A method and system for digital management of chemical waste liquid recycling process information

Abstract

The present invention relates to the field of waste liquid treatment technology, and in particular to a method and system for digital management of chemical waste liquid recovery process information. The method comprises the following steps: using an automatic sampler to collect chemical waste liquid recovery equipment parameters and save chemical waste liquid recovery equipment parameter information; using computer-aided engineering software to construct a digital three-dimensional model of chemical waste liquid treatment according to the chemical waste liquid recovery equipment parameter information; obtaining chemical waste liquid characteristic data and inputting it into the digital three-dimensional model of chemical waste liquid treatment, using simulation software to simulate the waste liquid recovery process, and performing quantitative evaluation of the waste liquid recovery results of the simulation process to obtain the evaluation results of the treatment process; judging whether the waste liquid recovery process meets the preset treatment standards according to the evaluation results of the treatment process. The present invention realizes efficient management and abnormal monitoring of the chemical waste liquid recovery process based on waste liquid treatment technology.

Description

Technical Field

[0001] The invention relates to the technical field of waste liquid treatment, and in particular to a method and system for digital management of chemical waste liquid recovery and treatment process information. Background Art

[0002] Traditional digital management systems for chemical wastewater treatment usually only monitor some key parameters (such as flow, temperature, pH value, etc.), and these monitoring data are scattered in different systems or equipment, lacking a unified management platform. For the treatment of special wastewaters such as tin-containing wastewater and etching solutions, the changes in multiple components and chemical reactions in the wastewater require all-round monitoring. Traditional systems often cannot achieve comprehensive and systematic monitoring and management, and are prone to omissions or errors. Traditional methods rely on manual or inefficient automated sensors for data collection, resulting in delayed data updates during wastewater treatment. For wastewater containing harmful substances (such as cuprous chloride and tin compounds), the treatment process needs to be monitored in real time to avoid environmental pollution, but traditional digital management systems often have data collection delays, making it difficult to respond to abnormal situations in the wastewater treatment process in a timely manner, thereby affecting the overall treatment effect. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for digital management of chemical waste liquid recycling process information to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for digital management of chemical waste liquid recycling process information includes the following steps:

[0005] Step S1: using an automatic sampler to collect chemical waste liquid recovery equipment parameters and save the chemical waste liquid recovery equipment parameter information; according to the chemical waste liquid recovery equipment parameter information, using computer-aided engineering software to construct a digital three-dimensional model of chemical waste liquid treatment;

[0006] Step S2: obtaining chemical waste liquid characteristic data and inputting them into a digital three-dimensional model of chemical waste liquid treatment, simulating the waste liquid recovery treatment process using simulation software, and quantitatively evaluating the waste liquid recovery results of the simulation process to obtain a treatment process evaluation result;

[0007] Step S3: judging whether the waste liquid recovery treatment process meets the preset treatment standard according to the treatment process evaluation result; if the treatment process evaluation result does not meet the preset treatment standard, adjusting the key parameters in the treatment process, and optimizing the digital three-dimensional model of chemical waste liquid treatment to obtain the digital optimized three-dimensional model of chemical waste liquid treatment;

[0008] Step S4: Upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device to monitor the waste liquid recovery process in real time. When an abnormality is detected in the waste liquid treatment process, an alarm signal is generated and a maintenance reminder for the chemical waste liquid recovery equipment is triggered. When the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery information.

[0009] Preferably, step S1 specifically comprises:

[0010] Step S11: using an automatic sampler to collect operating parameters of the chemical waste liquid recovery equipment in real time, thereby obtaining operating data of the chemical waste liquid recovery equipment;

[0011] Step S12: normalizing and processing the outliers of the chemical waste liquid recovery equipment operation data, and generating parameter information of the chemical waste liquid recovery equipment;

[0012] Step S13: According to the parameter information of the chemical waste liquid recovery equipment, the fluid flow channel and structural components of the chemical waste liquid recovery equipment are reconstructed in three-dimensional space using parametric modeling technology to obtain three-dimensional structural data of the equipment;

[0013] Step S14: acquiring fluid characteristic parameters; analyzing the fluid distribution inside the device by combining the fluid characteristic parameters and the three-dimensional structure data of the device with a fluid dynamics simulation method, thereby obtaining the fluid distribution data inside the device;

[0014] Step S15: Generate a digital three-dimensional model of chemical waste liquid treatment using computer-aided engineering tools based on the three-dimensional structure data of the equipment and the fluid distribution data inside the equipment.

[0015] Preferably, step S14 is specifically as follows:

[0016] Step S141: obtaining fluid characteristic parameters using a rheometer;

[0017] Step S142: Preliminary processing of the fluid characteristic parameters, including denoising, normalization and data fitting, to obtain preliminary processed fluid characteristic parameters;

[0018] Step S143: using computer-aided design software and computational fluid dynamics software, combined with the three-dimensional structure data of the equipment, to establish a fluid flow model;

[0019] Step S144: using computational fluid dynamics software, setting fluid flow boundary conditions and fluid properties according to the preliminary processed fluid characteristic parameters, thereby obtaining fluid dynamics simulation start data;

[0020] Step S145: numerically simulate the fluid dynamics simulation startup data using computational fluid dynamics software to calculate the velocity field, pressure field, and temperature field distribution of the fluid inside the device, thereby obtaining the fluid motion state data inside the device;

[0021] Step S146: Use visualization software to generate visualization of the internal fluid distribution of the device based on the internal fluid movement state data, thereby obtaining the internal fluid distribution data of the device.

[0022] Preferably, step S15 is specifically as follows:

[0023] Step S151: Start computer-aided engineering software and create a three-dimensional model project file of the equipment;

[0024] Step S152: importing the equipment 3D structure data into the equipment 3D model project file, and drawing the equipment 3D geometric model;

[0025] Step S153: adjusting the internal flow channels and internal pores of the equipment on the three-dimensional geometric model of the equipment according to the internal fluid distribution data of the equipment, so as to obtain a digital three-dimensional model of chemical waste liquid treatment.

[0026] Preferably, step S2 specifically comprises:

[0027] Step S21: Obtain chemical waste liquid sample data, use liquid chromatography to measure the viscosity of the chemical waste liquid sample, perform 15-20 measurements, record the viscosity value of each measurement, calculate the average value of all measurement values ​​as the final waste liquid viscosity value, and record the chemical waste liquid sample viscosity data;

[0028] Step S22: Use gas chromatography to perform cuprous chloride solution detection on the chemical waste liquid sample data, take out 10-50 ml of the waste liquid sample, filter it using a filter membrane with a pore size of 0.45-0.55 micron, and use a flame ionization detector to perform chromatographic column detection, wherein the column temperature of the gas chromatography is usually set at 70°C-120°C, so as to obtain cuprous chloride solution data;

[0029] Step S23: Integrate the chemical waste liquid sample viscosity data and cuprous chloride solution data to obtain chemical waste liquid characteristic data;

[0030] Step S24: inputting the chemical waste liquid characteristic data into the digital three-dimensional model of chemical waste liquid treatment, simulating the waste liquid recovery treatment process using simulation software, and quantitatively evaluating the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results.

[0031] Preferably, step S24 is specifically as follows:

[0032] Step S241: using a database tool to convert the chemical waste liquid characteristic data into a CSV file to obtain a chemical waste liquid characteristic CSV file;

[0033] Step S242: inputting the chemical waste liquid characteristic CSV file into the digital three-dimensional model of chemical waste liquid treatment, and uploading the digital three-dimensional model of chemical waste liquid treatment to the simulation software;

[0034] Step S243: performing waste liquid flow simulation in simulation software, wherein the flow type is set to turbulent flow type, and the flow velocity is set to 0.1 m / s to 2 m / s;

[0035] Step S244: performing a waste liquid mixing simulation in the simulation software, wherein the stirring speed is set to 50 rpm to 300 rpm and the mixing time is set to 1 min to 30 min;

[0036] Step S245: performing waste liquid separation simulation in simulation software, wherein the porosity of the separation medium is set to 30% to 60%, and the sedimentation rate is set to 1 mm / s to 20 mm / s;

[0037] Step S246: running the waste liquid recovery processing module in the simulation software, and recording the waste liquid separation efficiency data and the waste liquid processing time data at intervals of 10 seconds;

[0038] Step S247: If the waste liquid separation efficiency data and the waste liquid treatment time data reach the set recovery and treatment parameter target, stop the simulation process and output the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time;

[0039] Step S248: Perform a quantitative evaluation of the waste liquid recovery result based on the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time to obtain a treatment process evaluation result.

[0040] Preferably, step S248 is specifically as follows:

[0041] Calculating the waste liquid recovery efficiency according to the separation efficiency data table of the separation equipment to obtain waste liquid recovery efficiency data, and evaluating the waste liquid recovery efficiency on the waste liquid recovery efficiency data to obtain a waste liquid recovery efficiency evaluation result;

[0042] Calculate the processing energy consumption based on the waste liquid recovery efficiency data and the statistical information of the waste liquid processing time, so as to obtain the waste liquid processing energy consumption data, and evaluate the waste liquid processing energy consumption based on the waste liquid processing energy consumption data, so as to obtain the waste liquid processing energy consumption evaluation result;

[0043] Conduct equipment stability analysis based on waste liquid treatment energy consumption data and chemical waste liquid recovery equipment operation data to obtain equipment stability data, and conduct equipment stability evaluation on the equipment stability data to obtain equipment stability results;

[0044] The waste liquid recovery efficiency evaluation results, waste liquid treatment energy consumption evaluation results and equipment stability results are integrated to obtain the treatment process evaluation results.

[0045] Preferably, step S3 specifically comprises:

[0046] Step S31: Obtaining preset chemical waste liquid recycling and treatment standard data;

[0047] Step S32: making an abnormality judgment on the treatment process evaluation result according to the preset chemical waste liquid recovery treatment standard data, thereby obtaining an abnormal treatment process evaluation result;

[0048] Step S33: adjusting the temperature of the chemical waste liquid recovery equipment to 60-80° C. based on the abnormality handling process evaluation result, thereby obtaining temperature adjustment data;

[0049] Step S34: using a pump speed regulating device to adjust the flow rate of the abnormality handling process evaluation result to 2-5 m³ / h, thereby obtaining flow rate adjustment data;

[0050] Step S35: using a pH sensor to adjust the reagent dosage ratio based on the abnormality handling process evaluation result, adjusting the amount of sodium hydroxide added to 100-150ppm, thereby obtaining reagent dosage ratio adjustment data;

[0051] Step S36: adjusting key parameters of the digital three-dimensional model of chemical waste liquid treatment according to the temperature adjustment data, the flow adjustment data and the reagent addition ratio adjustment data, so as to obtain the digital optimized three-dimensional model of chemical waste liquid treatment.

[0052] Preferably, step S4 is specifically:

[0053] Step S41: using a flow chart modeling tool to digitally optimize the three-dimensional model of chemical waste liquid treatment to model the waste liquid recovery process, thereby obtaining a waste liquid recovery flow chart, and uploading the waste liquid recovery flow chart to the intelligent monitoring device;

[0054] Step S42: setting real-time monitoring points according to the waste liquid recovery flow chart, thereby obtaining real-time monitoring point data;

[0055] Step S43: Start the intelligent monitoring device to monitor waste liquid treatment abnormalities according to the real-time monitoring point data, thereby obtaining waste liquid treatment abnormality data, generating an alarm signal and triggering a maintenance reminder for the chemical waste liquid recovery equipment, and obtaining the waste liquid recovery equipment maintenance data;

[0056] Step S44: performing digital feedback identification on the maintenance data of the waste liquid recovery equipment, thereby obtaining digital feedback data;

[0057] Step S45: Upload the digital feedback data to the digital optimization three-dimensional model of chemical waste liquid treatment to execute the digital management task of chemical waste liquid recycling and treatment information.

