An on-line monitoring system and method for chemical oxygen demand

Abstract

The present application belongs to the technical field of water quality chemical oxygen demand monitoring, and provides an online monitoring system and method for chemical oxygen demand, comprising: an ozone preparation module, which adjusts the dissolution efficiency and reaction activity of prepared ozone water through closed-loop control of PH value and temperature; a chemical reaction module, which adopts a reaction channel and is provided with a plurality of oxidation-reduction potential probes for obtaining potential changes at different positions along the water sample flow direction; a flow rate regulation module, which is connected with the ozone preparation module, regulates the sample injection rate in real time, and matches the best oxidation interval of the ozone preparation module; an estimation module, which is connected with the chemical reaction module and the flow rate regulation module, obtains the estimated value of chemical oxygen demand based on the potential difference sequence obtained by the oxidation-reduction potential probe and the sample injection rate; and a compensation module, which dynamically corrects the estimated value of chemical oxygen demand according to the PH value, temperature and ozone water concentration, and completes the online monitoring of chemical oxygen demand.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of water quality chemical oxygen demand monitoring, and particularly relates to an online monitoring system and method for chemical oxygen demand. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Chemical oxygen demand (COD) is a key parameter for measuring the total amount of organic matter that can be oxidized by a strong oxidizing agent in water quality, and is widely used in water quality evaluation of municipal sewage, industrial wastewater, surface water and groundwater; the higher the value of chemical oxygen demand, the more serious the organic pollution in the water quality. Since the measurement of chemical oxygen demand represents an important basis for emission compliance and environmental regulation, it is necessary to strengthen the monitoring of chemical oxygen demand.

[0004] The traditional monitoring of chemical oxygen demand usually adopts potassium dichromate method (oxidation-titration method, GB 11914-89, equivalent to ISO 6060:1989) with high precision, strong reliability and oxidation ability for various organic matters, but there are the following problems: (1) It needs to be heated and refluxed for two hours under strong acid conditions, and the reaction time is long, so the response speed cannot meet the requirements of rapid feedback scenarios;

[0005] (2) It needs to go through multiple complex operations such as heating, refluxing, titration and end point judgment, and has high dependence on manual operation;

[0006] (3) It needs to use strong corrosive and carcinogenic chemicals such as chromium (VI), mercury salt and sulfuric acid, which will cause secondary pollution and harm;

[0007] (4) It is difficult to integrate with automatic control systems, and cannot be integrated to realize contact and online detection.

[0008] With the increasing demand for on-site emergency response, water quality scheduling and industrial wastewater process control, the traditional chemical oxygen demand monitoring method has been unable to meet the monitoring needs, and in recent years, various improved and alternative technologies have emerged, including:

[0009] a. Electrochemical method (such as:

[0010] catalytic anode method, boron-doped diamond (BDD) electrode method, etc.), which uses an electrode system to directly or indirectly oxidize oxidizable components in wastewater, and can be used for small-sized rapid monitoring, but its monitoring precision is easily affected by electrode pollution or passivation, and the electrode has a short service life and is difficult to analyze complex component wastewater;

[0011] ​b. Spectroscopy (such as: UV absorption method, photocatalytic luminescence method, etc.), by measuring the absorption or fluorescence intensity at a specific wavelength, the chemical oxygen demand is indirectly inferred quickly and non-contact, but the absorbance and COD value have strong sample dependence, turbidity, color and other factors seriously interfere with the results, the modeling accuracy is limited, and the popularization is difficult;

[0012] c. Ultrasonic assisted method, that is, using ultrasonic wave to enhance oxidation reaction or signal amplification to realize rapid determination; but the system structure is complex, the energy consumption is high, the stability is insufficient, and the commercialization degree is low;

[0013] d. Artificial intelligence assisted modeling method (such as: fuzzy logic, neural network, etc.), by training the mapping relationship between sensor signal and standard chemical oxygen demand value, the prediction accuracy is improved; but the generalization ability is limited, a large number of samples are needed to support, and the model migration between different water types is poor.

