviation of the electron transfer rate of the independent detection area in multiple monitoring cycles; Calculate the difference ratio of the dynamic stability values ​​of two independent detection areas within the regional detection pair to obtain the stability difference ratio; Based on the stable difference ratio, the dynamic consistency of the region detection pair is verified.

5. The chemical oxygen demand detection system for intermediate synthesis according to claim 4, characterized in that: The spatial propagation characteristics of the dissipation effect between the regions are identified as follows: Obtain the stable difference ratio and spatial uniformity of adjacent region detection pairs, and calculate the attenuation gradient of the stable difference ratio and spatial uniformity of adjacent region detection pairs; Based on the attenuation gradient of the stable difference ratio and spatial uniformity, the front propagation zone of the dispersion effect is proposed; The frontier propagation belt is used as the spatial propagation feature; Obtain the spectral curve of the spectral signal, extract the peak position shift of the spectral curve, and perform a shift test to determine whether it is a real dissipation effect; If there is a true dissipation effect, the effect interval of the front propagation zone is identified based on the attenuation gradient of the stable difference ratio and the spatial uniformity; A partitioned COD signal compensation strategy is formulated based on the effect interval.

6. The chemical oxygen demand detection system for intermediate synthesis according to claim 1, characterized in that: Also includes: Collaborative optimization module: Build an oxidation-spectrum collaborative optimization model to output real-time COD value and feed it back to the synthesis process control system.

7. The chemical oxygen demand detection system for intermediate synthesis according to claim 6, characterized in that: The oxidation-spectroscopy synergistic optimization model is constructed as follows: Data fusion and oxidation-spectroscopy coupling model establishment; Based on the oxidation efficiency of the nitro group, the corrected spectral signal and the partition compensation parameters, the dimensional differences were eliminated through standardization, abnormal data were removed, and a unified data matrix was established; The Michaelis-Menten equation was used to describe the oxidation rate of the nitro group, and the mapping relationship between absorbance, signal masking value and COD was established in combination with partial least squares regression. Parameter adaptive update and COD inversion calculation; Each time new nitro group concentration data is collected, the weight of historical data is adjusted by the forgetting factor, the gain matrix is ​​calculated, and the model coefficients are iteratively optimized to adapt to fluctuations in wastewater quality; Input the current spectral signal and oxidation efficiency data, calculate the real-time COD value through the oxidation-spectrum coupling model, and compare it with the high-performance liquid chromatography (HPLC) offline detection results. If the deviation is greater than 5%, the model self-correction is triggered; COD parameter feedback control; Based on the deviation between the real-time COD value and the target value, the oxidant dosage and reaction time are adjusted, and zoning control is implemented for the strong and weak effect areas. The control parameters are optimized through genetic algorithm to balance the COD removal rate and energy consumption.

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