[0058] Preferably, the present specification also provides a chemical waste liquid recycling process information digital management system, which is used to execute the chemical waste liquid recycling process information digital management method as described above, and the chemical waste liquid recycling process information digital management system includes:

[0059] The digital three-dimensional model building module for waste liquid treatment is used to collect the parameters of chemical waste liquid recovery equipment using an automatic sampler and save the parameter information of the chemical waste liquid recovery equipment; according to the parameter information of the chemical waste liquid recovery equipment, a digital three-dimensional model of chemical waste liquid treatment is built using computer-aided engineering software;

[0060] The waste liquid recovery and treatment simulation module is used to obtain the characteristic data of chemical waste liquid and input it into the digital three-dimensional model of chemical waste liquid treatment, simulate the waste liquid recovery and treatment process by using simulation software, and quantitatively evaluate the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results;

[0061] The model optimization module is used to determine whether the waste liquid recovery treatment process meets the preset treatment standards based on the treatment process evaluation results; if the treatment process evaluation results do not meet the preset treatment standards, the key parameters in the treatment process are adjusted, and the digital three-dimensional model of chemical waste liquid treatment is optimized to obtain a digital optimized three-dimensional model of chemical waste liquid treatment;

[0062] The waste liquid recovery information digital management module is used to upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device, monitor the waste liquid recovery and treatment process in real time, and generate an alarm signal and trigger a maintenance reminder for the chemical waste liquid recovery equipment when an abnormality is detected in the waste liquid treatment process; when the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery and treatment information.

[0063] The present invention discloses a digital management system for chemical waste liquid recycling process information. The system can realize any digital management method for chemical waste liquid recycling process information of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the digital management method for chemical waste liquid recycling process information. The internal modules of the system cooperate with each other to improve the waste liquid recycling efficiency.

[0064] The present invention collects the operation data of chemical waste liquid recovery equipment in real time by using an automatic sampler, and performs normalization and outlier processing through a data processing program, so that the parameter information of the equipment is more accurate and effective. The three-dimensional model construction step generates accurate equipment internal fluid distribution data through fluid dynamics simulation and computer-aided engineering software, providing detailed spatial data support for the optimization of waste liquid treatment. The flow, mixing and separation effects of the waste liquid recovery process are simulated by combining the physical and chemical properties data of the waste liquid through simulation software, and quantitative evaluation is performed, which can not only effectively predict the treatment effect of the waste liquid, but also provide data basis for subsequent optimization. By comparing the evaluation results with the preset treatment standards, problems in the treatment process can be identified in time, and key parameters such as temperature, flow rate and dosage of the agent can be adjusted, so as to optimize the waste liquid recovery process and ensure the best treatment effect. By uploading the optimized digital three-dimensional model to the intelligent monitoring device, real-time monitoring and abnormal alarm are carried out. When the waste liquid recovery process is abnormal, an alarm signal can be generated in time and an equipment maintenance reminder can be triggered, effectively avoiding the low efficiency of waste liquid recovery or environmental pollution caused by equipment failure or improper operation. The modular design of the system enables the various functional modules to work together. Through real-time data feedback and optimization model adjustment, the intelligence and automation of the waste liquid recovery process are improved, human intervention is reduced, and the overall processing capacity and response speed of the system are improved. Overall, this method and system can provide more accurate and efficient waste liquid recovery management, which is suitable for the treatment of complex chemical waste liquids, especially in the treatment of wastewater containing hazardous substances, which can effectively reduce environmental risks and improve the overall benefits of industrial wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0066] Figure 1 A schematic flow chart of the steps of the method for digital management of information in the chemical waste liquid recycling process of the present invention;

[0067] Figure 2 Detailed flowchart of step S1 in the present invention.

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0070] To achieve this, please refer to Figure 1 to Figure 2 The present invention provides a method for digital management of chemical waste liquid recycling process information, the method comprising the following steps:

[0071] Step S1: using an automatic sampler to collect chemical waste liquid recovery equipment parameters and save the chemical waste liquid recovery equipment parameter information; according to the chemical waste liquid recovery equipment parameter information, using computer-aided engineering software to construct a digital three-dimensional model of chemical waste liquid treatment;

[0072] In this embodiment, during the operation of the chemical waste liquid recovery equipment, the operating parameters of the chemical waste liquid recovery equipment are collected every 5 minutes by an industrial-grade automatic sampler (such as an online automatic sampling device), including but not limited to flow rate (unit: liter / minute), temperature (unit: degrees Celsius), pressure (unit: Pascal) and liquid viscosity (unit: Pa·second). The collected parameters are connected to the main control computer via Ethernet and stored in a device-specific database (such as a MySQL database). The database fields include device number, sampling time, parameter type and corresponding values. Subsequently, the parameter information in the database is imported using the installed computer-aided engineering software (such as ANSYS or AutoCAD), and modeling is performed one by one corresponding to the structural features of the equipment. In the modeling, the equipment structure part uses parametric dimensions (such as the waste liquid recovery tank width of 500mm and the height of 800mm), and the fluid characteristics part is given real-time attributes based on the database values. Finally, the construction of the digital three-dimensional model of chemical waste liquid treatment is completed, and it is stored in STEP format for subsequent operation calls.

[0073] Step S2: obtaining chemical waste liquid characteristic data and inputting them into a digital three-dimensional model of chemical waste liquid treatment, simulating the waste liquid recovery treatment process using simulation software, and quantitatively evaluating the waste liquid recovery results of the simulation process to obtain a treatment process evaluation result;

[0074] In this embodiment, on the basis of completing the digital three-dimensional model of chemical waste liquid treatment, the characteristic data of the chemical waste liquid are obtained, including chemical component concentration (such as benzene content 200ppm), density (such as 1.2 g / cm³), viscosity (such as 0.5 Pa·s), initial boiling point (such as 65°C) and final boiling point (such as 110°C), which are collected by high-precision analysis equipment (such as gas chromatograph and densitometer), and the analysis results are recorded in a standardized Excel file. Subsequently, the above chemical waste liquid characteristic data are loaded into the digital three-dimensional model as input conditions, and simulation software (such as COMSOL or Fluent) is called to simulate the waste liquid recovery process. During the simulation process, boundary conditions are set, such as inlet and outlet pressure difference of 0.1 MPa, treatment temperature range of 60°C to 90°C, etc., and the fluid flow and heat transfer characteristics are analyzed using the discrete solution method. After the treatment is completed, the simulation output data (such as fluid stability, gas emissions) are quantitatively evaluated using Matlab software, and an evaluation report is generated to output specific treatment process evaluation results, such as a recovery efficiency of 92% and gas emissions below environmental protection standards (such as 10 mg / m³).

[0075] Step S3: judging whether the waste liquid recovery treatment process meets the preset treatment standard according to the treatment process evaluation result; if the treatment process evaluation result does not meet the preset treatment standard, adjusting the key parameters in the treatment process, and optimizing the digital three-dimensional model of chemical waste liquid treatment to obtain the digital optimized three-dimensional model of chemical waste liquid treatment;

[0076] In this embodiment, according to the evaluation results of the treatment process, the recovery efficiency (such as 92%) is compared with the preset treatment standard (such as the recovery efficiency must be ≥95%) to determine whether the requirements are met. If the result does not meet the standard, determine the key parameters in the simulation results (such as the flow rate is set to 1.2 L / min, resulting in insufficient mass transfer), and use the parameter optimization tool based on the genetic algorithm to readjust the key parameters (such as increasing the flow rate to 1.5 L / min and increasing the treatment temperature to 70°C). The optimized parameters are input into the digital three-dimensional model again, and the simulation is re-performed to observe the impact of the adjustment of key parameters on the waste liquid recovery and treatment performance until the evaluation results meet the treatment standards (such as the recovery efficiency is increased to 96%). After the optimization is completed, a digital optimized three-dimensional model of chemical waste liquid treatment is generated and stored in a dedicated management system in a specific format (such as IGES or Parasolid).

[0077] Step S4: Upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device to monitor the waste liquid recovery process in real time. When an abnormality is detected in the waste liquid treatment process, an alarm signal is generated and a maintenance reminder for the chemical waste liquid recovery equipment is triggered. When the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery information.

[0078] In this embodiment, the optimized digital optimization three-dimensional model of chemical waste liquid treatment is imported into an intelligent monitoring device (such as a Siemens PLC control system), and the waste liquid treatment process is monitored in real time through the SCADA software. The monitoring parameters include flow, temperature, pressure and waste liquid properties, and the monitoring threshold is set, such as flow 1.2-1.5 L / min, temperature 60-70°C, and pressure not exceeding 0.15MPa. When the range is exceeded, the abnormal detection module is triggered. If an abnormality is detected (such as a flow rate drop to 1.1 L / min), the system automatically generates an alarm signal, transmits the alarm information to the terminal of the operation and maintenance personnel through industrial Ethernet, and starts the self-test function of the chemical waste liquid recovery equipment. After the self-test is completed, the cause of the abnormality (such as filter blockage) is recorded and the operation and maintenance personnel are reminded to replace the filter. After resuming normal operation, the real-time monitoring data (such as flow rate recovery to 1.3 L / min) is synchronously uploaded to the digital optimization three-dimensional model of chemical waste liquid treatment, and a detailed report of the recovery process is generated, including the working status of the equipment, changes in the properties of the waste liquid, and the optimized system performance, providing digital management support for chemical waste liquid recovery and treatment tasks.

[0079] Preferably, step S1 specifically comprises:

[0080] Step S11: using an automatic sampler to collect operating parameters of the chemical waste liquid recovery equipment in real time, thereby obtaining operating data of the chemical waste liquid recovery equipment;

[0081] In this embodiment, during the operation of the chemical waste liquid recovery equipment, an industrial-grade automatic sampler (such as a sampler equipped with a flow sensor and a temperature sensor) is used to collect equipment operating parameters in real time at fixed time intervals (for example, every 10 seconds). The collected data includes the flow rate (unit: liter / minute), pressure (unit: Pascal), temperature (unit: Celsius), viscosity of the waste liquid (unit: Pa·second) at the inlet and outlet of the equipment, and the chemical properties of the waste liquid (such as benzene content 200ppm, pH value 6.5). The automatic sampler performs preliminary storage of the collected data through an embedded control module (such as an ARM Cortex-M series microprocessor) and uploads the data to the central monitoring system through the Modbus protocol. During the data collection process, the upper and lower flow thresholds (such as 0.5 L / min to 5 L / min) and the upper and lower pressure thresholds (such as 10 kPa to 500 kPa) are set, and the data exceeding the threshold will be automatically marked as an abnormal value for subsequent processing. The sampling results are saved in the industrial database system in CSV format, and the fields include timestamp, parameter type and corresponding value.

[0082] Step S12: normalizing and processing the outliers of the chemical waste liquid recovery equipment operation data, and generating parameter information of the chemical waste liquid recovery equipment;

[0083] In this embodiment, the chemical waste liquid recovery equipment operation data collected in step S11 is imported into the data processing program, and a standardized data processing tool (such as a data preprocessing script written in Python) is used. First, each set of operation data is normalized using the formula (x-xmin) / (xmax-xmin), where x represents the actual data value, xmin and xmax represent the minimum and maximum values ​​of the same type of parameters, respectively, and the data range is adjusted to [0,1] after normalization. Subsequently, the data is detected for outliers using the triple standard deviation method (3σ method), the mean and standard deviation of each set of data are calculated, and data exceeding the mean ±3 times the standard deviation is defined as an outlier. For the detected outliers, the Lagrange interpolation method is used for repair, and the missing or abnormal data is supplemented by calculating the surrounding time point data. After the processing is completed, the normalized and abnormal chemical waste liquid recovery equipment parameter information is generated and saved as an SQL data table.

