Hydrogen peroxide purification process state monitoring method

Abstract

The invention relates to the technical field of state monitoring, and discloses a hydrogen peroxide purification process state monitoring method. The method comprises the following steps: performing multi-point real-time monitoring and hydrogen peroxide decomposition risk target factor calculation on the temperature, pressure, conductivity and organic matter content of hydrogen peroxide purification equipment to obtain an evaluation result of the influence of temperature gradient and impurity content on hydrogen peroxide stability; carrying out predictive control parameter calculation on the heating power, the cooling flow and the feeding rate to obtain a safe operation parameter range of the equipment; and performing dynamic safety boundary calculation on the temperature distribution and the concentration boundary in the purification process to obtain a hydrogen peroxide decomposition risk early warning index. Through construction of the dynamic safety boundary and calculation of the risk early warning index, stable operation of the hydrogen peroxide purification equipment is realized, so that the production process of the electronic-grade high-purity hydrogen peroxide is safer, more stable and more efficient.

Description

Technical Field

[0001] The present application relates to the technical field of condition monitoring, and in particular to a method for condition monitoring of a hydrogen peroxide purification process. Background Art

[0002] With the rapid development of the electronics industry, the purity requirements for electronic-grade hydrogen peroxide are becoming increasingly stringent. A variety of purification techniques, including distillation, adsorption, ion exchange resins, membrane separation, crystallization, and extraction, are required to remove metal ions, organic matter, and other impurities from the solution. However, these purification methods often face challenges in hydrogen peroxide instability in industrial applications. In particular, factors such as temperature fluctuations, catalytic effects of metal impurities, and interference from organic matter can accelerate the decomposition reaction of hydrogen peroxide, reducing product purity and posing safety risks.

[0003] Current control methods for the hydrogen peroxide purification process rely primarily on traditional fixed-parameter PID control and manual experience, lacking a deep understanding of the decomposition kinetics of hydrogen peroxide and scientific monitoring methods. These traditional methods are unable to accurately capture the impact of multiple factors, such as temperature gradients, metal ion catalytic effects, and organic interference, on hydrogen peroxide stability, making it difficult for the control system to respond promptly to potential risks. Furthermore, existing monitoring technologies lack the accuracy to accurately detect impurity levels in real time, and the data correlation analysis capabilities of multi-point distributed monitoring networks are limited, making it difficult to accurately assess the global status of the equipment. This makes it difficult to precisely define the safety boundaries of the purification process and lacks a scientific basis for control decisions. Summary of the Invention

[0004] The present application provides a method for monitoring the status of a hydrogen peroxide purification process. The present application establishes a full life cycle monitoring system for the hydrogen peroxide purification process. This not only solves the problem that traditional methods cannot accurately capture the coupling effects of multiple factors, but also achieves stable operation of hydrogen peroxide purification equipment through the construction of dynamic safety boundaries and the calculation of risk warning indicators, making the production process of electronic-grade high-purity hydrogen peroxide safer, more stable and efficient.

[0005] In a first aspect, the present application provides a method for monitoring the status of a hydrogen peroxide purification process, the method comprising: Conduct multi-point real-time monitoring of the temperature, pressure, conductivity, and organic matter content of hydrogen peroxide purification equipment to obtain equipment operating status data; Based on the equipment operating status data, calculating and analyzing the target risk factors for hydrogen peroxide decomposition in the hydrogen peroxide purification equipment to obtain an assessment result of the impact of temperature gradient and impurity content on hydrogen peroxide stability; Based on the impact assessment results, characteristic signals of metal ions and organic matter are extracted to obtain real-time values ​​of impurity content, and predictive control parameter calculations are performed on the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment to obtain a safe operating parameter range for the equipment; By utilizing the safe operating parameter range of the equipment, dynamic safety boundary calculations are performed on the temperature distribution and concentration boundary during the purification process to obtain a hydrogen peroxide decomposition risk warning indicator.

[0006] The technical solution provided in this application forms a multi-point real-time monitoring network by placing sensor arrays at target locations on the equipment. A heat-mass coupling transfer model is established, and the temperature gradient influencing factor is introduced, incorporating the concept of spatial distribution into the kinetic analysis of hydrogen peroxide decomposition. Wavelet decomposition and adaptive Kalman filtering techniques are used to extract characteristic signals of metal ions and organic matter, solving the problem of accurately monitoring impurity content. A control system with a hierarchical optimization structure is constructed based on predictive control theory, achieving coordinated processing of hard and soft constraints. Safety margins are calculated by solving the Lyapunov inequality for heat and mass transfer, forming a dynamically updated safe operating area map, ensuring that the hydrogen peroxide purification process balances safety and efficiency. Multi-point monitoring, kinetic modeling, impurity monitoring, predictive control, and safety margin calculation are organically combined to establish a full lifecycle monitoring system for the hydrogen peroxide purification process. This not only addresses the inability of traditional methods to accurately capture the coupling effects of multiple factors, but also ensures stable operation of the hydrogen peroxide purification equipment through the construction of dynamic safety margins and the calculation of risk warning indicators, making the production process of electronic-grade high-purity hydrogen peroxide safer, more stable, and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0008] Figure 1 This is a schematic diagram of an embodiment of a method for monitoring the status of a hydrogen peroxide purification process in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The embodiments of the present application provide a method for monitoring the state of a hydrogen peroxide purification process. The terms "first," "second," "third," "fourth," and the like (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products, or apparatus.