[0014] In summary, the existing chemical oxygen demand monitoring has the following problems: the mature degree of non-toxic and green alternative scheme is not high; fast response and precision are difficult to achieve; the system anti-interference ability and self-maintenance ability are generally insufficient; the intelligent level and integration ability need to be improved, especially in complex wastewater (such as high salt and high turbidity) or outdoor extreme environment. SUMMARY

[0015] To solve the above problems, the present application provides an online monitoring system and method for chemical oxygen demand, based on the chemical oxidation path of ozone, the dissolution efficiency and reaction stability of ozone are maintained through closed-loop control; through multi-point sensing, modeling estimation and self-learning control method, the measurement accuracy and adaptive ability of chemical oxygen demand are improved, realizing the monitoring of chemical oxygen demand with green, fast, intelligent and robust.

[0016] According to some embodiments, the first aspect of the present application provides an online monitoring system for chemical oxygen demand, which adopts the following technical scheme:

[0017] An online monitoring system for chemical oxygen demand, comprising:

[0018] An ozone preparation module comprising an air source and an ozone generator in communication with each other, for preparing ozone water, and adjusting the dissolution efficiency and reaction activity of the prepared ozone water through closed-loop control of pH value and temperature;

[0019] A chemical reaction module adopting a reaction channel in communication with the ozone preparation module, the reaction channel being provided with a plurality of oxidation-reduction potential probes for acquiring potential changes at different positions along the water sample flow direction;

[0020] A flow rate regulation module is connected with the ozone preparation module to regulate the sample injection rate in real time and match the optimal oxidation interval of the ozone preparation module.

[0021] An estimation module is connected with the chemical reaction module and the flow rate regulation module to construct a reaction rate function based on the potential difference sequence obtained by the oxidation-reduction potential probe, combine the sample injection rate, and obtain the estimated value of the chemical oxygen demand.

[0022] A compensation module is connected with the ozone preparation module and the estimation module to dynamically correct the estimated value of the chemical oxygen demand according to the pH value, temperature, and ozone water concentration, and complete the online monitoring of the chemical oxygen demand.

[0023] As a further technical limitation, in the estimation module, the response of the oxidation-reduction potential probe is tested by using a double-pulse injection method to construct the relationship between the potential measured by the oxidation-reduction potential probe and the injection volume, combine a fuzzy logic control algorithm to adjust the sample injection rate in real time, obtain the optimal sample injection rate, monitor the change of the potential measured by the oxidation-reduction potential probe, and correct the sample injection rate in real time. The reaction rate function is constructed by the potential measured by each oxidation-reduction potential probe, and the estimated value of the chemical oxygen demand is calculated in real time by using the recursive least squares method.

[0024] Further, in the estimation module, during the estimation of the chemical oxygen demand, the reaction rate function and the parameter vector are updated according to the potential measured by the oxidation-reduction potential probe, the chemical oxygen demand is adaptively calibrated according to the parameter vector, the estimation of the chemical oxygen demand is completed, and the estimated value of the chemical oxygen demand is obtained.

[0025] Further, in the estimation module, when the residual error of the obtained estimated value of the chemical oxygen demand exceeds the residual threshold, the self-calibration of the data is automatically triggered, the parameter vector is refitted based on the least squares method, and the estimation of the chemical oxygen demand is completed by combining the refitted parameter vector.

[0026] As a further technical limitation, in the compensation module, the dynamic correction of the estimated value of the chemical oxygen demand uses a dynamic compensation adjustment and regularization strategy to strengthen the least squares method, updates the weights of the long short-term memory neural network through a hybrid framework of the least squares method and the long short-term memory neural network, updates the real-time estimated value of the chemical oxygen demand, and completes the online monitoring of the chemical oxygen demand according to the obtained real-time estimated value of the chemical oxygen demand.

[0027] As a further technical limitation, the ozone preparation module uses a multivariable control strategy to complete the closed-loop control of ozone water preparation by compensating for the strong coupling between the pH value and the temperature.