[0084] Step S13: According to the parameter information of the chemical waste liquid recovery equipment, the fluid flow channel and structural components of the chemical waste liquid recovery equipment are reconstructed in three-dimensional space using parametric modeling technology to obtain three-dimensional structural data of the equipment;

[0085] In this embodiment, parametric modeling technology is used to perform three-dimensional reconstruction of the fluid flow channel and structural components of the chemical waste liquid recovery equipment according to the parameter information of the chemical waste liquid recovery equipment. In the specific implementation process, the normalized parameter information is loaded using a three-dimensional modeling software (such as SolidWorks or CATIA). Taking the fluid flow channel as an example, a geometric sketch based on the flow channel size (such as a diameter of 10 mm and a length of 1 m) is established, and a three-dimensional solid model is formed by an extrusion command; at the same time, according to the actual structural data of the equipment, such as the height (300 mm) and thickness (10 mm) of the pump body, the external structural components of the equipment are constructed. Parametric modeling uses a mathematical constraint method to associate the fluid flow channel with the external structure through an expression, for example, the flow channel diameter D satisfies the constraints of the equipment input parameters: D=f(Q), where Q represents the flow rate. All three-dimensional structural data are exported as STEP format files, containing detailed geometric and dimensional information.

[0086] Step S14: acquiring fluid characteristic parameters; analyzing the fluid distribution inside the device by combining the fluid characteristic parameters and the three-dimensional structure data of the device with a fluid dynamics simulation method, thereby obtaining the fluid distribution data inside the device;

[0087] In this embodiment, fluid characteristic parameters are obtained from physical and chemical experiments, including density (such as 1.2g / cm³), viscosity (such as 0.6Pa·s), specific heat capacity (such as 4.2kJ / kg·K) and thermal conductivity (such as 0.58W / m·K). The experiment uses instruments such as a density meter, a viscometer and a thermal conductivity analyzer to obtain the above parameters, and the data accuracy is controlled within the range of ±0.01. Combined with the established three-dimensional structure data, the fluid dynamics simulation method (CFD, Computational Fluid Dynamics) is used to analyze the internal fluid distribution of the device. The fluid boundary conditions are set by CFD software (such as Fluent or OpenFOAM), for example, the flow rate is set to 1.2m / s, the inlet pressure is 0.1MPa, and the outlet pressure difference is 0.05MPa, and the finite volume method is used to discretely solve the fluid flow. During the solution process, key performance indicators such as velocity field, pressure field and temperature field distribution are monitored, and finally the internal fluid distribution data of the device is generated, and the output format is VTK file.

[0088] Step S15: Generate a digital three-dimensional model of chemical waste liquid treatment using computer-aided engineering tools based on the three-dimensional structure data of the equipment and the fluid distribution data inside the equipment.

[0089] In this embodiment, the three-dimensional structure data of the device generated in step S13 (including the geometric dimensions of the fluid flow channel and structural components of the device) and the internal fluid distribution data of the device generated in step S14 (including the specific numerical distribution of the velocity field, pressure field, and temperature field) are respectively imported into a computer-aided engineering tool (such as ANSYS Workbench). First, create an engineering project in ANSYS, and import the three-dimensional structure data of the device (file format is STEP or IGES) and the fluid distribution data (file format is VTK or CSV) respectively. In the geometry module, the imported device geometry data is meshed, using an unstructured tetrahedral mesh, and the mesh size is set to 1 mm. The key parts (such as the fluid inlet and the bend area) adopt a local refinement strategy to further reduce the mesh size to 0.1 mm to ensure that the accuracy meets the analysis requirements. In the boundary condition definition stage, the fluid distribution data is mapped to the nodes of the geometric model. The specific operation is: the fluid pressure field data is used as the surface load of the inner wall of the device and applied to the corresponding nodes of the three-dimensional structure model. For example, the pressure at the fluid inlet is 0.2 MPa, and at the outlet is 0.1 MPa. The pressure distribution in the middle area is applied by linear interpolation according to the node values ​​in the VTK file; the fluid temperature field (inlet temperature is 25°C, outlet is 50°C) is loaded on the nodes of the fluid contact surface, and the wall heat conductivity coefficient is set to 50 W / m²·K to reflect the heat exchange between the device and the external environment. After entering the solution module, select the multi-physics field collaborative solution mode of static structural analysis and thermal fluid analysis. In static structural analysis, set the material properties, such as the main material of the equipment is stainless steel (elastic modulus is 210GPa, Poisson's ratio is 0.3, and density is 7,800 kg / m³). In thermal fluid analysis, set the fluid characteristic parameters, including specific heat capacity of 4.2 kJ / kg·K and viscosity of 0.6 Pa·s. During the analysis, the finite element method (FEM) is used to discretize the device structure, solve the stress field caused by fluid load and the displacement field caused by thermal expansion, and monitor the maximum stress point and deformation. For example, the maximum stress on the equipment wall is 150 MPa, and the maximum displacement is 0.5 mm. After the calculation is completed, the post-processing module is used to generate a visual report of the digital 3D model, including pressure field distribution cloud map, temperature field distribution cloud map, stress distribution cloud map and deformation field cloud map. The result data is saved in Parasolid format (.x_t) and general STEP format (.stp) to ensure that other design and analysis tools can be seamlessly called. The generated digital 3D model is uploaded to the equipment management system, and the real-time update function of the model is set. That is, when the equipment operating status changes, the 3D model can be updated by mapping the new data to keep the model consistent with the actual status of the equipment.

[0090] Preferably, step S14 is specifically as follows:

[0091] Step S141: obtaining fluid characteristic parameters using a rheometer;

[0092] In this embodiment, fluid property measurement equipment is used to collect key physical parameters of the fluid, and these equipment include a rheometer, a densitometer, a thermal conductivity tester, a specific heat capacity tester, and a surface tension meter. First, the fluid sample to be tested is placed in a constant temperature control device, the temperature is set to the standard room temperature of 25°C, and the dynamic viscosity is measured by the rotation method and the oscillation method using a rheometer, the measurement range is set to 0.01 to 10Pa·s, and the data accuracy is controlled to ±0.001Pa·s; then, the density of the fluid is measured under normal pressure conditions using a densitometer according to the ASTM D4052 standard, and the test values ​​under 5 different temperature gradients are taken, and the average value is calculated, and the density measurement accuracy is controlled to ±0.1 kg / m³; then, a thermal conductivity tester is used to measure the thermal conductivity of the fluid by the transient plane heat source method, the test range is 0.1 to 10 W / (m·K), and the data is recorded with an accuracy of ±0.01W / (m·K); finally, a specific heat capacity tester is used to measure the specific heat capacity of the sample under constant pressure conditions, according to ISO The data is collected according to the 11357-4 standard with an accuracy of ±0.5 J / (g·K), and the surface tension of the fluid is measured by the hanging drop method in combination with a surface tension meter, and the surface tension value is recorded with an accuracy of ±0.01 mN / m. Through the above process, the characteristic parameters of the fluid are fully obtained and recorded in a dedicated data recording system to ensure that the data format meets the requirements for subsequent analysis and use.

[0093] Step S142: Preliminary processing of the fluid characteristic parameters, including denoising, normalization and data fitting, to obtain preliminary processed fluid characteristic parameters;

[0094] In this embodiment, the fluid characteristic parameters are imported into data analysis software (such as MATLAB or Python's Pandas library) for processing. The processing process includes three main stages: denoising, normalization, and data fitting. First, the random errors in the original data are filtered out by the wavelet denoising algorithm. The denoising parameter is set to 5 decomposition layers, and the threshold is selected by the soft threshold method; then, the Min-Max normalization method is used to standardize the data values ​​to the [0,1] interval for the stability of subsequent calculations. The formula is X′=(X−Xmin) / (Xmax−Xmin), where Xmin and Xmax are the minimum and maximum values ​​of the data, respectively; finally, the processed data is fitted based on the polynomial fitting algorithm, and the third-order polynomial is selected to fit the changing trend of the fluid characteristic parameters. The fitting accuracy is controlled below 0.01 by the mean square error, thereby generating a preliminary processing fluid characteristic parameter data set.

[0095] Step S143: using computer-aided design software and computational fluid dynamics software, combined with the three-dimensional structure data of the equipment, to establish a fluid flow model;

[0096] In this embodiment, computer-aided design software (such as SolidWorks) and computational fluid dynamics software (such as ANSYS Fluent) are used to establish a fluid flow model. In the specific operation, the three-dimensional structure data of the device is first imported into SolidWorks, and the fluid flow channel part is separated into the internal fluid space by the boundary extraction tool; then, the extracted internal space file is imported into ANSYS Fluent in Parasolid format and set as the fluid analysis area. The basic physical model of fluid flow is defined in this area, and the model includes laminar flow, turbulent flow and heat conduction models. The standard k-ε model is selected to describe turbulent motion, the turbulence intensity is set to 5%, the turbulent viscosity ratio is set to 10, and the heat conduction equation is activated to consider the influence of the temperature field.

[0097] Step S144: using computational fluid dynamics software, setting fluid flow boundary conditions and fluid properties according to the preliminary processed fluid characteristic parameters, thereby obtaining fluid dynamics simulation start data;

[0098] In this embodiment, based on the preliminary processing of fluid characteristic parameters, combined with information such as fluid density, viscosity, thermal conductivity and specific heat capacity, the fluid boundary conditions and property parameters are set in ANSYS Fluent. The specific boundary conditions include the fluid inlet velocity and outlet pressure. The inlet velocity is set to 2m / s, the outlet pressure is set to normal pressure (101325 Pa), and the data in the fluid physical property database are used to assign values ​​to the fluid material properties. The density is set to 800kg / m³, the viscosity is 0.02Pa·s, the thermal conductivity is 0.15W / (m·K), and the specific heat capacity is 2000J / (kg·K). After the setting is completed, the integrity and correctness of the boundary condition setting are verified by the inspection tool, and saved as the fluid dynamics simulation startup data.

[0099] Step S145: numerically simulate the fluid dynamics simulation startup data using computational fluid dynamics software to calculate the velocity field, pressure field, and temperature field distribution of the fluid inside the device, thereby obtaining the fluid motion state data inside the device;

[0100] In this embodiment, computational fluid dynamics software is used to perform numerical simulation on the simulation startup data. The finite volume method is used to discretize the fluid domain, and the computational grid is divided into an unstructured tetrahedral grid. The grid size is set to 1 mm, and the convergence of the calculation results is ensured by a grid independence test. Subsequently, a transient simulation is started, and the simulation time is set to 10 seconds with a time step of 0.01 seconds. During the simulation process, the velocity field, pressure field, and temperature field distribution inside the device are recorded, and the changing trend of the data is monitored through specific points, such as velocity fluctuations at the inlet and temperature gradients at the outlet. The simulation results are exported in CSV format, containing the global fluid motion state data at each time step.

[0101] Step S146: Use visualization software to generate visualization of the internal fluid distribution of the device based on the internal fluid movement state data, thereby obtaining the internal fluid distribution data of the device.

[0102] In this embodiment, visualization software (such as ParaView) is used to process and visualize the simulation results. The fluid motion state data is imported into ParaView, the velocity field, pressure field and temperature field distribution data of the fluid are loaded, and the section tool is used to generate a cross-sectional view at key locations (such as the inlet, corner and outlet). At the same time, streamlines and isotherms are used to represent the motion trajectory and temperature distribution of the fluid, and different colors are used to identify the velocity and pressure changes of the fluid. Finally, a visual image of the fluid distribution inside the device is generated, and it is exported in PNG format for subsequent analysis, completing the generation of the fluid distribution data inside the device.

[0103] Preferably, step S15 is specifically as follows:

[0104] Step S151: Start computer-aided engineering software and create a three-dimensional model project file of the equipment;

[0105] In this embodiment, computer-aided engineering software, such as ANSYS Workbench, is started to create a new project file for the construction of a three-dimensional model of chemical waste liquid treatment equipment. When the project is created, the storage path is specified in the project file directory, and the naming rule adopts the equipment name plus timestamp format (for example: ChemWaste_20250116) for subsequent management and tracking. At the same time, specific modules are set for the project, such as structural analysis modules, fluid analysis modules, and multi-physics field coupling modules. Subsequently, the calculation parameters of the project are configured in the "Project Settings" of the project page, including the unit system (such as millimeters, kilograms, seconds), numerical precision (set to five decimal places), and solver type (direct solver is selected). After the project file is created, the software interface will display a blank analysis flow chart, waiting for data input and geometric model import operations.