[0010] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for monitoring the status of a hydrogen peroxide purification process includes: Step S101: Perform multi-point real-time monitoring of the temperature, pressure, conductivity, and organic matter content of the hydrogen peroxide purification equipment to obtain equipment operating status data; It is understandable that the execution subject of this application can be a hydrogen peroxide purification process status monitoring system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0011] Specifically, an array of temperature sensors is placed at key locations within the hydrogen peroxide purification equipment, such as the reaction zone, separation zone, and discharge port. The spacing between the temperature sensors is maintained between 10 and 15 cm to form a temperature gradient monitoring network. These sensors continuously collect temperatures in different regions, acquiring temperature distribution data within the equipment. During data acquisition, an adaptive sampling strategy is employed to dynamically adjust the sampling frequency based on the rate of temperature change, increasing the sampling frequency during periods of drastic temperature fluctuations and decreasing it during periods of stability, thereby generating temperature gradient distribution data. Furthermore, conductivity sensors are installed at the feed inlet, reaction zone, separation zone, and discharge port of the hydrogen peroxide purification equipment. These sensors monitor changes in the solution's conductivity in real time, generating solution conductivity distribution data that reflects concentration variations in each region and trends in impurity content. Based on this solution conductivity distribution data, near-infrared spectroscopy is used to analyze the organic matter content in the solution. Combined with data analysis methods such as principal component analysis or partial least squares regression, characteristic organic matter signals are extracted to generate spectral data on the composition of the organic matter in the solution. Inductively coupled plasma mass spectrometry (ICP-MS) was performed on solution samples from the hydrogen peroxide purification equipment. Using highly sensitive ICP-MS technology, the types and contents of metal ions in the samples were accurately analyzed, generating distribution data for metal impurities. A spatial distribution model of metal ions in different regions was then constructed using a feature mapping matrix to reflect the spatial variation patterns of different metal impurities. Pressure sensors were installed in the high-temperature and high-pressure zones of the hydrogen peroxide purification equipment. These sensors monitor pressure fluctuations during the process in real time, capturing pressure trend data during equipment operation. This data helps identify abnormal or hazardous pressure conditions within the reaction zone. Data on equipment temperature gradients, solution conductivity distribution, organic composition spectra, metal impurity distribution, and pressure trend data were preprocessed, including outlier removal, data smoothing, and normalization, to remove noise and ensure data consistency. During data preprocessing, methods such as sliding mean, Kalman filtering, or wavelet decomposition were used to smooth the data and remove outliers to ensure data quality. The processed data was then integrated into equipment operating status data.

[0012] Step S102: Calculate and analyze the target risk factors for hydrogen peroxide decomposition in the hydrogen peroxide purification equipment based on the equipment operating status data to obtain an assessment result of the impact of temperature gradient and impurity content on hydrogen peroxide stability; Specifically, the temperature and concentration correlation analysis of the equipment operating status data was conducted. By coupling the temperature data of the reaction zone, separation zone and discharge port with the solution concentration data, the intrinsic correlation between temperature changes and hydrogen peroxide concentration distribution was revealed, and the regularity data of concentration distribution was obtained. These data were combined to analyze the concentration variation trend in different areas. According to the hydrogen peroxide concentration distribution regularity data, the reaction rate under different temperature conditions was fitted. Combined with the experimental data, a mathematical expression model of reaction rate reflecting the influence of temperature gradient was established through data fitting method. This model can capture the influence of temperature change on reaction rate and take into account the nonlinear change factors caused by temperature gradient. Based on the mathematical expression model of reaction rate, the hydrogen peroxide purification equipment was divided into multiple control units, and the equipment space was divided into several small areas. A local state equation was established in each control unit to form a hydrogen peroxide decomposition spatial distribution model, which describes the concentration change and temperature response characteristics of hydrogen peroxide in different areas. Based on the distribution data of metal impurities in the equipment, the catalytic coefficient of the metal ions was calculated by analyzing the metal ion concentration and its distribution in different regions. A temperature correction factor was introduced based on temperature variations to quantify the catalytic effect of metal ions on the hydrogen peroxide decomposition process, effectively revealing the promoting effect of different metal ions on the reaction rate under different temperature conditions. Using organic matter composition data obtained from near-infrared spectroscopy, principal component analysis was used to extract and classify organic matter characteristics. A mathematical model for the influence of organic matter on hydrogen peroxide stability was established. Based on the stability characteristics of different organic matter, an assessment value of the impact of organic matter interference on stability was calculated, quantifying the perturbation effect of organic matter on hydrogen peroxide stability under different conditions. The spatial distribution model of hydrogen peroxide decomposition, the quantitative results of the metal ion catalytic effect, and the assessment value of organic matter interference were used for stability analysis. Using the Lyapunov stability analysis method, the system stability was evaluated by combining multi-dimensional data of temperature, concentration, and impurity content variations to determine the stability of the hydrogen peroxide decomposition process under different conditions. By jointly analyzing these data, the overall impact of temperature gradient and impurity content on the decomposition stability of hydrogen peroxide is revealed. The evaluation results can quantify the stability change trend of hydrogen peroxide under different operating conditions and identify potential instability factors.

[0013] Step S103: Based on the impact assessment results, metal ion and organic matter characteristic signals are extracted to obtain real-time values ​​of impurity content, and predictive control parameter calculations are performed on the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment to obtain a safe operating parameter range for the equipment; Specifically, a basic signal separation model is constructed, encompassing the hydrogen peroxide (H2O2) signal, impurity signals, and noise signals. This model decomposes the raw monitoring data into a target signal, an impurity interference signal, and a background noise signal. Wavelet decomposition is then applied to the signal separation model. By selecting appropriate wavelet basis functions, such as the Daubechies wavelet basis, the signal is decomposed at multiple scales to obtain characteristic wavelet coefficients at different frequency bands. These wavelet coefficients reflect the characteristic variations of the signal at different scales, allowing the extraction of characteristic information about metal ion impurities. Based on these characteristic wavelet coefficients, a characteristic mapping matrix for metal ion impurities is constructed. The characteristic response patterns of different metal ion types in the sensor network are mapped and summarized. By learning and training the characteristic mapping matrix, a characteristic identification standard for metal ions is formed, enabling accurate identification of the response patterns of the characteristic signals of different metal ions. Based on the impact assessment results, a state equation and an observation equation are created, and a dynamic filtering model for impurity concentration changes is established. This filtering model dynamically estimates the impurity concentration using an adaptive Kalman filter algorithm. The model is continuously revised and updated based on real-time observation data, ensuring accurate estimation under complex operating conditions. Using a dynamic impurity concentration filtering model, principal component analysis (PCA) is performed on the spectral data. Feature dimensionality reduction is performed on the spectral data to extract eigenvectors of organic impurities from the spectral data. These eigenvectors are then fed into a subsequent impurity concentration calculation model. Simultaneously, the characteristic identification criteria for metal ions and the eigenvectors of organic impurities are input into a neural network mapping function, which then fits the impurity concentration values. After extensive training with a large number of samples, the neural network demonstrates strong generalization capabilities, adapting well to varying operating conditions during the impurity concentration calculation process. Confidence intervals for impurity concentrations are calculated, yielding real-time values ​​of impurity content. Based on these real-time impurity content values, predictive control parameters for the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment are calculated. Using a model-based predictive control approach, the equipment's operating status is predicted at multiple future moments. Control variables are optimized based on the optimal control objective. By combining the impurity content trend with the equipment's physical boundary constraints, a safe operating parameter range is calculated to meet the equipment's safe operation requirements. This includes the optimal setpoint for heating power, the adjustable range for cooling flow rate, and the dynamic curve for feed rate.