[0028] According to some embodiments, a second aspect of the present application provides an online monitoring method of chemical oxygen demand, which adopts the online monitoring system of chemical oxygen demand provided by the first aspect, and adopts the following technical solution:

[0029] An online monitoring method of chemical oxygen demand, comprising the following steps:

[0030] Ozone water is prepared, reaction conditions are adjusted, and the prepared ozone water is mixed with a water sample and then injected into a reaction channel;

[0031] Potential data of different oxidation-reduction potential probes are acquired, and a potential difference sequence is constructed;

[0032] An estimated value of chemical oxygen demand is calculated according to the constructed potential difference sequence;

[0033] The obtained estimated value of chemical oxygen demand is dynamically corrected, and the online monitoring of chemical oxygen demand is completed.

[0034] According to some embodiments, a third aspect of the present application provides a computer readable storage medium, which adopts the following technical solution:

[0035] A computer readable storage medium, which stores a program, and the program is executed by a processor to realize the steps in the online monitoring method of chemical oxygen demand according to the second aspect of the present application.

[0036] According to some embodiments, a fourth aspect of the present application provides an electronic device, which adopts the following technical solution:

[0037] An electronic device, which comprises a memory, a processor, and a program stored in the memory and running on the processor, and the processor realizes the steps in the online monitoring method of chemical oxygen demand according to the second aspect of the present application when executing the program.

[0038] According to some embodiments, a fifth aspect of the present application provides a computer program product, which adopts the following technical solution:

[0039] A computer program product, which comprises software code, and the program in the software code realizes the steps in the online monitoring method of chemical oxygen demand according to the second aspect of the present application.

[0040] Compared with the prior art, the present application has the following beneficial effects:

[0041] The present application is based on the chemical oxidation path of ozone, maintains the dissolution efficiency and reaction stability of ozone through closed-loop control, improves the measurement accuracy and self-adaptive ability of chemical oxygen demand through multi-point sensing, modeling estimation and self-learning control method, and realizes the monitoring of chemical oxygen demand with green, rapid, intelligent and robust characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the present embodiments and are incorporated in and constitute a part of this specification. The embodiments illustrated in the drawings are provided and serve as an example of the present embodiments, and do not constitute an undue limitation on the present embodiments.

[0043] Figure 1 A schematic diagram of the relationship between the rising rate of the conventional chemical oxidation reaction and the oxidation-reduction potential ORP;

[0044] Figure 2 A schematic diagram of the structure of the online monitoring system for chemical oxygen demand in Embodiment One of the present application;

[0045] Figure 3 A schematic diagram of the structure of the ozone water generator sensor and actuator in Embodiment One of the present application;

[0046] Figure 4(a) is a schematic diagram of the structure of the pump system in Embodiment One of the present application;

[0047] Figure 4(b) is a schematic diagram of the structure of the flow mixer in Embodiment One of the present application;

[0048] Figure 5(a) is a schematic diagram of the columnar injection pulse of the sample in Embodiment One of the present application;

[0049] Figure 5(b) is a schematic diagram of the broken line of the ΔORP2 reading in Embodiment One of the present application;

[0050] Figure 6 A schematic diagram of the analysis of the ΔORP reading in Embodiment One of the present application;

[0051] Figure 7 A flowchart of the self-adaptive calibration in Embodiment One of the present application;

[0052] Figure 8 A schematic diagram of the hybrid architecture compatible with RLS and LSTM neural networks in Embodiment One of the present application. DETAILED DESCRIPTION

[0053] The present application will be further described with reference to the accompanying drawings and embodiments.

[0054] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0055] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0056] In the present application, the terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom" and the like indicate the orientation or positional relationship shown in the drawings, which are only the relationship words determined for the purpose of describing the structural relationship of the components or elements of the present application, and are not intended to specify any component or element in the present application, and cannot be understood as a limitation of the present application.

[0057] In the present application, the terms such as "fixedly connected", "connected", "connected" and the like should be understood broadly, which means that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. For relevant researchers or technicians in the art, the specific meaning of the above terms in the present application can be determined according to the specific circumstances, and cannot be understood as a limitation of the present application.

[0058] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0059] Embodiment one

[0060] The embodiment one of the present application introduces an online monitoring system for chemical oxygen demand.

[0061] In the process of chemical oxidation reaction, the up-taking rate is usually used to describe the change of oxidation degree with time, as shown in Figure 1 , which is generally approximated by a second-order equation, i.e. ; by measuring the change of oxidation-reduction potential (ORP), the degree of oxidation reaction can be reflected; the potential value measured by the ORP probe at different time points can be used to inverse the oxidation rate function f(t).