[0106] Step S152: importing the equipment 3D structure data into the equipment 3D model project file, and drawing the equipment 3D geometric model;

[0107] In this embodiment, the three-dimensional structure data of the device is imported into the three-dimensional model project file of the device, and the file import function is used to select the three-dimensional structure data file in Parasolid format (extension name .x_t) or STEP format (extension name .stp). After importing, the geometry editing module (such as ANSYS SpaceClaim or SolidWorks) is enabled, the geometric features in the three-dimensional structure data of the device are loaded into the visualization interface, and the integrity of the three-dimensional geometry structure is checked. The specific steps of the geometry check include boundary continuity check, facet closure detection, and feature size consistency verification. For example, the closed boundary of the fluid flow channel is confirmed to be complete by the automatic detection tool. If an unclosed surface is found, the "Merge Boundary" tool is used to repair it. After the geometry import is completed, the geometric structure of the device is drawn by the three-dimensional drawing tool, including the specific layout of the device shell, internal flow channel and pores. During the drawing process, unique identifiers are assigned to different components, such as the shell number is "Shell_001" and the internal flow channel number is "FlowPath_001", and the colors are distinguished according to the function of the device, such as the shell is represented by gray and the flow channel is represented by blue, so as to facilitate the visualization of subsequent operations and analysis.

[0108] Step S153: adjusting the internal flow channels and internal pores of the equipment on the three-dimensional geometric model of the equipment according to the internal fluid distribution data of the equipment, so as to obtain a digital three-dimensional model of chemical waste liquid treatment.

[0109] In this embodiment, the three-dimensional geometric model of the device is adjusted according to the three-dimensional structural data of the device and the fluid distribution data inside the device. The adjustment work focuses on internal flow channel optimization and pore adjustment, and detailed local modifications are made to various areas of the geometric model through the fluid distribution data. First, the fluid distribution data is loaded into the multi-physics field module in the software in a tabular format (such as a CSV file), and mapped to the internal nodes of the device geometric model according to the correspondence between the position coordinates (x, y, z) and physical quantities (such as pressure, velocity, temperature). Subsequently, according to the gradient change of the velocity distribution in the flow channel, a widened flow channel design is adopted for the high-speed section (velocity gradient greater than 500Pa / m), and the specific adjustment amount of the flow channel width is determined according to ; Calculation, where ΔP is the fluid pressure change value, G is the flow channel shape coefficient, and μ is the fluid viscosity; for the low-speed section (speed less than 0.2m / s), the flow velocity is increased by reducing the flow channel cross-sectional area, and the reduction ratio of the cross-sectional area is set to 10%. After completing the flow channel optimization, the pore size is adjusted according to the distribution of the fluid pressure field for the pore area inside the equipment. For the pore area with a pressure greater than 5000 Pa in the pressure field, its radius is reduced by 20% to reduce the risk of fluid retention; for the area with a pressure less than 1000Pa, the pore radius is increased by 15% to improve fluid permeability. After the pore adjustment is completed, the entire geometric model is tested for continuity again to ensure that the geometric changes of the flow channel and pores do not cause damage to the closed boundary.

[0110] Preferably, step S2 specifically comprises:

[0111] Step S21: Obtain chemical waste liquid sample data, use liquid chromatography to measure the viscosity of the chemical waste liquid sample, perform 15-20 measurements, record the viscosity value of each measurement, calculate the average value of all measurement values ​​as the final waste liquid viscosity value, and record the chemical waste liquid sample viscosity data;

[0112] In the present embodiment, a sample is extracted from a chemical waste liquid storage container by a standard sampler to ensure that the volume of each sampling is between 100 ml and 200 ml. Then, the waste liquid sample is divided into 15 to 20 small samples, each of which is 10 ml, and is placed in a clean, uncontaminated sample bottle. Then, the liquid chromatograph is started and preheated to a stable state, ensuring that the temperature of the chromatographic column is controlled between 35 ° C and 45 ° C, and the flow rate of the mobile phase is 0.5 ml / min. Each small sample is injected into the liquid chromatograph one by one through the injector for viscosity measurement, and the measurement result is recorded each time. In order to ensure that the measurement result is accurate, before each measurement, the equipment is calibrated with a standard solution of known viscosity to ensure that the equipment is in the correct working state. After the measurement is completed, the viscosity value of each time is recorded. This process is performed at least 15 to 20 times, and the viscosity value of each time is recorded. After all measurements are completed, the average value of these viscosity values ​​is calculated. The calculation method is to add up the viscosity values ​​of each measurement and then divide it by the total number of measurements (i.e. 15 to 20 times) to obtain an average viscosity value. Specifically, the viscosity values ​​of each measurement are added together and then divided by the number of measurements. The result is the final viscosity value of the chemical waste liquid. Ultimately, this average viscosity value will be stored in a CSV file as the representative viscosity data of the chemical waste liquid for subsequent data integration and analysis.

[0113] Step S22: Use gas chromatography to perform cuprous chloride solution detection on the chemical waste liquid sample data, take out 10-50 ml of the waste liquid sample, filter it using a filter membrane with a pore size of 0.45-0.55 micron, and use a flame ionization detector to perform chromatographic column detection, wherein the column temperature of the gas chromatography is usually set at 70°C-120°C, so as to obtain cuprous chloride solution data;

[0114] In this embodiment, a gas chromatograph (such as Shimadzu GC-2010 Plus) is used to detect cuprous chloride solution on a chemical waste liquid sample. First, 10 ml to 50 ml of sample solution is taken from the remaining sample in step S21, and filtered through a filter membrane with a pore size of 0.45 microns to 0.55 microns. The filtered sample solution is placed in a sample bottle with a cover to avoid contamination. Start the gas chromatograph, and set the column temperature to a linear heating program of 70°C to 120°C, the heating rate is 10°C per minute, and the detector type is set to a flame ionization detector (FID). The filtered sample solution is injected into the chromatographic column of the gas chromatograph (such as a chromatographic column filled with a high-resolution silica gel stationary phase) through an automatic sampler. After the detection is completed, the cuprous chloride concentration data is exported from the chromatographic software. The specific format is a concentration data sequence recorded once per second, which is saved as a CSV file in the form of time-concentration pairs. During the test, if an abnormal peak is found (the peak height exceeds the baseline by more than 10 times), use standard cuprous chloride solution (concentration 1.0 mg / L) to recalibrate the instrument to ensure measurement accuracy.

[0115] Step S23: Integrate the chemical waste liquid sample viscosity data and cuprous chloride solution data to obtain chemical waste liquid characteristic data;

[0116] In this embodiment, the viscosity data of the chemical waste liquid sample is integrated with the cuprous chloride solution data obtained in step S22. The two CSV files are read separately using a data analysis tool (such as the Pandas library in Python), and the data are aligned according to the sample number. The statistical characteristic values ​​of the viscosity data and the cuprous chloride concentration data are calculated separately, including the maximum value, minimum value, average value and standard deviation, and saved in a tabular form as a "waste liquid characteristic statistics table". Combining the viscosity and concentration data, a chemical waste liquid characteristic parameter matrix is ​​constructed, and the matrix format is that each column corresponds to a physical parameter (such as viscosity, cuprous chloride concentration), and each row is the data of a sample. The characteristic parameter matrix is ​​reduced in dimension by the principal component analysis (PCA) method, converted into a 2D characteristic distribution map, saved in a vector graphic format (such as SVG), and accompanied by a corresponding numerical matrix to support further analysis.

[0117] Step S24: inputting the chemical waste liquid characteristic data into the digital three-dimensional model of chemical waste liquid treatment, simulating the waste liquid recovery treatment process using simulation software, and quantitatively evaluating the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results.

[0118] In this embodiment, the integrated chemical waste liquid characteristic data is input into the digital three-dimensional model of chemical waste liquid treatment. Start the simulation software (such as COMSOL Multiphysics) and load the three-dimensional model of the equipment, import the waste liquid characteristic data into the simulation module in a table form, and assign physical meaning to each waste liquid characteristic parameter in the parameter setting interface, such as setting the viscosity parameter as the viscosity coefficient of the fluid dynamics equation, and setting the cuprous chloride concentration as the input variable of the chemical reaction rate equation. Configure boundary conditions according to the design parameters of the waste liquid treatment equipment (such as flow channel size, reaction zone volume, etc.), for example, the inlet velocity is set to 0.1 m / s and the outlet pressure is 101325 Pa. After starting the simulation, record the distribution of key parameters in the waste liquid treatment process, such as the velocity field, concentration field and product concentration changes in the reaction zone, and set the simulation time to 1000 seconds and the time step to 1 second. After the simulation is completed, the simulation results are exported as numerical tables (such as Excel format) and three-dimensional visualization images (such as VTK format), and data analysis tools are used to quantitatively evaluate the quality of the recovered products. For example, the total flow rate and component concentration of the outlet flow channel in the simulation results are integrated to obtain the processing efficiency and total product data, and these evaluation results are stored in the "Processing Result Evaluation Report" file for subsequent optimization.

[0119] Preferably, step S24 is specifically as follows:

[0120] Step S241: using a database tool to convert the chemical waste liquid characteristic data into a CSV file to obtain a chemical waste liquid characteristic CSV file;

[0121] In this embodiment, the conversion process of chemical waste liquid characteristic data first uses a database management system (such as MySQL or Microsoft SQL Server) to extract the waste liquid characteristic data stored in the database. These data include the physical and chemical properties of the waste liquid, such as viscosity, pH value, temperature, salinity, dissolved substance concentration, etc. Using database tools, the data is sorted according to specific fields (such as sample number, test items, measurement values, etc.) and exported to CSV format. To ensure the accuracy of the data, the title of each column is clear when it is exported, such as "sample number", "viscosity value", "pH value", etc. When exporting data, it is necessary to verify the correctness of the type and content of each field to ensure that there are no omissions or duplications. Finally, the entire waste liquid characteristic data is saved in CSV file format as input data for subsequent input into the digital three-dimensional model of chemical waste liquid treatment.

[0122] Step S242: inputting the chemical waste liquid characteristic CSV file into the digital three-dimensional model of chemical waste liquid treatment, and uploading the digital three-dimensional model of chemical waste liquid treatment to the simulation software;

[0123] In this embodiment, the CSV file is used as input to enter the digital three-dimensional model of chemical waste liquid treatment. Use digital modeling software (such as AutoCAD, SolidWorks, COMSOL, etc.) to open the three-dimensional model of waste liquid treatment. This model usually includes waste liquid treatment equipment and process flow lines, such as reaction tanks, separators, sedimentation tanks, etc. The way to input data is to import the data in the CSV file into the model through the import function provided by the software. According to the different characteristics of the waste liquid, the characteristic data (such as viscosity, temperature, pH value, etc.) will be associated with the corresponding model part. For example, the viscosity value will affect the flow rate in the flow pipe and reactor, and the temperature and pH value will affect the rate of the chemical reaction or the sedimentation process. During the import process, the software will perform data mapping and automatically match each data point in the CSV file to the corresponding position of the three-dimensional model. In this way, the characteristic data of the chemical waste liquid is included in the entire digital model for subsequent simulation analysis.

[0124] Step S243: performing waste liquid flow simulation in simulation software, wherein the flow type is set to turbulent flow type, and the flow velocity is set to 0.1 m / s to 2 m / s;

[0125] In this embodiment, enter the simulation software (such as ANSYS Fluent) to simulate the flow of waste liquid. Set the flow type to turbulent type, which is suitable for waste liquid with high-speed flow and complex flow channel structure. In the turbulent simulation, the flow of the waste liquid presents irregular velocity and pressure distribution, and the fluctuation of flow rate is more obvious. The flow rate range is set to 0.1m / s to 2m / s, and the flow rate is selected according to the actual flow rate range of the waste liquid treatment equipment. The setting of the flow rate depends on the concentration, viscosity and flow channel design of the waste liquid. During the simulation process, the appropriate flow rate value is adjusted according to the properties of the waste liquid. At this time, the simulation software will simulate the flow path, turbulence phenomenon and its interaction with the internal structure of the equipment of the waste liquid according to the set flow rate range.