[0014] A state-space model is constructed based on real-time numerical values ​​of impurity content. This model incorporates key control variables such as hydrogen peroxide concentration, temperature distribution, impurity concentration, and heating power, cooling flow rate, and feed rate. By analyzing the heat and mass transfer relationships of hydrogen peroxide under different operating conditions, the complex internal dynamics of the device are mathematically modeled using state equations and observation equations, forming a state-space model that describes the device's operational state changes. Based on the state-space model, prediction and control domain parameters are set. The prediction domain determines the number of steps to predict the future state, while the control domain defines the duration over which control adjustments can be made. By setting these parameters and combining them with real-time monitoring data, the system behavior is predicted at multiple moments in the future, resulting in a predicted sequence of system behavior that reflects the dynamic changes in the device's operational state over a period of time. A control objective function is then designed for this predicted sequence. During the objective function design process, a weight matrix for heating power, cooling flow rate, and feed rate is introduced, and constraint slack variables are considered to enhance the system's flexible control capabilities. This objective function ensures that the system achieves optimal control of heating, cooling, and feed rates while meeting safety margins and physical constraints, thereby achieving the goal of maintaining hydrogen peroxide stability. At the same time, based on the real-time numerical value of the impurity content and the previously obtained impact assessment results, the physical limitations and safety boundaries of the equipment are used as hard constraints. These hard constraints include temperature limits, pressure limits, impurity concentration safety thresholds, and equipment load capacity. Product quality indicators and efficiency targets are set as soft constraints. These soft constraints allow for moderate relaxation under extreme conditions to ensure the overall operational safety and stability of the system. The hierarchical constraint set can effectively balance the relationship between equipment operation safety and production efficiency, thereby providing reasonable boundary conditions for multi-objective optimization. On this basis, a two-layer optimization algorithm is executed using the constructed optimization solution objective function and hierarchical constraint set. The first-layer optimization algorithm prioritizes solving the optimal solution under the premise of satisfying the hard constraints, ensuring that the equipment operation safety and physical limits are not exceeded. The second-layer optimization algorithm further optimizes the soft constraints under the condition that the hard constraints are satisfied, thereby improving product quality and system operation efficiency. The optimal control instruction sequence is processed through a fast channel and a slow channel. The fast channel is primarily responsible for quickly responding to emergency situations such as temperature anomalies and pressure fluctuations. When the system detects a sudden temperature change or pressure fluctuation, the fast channel immediately adjusts the control instructions to ensure safe operation of the equipment. The slow channel is used to handle long-term issues such as concentration adjustment and impurity control. By gradually optimizing the control variables, precise control of hydrogen peroxide concentration and impurity content is achieved, thereby ensuring the stability and safety of the entire purification process. The optimal control instructions, after coordinated processing by the fast and slow channels, are used to obtain the safe operating parameter range of the equipment.

[0015] The control performance indicator and constraint violation penalty terms of the optimization objective function are decomposed to form a hierarchical objective function structure. This structure separates the control objective from the constraints of the system and introduces a penalty term to quantify the degree of constraint violation, providing a clear computational framework for the subsequent two-level optimization process. After completing the hierarchical decomposition of the objective function, the hard constraints in the hierarchical constraint set are converted into linear matrix inequalities. Physical constraints and safety boundary conditions, including equipment physical limitations, temperature boundaries, pressure limits, and impurity concentration safety thresholds, are expressed as linear inequality matrices. This allows the first-level optimization problem to be effectively addressed during the solution process. The matrix describes the key safety conditions that must be met for the equipment operating state in different regions and time periods. Based on the hierarchical objective function structure and the constraint matrix of the first-level optimization problem, the interior point method is used to solve the first-level optimization problem. Through the iterative optimization mechanism of the interior point method, an initial feasible solution that satisfies the hard constraints is quickly found. Using an initial feasible solution that satisfies the hard constraints, a soft constraint violation metric function is constructed to numerically quantify soft constraints such as product quality indicators, energy efficiency targets, and impurity content control accuracy. The degree of soft constraint violation is calculated to assess the system's potential to optimize the soft constraint objectives while satisfying the hard constraints. This soft constraint optimization space provides a sufficient solution space for subsequent quadratic optimization, allowing the overall system performance to be further optimized without violating the hard constraints. Within this soft constraint optimization space, a sequential quadratic programming algorithm is used to iteratively optimize the hierarchical objective function structure. The sequential quadratic programming algorithm is an effective optimization algorithm for nonlinear constraints. In each iteration, the algorithm approximates the original nonlinear problem into a quadratic programming subproblem, continuously optimizing the objective function by solving the subproblems while maintaining the satisfaction of the hard constraints. During the optimization process, the sequential quadratic programming algorithm dynamically adjusts the weight coefficient of the soft constraint violation penalty term to balance the improvement of the soft constraint objectives with the strict satisfaction of the hard constraints, thereby ensuring the stability and safety of the system under complex operating conditions. Through multiple iterations of the sequential quadratic programming algorithm within the soft constraint optimization space, an optimal control instruction sequence is obtained while satisfying the hard constraints.

[0016] Step S104: Using the equipment safe operating parameter range, dynamically calculate the temperature distribution and concentration boundary during the purification process to obtain a hydrogen peroxide decomposition risk warning indicator.