[0062] Based on this, the embodiment proposes an online monitoring system for chemical oxygen demand as shown in Figure 2 , which includes an ozone preparation module, a chemical reaction module, a flow rate control module, an intelligent estimation platform and a remote communication module, and can be used for fixed online monitoring, is also suitable for portable scenarios, and provides an integrated solution for environmental monitoring, smart water, emergency response and other applications; the measurement system for COD is realized by combining the strong oxidation reaction of ozone and the real-time monitoring of multiple ORP points, and specifically includes:

[0063] The green ozone water preparation module includes an air source, an ozone generator, an acid addition system, and temperature / liquid level / pressure sensors. Using air as the gas source, it prepares a high-concentration ozone water (O3) solution through an ozone generator and an acidification reaction condition control module (pH<4). The ozone dissolution efficiency and reaction stability are maintained through closed-loop control.

[0064] The multi-point ORP reaction channel consists of a mixing pump, a sample pump, and a thin tubular reactor. The reactor is equipped with multiple ORP probes (ORP1–ORP4) to record the ΔORP value during the reaction process. The reactor adopts a thin tubular design and is equipped with multiple sets of ORP electrodes. The potential trajectory is captured by combining flow rate and time.

[0065] The flow rate control module is based on a dual-pulse mechanism during the startup phase. F min / F max Pulse injection generates ΔORP response curves, and fuzzy logic algorithms are used to quickly calculate the optimal sample injection rate. F opt Sample flow rate;

[0066] Estimation and calibration module: Based on the ΔORP sequence, the rising rate function is updated in real time using recursive least squares (RLS). f (t) parameters θ Combined with steady-state time T steady Compared to flow rate Estimate COD value.

[0067] The compensation and error control module incorporates factors such as temperature, pH, O3 concentration, and turbidity to establish a COD–ΔORP–flow rate relationship database for multivariate dynamic compensation; it also incorporates a residual feedback mechanism and sliding window filtering to dynamically correct model parameters and incorporates sensor signals such as temperature / pH / O3 concentration for modeling compensation.

[0068] The communication and remote monitoring module supports Modbus / 4G / NB-IoT protocols and has functions for remote status reporting, maintenance diagnosis, and cloud platform integration.

[0069] The online monitoring system in this embodiment adopts a modular packaging structure, which is compact, easy to maintain, and suitable for deployment in fixed sites and portable scenarios.

[0070] As one or more implementation methods, the preparation of ozone water requires precise control of water temperature, water level, pH value, pressure, and O3 concentration to ensure the consistency and repeatability of the reaction. For example... Figure 3As shown, five core monitoring points (ORP, conductivity, temperature, liquid level, and pressure) and four actuators (water pump, ozone pump, acid pump, and heater) are set up; a 3×3 multivariable control strategy is adopted to compensate for the strong coupling between variables and realize full-process automation, adaptive adjustment and steady-state tracking.

[0071] To ensure accurate COD estimation for various samples in the reactor, this embodiment employs the pump system and mixer structure shown in Figures 4(a) and 4(b), respectively. A flow rate intelligent adjustment mechanism based on the ORP signal and rapid calculation of the optimal sample flow rate F, as shown in Figures 5(a) and 5(b), are used. opt Compared with the initial COD estimate (COD E ); Specifically:

[0072] The startup phase employs a dual-pulse injection method (F min With F max Test the ORP2 response and construct ΔORP2-V min / V max Relationship curve;

[0073] Using fuzzy logic algorithms to quickly calculate the optimal sample flow rate F opt Compared with the initial COD estimate (COD E );

[0074] During operation, the ORP2 value is continuously monitored and the sample pump speed is adjusted in real time to ensure that the ORP value remains within the linear detection range.

[0075] Membership functions are constructed using two-point ORP readings, and a fuzzy control rule base is established to achieve rapid system response and improve measurement accuracy.