[0126] Step S244: performing a waste liquid mixing simulation in a simulation software, wherein the stirring speed is set to 50 rpm to 300 rpm and the mixing time is set to 1 min to 30 min;

[0127] In this embodiment, according to the simulation target, the stirring speed is set to 50rpm to 300rpm, and this range is set according to the viscosity and treatment requirements of the waste liquid. A lower stirring speed is suitable for low-viscosity waste liquid, while a higher stirring speed is used for high-viscosity waste liquid. During the setting process, a suitable stirring speed range is selected considering the mixing effect of different waste liquid components and the influence of speed on the final treatment result. The mixing time is set to 1 minute to 30 minutes, and the time range is determined according to the reaction characteristics of the components in the waste liquid. A longer mixing time helps to enhance the uniformity of the waste liquid and promote effective reaction.

[0128] Step S245: performing waste liquid separation simulation in simulation software, wherein the porosity of the separation medium is set to 30% to 60%, and the sedimentation rate is set to 1 mm / s to 20 mm / s;

[0129] In this embodiment, the porosity of the separation medium is set to 30% to 60%, and this range is adjusted according to the physical properties of the selected medium and the properties of the waste liquid. The porosity will affect the sedimentation and separation efficiency of solid particles in the waste liquid. Too low porosity leads to low separation efficiency, while too high porosity affects the fluidity of the medium. The sedimentation rate is set to 1mm / s to 20mm / s, which is determined according to the size and density of the particles in the waste liquid. The sedimentation rate of the particles is closely related to the flow properties of the waste liquid. The sedimentation rate can be adjusted during the simulation process to achieve the desired separation effect.

[0130] Step S246: running the waste liquid recovery processing module in the simulation software, and recording the waste liquid separation efficiency data and the waste liquid processing time data at intervals of 10 seconds;

[0131] In this embodiment, the operation of the waste liquid recovery and processing module in the simulation software records the waste liquid separation efficiency data and the waste liquid processing time data every 10 seconds. The waste liquid separation efficiency is calculated by real-time monitoring of the removal of solid matter in the waste liquid, and the waste liquid processing time records the time required for the entire processing process. By setting the simulation parameters, the simulation software can accurately track the performance of the waste liquid at each processing stage and record the necessary data. The recording frequency of every 10 seconds can ensure that the details of the process are fully captured and data can be obtained in a timely manner at each stage of the processing process.

[0132] Step S247: If the waste liquid separation efficiency data and the waste liquid treatment time data reach the set recovery and treatment parameter target, stop the simulation process and output the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time;

[0133] In this embodiment, if the waste liquid separation efficiency data and waste liquid processing time data reach the preset recycling processing parameter target, the simulation process stops. At this time, the simulation software will output the separation efficiency data table of the separation equipment, which lists in detail the separation efficiency and corresponding processing time of each stage in the entire simulation process. The output results include the separation efficiency percentage, processing time, and equipment parameters of each stage, which provide a basis for subsequent data analysis and equipment tuning.

[0134] Step S248: Perform a quantitative evaluation of the waste liquid recovery result based on the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time to obtain a treatment process evaluation result.

[0135] In this embodiment, a quantitative evaluation of the waste liquid recovery results is performed based on the output separation efficiency data table and the statistical information of the waste liquid treatment time. The quantitative evaluation is based on the statistical information of the output data, such as the average value, maximum value, minimum value, etc. of the separation efficiency and the treatment time, combined with the set process goals, to conduct a comprehensive analysis of the recovery treatment results. This evaluation not only helps to verify whether the treatment process meets the target requirements, but also provides necessary information for optimizing the recovery process. In the evaluation process, it involves calculating the standard deviation of the separation efficiency, the range of variation of the treatment time and other indicators, so as to comprehensively evaluate the stability and effect of the waste liquid treatment process.

[0136] Preferably, step S248 is specifically as follows:

[0137] Calculating the waste liquid recovery efficiency according to the separation efficiency data table of the separation equipment to obtain waste liquid recovery efficiency data, and evaluating the waste liquid recovery efficiency on the waste liquid recovery efficiency data to obtain a waste liquid recovery efficiency evaluation result;

[0138] In this embodiment, the separation efficiency data of each separation stage is collected. The data table provides the separation efficiency of different processing stages, for example, the recovery efficiency of the initial stage is 80%, the mid-stage is 85%, and the final stage is 90%. According to the processing time of the waste liquid and the characteristics of the processing stage, the weight of each stage is calculated. For example, the initial stage accounts for 30% of the total processing time, the mid-stage accounts for 40%, and the final stage accounts for 30%. By weighted average method, the separation efficiency of each stage is summarized and the final waste liquid recovery efficiency is calculated:

[0139] Total recovery efficiency = (0.8 × 30% + 0.85 × 40% + 0.9 × 30%);

[0140] The final recovery efficiency data is then obtained. Next, the actual calculated waste liquid recovery efficiency is compared with the predetermined recovery efficiency target (e.g. 90%). By calculating the error value or deviation, it is determined whether the waste liquid recovery efficiency has reached the preset target. If the deviation is less than 5%, the evaluation result is "high efficiency", if the deviation is between 5%-10%, it is "normal", and if it exceeds 10%, it is "low efficiency". This provides a basis for subsequent operational adjustments.

[0141] Calculate the processing energy consumption based on the waste liquid recovery efficiency data and the statistical information of the waste liquid processing time, so as to obtain the waste liquid processing energy consumption data, and evaluate the waste liquid processing energy consumption based on the waste liquid processing energy consumption data, so as to obtain the waste liquid processing energy consumption evaluation result;

[0142] In this embodiment, according to the operating parameters such as the power of the equipment, the flow rate of the waste liquid, and the stirring speed, the energy consumption calculation formula of the equipment is adopted: E=P×T; wherein E is the processing energy consumption (in kWh), P is the power of the equipment (in watts), and T is the waste liquid processing time (in hours). For example, if the power of the equipment is 200 watts and the waste liquid processing time is 1 hour, the energy consumption per hour is calculated to be 0.2 kWh. Then, combined with factors such as the waste liquid flow rate (such as 1.2 m / s) and the stirring speed (such as 150 rpm), considering that the changes in these parameters will affect the processing energy consumption, the parameters in the energy consumption calculation formula are further adjusted to accurately calculate the energy consumption of each link in the processing process. Next, the goal of the waste liquid treatment energy consumption assessment is to ensure that the energy consumption per ton of waste liquid does not exceed the predetermined standard (such as the energy consumption per ton of waste liquid should be controlled within 50 kWh). By comparing the actual energy consumption with the preset standard, if the actual energy consumption exceeds the standard, the evaluation result is "inefficient"; if the energy consumption is less than the standard value, it is evaluated as "efficient". This process helps to identify which links have higher energy consumption and provides a basis for subsequent optimization.

[0143] Conduct equipment stability analysis based on waste liquid treatment energy consumption data and chemical waste liquid recovery equipment operation data to obtain equipment stability data, and conduct equipment stability evaluation on the equipment stability data to obtain equipment stability results;

[0144] In this embodiment, key data of the equipment during operation are obtained, such as the operating temperature, pressure, vibration level, power fluctuation, etc. of the equipment. For example, the temperature fluctuation during the operation of the equipment should be controlled within the range of ±5°C, and the vibration amplitude should be less than 2mm / s. By installing sensors, the various parameters of the equipment are monitored in real time, and the operation data of the equipment is transmitted to the data analysis system in real time. If the temperature fluctuation exceeds the set range or the vibration amplitude exceeds 2mm / s during the long-term operation of the equipment, it is considered that the equipment has a stability problem. The equipment stability data includes the operating time, the number of failures, the fluctuation of equipment parameters, etc., which are used to determine whether the equipment meets the requirements of long-term stable operation. By counting the frequency of equipment failures (such as a failure once every 1000 hours), the average stable operation time of the equipment is calculated, and combined with the operating parameters of the equipment, the equipment stability data is finally obtained. When evaluating these data, the standard of equipment stability is set, such as the number of equipment failures per year does not exceed 4 times, and the evaluation result is "stable" when the equipment stability is excellent, otherwise it is "unstable".

[0145] The waste liquid recovery efficiency evaluation results, waste liquid treatment energy consumption evaluation results and equipment stability results are integrated to obtain the treatment process evaluation results.

[0146] In this embodiment, the weight of each evaluation result is set. For example, the weight of the waste liquid recovery efficiency evaluation is 40%, the weight of the waste liquid treatment energy consumption evaluation is 30%, and the weight of the equipment stability evaluation is 30%. By combining the evaluation results of each item and performing weighted calculation based on the actual score of each evaluation result. For example, if the waste liquid recovery efficiency evaluation score is 85%, the waste liquid treatment energy consumption evaluation score is 90%, and the equipment stability evaluation score is 80%, then the treatment process evaluation result is obtained based on the weight calculation:

[0147] Comprehensive evaluation result = (0.85 × 40% + 0.9 × 30% + 0.8 × 30%);

[0148] Finally, a comprehensive evaluation value is obtained, which is used to evaluate the overall efficiency and stability of the entire waste liquid recovery process. The comprehensive evaluation result is a comprehensive evaluation index. If the value exceeds 80%, the entire waste liquid recovery process is considered to be efficient; if it is between 60% and 80%, it is a normal treatment process; if it is less than 60%, it is inefficient and needs to be optimized and improved.

[0149] Preferably, step S3 specifically comprises:

[0150] Step S31: Obtaining preset chemical waste liquid recycling and treatment standard data;

[0151] In this embodiment, it is necessary to define and obtain a set of standardized waste liquid recovery and treatment indicators, including waste liquid treatment efficiency, energy consumption, temperature, flow rate, pH value and amount of reagent added. Such preset standards are obtained by consulting industry specifications, standard documents or collecting from historical data. For example, the recovery efficiency of waste liquid should reach more than 85%, the temperature standard should be set between 60-80°C, the flow rate should be controlled between 2-5 m³ / h, and the reagent addition ratio is 100-150ppm. All standard data are stored in a data table or database, and it is ensured that these standard data can provide a reference for the evaluation of subsequent processing processes. The acquisition of standard data not only involves the setting of target values, but also combines the actual operating environment, considers the errors or fluctuations that occur in the actual processing process, and ensures the comprehensiveness and applicability of the standard data.

[0152] Step S32: making an abnormality judgment on the treatment process evaluation result according to the preset chemical waste liquid recovery treatment standard data, thereby obtaining an abnormal treatment process evaluation result;

[0153] In this embodiment, real-time evaluation data of the current waste liquid treatment process is collected, including waste liquid recovery efficiency, energy consumption, temperature, flow rate, dosage of reagents, etc. By comparing these data with the preset standard data, it is determined whether each indicator is within the specified range. For example, if the waste liquid recovery efficiency is less than 85%, the temperature exceeds 80°C or is lower than 60°C, the flow rate is lower than 2m³ / h or higher than 5m³ / h, and the amount of reagent added is lower than 100ppm or higher than 150ppm, the treatment process is judged to be abnormal. In order to perform abnormal judgment, a threshold setting method is adopted, and all data points are compared with the threshold. The standard value, threshold and error range of each parameter are set in advance. If they exceed the range, they are marked as abnormal. The evaluation results are displayed through the data output system, showing the abnormal treatment process.

[0154] Step S33: adjusting the temperature of the chemical waste liquid recovery equipment to 60-80° C. based on the abnormality handling process evaluation result, thereby obtaining temperature adjustment data;

[0155] In this embodiment, the temperature of the waste liquid in the device is monitored in real time by a temperature sensor and compared with a preset standard temperature range (60-80°C). If the temperature exceeds this range, the temperature control system is started to adjust it. The system adjusts the temperature of the waste liquid by a heating or cooling device. For example, if the temperature in the device is 85°C, the cooling system is started to lower the temperature to within the standard range. The adjustment process is achieved through closed-loop control technology, and the temperature sensor provides real-time feedback information to ensure that the temperature is stable within the target range. During the adjustment process, the equipment will be accurately adjusted according to the preset standard data to avoid excessive temperature fluctuations and ensure that the treatment process runs stably within the most suitable temperature range.