[0017] Specifically, a stability boundary calculation is performed based on the safe operating parameter range of the equipment. By coupling the equipment's heat and mass transfer characteristics, impurity concentration changes, and the dynamic characteristics of the reaction rate, a state boundary equation (SBE) is derived to characterize the system's stability. This SBE accurately describes the stability variations of the hydrogen peroxide decomposition process under different temperature, concentration, and impurity content conditions. Based on the SBE, the state space of the hydrogen peroxide purification process is divided into three regions: a safe operating zone, a warning zone, and a danger zone. A hierarchical safety zone division is performed based on the critical thresholds of the state variables. The safe operating zone corresponds to the region where the system state is completely within the stability boundary, the warning zone represents the region where the system state is close to the critical boundary but has not yet crossed the safety limit, and the danger zone indicates that the system state has exceeded the safety boundary and is at high risk of decomposition. This multi-level safety zone division effectively identifies the safety level of the system's operating state. Based on the hierarchical safety zone division results, a temperature gradient-guided concentration boundary constraint mathematical model is established. This model quantifies the relationship between temperature, concentration, and impurity content, and sets differentiated impurity content limits for different temperature ranges. This forms a multidimensional safety constraint relationship encompassing temperature, concentration, and impurities, dynamically reflecting the system's sensitivity to impurity concentration changes under different temperature conditions. A risk assessment function, encompassing temperature, concentration, metal ions, organic matter, and equipment status index, is constructed using safety constraint relationships. This risk assessment function considers multiple key parameters and calculates a quantified risk value for the system through multidimensional data fusion, comprehensively reflecting the system's current stability and potential risk level. Based on this multidimensional risk quantification value, adaptive adjustments to the boundary constraints are implemented. During system operation, the safe operating boundary is dynamically updated by continuously monitoring the changing trends of key parameters. This update process comprehensively considers changes in temperature gradients, impurity concentrations, and reaction rates, ensuring that the safety boundary remains consistent with the system's actual operating state. After the dynamic update is complete, the constraint violation probability of the updated safe operating boundary is calculated. By predicting the state evolution over multiple future time steps, the probability of the system violating the safety boundary is calculated, resulting in a warning indicator for hydrogen peroxide decomposition risk. This function quantifies the probability of anomalies occurring during system operation and triggers an alarm mechanism when the risk exceeds the threshold, enabling timely preventive control measures to be implemented and prevent hydrogen peroxide decomposition accidents.

[0018] In the embodiments of the present application, a multi-point real-time monitoring network is formed by setting up sensor arrays at target locations on the equipment. A heat-mass coupling transfer model is established and the temperature gradient influencing factor is introduced, incorporating the concept of spatial distribution into the kinetic analysis of hydrogen peroxide decomposition. Wavelet decomposition and adaptive Kalman filtering techniques are used to extract characteristic signals of metal ions and organic matter, solving the problem of accurately monitoring impurity content. A control system with a hierarchical optimization structure is constructed based on predictive control theory, achieving coordinated processing of hard and soft constraints. The safety margin is calculated by solving the heat and mass transfer Lyapunov inequality, forming a dynamically updated safe operating area map, ensuring that the hydrogen peroxide purification process balances safety and efficiency. Multi-point monitoring, kinetic modeling, impurity monitoring, predictive control, and safety margin calculation are organically combined to establish a full life cycle monitoring system for the hydrogen peroxide purification process. This not only solves the problem that traditional methods cannot accurately capture the coupling effects of multiple factors, but also achieves stable operation of the hydrogen peroxide purification equipment through the construction of dynamic safety margins and the calculation of risk warning indicators, making the production process of electronic-grade high-purity hydrogen peroxide safer, more stable, and more efficient.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: A temperature sensor array is set up at the target locations of the reaction zone, separation zone and discharge port of the hydrogen peroxide purification equipment to continuously collect temperature distribution through the temperature sensor array to obtain equipment temperature gradient data; Conductivity sensors are installed at the feed inlet, reaction zone, separation zone and discharge port of the hydrogen peroxide purification equipment to detect the change of solution conductivity in real time and obtain solution conductivity distribution data; Based on the solution conductivity distribution data, the organic matter content in the solution is scanned and analyzed using near-infrared spectroscopy technology to obtain the organic matter composition spectrum data; Perform inductively coupled plasma mass spectrometry on solution samples from hydrogen peroxide purification equipment to analyze the type and content of metal ions and obtain metal impurity distribution data; Install pressure sensors in the high-temperature and high-pressure areas of the hydrogen peroxide purification equipment to monitor process pressure fluctuations and obtain pressure change trend data; The equipment temperature gradient data, solution conductivity distribution data, organic matter composition spectrum data, metal impurity distribution data and pressure change trend data are processed by outlier elimination and standardization to obtain the equipment operation status data.