[0076] Since the ORP1 reading will remain constant, its reading is a function of all other ORP readings. For a given optimal flow rate for the sample, the flow rate of the mixed solution within the chamber is... F for F = F 03 + F s ;in, F O3 Ozone water flow rate, F S Inject flow rate into the sample.

[0077] When using a 3-pulse sequence sample injection scheme, such as Figure 6 As shown, nine useful data points of ΔORP can be obtained from the sample pulse sequence injection; in this embodiment, the COD value of a given sample is a function of the ORP reading, the sample injection flow rate, and the ozone water flow rate; that is... ;in, is the steady state value of the predicted rising rate curve at the kth sampling instant, whose parameters are can be calculated by recursive least square method. At the sampling instant, combine each ORP measurement chamber reading to generate a ΔORP sequence to approximate the rising rate, as shown in detail in Figure 6 , the optimal solution of the rising rate is obtained by least square method.

[0078] For online application, the COD value can change all the time. In order to maintain the accuracy of the measurement, the optimal sample injection flow rate should also be adjusted accordingly. The readings of ORP1, ORP2, ORP3 and ORP4 are obtained in a continuous manner, and the COD value is calculated in real time by recursive least square method, as shown in detail in Figure 7 , in particular:

[0079] Whenever a new data point is obtained, the estimated value of θ is updated without the need to recalculate from scratch;

[0080] Substitute these measurements into the model, and use recursive least square method (RLS) to estimate the parameter vector θ that describes the relationship between the input and output;

[0081] Substitute the estimated value of ΔORP into the calibration formula to calculate the new COD value.

[0082] It should be noted that the COD value can change over time, and the current optimal injection flow rate can become unsuitable. To cope with this change, the embodiment can check whether the currently estimated ORP falls within a defined linear range of ORP; if not, the flow rate can be adjusted up or down with the latest measurement results or the optimal flow rate can be re-estimated from scratch by applying the fuzzy logic method.

[0083] The value of ，k is different for different conditions. To quickly calculate the COD value, rewrite the formula as and develop a database; that is,

[0084] Since the temperature, pressure and sample injection flow rate are fixed, tests are conducted to find the value of k under different COD solution concentrations. Since potassium dichromate solution is widely used for COD calibration, the value of k is also determined by potassium dichromate. After preparing these potassium dichromate solutions for COD samples, the value measured by standard titration method is used as the final reference COD value to determine the value of k . The COD values of different potassium dichromate solution concentrations are calculated by the formula ; in particular:

[0085] The flow rate of ozone water is set to make ozone water flow slowly in the chemical reactor at the designed speed;

[0086] The optimal sample flow rate is selected for each potassium dichromate solution with known COD value, and the equation is used to calculate the value of k;

[0087] The COD is measured by the standard titration method based on the formula to determine the value of k;

[0088] The above steps are repeated with different concentrations to deduce the COD-k relationship and build an empirical database;

[0089] For different application samples, the value of k is corrected by comparison with the standard titration method and input into the database.

[0090] To ensure the estimation accuracy of the recursive least squares (RLS) model in online monitoring scenarios, the error control and generalization mechanism shown in Figure 8 is used, that is,

[0091] (1) Sliding window data filtering mechanism, that is, the ORP original data is filtered using a sliding window average and median filter, outliers and short-term disturbances are removed, and only the ΔORP points that meet the linear response interval are retained to enter the modeling process to prevent abnormal values from polluting the model.

[0092] (2) Error feedback correction mechanism, after completing a round of COD estimation, the system calculates the residual error between the estimated value and the empirical data; if the error exceeds the set threshold (such as ±5%), the model self-calibration module is automatically triggered to re-fit the parameters . The system can learn the error distribution trend and dynamically adjust the forgetting factor λ to enhance the convergence.

[0093] (3) Multi-model cross check mechanism, for extreme wastewater scenarios (such as high salt and high temperature), the system preloads multiple pre-trained models and performs parallel fitting, compares the fitting residuals of different model outputs, and automatically selects the optimal solution as the current estimation benchmark to reduce the error risk.

[0094] In addition, to improve the adaptability in different types of wastewater and complex environments, the following generalization mechanisms are also constructed in the implementation:

[0095] (1) Multi-sample training database, that is, by collecting typical sewage (such as domestic sewage, printing and dyeing, electroplating, pharmaceuticals, etc.) to establish an offline modeling sample set covering COD 10-10000 ppm, and calibrating the model by titration.