[0156] Step S34: using a pump speed regulating device to adjust the flow rate of the abnormality handling process evaluation result to 2-5 m³ / h, thereby obtaining flow rate adjustment data;

[0157] In this embodiment, when the flow control adjustment is performed, the waste liquid flow is monitored in real time by the flow sensor, and the current flow data is compared according to the preset standard flow range (2-5m³ / h). If the flow exceeds 5m³ / h or is lower than 2m³ / h, it is adjusted by the pump speed regulating device. The pump speed regulating device can accurately control the speed of the pump, thereby adjusting the flow of the waste liquid. For example, if the current flow is 6m³ / h, the regulating device will reduce the pump speed until the flow drops to the specified standard range. This process is implemented through a flow control system, and the flow sensor and the pump speed regulating system cooperate with each other to ensure that the flow control is within the optimal range to avoid abnormal flow affecting the waste liquid recovery effect. The entire process uses the closed-loop control principle to ensure that the flow is maintained in the specified 2-5m³ / h range.

[0158] Step S35: using a pH sensor to adjust the reagent dosage ratio based on the abnormality handling process evaluation result, adjusting the amount of sodium hydroxide added to 100-150ppm, thereby obtaining reagent dosage ratio adjustment data;

[0159] In this embodiment, when adjusting the dosage ratio of the reagent, the pH value of the waste liquid is detected in real time by a pH sensor, and the current pH value of the waste liquid is compared with the set standard pH range (usually neutral or weakly alkaline). If the pH value deviates from the standard range (for example, the pH value is too low or too high), an automated reagent dosing device is used for adjustment. According to the pH value of the waste liquid, chemical agents such as sodium hydroxide are added to adjust the amount of the reagent added. Usually, if the pH value is lower than the lower limit of the standard range, the dosage of sodium hydroxide is increased; if the pH value is higher than the upper limit of the standard, the dosage of the reagent is reduced. By controlling the dosage of sodium hydroxide to 100-150ppm, the reagent dosing system can ensure that the pH value of the waste liquid is within the standard range, avoiding poor waste liquid treatment effect due to inaccurate dosage of reagents. The amount of reagent added is adjusted according to the difference between the real-time pH value and the preset standard to ensure that the chemical properties of the waste liquid meet the treatment requirements.

[0160] Step S36: adjusting key parameters of the digital three-dimensional model of chemical waste liquid treatment according to the temperature adjustment data, the flow adjustment data and the reagent addition ratio adjustment data, so as to obtain the digital optimized three-dimensional model of chemical waste liquid treatment.

[0161] In this embodiment, temperature adjustment data, flow adjustment data and reagent addition ratio adjustment data are collected. These data will be input as input parameters into the digital three-dimensional model of waste liquid treatment. Through numerical simulation and optimization calculation, key parameters in the model, such as the heating system of the equipment, fluid dynamic characteristics and reagent reaction rate, are adjusted. For example, if the temperature adjustment data indicates that the heating device needs to increase the power, the power parameters of the heating system are adjusted through model optimization; if the flow adjustment data requires the flow regulation system to increase the flow, the diameter of the pipeline and the power of the pump are adjusted in the model; if the reagent addition ratio needs to be increased, the reaction process is optimized by adjusting the reagent addition amount in the model. The whole process adopts a numerical optimization algorithm, and by adjusting the multi-dimensional parameters of the treatment process, an optimized digital three-dimensional model of waste liquid treatment is finally obtained, thereby improving the stability and efficiency of the entire waste liquid recovery process.

[0162] Preferably, step S4 is specifically:

[0163] Step S41: using a flow chart modeling tool to digitally optimize the three-dimensional model of chemical waste liquid treatment to model the waste liquid recovery process, thereby obtaining a waste liquid recovery flow chart, and uploading the waste liquid recovery flow chart to the intelligent monitoring device;

[0164] In this embodiment, each step of the waste liquid treatment process is digitally modeled by a flowchart modeling tool (such as Visio, Lucidchart or a custom-developed process modeling tool). Each step includes key operations such as pre-treatment, separation, concentration, and reuse of waste liquid recovery. According to various parameters of chemical waste liquid recovery, such as temperature, flow rate, pH value, dosage of reagents, etc., the operation details of each stage and its related input and output data are marked respectively. In the modeling process, it is first necessary to obtain the processing standards and equipment capabilities of chemical waste liquid recovery, and set the standard operating procedures for each link. Then, these data are presented as a flowchart through a graphical interface, in which the connection between each operation node is represented by an arrow to indicate the data flow and material flow. After completing the flowchart, it is exported using a suitable format (such as XML or JSON) and uploaded to the intelligent monitoring device to ensure that the subsequent monitoring device can automatically identify and process the data flow.

[0165] Step S42: setting real-time monitoring points according to the waste liquid recovery flow chart, thereby obtaining real-time monitoring point data;

[0166] In this embodiment, it is necessary to determine the monitoring points of each link according to each processing link in the waste liquid recovery flow chart. Each monitoring point needs to reflect the real-time status of key operations, including temperature, flow, pH value, reagent addition ratio, etc. In order to set the monitoring points, it is necessary to understand the equipment configuration and operation standards of each link in detail. For example, in the pretreatment link of waste liquid recovery, it is necessary to set a temperature monitoring point, in the separation link, it is necessary to set a flow monitoring point, and in the reagent addition link, it is necessary to set a pH value monitoring point. Through the monitoring system configuration interface, these point positions and corresponding monitoring parameters are input into the system to ensure that the system can obtain key data of each link in real time. The system will encode and identify these monitoring points according to the waste liquid recovery flow chart to facilitate subsequent real-time data acquisition and processing.

[0167] Step S43: Start the intelligent monitoring device to monitor waste liquid treatment abnormalities according to the real-time monitoring point data, thereby obtaining waste liquid treatment abnormality data, generating an alarm signal and triggering a maintenance reminder for the chemical waste liquid recovery equipment, and obtaining the waste liquid recovery equipment maintenance data;

[0168] In this embodiment, when the intelligent monitoring device is started, real-time parameter data such as temperature, flow rate, pH value, and dosage ratio of the waste liquid treatment process are obtained through real-time monitoring point data. These data will be continuously uploaded to the intelligent monitoring device for analysis. The system uses threshold settings and predetermined standard data to compare and detect abnormalities in real-time data. For example, if the temperature exceeds 80°C or is lower than 60°C, the flow rate is lower than 2m³ / h or exceeds 5m³ / h, and the pH value is lower than 100ppm or exceeds 150ppm, the system will mark it as an abnormal state and immediately trigger the alarm mechanism. The alarm signal will be displayed through the interface, and the automatic maintenance reminder function of the chemical waste liquid recovery equipment will be activated, prompting that equipment maintenance operations are required. The alarm signal and equipment maintenance reminder content are fed back according to the equipment status, processing process and set thresholds to ensure that the staff can understand the abnormal situation of the equipment in a timely manner.

[0169] Preferably, step S43 further includes the following steps:

[0170] Step S431: starting the intelligent monitoring device to extract waste liquid monitoring features according to the real-time monitoring point data, thereby obtaining waste liquid monitoring data;

[0171] In this embodiment, when the intelligent monitoring device is started, the monitoring device is first initialized and configured to ensure that all monitoring sensors (such as pH sensors, temperature sensors, dissolved oxygen sensors, etc.) are in normal working condition. The point data acquisition of the monitoring device should cover the key links of the waste liquid treatment system, including the water inlet, wastewater pool, and the inlet and outlet of the treatment equipment. Each monitoring point collects data once a minute and transmits the data synchronously to the central monitoring system. In this step, a feature extraction algorithm (such as an analysis method based on time domain and frequency domain) is used to process the real-time monitoring point data to calculate the mean, standard deviation, maximum value, minimum value and other characteristic values ​​of the data. By setting a preset standard threshold, such as a pH value threshold of 6-8 and a temperature threshold of 20℃-40℃, it is judged whether the waste liquid is normal. These extracted characteristic values ​​will constitute the waste liquid monitoring data for subsequent analysis.

[0172] Step S432: acquiring waste liquid historical monitoring abnormal data, and classifying the waste liquid monitoring data into tin-containing wastewater abnormalities according to the waste liquid historical monitoring abnormal data, thereby obtaining tin-containing wastewater abnormal monitoring data;

[0173] In this embodiment, historical waste liquid monitoring data is retrieved from the database, and data cleaning is performed to remove noise data or incomplete data. Then, based on the historical monitoring abnormal data, the situation of high concentration of tin element in the waste liquid is screened out. The tin content can be detected by chemical analysis or online heavy metal analysis instrument, and the concentration threshold of tin is set. For example, when the tin concentration in the waste liquid exceeds 0.5mg / L, it is expressed as abnormal tin-containing wastewater. The historical abnormal data will be used as a reference for formulating the abnormal classification standard of tin-containing wastewater. In actual operation, the real-time monitoring data is compared with the historical abnormal data. If the tin concentration in the waste liquid data at a certain moment exceeds the set threshold (0.5mg / L), it is marked as abnormal tin-containing wastewater, and the relevant time and data are recorded. The abnormal monitoring data of tin-containing wastewater finally obtained includes information such as the monitoring time of the waste liquid, the tin concentration value, and the type of waste liquid.

[0174] Step S433: collecting acid gas according to the real-time monitoring point data, thereby obtaining acid gas data;

[0175] In this embodiment, the gas sensor of the real-time monitoring device is activated to collect the concentration data of acidic gas. The monitoring point of acidic gas is set at the exhaust port of the waste liquid treatment system or the key position of the ventilation system to ensure that the acidic components in the waste gas can be accurately collected. The detection of acidic gas adopts chemical gas sensors, such as sulfur dioxide (SO2), nitrogen oxides (NO X ) sensors, etc., to monitor their concentration in real time. During the data collection process, set the concentration of acidic gas to be sampled once a minute, and record the electrical signal or digital signal output by the sensor and convert it into a concentration value. Set the threshold of acidic gas concentration. For example, if the SO2 concentration exceeds 100ppm, it is a high concentration of acidic gas and an alarm is required. The collected acidic gas data includes concentration, gas type and collection time, which will be further used for gas impact elimination.

[0176] Step S434: eliminating gas factors from abnormal monitoring data of tin-containing wastewater according to the acid gas data, thereby obtaining abnormal data of wastewater treatment;

[0177] In this embodiment, the acid gas data is first cross-checked with the abnormal monitoring data of tin-containing wastewater. If the acid gas concentration exceeds the set threshold while the waste liquid monitoring is abnormal, it indicates that the acid gas has an impact on the composition of the waste liquid. By standardizing the concentration of acid gases, for example, when the SO2 concentration value exceeds 100ppm, it is considered that the gas factor has a greater impact on the wastewater. At this time, the abnormal data of the wastewater needs to be re-evaluated and eliminated. By using the elimination method, the abnormal data of the waste liquid in the time period with a higher acid gas concentration is eliminated, and then the tin concentration is confirmed by other chemical analyses to ensure that these data are not misled by the influence of acid gases. Finally, after the gas factor is eliminated, the abnormal data of waste liquid treatment obtained will include the tin concentration in the waste liquid, the type of wastewater, and the final data after excluding the influence of acid gas, and the relevant monitoring time and processing results will be saved.

[0178] Step S435: Generate an alarm signal according to the waste liquid treatment abnormal data and trigger a maintenance reminder for the chemical waste liquid recovery equipment to obtain the waste liquid recovery equipment maintenance data.