[0020] Specifically, a temperature sensor array is arranged at key locations such as the reaction zone, separation zone, and discharge port of the hydrogen peroxide purification equipment. The spacing between these temperature sensors is maintained between 10-15 cm to form a high-density temperature monitoring network. The array continuously collects the temperature of different areas inside the equipment and adopts an adaptive sampling strategy during the data collection process. When the equipment is running stably, the sampling frequency is reduced to reduce the amount of data, and when the temperature changes drastically, the sampling frequency is automatically increased to capture key data. By real-time monitoring and continuous collection of temperature data at different locations, the temperature gradient distribution data inside the equipment is obtained. At the same time, conductivity sensors are respectively set at the feed inlet, reaction zone, separation zone, and discharge port of the hydrogen peroxide purification equipment. The conductivity sensors detect the conductivity changes of the solution in real time, and obtain the conductivity distribution data of the solution at different locations, reflecting the changing trend of the solution concentration and the spatial distribution of the solute content. By combining the conductivity changes at different locations, the material migration law and reaction kinetic characteristics of the solution during the purification process are analyzed. After obtaining solution conductivity distribution data, near-infrared spectroscopy is used to scan and analyze the organic matter content in the solution. Near-infrared spectroscopy accurately detects characteristic absorption peaks of organic matter at different wavelengths. Combined with data analysis methods such as principal component analysis or partial least squares regression, the spectral data is reduced in dimension and key characteristic signals are extracted to obtain spectral data on the composition of the organic matter. Inductively coupled plasma mass spectrometry is performed on solution samples from the hydrogen peroxide purification equipment. This technology detects the type and concentration of metal ions in the solution with extremely high sensitivity and distinguishes the spatial distribution characteristics of different metal ions at different locations. By integrating metal impurity data with temperature, conductivity, and spectral data, the catalytic effect of metal impurities on the hydrogen peroxide decomposition reaction and potential decomposition risks are analyzed. Furthermore, pressure sensors are installed in the high-temperature and high-pressure zones of the hydrogen peroxide purification equipment to monitor pressure fluctuations in real time during the process. These pressure sensors provide trend data on pressure changes during operation, reflecting pressure fluctuations within the equipment and identifying potential safety hazards, such as abnormal equipment fluctuations or reaction instability. Preprocessing is performed on equipment temperature gradient data, solution conductivity distribution data, organic composition spectrum data, metal impurity distribution data, and pressure change trend data, including outlier removal and data standardization. Multi-dimensional analysis of the collected data is performed to remove abnormal data points caused by equipment failure or data acquisition anomalies. Data smoothing techniques such as sliding mean, Kalman filtering, or wavelet decomposition are used to reduce noise and detect outliers, thereby ensuring data accuracy and stability. All types of data are standardized, and data of different physical quantities are normalized to a unified scale to facilitate subsequent data fusion and modeling analysis. During the data standardization process, methods such as maximum and minimum normalization are used to ensure that data from different sources can be effectively compared and analyzed in a unified data space.The data after outlier removal and standardization processing forms the equipment's operating status data.

[0021] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Conduct temperature and concentration correlation analysis on the equipment operating status data to obtain hydrogen peroxide concentration distribution data; Based on the hydrogen peroxide concentration distribution data, the reaction rates under different temperature conditions were fitted to obtain a mathematical expression for the reaction rate that includes the temperature gradient factor. Based on the mathematical expression of reaction rate, the hydrogen peroxide purification equipment is divided into multiple control units. A local state equation matrix is ​​established based on the multiple control units to obtain the spatial distribution model of hydrogen peroxide decomposition. Based on the metal impurity distribution data, the catalytic coefficient of the metal ion and the temperature correction function are calculated to obtain the quantitative results of the metal ion catalytic effect; Using the spectral data of organic matter composition, a mathematical model of the effect of organic matter on the stability of hydrogen peroxide was constructed and the organic matter stability impact index was calculated to obtain the organic matter interference assessment value. Lyapunov stability analysis was performed on the spatial distribution model of hydrogen peroxide decomposition, the quantitative results of metal ion catalytic effect, and the numerical evaluation of organic matter interference, and the evaluation results of the effects of temperature gradient and impurity content on the stability of hydrogen peroxide were obtained.

[0022] Specifically, the device's operating status data was analyzed for correlations between temperature and concentration. By coupling temperature distribution data from different regions of the device, such as the reaction zone, separation zone, and discharge port, with hydrogen peroxide concentration variation data, the inherent correlation between temperature variation and concentration distribution was revealed, resulting in a regular distribution of hydrogen peroxide concentration. Data fitting was performed using multivariate nonlinear regression or machine learning algorithms such as support vector regression and random forests. Dynamic corrections were performed based on historical experimental data to ensure that the resulting concentration distribution accurately reflected the variation in hydrogen peroxide concentration at various spatial locations under different operating conditions. Based on the hydrogen peroxide concentration distribution data, the reaction rates under different temperature conditions were fitted. By performing nonlinear curve fitting on the temperature and concentration data, a mathematical expression for the reaction rate was derived that included the temperature gradient factor. This expression captured the nonlinear reaction rate variation caused by the temperature gradient during hydrogen peroxide decomposition. To better reflect the effect of the temperature gradient on the reaction rate, the reaction rate expression was modified to include a term with the temperature gradient modulus, making it sensitive to temperature variations at different spatial locations and characterizing the localized reaction rate variations caused by uneven temperature distribution. Based on the mathematical expression of reaction rate, the hydrogen peroxide purification equipment is divided into multiple control units. The spatial distribution of the entire purification equipment is finely divided, and a local state equation matrix is ​​established in each control unit. This local state equation matrix contains the changing relationship between concentration, temperature, reaction rate, and impurity concentration. By solving the state equation for each control unit, a spatial distribution model of hydrogen peroxide decomposition is obtained, describing the dynamic changes in hydrogen peroxide concentration at different locations and times, and predicting the spatial diffusion characteristics and reaction heat distribution of the system under different conditions. At the same time, the catalytic coefficient and temperature correction function of the metal ion are calculated based on the metal impurity distribution data. The metal ion concentration distribution data obtained by inductively coupled plasma mass spectrometry are combined with the reaction kinetic model to analyze the catalytic effect of different metal ions on the hydrogen peroxide decomposition reaction. The correction effect of temperature changes on the metal ion catalytic effect is quantified by establishing a temperature correction function. This calculation process can quantify the catalytic effect of metal ions, thereby revealing the enhancing or inhibiting effect of different metal impurities on the hydrogen peroxide decomposition reaction rate under different temperature conditions. The spectral data of organic matter composition is used to construct a mathematical model of the influence of organic matter on the stability of hydrogen peroxide. By performing data dimensionality reduction techniques such as principal component analysis or partial least squares regression on the spectral data, the characteristic signals of organic matter are extracted, and a quantitative model of the influence of organic matter on the stability of hydrogen peroxide is established. On this basis, the organic matter stability impact index is calculated to obtain an interference assessment value of organic matter on the system stability.A comprehensive Lyapunov stability analysis was conducted on the spatial distribution model of hydrogen peroxide decomposition, the quantitative results of the metal ion catalytic effect, and the numerical value of the organic interference assessment. By constructing a Lyapunov function to analyze the stability of the system, the stability trend of the system under different temperature, concentration, and impurity content conditions was determined. The time rate of change of the Lyapunov function was analyzed to determine whether the system was in a stable state. By solving the system's Lyapunov function, the boundary conditions of the system stability were clarified, and the key factors leading to system instability were identified. The impact of temperature gradient and impurity content on hydrogen peroxide stability was evaluated.