[0096] ​(2) Environmental parameter compensation modeling, introducing variables such as temperature, pH value, and ozone concentration into the model input to construct an estimation model ; wherein k is a sample characteristic compensation factor, which can be obtained by table lookup or interpolation.

[0097] (3) Structure adaptive and neural network fusion mechanism, introducing dynamic step adjustment and regularization strategy to strengthen the robustness of RLS, and reserving the interface for introducing incremental neural network and RLS fusion to improve the adaptability of the model to nonlinear complex working conditions.

[0098] (4) Model verification and online correction mechanism, automatically recording the residual trend between the results and the titration method, and triggering the recalibration or model switching strategy according to the error accumulation.

[0099] Example analysis

[0100] To test the performance of the method, three samples from different industrial fields (Sample 1: domestic wastewater, Sample 2: food industry wastewater, and Sample 3: chemical industry wastewater) were collected, and the comparison results measured by the experimental platform and the standard titration method are listed in Table 1.

[0101] Table 1 Comparison of detection results of two methods

[0102]

[0103] Based on the test results, the experimental platform can determine the COD value in a very short time, the fast determination only takes a few minutes, and the accurate determination is less than 20 minutes; it has high measurement accuracy in a wide range of applications, and for COD values of 200 ppm to 10000 ppm, the measurement error is kept below 3%; the system can automatically adjust and self-calibrate to ensure long-term operation consistency.

[0104] This embodiment is based on the chemical oxidation path of ozone, and maintains the dissolution efficiency and reaction stability of ozone through closed-loop control; through multi-point sensing, modeling estimation and self-learning control method, the measurement accuracy and self-adaptive ability of chemical oxygen demand are improved, realizing the monitoring of chemical oxygen demand with green, fast, intelligent and robust.

[0105] Example two

[0106] The embodiment two of the present application introduces an online monitoring method of chemical oxygen demand.

[0107] An online monitoring method of chemical oxygen demand, comprising the following steps:

[0108] Preparation of ozone water, adjustment of reaction conditions, mixing of the prepared ozone water with water sample and injection into the reaction channel;

[0109] Obtaining potential data of different redox potential probes, and constructing a potential difference sequence;

[0110] According to the constructed potential difference sequence, an estimated value of the chemical oxygen demand is calculated;

[0111] The obtained estimated value of the chemical oxygen demand is dynamically corrected, and the online monitoring of the chemical oxygen demand is completed.

[0112] The detailed steps are the same as the working principle of the online monitoring system of the chemical oxygen demand provided in the first embodiment, and will not be repeated here.

[0113] Embodiment three

[0114] The embodiment three of the present application provides a computer readable storage medium.

[0115] A computer readable storage medium, which stores a program, the program is executed by a processor to realize the steps in the online monitoring method of the chemical oxygen demand provided in the second embodiment of the present application.

[0116] The detailed steps are the same as the online monitoring method of the chemical oxygen demand provided in the second embodiment, and will not be repeated here.

[0117] Embodiment four

[0118] The embodiment four of the present application provides an electronic device.

[0119] An electronic device, comprising a memory, a processor and a program stored in the memory and running on the processor, wherein the processor executes the program to realize the steps in the online monitoring method of the chemical oxygen demand provided in the second embodiment of the present application.

[0120] The detailed steps are the same as the online monitoring method of the chemical oxygen demand provided in the second embodiment, and will not be repeated here.

[0121] Embodiment five

[0122] The embodiment five of the present application provides a computer program product.

[0123] A computer program product, comprising software code, wherein the program in the software code executes the steps in the online monitoring method of the chemical oxygen demand provided in the second embodiment of the present application.

[0124] The detailed steps are the same as the online monitoring method of the chemical oxygen demand provided in the second embodiment, and will not be repeated here.

[0125] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0126] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0127] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0129] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Accordingly, the appended claims are intended to embrace all such modifications and variations as fall within the scope of the application.

[0130] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application encompass such modifications and changes as fall within the scope of the claims and their equivalents.