[0179] In this embodiment, according to the abnormal data of waste liquid treatment, when the tin concentration in the waste liquid exceeds the set threshold (such as 0.5 mg / L) and is not affected by gas factors, the alarm signal is immediately triggered. The generation of the alarm signal includes the equipment self-check function. When the waste liquid is detected to be abnormal, the monitoring system generates an alarm according to the set rules and notifies in real time through the display, email or text message. Subsequently, the system determines whether the equipment needs maintenance based on the abnormal data. If the tin concentration is abnormal and the waste liquid treatment does not meet the standards, the system will trigger the maintenance reminder of the chemical waste liquid recovery equipment, reminding the maintenance personnel to check the operating status of the equipment, the working condition of the filter element or pump, etc. The system records the maintenance data, including the alarm time, tin concentration value, alarm type, maintenance requirements and other information, to provide a basis for subsequent equipment maintenance work.

[0180] Step S44: performing digital feedback identification on the maintenance data of the waste liquid recovery equipment, thereby obtaining digital feedback data;

[0181] In this embodiment, the maintenance data of the waste liquid recovery equipment is processed by a digital feedback identification system. During this process, the maintenance data of the equipment, including equipment status, fault code, alarm history, maintenance record, etc., will be converted into digital signals and input into the digital feedback identification system. First, the system obtains the current fault status of the equipment or the specific problem that needs maintenance by scanning the fault code and sensor signal of the equipment. For example, if a temperature exceeding the standard fault is detected, the system will record the information and generate a corresponding maintenance task. The feedback data includes the maintenance task number, task description, processing steps, recommended maintenance time, etc. The equipment fault information is digitally stored and a historical data archive is generated. The digital data will be used for subsequent management systems, fault analysis and equipment maintenance planning.

[0182] Preferably, step S44 further includes the following steps:

[0183] Step S441: extracting equipment operation status features according to the waste liquid recovery equipment maintenance data, thereby obtaining equipment operation status data;

[0184] In this embodiment, a data acquisition system is used to monitor the operating parameters of the equipment in real time and record the operating conditions of the equipment, including but not limited to temperature, pressure, flow, current, voltage and other data. These data are collected by sensors and stored in the database. Next, through data processing tools, such as the Pandas library in Python, the maintenance data of the equipment is cleaned and denoised, and key operating parameter features are extracted, such as the average temperature of the equipment, the maximum working pressure and the current fluctuation range. These features can be used to determine the health status of the equipment through set threshold standards (such as the normal temperature range of the equipment is 30°C to 50°C, and the current fluctuation does not exceed ±5%), providing data support for subsequent steps.

[0185] Step S442: extracting fault warning indicators according to the equipment operation status data, thereby obtaining fault warning indicator data;

[0186] In this embodiment, time series analysis methods, such as sliding windows and Fourier transforms, are used to periodically analyze the operation data of the equipment and extract abnormal fluctuation patterns in the operation of the equipment. First, the data such as the temperature and pressure of the equipment are smoothed to remove short-term fluctuations and obtain a more stable operation curve. Then, signal processing methods, such as fast Fourier transform (FFT), are used to extract the frequency characteristics in the signal to determine whether there are abnormal vibration or noise patterns. These frequency components indicate the occurrence of equipment failures. In addition, combined with the service life of the equipment and historical fault records, machine learning algorithms, such as support vector machines (SVM), are used to classify the operation data of the equipment and extract fault warning indicators. These indicators include excessive temperature, low pressure, abnormal vibration, etc. of the equipment, and corresponding warning signals are generated.

[0187] Step S443: identifying equipment maintenance requirements according to the fault warning indicator data, thereby obtaining maintenance requirement data;

[0188] In this embodiment, a threshold standard is set for each fault warning indicator. For example, when the temperature exceeds 50°C, it indicates that the equipment has an overheating problem and needs to be inspected; when the vibration frequency abnormality exceeds a certain range, it indicates that the parts of the equipment are severely worn and also need maintenance. By comparing the current status data of the equipment with the set threshold, it is possible to identify which equipment requires further maintenance or replacement of certain parts. Maintenance requirement data includes specific fault descriptions (such as "temperature is too high", "pressure sensor failure") and corresponding maintenance actions (such as "replace sensor", "check cooling system"). These maintenance requirement data will be automatically generated and recorded in the maintenance management system to form a maintenance work order, providing a basis for subsequent maintenance operations.

[0189] Preferably, step S443 further includes the following steps:

[0190] Perform equipment status analysis based on fault warning indicator data to obtain equipment status data;

[0191] In this embodiment, fault warning indicator data from the equipment is collected, including but not limited to various sensor data such as temperature, pressure, vibration, flow, power, etc. The acquisition frequency of each sensor should meet the requirements of the equipment operation characteristics, for example, vibration data is collected once per second and temperature data is collected once per minute. These data are summarized in real time through the sensor data acquisition system. During the data processing process, data cleaning technology is used to remove invalid or noise data to ensure data quality. Then, basic descriptive statistical analysis is performed on each indicator using statistical analysis methods (such as mean, standard deviation, maximum value, minimum value) to identify the operating status of the equipment. To ensure the accuracy of the analysis, specific analysis thresholds can be set. For example, when the temperature exceeds 45°C, it means that the equipment enters the warning state, and when the vibration frequency exceeds the specified range (such as 20Hz), it indicates that the equipment has signs of failure. Finally, the status data of the equipment is generated, including whether the equipment is in normal working condition and whether there is an abnormality or potential failure risk.

[0192] Performing fault risk assessment on equipment status data to obtain fault risk assessment data;

[0193] In this embodiment, the equipment status data is used to perform fault risk assessment. First, by analyzing the abnormal fluctuations of various indicators of the equipment, a risk assessment model such as fault tree analysis (FTA) or analytic hierarchy process (AHP) is used to determine the risk weights of different equipment components, combined with historical data of equipment operation and industry standards. For each equipment component, the failure probability of each component is calculated based on its fault warning data (such as temperature, pressure, vibration, etc.). For example, by calculating the frequency of equipment temperature exceeding 40°C, combined with historical data, the failure probability of the equipment due to excessive temperature is determined. Then, by weighted calculation of the risks of all equipment components, the overall failure risk level of the equipment is comprehensively evaluated. The evaluation results include the failure risk score of each equipment component, and the components are ranked according to the score, providing a basis for the next step of key component identification. The fault risk assessment data specifically includes information such as risk level and time interval of failure.

[0194] Identify key equipment components based on failure risk assessment data to obtain key component data;

[0195] In this embodiment, a risk threshold is set. For example, if the failure risk score of an equipment component exceeds 0.8, it is considered a high-risk component; a component with a score between 0.5 and 0.8 is a medium-risk component; and a component with a score below 0.5 is a low-risk component. By screening the failure risk assessment data, high-risk components are selected as key components. For example, for high-temperature equipment, temperature sensors and cooling systems are key components; for mechanical transmission equipment, vibration sensors and motors are key components. Combined with the equipment structure diagram, the failure risk score of each component is marked one by one, and by comparison, a specific list of key components is obtained. At this point, the key component data will include information such as component name, component number, failure risk score, etc., which will serve as the basis for subsequent equipment maintenance priority sorting and maintenance strategy formulation.

[0196] Sort equipment maintenance priorities based on key component data to obtain maintenance priority data;

[0197] In this embodiment, each key component is scored based on factors such as its failure risk score, impact range, and maintenance cost. For example, equipment components with a failure risk score exceeding 0.8 have the highest priority; those with a score between 0.5 and 0.8 have a medium priority; and those with a score below 0.5 have the lowest priority. At the same time, weighting can also be performed based on factors such as the importance of the production line where the equipment is located and the impact on the overall production process. For example, the failure of a key equipment component will cause the entire line to shut down, and its priority should be increased at this time. After the score calculation is completed, all key components are sorted according to priority. The maintenance priority data finally generated will include information such as the maintenance priority of each key component, the expected time of failure, and the recommended maintenance time.

[0198] Maintenance demand forecasting is performed based on maintenance priority data to obtain maintenance demand data.

[0199] In this embodiment, the scheduled maintenance time of each key component is calculated based on the workload, operating time and historical failure data of the equipment, combined with the maintenance cycle of the equipment. For example, if the transmission system of the equipment has worked for 5,000 hours, and historical data shows that its failure rate rises sharply after 6,000 hours, the system will automatically predict that preventive maintenance will be performed after 5,000 hours. Next, the maintenance history data, failure mode and priority ranking of the equipment are used to predict future maintenance needs. In specific implementation, the failure risk of the equipment in the future can be predicted by time series analysis methods, and the maintenance strategy can be adjusted in combination with the current operating status of the equipment and maintenance priority data. Finally, the generated maintenance demand data will include information such as the maintenance time, required spare parts, personnel arrangements and maintenance resources of each equipment component.

[0200] Step S444: Formulate an equipment maintenance plan according to the maintenance requirement data, thereby obtaining equipment maintenance plan data;

[0201] In this embodiment, according to each maintenance requirement, the relevant equipment maintenance manual and maintenance history are searched to clarify the specific operation steps. For example, for the problem of excessive temperature of the equipment, the maintenance plan should include operation steps such as checking the cooling system, replacing the temperature sensor, and cleaning the radiator. For the failure of the equipment pressure sensor, the maintenance plan should include operations such as disconnecting the power supply, removing the sensor, replacing parts and recalibrating. Each maintenance plan needs to list in detail the required tools, spare parts and estimated maintenance time to ensure that maintenance personnel can perform maintenance tasks efficiently. At the same time, combined with the workload and production plan of the equipment, the maintenance plan should also consider minimizing the equipment downtime and provide clear steps for restarting the equipment. All maintenance plans will be recorded in detail and used as part of the equipment maintenance plan to ensure that the equipment can be restored to normal working condition to the greatest extent possible during the maintenance process.

[0202] Step S445: Perform digital feedback identification according to the equipment maintenance plan data to obtain digital feedback data.

[0203] In this embodiment, a digital management system is used to encode the maintenance plan to ensure that the execution of each maintenance task can be tracked and recorded. Through an integrated equipment maintenance system (such as CMMS, a computerized maintenance management system), each maintenance work will automatically generate a work order according to the predetermined plan, and update the maintenance history of the equipment after completion. In addition, digital feedback also includes the update of all key parameters of the equipment during the maintenance process, such as the equipment temperature and pressure recovery after maintenance. All information is stored in digital form for future maintenance decisions. These feedback data will be uploaded through a cloud platform or enterprise resource planning (ERP) system to ensure real-time updating and cross-departmental sharing of maintenance information, further enhancing the intelligence of equipment management.

[0204] Step S45: Upload the digital feedback data to the digital optimization three-dimensional model of chemical waste liquid treatment to execute the digital management task of chemical waste liquid recycling and treatment information.

[0205] In this embodiment, when uploading digital feedback data to the digital optimization three-dimensional model of chemical waste liquid treatment, it is first necessary to integrate the digital feedback data obtained from equipment maintenance with the three-dimensional model of chemical waste liquid treatment. This process involves dynamically updating the equipment status and operating parameters in the model. Through the system interface, the latest maintenance data of the equipment (such as temperature adjustment, flow control, changes in the dosage of the reagent, etc.) is uploaded to the optimization model to reflect the changes in the equipment and the treatment process in real time. The data includes the current status of the equipment, the time when the maintenance is completed, the next operation suggestion, etc., and the model will optimize and adjust the waste liquid treatment process accordingly based on these data. By uploading these feedback data to the three-dimensional model, it is possible to ensure the synchronous update of the equipment and the waste liquid treatment process, thereby realizing the digital management and process optimization of waste liquid recovery and treatment information.

[0206] Preferably, the present specification also provides a chemical waste liquid recycling process information digital management system, which is used to execute the chemical waste liquid recycling process information digital management method as described above, and the chemical waste liquid recycling process information digital management system includes:

[0207] The digital three-dimensional model building module for waste liquid treatment is used to collect the parameters of chemical waste liquid recovery equipment using an automatic sampler and save the parameter information of the chemical waste liquid recovery equipment; according to the parameter information of the chemical waste liquid recovery equipment, a digital three-dimensional model of chemical waste liquid treatment is built using computer-aided engineering software;

[0208] The waste liquid recovery and treatment simulation module is used to obtain the characteristic data of chemical waste liquid and input it into the digital three-dimensional model of chemical waste liquid treatment, simulate the waste liquid recovery and treatment process by using simulation software, and quantitatively evaluate the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results;

[0209] The model optimization module is used to determine whether the waste liquid recovery treatment process meets the preset treatment standards based on the treatment process evaluation results; if the treatment process evaluation results do not meet the preset treatment standards, the key parameters in the treatment process are adjusted, and the digital three-dimensional model of chemical waste liquid treatment is optimized to obtain a digital optimized three-dimensional model of chemical waste liquid treatment;

[0210] The waste liquid recovery information digital management module is used to upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device, monitor the waste liquid recovery and treatment process in real time, and generate an alarm signal and trigger a maintenance reminder for the chemical waste liquid recovery equipment when an abnormality is detected in the waste liquid treatment process; when the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery and treatment information.