[0023] In a specific embodiment, the process of executing step S103 may specifically include the following steps: A basic signal separation model including hydrogen peroxide main signal, impurity signal and noise signal is constructed, and the basic signal separation model is subjected to wavelet decomposition to obtain characteristic wavelet coefficients of different frequency bands; Based on the characteristic wavelet coefficients, a feature mapping matrix for metal ion impurities is constructed. The characteristic response patterns of various metal ions in the sensor network are recorded according to the feature mapping matrix to obtain the metal ion feature recognition standard. Based on the impact assessment results, the state equation and observation equation are created, and a dynamic filtering model for impurity concentration is established. The impurity concentration dynamic filtering model is used to perform principal component analysis on the spectral data to obtain the organic impurity feature vector; The metal ion feature recognition standard and the organic impurity feature vector are input into the neural network mapping function to calculate the impurity concentration value and its confidence interval to obtain the real-time value of the impurity content; Based on the real-time value of impurity content, predictive control parameter calculations are performed on the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment to obtain the equipment's safe operating parameter range.

[0024] Specifically, a basic signal separation model was constructed, which includes the hydrogen peroxide (H2O2) signal, impurity signals, and noise signals. This model analyzes multivariate monitoring data and decomposes the collected composite signal to distinguish the H2O2 signal, impurity signals, and environmental noise signals. To achieve this goal, wavelet decomposition technology was used to perform multiscale processing on the basic signal separation model. By selecting appropriate wavelet basis functions (e.g., Daubechies wavelet basis), the original signal was decomposed into wavelet coefficients at different frequency bands. Feature extraction and filtering were performed on the wavelet coefficients at different scales to obtain the characteristic wavelet coefficients of each signal component. Wavelet decomposition effectively captures the signal's varying characteristics at different frequencies and time scales, thereby enabling accurate extraction of useful information from complex signals. A feature mapping matrix for metal ion impurities was constructed based on the characteristic wavelet coefficients at different frequency bands. This feature mapping matrix maps and quantifies the characteristic response patterns of different metal ion impurities in the sensor network. The unique characteristic signals of different metal ions were identified through the characteristic signal responses at different frequencies and spatial locations. The feature mapping matrix is ​​fitted and trained with historical sample data to obtain a metal ion feature recognition standard. This standard encompasses the characteristic response patterns of different metal ions under different operating conditions and serves as the basis for subsequent real-time identification of metal ion impurity signals. Based on the impact assessment results, a state equation and observation equation are created, and a dynamic filtering model for impurity concentration is established. This filtering model dynamically estimates the impurity concentration by fusing the system state with the observed data and continuously updates the system state based on real-time data. During the construction of the filtering model, methods such as Kalman filtering, extended Kalman filtering, or particle filtering are used to estimate and correct the system state, enabling the model to maintain high accuracy under noise interference and nonlinear conditions. The impurity concentration dynamic filtering model is used to perform principal component analysis on the spectral data. By performing feature dimensionality reduction on the multidimensional spectral data, the characteristic vectors of organic impurities are extracted, effectively describing the composition and content changes of organic impurities in the solution. The metal ion feature identification criteria and organic impurity feature vectors are input into a neural network mapping function to calculate the impurity concentration value and its confidence interval. By constructing a multi-layer feedforward neural network or convolutional neural network, deep learning training is performed on the input feature data, allowing the network to learn the complex mapping relationship between different metal ion and organic impurity signals and actual concentration values, thereby quickly and accurately calculating the impurity concentration value during real-time monitoring. At the same time, in order to improve the credibility of the calculation results, combined with the calculation method of the confidence interval, a statistical model is constructed to estimate the confidence interval of the predicted concentration value, quantify the uncertainty of the prediction result, and trigger an alarm mechanism in the event of abnormal fluctuations to ensure that the system always remains within the safety boundary during operation.Based on real-time data on impurity levels, predictive control parameters for the heating power, cooling flow rate, and feed rate of a hydrogen peroxide purification system are calculated. Using a model-based predictive control approach, the control model incorporates the system's state-space equations, observation equations, and external disturbances into the control framework. By predicting the system state at multiple moments in the future, control parameters are adjusted in advance, achieving optimal control of heating power, cooling flow rate, and feed rate. To improve control accuracy, the control targets are dynamically adjusted based on real-time impurity concentration data, ensuring that the system maintains the optimal reaction temperature and concentration distribution under varying impurity concentrations and mitigating the risk of hydrogen peroxide decomposition. During the calculation process, the weight matrices for heating power, cooling flow rate, and feed rate are combined with the control targets, and constraint slack variables are introduced to ensure optimal operating parameter calculation while meeting safety margins. This approach dynamically adjusts control parameters and maintains system stability under varying operating conditions. Based on the results of the predictive control parameter calculations, the safe operating parameter range for the equipment is determined.

[0025] In a specific embodiment, the step of performing predictive control parameter calculation for the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment based on the real-time value of the impurity content to obtain the safe operating parameter range of the equipment may specifically include the following steps: Based on the real-time values ​​of impurity content, a state space model including hydrogen peroxide concentration, temperature distribution, impurity concentration and control variables is constructed; Based on the state space model, the prediction time domain and control time domain parameters are set, and state prediction is performed according to the prediction time domain and control time domain parameters to obtain the system behavior prediction sequence; The control objective function is designed for the system behavior prediction sequence, and the weight matrix and constraint slack variables of heating power, cooling flow and feed rate are introduced to obtain the optimized solution objective function; Based on the real-time numerical value of impurity content and the impact assessment results, the physical limitations and safety boundaries of the equipment are set as hard constraints, and the product quality indicators and efficiency targets are set as soft constraints to obtain a hierarchical constraint condition set; A two-level optimization algorithm is implemented using the optimization solution of the objective function and the hierarchical constraint set. The optimal solution under the hard constraints is first solved, and then the soft constraints are optimized to obtain the optimal control instruction sequence. The optimal control instruction sequence is processed through the fast channel and the slow channel to obtain the safe operating parameter range of the equipment. The fast channel directly responds to temperature anomalies and pressure fluctuations, and the slow channel handles concentration adjustment and impurity control.