[0131] The above only shows the preferred embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An on-line monitoring system for chemical oxygen demand, characterized by, The application relates to an online monitoring method for chemical oxygen demand. The application comprises the following steps: An ozone preparation module is used to prepare ozone water, and the dissolution efficiency and reaction activity of the ozone water are adjusted by closed-loop control of pH value and temperature; A chemical reaction module is used to obtain different oxidation-reduction potential data of different oxidation-reduction potential probes arranged along the water sample flow direction in a reaction channel connected with the ozone preparation module; A flow rate regulation module is connected with the ozone preparation module to regulate the sample injection rate in real time and match the optimal oxidation interval of the ozone preparation module; An estimation module is connected with the chemical reaction module and the flow rate regulation module, a reaction rising rate function is constructed based on the potential difference sequence obtained by the oxidation-reduction potential probes, and the estimation value of the chemical oxygen demand is obtained by combining the sample injection rate; A compensation module is connected with the ozone preparation module and the estimation module, the estimation value of the chemical oxygen demand is dynamically modified according to the pH value, temperature and ozone water concentration, and the online monitoring of the chemical oxygen demand is completed; in the estimation module, the response of the oxidation-reduction potential probe is tested by using a double-pulse injection method, the relationship between the potential measured by the oxidation-reduction potential probe and the injection volume is constructed, the sample injection rate is adjusted in real time by combining a fuzzy logic control algorithm, the optimal sample injection rate is obtained, the change of the potential measured by the oxidation-reduction potential probe is monitored, and the sample injection rate is dynamically modified in real time; the reaction rising rate function is constructed by the potential measured by each oxidation-reduction potential probe, and the estimation value of the chemical oxygen demand is calculated in real time by using a recursive least square method; 2. An on-line monitoring system for chemical oxygen demand as claimed in claim 1 wherein, In the compensation module, the dynamic modification of the estimation value of the chemical oxygen demand adopts a dynamic compensation adjustment and regularization strategy to strengthen the least square method, the weight of a long short-term memory neural network is updated online by using a hybrid framework of the least square method and the long short-term memory neural network, the real-time estimation value of the chemical oxygen demand is updated, and the online monitoring of the chemical oxygen demand is completed according to the obtained real-time estimation value of the chemical oxygen demand.

3. An on-line monitoring system for chemical oxygen demand as claimed in claim 2, wherein, In the estimation module, in the process of estimating the chemical oxygen demand, the reaction rising rate function and the parameter vector are updated according to the potential measured by the oxidation-reduction potential probe, the chemical oxygen demand is adaptively calibrated according to the parameter vector, the estimation of the chemical oxygen demand is completed, and the estimation value of the chemical oxygen demand is obtained.

4. An on-line monitoring system for chemical oxygen demand as claimed in claim 1 wherein, In the estimation module, when the estimation residual of the chemical oxygen demand exceeds a residual threshold, the self-calibration of data is automatically triggered, the parameter vector is refitted based on the least square method, and the estimation of the chemical oxygen demand is completed by combining the refitted parameter vector.

5. A method for on-line monitoring of chemical oxygen demand using the on-line monitoring system for chemical oxygen demand according to any one of claims 1 to 4, characterized by, The ozone preparation module adopts a multivariable control strategy to complete the closed-loop control of ozone water preparation by compensating the strong coupling between the pH value and the temperature. The application comprises the following steps: Ozone water is prepared, the reaction conditions are adjusted, the prepared ozone water is mixed with a water sample, and then the mixture is injected into a reaction channel; Potential data of different oxidation-reduction potential probes are obtained, and a potential difference sequence is constructed; The estimation value of the chemical oxygen demand is calculated according to the constructed potential difference sequence; The obtained estimation value of the chemical oxygen demand is dynamically modified, and the online monitoring of the chemical oxygen demand is completed.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the steps of the method for on-line monitoring of chemical oxygen demand as claimed in claim 5.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor implements the steps of the method for on-line monitoring of chemical oxygen demand as claimed in claim 5 when executing the program.

8. A computer program product comprising software code, characterized in that, The program in the software code implements the steps of the method for on-line monitoring of chemical oxygen demand as claimed in claim 5.

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