Claims

1. A method for digital management of chemical waste liquid recycling process information, characterized in that: Applied to chemical waste liquid recovery equipment, the chemical waste liquid recovery equipment is equipped with an automatic sampler and an intelligent monitoring device, and the chemical waste liquid recovery process information digital management method includes the following steps: Step S1: using an automatic sampler to collect chemical waste liquid recovery equipment parameters and save the chemical waste liquid recovery equipment parameter information; according to the chemical waste liquid recovery equipment parameter information, using computer-aided engineering software to construct a digital three-dimensional model of chemical waste liquid treatment; Step S2: Acquire chemical waste liquid characteristic data and input it into the digital three-dimensional model of chemical waste liquid treatment, use simulation software to simulate the waste liquid recovery treatment process, and perform quantitative evaluation of the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results. Step S2 is specifically as follows: Step S21: Obtain chemical waste liquid sample data, use liquid chromatography to measure the viscosity of the chemical waste liquid sample, perform 15-20 measurements, record the viscosity value of each measurement, calculate the average value of all measurement values ​​as the final waste liquid viscosity value, and record the chemical waste liquid sample viscosity data; Step S22: Use gas chromatography to perform cuprous chloride solution detection on the chemical waste liquid sample data, take out 10-50 ml of the waste liquid sample, filter it using a filter membrane with a pore size of 0.45-0.55 micron, and use a flame ionization detector to perform chromatographic column detection, wherein the column temperature of the gas chromatography is set to 70°C-120°C, so as to obtain cuprous chloride solution data; Step S23: Integrate the chemical waste liquid sample viscosity data and cuprous chloride solution data to obtain chemical waste liquid characteristic data; Step S24: inputting the chemical waste liquid characteristic data into the digital three-dimensional model of chemical waste liquid treatment, simulating the waste liquid recovery treatment process using simulation software, and quantitatively evaluating the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results; Step S3: judging whether the waste liquid recovery treatment process meets the preset treatment standard according to the treatment process evaluation result; if the treatment process evaluation result does not meet the preset treatment standard, adjusting the key parameters in the treatment process, and optimizing the digital three-dimensional model of chemical waste liquid treatment to obtain the digital optimized three-dimensional model of chemical waste liquid treatment; Step S4: Upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device to monitor the waste liquid recovery process in real time. When an abnormality is detected in the waste liquid treatment process, an alarm signal is generated and a maintenance reminder for the chemical waste liquid recovery equipment is triggered. When the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery information.

2. The method for digital management of chemical waste liquid recycling process information according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: using an automatic sampler to collect operating parameters of the chemical waste liquid recovery equipment in real time, thereby obtaining operating data of the chemical waste liquid recovery equipment; Step S12: normalizing and processing the outliers of the chemical waste liquid recovery equipment operation data, and generating parameter information of the chemical waste liquid recovery equipment; Step S13: According to the parameter information of the chemical waste liquid recovery equipment, the fluid flow channel and structural components of the chemical waste liquid recovery equipment are reconstructed in three-dimensional space using parametric modeling technology to obtain three-dimensional structural data of the equipment; Step S14: acquiring fluid characteristic parameters; analyzing the fluid distribution inside the device by combining the fluid characteristic parameters and the three-dimensional structure data of the device with a fluid dynamics simulation method, thereby obtaining the fluid distribution data inside the device; Step S15: Generate a digital three-dimensional model of chemical waste liquid treatment using computer-aided engineering tools based on the three-dimensional structure data of the equipment and the fluid distribution data inside the equipment.

3. The method for digital management of chemical waste liquid recycling process information according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: obtaining fluid characteristic parameters using a rheometer; Step S142: Preliminary processing of the fluid characteristic parameters, including denoising, normalization and data fitting, to obtain preliminary processed fluid characteristic parameters; Step S143: using computer-aided design software and computational fluid dynamics software, combined with the three-dimensional structure data of the equipment, to establish a fluid flow model; Step S144: using computational fluid dynamics software, setting fluid flow boundary conditions and fluid properties according to the preliminary processed fluid characteristic parameters, thereby obtaining fluid dynamics simulation start data; Step S145: numerically simulate the fluid dynamics simulation startup data using computational fluid dynamics software to calculate the velocity field, pressure field, and temperature field distribution of the fluid inside the device, thereby obtaining the fluid motion state data inside the device; Step S146: Use visualization software to generate visualization of the internal fluid distribution of the device based on the internal fluid movement state data, thereby obtaining the internal fluid distribution data of the device.

4. The method for digital management of chemical waste liquid recycling process information according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: Start computer-aided engineering software and create a three-dimensional model project file of the equipment; Step S152: importing the equipment 3D structure data into the equipment 3D model project file, and drawing the equipment 3D geometric model; Step S153: adjusting the internal flow channels and internal pores of the equipment on the three-dimensional geometric model of the equipment according to the internal fluid distribution data of the equipment, so as to obtain a digital three-dimensional model of chemical waste liquid treatment.

5. The method for digital management of chemical waste liquid recycling process information according to claim 1, characterized in that: Step S24 is specifically as follows: Step S241: using a database tool to convert the chemical waste liquid characteristic data into a CSV file to obtain a chemical waste liquid characteristic CSV file; Step S242: inputting the chemical waste liquid characteristic CSV file into the digital three-dimensional model of chemical waste liquid treatment, and uploading the digital three-dimensional model of chemical waste liquid treatment to the simulation software; Step S243: performing waste liquid flow simulation in simulation software, wherein the flow type is set to turbulent flow type, and the flow velocity is set to 0.1 m / s to 2 m / s; Step S244: performing a waste liquid mixing simulation in the simulation software, wherein the stirring speed is set to 50 rpm to 300 rpm and the mixing time is set to 1 min to 30 min; Step S245: performing waste liquid separation simulation in simulation software, wherein the porosity of the separation medium is set to 30% to 60%, and the sedimentation rate is set to 1 mm / s to 20 mm / s; Step S246: running the waste liquid recovery processing module in the simulation software, and recording the waste liquid separation efficiency data and the waste liquid processing time data at intervals of 10 seconds; Step S247: If the waste liquid separation efficiency data and the waste liquid treatment time data reach the set recovery and treatment parameter target, stop the simulation process and output the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time; Step S248: Perform a quantitative evaluation of the waste liquid recovery result based on the separation efficiency data table of the separation equipment and the statistical information of the waste liquid treatment time to obtain a treatment process evaluation result.

6. The method for digital management of chemical waste liquid recycling process information according to claim 5, characterized in that: Step S248 is specifically as follows: Calculating the waste liquid recovery efficiency according to the separation efficiency data table of the separation equipment to obtain waste liquid recovery efficiency data, and evaluating the waste liquid recovery efficiency on the waste liquid recovery efficiency data to obtain a waste liquid recovery efficiency evaluation result; Calculate the processing energy consumption based on the waste liquid recovery efficiency data and the statistical information of the waste liquid processing time, so as to obtain the waste liquid processing energy consumption data, and evaluate the waste liquid processing energy consumption based on the waste liquid processing energy consumption data, so as to obtain the waste liquid processing energy consumption evaluation result; Conduct equipment stability analysis based on waste liquid treatment energy consumption data and chemical waste liquid recovery equipment operation data to obtain equipment stability data, and conduct equipment stability evaluation on the equipment stability data to obtain equipment stability results; The waste liquid recovery efficiency evaluation results, waste liquid treatment energy consumption evaluation results and equipment stability results are integrated to obtain the treatment process evaluation results.

7. The method for digital management of chemical waste liquid recycling process information according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Obtaining preset chemical waste liquid recycling and treatment standard data; Step S32: making an abnormality judgment on the treatment process evaluation result according to the preset chemical waste liquid recovery treatment standard data, thereby obtaining an abnormal treatment process evaluation result; Step S33: adjusting the temperature of the chemical waste liquid recovery equipment to 60-80° C. based on the abnormality handling process evaluation result, thereby obtaining temperature adjustment data; Step S34: using a pump speed regulating device to adjust the flow rate of the abnormality handling process evaluation result to 2-5 m³ / h, thereby obtaining flow rate adjustment data; Step S35: using a pH sensor to adjust the reagent dosage ratio based on the abnormality handling process evaluation result, adjusting the amount of sodium hydroxide added to 100-150ppm, thereby obtaining reagent dosage ratio adjustment data; Step S36: adjusting key parameters of the digital three-dimensional model of chemical waste liquid treatment according to the temperature adjustment data, the flow adjustment data and the reagent addition ratio adjustment data, so as to obtain the digital optimized three-dimensional model of chemical waste liquid treatment.

8. The method for digital management of chemical waste liquid recycling process information according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: using a flow chart modeling tool to digitally optimize the three-dimensional model of chemical waste liquid treatment to model the waste liquid recovery process, thereby obtaining a waste liquid recovery flow chart, and uploading the waste liquid recovery flow chart to the intelligent monitoring device; Step S42: setting real-time monitoring points according to the waste liquid recovery flow chart, thereby obtaining real-time monitoring point data; Step S43: Start the intelligent monitoring device to monitor waste liquid treatment abnormalities according to the real-time monitoring point data, thereby obtaining waste liquid treatment abnormality data, generating an alarm signal and triggering a maintenance reminder for the chemical waste liquid recovery equipment, and obtaining the waste liquid recovery equipment maintenance data; Step S44: performing digital feedback identification on the maintenance data of the waste liquid recovery equipment, thereby obtaining digital feedback data; Step S45: Upload the digital feedback data to the digital optimization three-dimensional model of chemical waste liquid treatment to execute the digital management task of chemical waste liquid recycling and treatment information.

9. A digital management system for chemical waste liquid recycling process information, characterized in that: Used to implement the chemical waste liquid recycling process information digital management method as claimed in claim 1, the chemical waste liquid recycling process information digital management system comprises: The digital three-dimensional model building module for waste liquid treatment is used to collect the parameters of chemical waste liquid recovery equipment using an automatic sampler and save the parameter information of the chemical waste liquid recovery equipment; according to the parameter information of the chemical waste liquid recovery equipment, a digital three-dimensional model of chemical waste liquid treatment is built using computer-aided engineering software; The waste liquid recovery and treatment simulation module is used to obtain the characteristic data of chemical waste liquid and input it into the digital three-dimensional model of chemical waste liquid treatment, simulate the waste liquid recovery and treatment process by using simulation software, and quantitatively evaluate the waste liquid recovery results of the simulation process to obtain the treatment process evaluation results; The model optimization module is used to determine whether the waste liquid recovery treatment process meets the preset treatment standards based on the treatment process evaluation results; if the treatment process evaluation results do not meet the preset treatment standards, the key parameters in the treatment process are adjusted, and the digital three-dimensional model of chemical waste liquid treatment is optimized to obtain a digital optimized three-dimensional model of chemical waste liquid treatment; The waste liquid recovery information digital management module is used to upload the digitally optimized three-dimensional model of chemical waste liquid treatment to the intelligent monitoring device, monitor the waste liquid recovery and treatment process in real time, and generate an alarm signal and trigger a maintenance reminder for the chemical waste liquid recovery equipment when an abnormality is detected in the waste liquid treatment process; when the waste liquid treatment returns to normal, the digital feedback data is uploaded to the digitally optimized three-dimensional model of chemical waste liquid treatment to perform the digital management task of chemical waste liquid recovery and treatment information.

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