[0026] Specifically, a state-space model is constructed, which includes hydrogen peroxide concentration, temperature distribution, impurity concentration, and control variables. This state-space model describes the dynamic changes in the hydrogen peroxide purification process by combining key system variables with control inputs. When constructing the model, factors such as the system's heat and mass transfer characteristics, reaction rate variations, impurity concentration distribution, and equipment control strategy are all incorporated into the state equation. This equation describes the system's state evolution at different time steps. Temperature, conductivity, organic matter spectral data, and metal ion concentration data collected by sensors are then linked to the system state using observation equations to form the state-space model. After establishing the state-space model, the prediction and control time domain parameters are set based on the model. The prediction time domain determines the length of time over which the system's future state can be predicted, while the control time domain defines the time range within which control adjustments can be made. To ensure stable system operation under complex operating conditions, the prediction time domain is set long to capture potential future system changes, while the control time domain is optimized based on the equipment's response characteristics to ensure that the control system can quickly make adjustments when necessary. After setting these parameters, the system state is predicted based on the state-space model. Iterative calculations are performed for multiple future time steps to generate a predicted sequence of system behavior, reflecting the changing trends of hydrogen peroxide concentration, temperature distribution, impurity concentration, and equipment control variables under different future operating conditions. A control objective function is designed for this predicted sequence. Weight matrices for heating power, cooling flow rate, and feed rate, as well as constraint slack variables, are introduced into the control objective function. By defining the objective function, the system's operating objectives are quantified relative to the changes in the control variables. The weight matrix is ​​used to balance the influence of different control variables, ensuring that the control strategy achieves optimal performance while meeting safety requirements. Furthermore, to address inevitable constraint conflicts and unexpected situations, constraint slack variables are introduced. These variables relax some soft constraints when necessary, providing greater flexibility and mitigating the risk of system instability caused by overly strict constraints. In constructing the objective function, based on the real-time values ​​of impurity content and impact assessment results, the equipment's physical limitations and safety margins are set as hard constraints, while product quality indicators and efficiency targets are set as soft constraints, forming a hierarchical constraint set. Hard constraints include the physical limits of the equipment, such as upper temperature limits, pressure limits, impurity concentration thresholds, and system response speed. These conditions must be met at all times; failure to do so threatens the safety of the equipment's operation. Soft constraints, on the other hand, include objectives such as product quality, energy efficiency optimization, and feed rate stability. These objectives are adjusted as necessary to ensure the safety and stability of the overall system operation. Through the hierarchical management of hard and soft constraints, product quality and operational efficiency are optimized while maintaining the system's safety margins, achieving a multi-objective balance in the hydrogen peroxide purification process.A two-level optimization algorithm is implemented to solve the optimal control strategy, utilizing the optimized objective function and a hierarchical set of constraints. In the first level of optimization, the optimal solution under hard constraints is prioritized. Using interior point methods or linear matrix inequality solvers, an initial feasible solution that meets physical constraints is found while ensuring the system's safety margins are maintained. Once this initial solution is obtained, the second level of optimization proceeds to further improve system efficiency and product quality by optimizing soft constraints. The objective function is iteratively optimized using sequential quadratic programming or gradient descent algorithms. While satisfying the hard constraints, the system's control variables are dynamically adjusted to obtain the optimal control instruction sequence. The optimal control instruction sequence is processed hierarchically through fast and slow channels. The fast channel provides rapid response to emergency situations such as temperature anomalies and pressure fluctuations. When the system detects a dramatic change in temperature or pressure, the fast channel immediately adjusts the heating power or cooling flow to prevent the system from entering the hazardous zone and ensure safe equipment operation. The slow channel, on the other hand, handles long-term issues such as concentration adjustment and impurity control. By slowly adjusting the feed rate, catalyst addition, and reaction time, the slow channel achieves precise control of system concentration and impurity content over long periods, thereby ensuring the stability of the hydrogen peroxide purification process. The optimal control instruction sequence, coordinated between the fast and slow channels, determines the safe operating parameter range for the equipment.

[0027] In a specific embodiment, the execution step utilizes the optimization solution objective function and the hierarchical constraint condition set to execute a two-layer optimization algorithm, first solving the optimal solution under the hard constraints, and then optimizing the soft constraints to obtain the optimal control instruction sequence. The process may specifically include the following steps: Decomposing the control performance index and constraint violation penalty term of the optimization objective function to obtain a hierarchical objective function structure; The hard constraints in the hierarchical constraint set are converted into linear matrix inequality form, and the physical limitations of the equipment and safety boundary conditions are processed to obtain the constraint matrix of the first-level optimization problem; Based on the hierarchical objective function structure and the constraint matrix of the first-level optimization problem, the interior point method is used to solve the first-level optimization problem and obtain the initial feasible solution that meets the hard constraints. Using the initial feasible solution that satisfies the hard constraints, a soft constraint violation metric function is constructed to numerically quantify product quality indicators and efficiency targets, thereby obtaining the soft constraint optimization space. In the soft-constraint optimization space, the hierarchical objective function structure is iteratively optimized by the sequential quadratic programming algorithm. The soft constraint indicators are analyzed while the hard constraints are satisfied, and the optimal control instruction sequence is obtained.

[0028] Specifically, the optimization objective function is decomposed into control performance indicators and constraint violation penalties to obtain a hierarchical objective function structure. By separating the control objective and constraint violation penalties, the optimization problem is divided into two layers. The first layer focuses on ensuring system operation within safety boundaries by strictly satisfying hard constraints, ensuring stable equipment operation within physical limits and safety boundaries. The second layer further optimizes system efficiency and product quality while maintaining safety, adjusting soft constraints to achieve optimal overall performance. This hierarchical objective function design approach effectively balances safety and efficiency under complex operating conditions. After the objective function decomposition is completed, the hard constraints in the hierarchical constraint set are converted into linear matrix inequalities for easier processing during the optimization process. Hard constraints include physical limitations and safety boundary conditions of the equipment, such as the upper temperature limit of the reaction zone, pressure limit, impurity concentration threshold, and maximum load capacity of the equipment. These conditions must always be met to ensure system safety. By converting the hard constraints into linear matrix inequalities, the complex nonlinear constraint problem is transformed into a standard convex optimization problem, making it easier to handle during the optimization process. Constraint matrices in the form of linear matrix inequalities offer excellent computational efficiency and robustness. This matrix-based inequality representation precisely defines the physical limits and safe operating boundaries of the equipment in multidimensional space, ensuring strict adherence to hard constraints during the first-level optimization process. Based on the hierarchical objective function structure and the constraint matrix of the first-level optimization problem, the interior point method is employed to solve the first-level optimization problem. The interior point method is an efficient convex optimization algorithm that gradually approaches the optimal solution by finding a balance between the objective function and the constraints. During the solution process, the interior point method effectively avoids direct collisions with the constraint boundaries by introducing a barrier function, while ensuring rapid convergence of the optimization path within the feasible region. The iterative solution mechanism of the interior point method provides strong computational power for large-scale optimization problems. By gradually adjusting the optimization step size and direction, the method ensures that an initial feasible solution that satisfies the hard constraints is obtained within a limited computational time. Using this initial feasible solution that satisfies the hard constraints, a soft constraint violation metric function is constructed to numerically quantify product quality indicators and efficiency targets, thereby forming a soft constraint optimization space. Soft constraints include target solution purity, energy consumption optimization requirements, production stability, and economic efficiency. In actual operation, violations of soft constraints do not directly lead to equipment safety issues, but they can affect product quality and the economic benefits of system operation. To quantify the degree of soft constraint violations, a violation metric function is constructed to convert the deviation from the soft constraint into a quantitative violation cost. This function clarifies the costs associated with different soft constraint violations, providing a quantitative optimization objective for the optimization process.In the soft-constrained optimization space, each soft constraint corresponds to a violation metric, and the optimization goal is to improve system efficiency while minimizing the violation metric. Within this soft-constrained optimization space, a sequential quadratic programming algorithm is used to iteratively optimize the hierarchical objective function structure. Sequential quadratic programming is an algorithm for solving nonlinear optimization problems. It gradually linearizes the original nonlinear problem, breaking it down into a series of quadratic programming subproblems. In each iteration, the sequential quadratic programming algorithm linearizes the objective function and constraints based on the current solution position, forming a local quadratic programming problem. Solving this problem determines the optimization direction and step size for the current solution, and continuously updates the current solution position. Through multiple iterations, the sequential quadratic programming algorithm gradually approaches the global optimal solution while ensuring that the hard constraints are consistently satisfied during the optimization process. Furthermore, to improve optimization efficiency, an adaptive adjustment mechanism is introduced into the algorithm to dynamically adjust the weight coefficients in the objective function based on the degree of soft constraint violation, thereby balancing control performance and safety during the optimization process. After multiple rounds of iterative optimization, an optimal control instruction sequence is ultimately obtained that fully optimizes the soft constraints while satisfying the hard constraints. This optimal control instruction sequence can ensure that the hydrogen peroxide purification process operates stably within physical boundaries and safety limits, effectively improve product quality and system operation efficiency, and maximize economic benefits through reasonable adjustment of soft constraints.

[0029] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Based on the equipment's safe operating parameter range, stability boundary calculations are performed to obtain the state boundary equation that characterizes the system's stability. According to the state boundary equation, the state space of the hydrogen peroxide purification process is divided into three areas: safe operation area, warning area and danger area, and the hierarchical safety area division result is obtained; Based on the results of the hierarchical safety zone division, a temperature gradient-guided concentration boundary constraint mathematical model was established to set differentiated impurity content limits for different temperature intervals, and obtain a safety constraint relationship involving temperature, concentration, and impurities. Using safety constraint relationships, a risk assessment function that includes temperature, concentration, metal ions, organic matter, and equipment status index is constructed, and multi-dimensional risk quantification values ​​are calculated based on the risk assessment function; Adaptive adjustment of boundary constraints is performed according to the multi-dimensional risk quantification values ​​to obtain a dynamically updated safe operation boundary. The constraint violation probability of the dynamically updated safe operation boundary is calculated to obtain a hydrogen peroxide decomposition risk warning indicator.

[0030] Specifically, by analyzing key parameters in the hydrogen peroxide purification process, including temperature distribution, concentration changes, impurity concentration, and equipment operating status, a state boundary equation was constructed. This state boundary equation mathematically describes the system's operating state and safety boundary conditions. By introducing heat and mass transfer equations, reaction kinetics models, and impurity diffusion equations, the equipment's heat transfer, concentration distribution, and impurity changes were coupled and modeled to form a boundary equation that characterizes the system's stability. This equation was parameter-fitted using experimental data and verified by combining Lyapunov stability theory to ensure that the system's state changes remain within a safe range under different operating conditions, thereby avoiding the risk of system instability caused by fluctuations in temperature, concentration, or impurity concentrations. According to the state boundary equation, the state space of the hydrogen peroxide purification process is divided into three regions: safe operating zone, warning zone, and danger zone. The safe operating zone corresponds to the region where the system operating state is completely within the stability boundary. When the system operates in this region, the changes in various parameters remain within the safety threshold and do not pose a risk to equipment operation. The warning zone represents the region where the system state gradually approaches the critical boundary. The system is still controllable, but the parameter changes are approaching the safety boundary, requiring the introduction of an early warning mechanism and necessary parameter adjustments. The danger zone represents the region where the system state has exceeded the safety boundary and is in a highly dangerous state, with the risk of uncontrolled hydrogen peroxide decomposition. When the system enters the danger zone, emergency control measures are immediately implemented. Key control parameters such as heating power, cooling flow rate, and feed rate are adjusted through fast channels to bring the system state back to the safe operating zone. Based on the results of the hierarchical safety zone division, a temperature gradient-guided concentration boundary constraint mathematical model is established. By introducing the dynamic relationship between temperature, concentration, and impurity content, this model sets differentiated impurity content limits for different temperature ranges, thereby accurately describing the system's safe operating boundary under different temperature conditions. In this mathematical model, temperature gradients directly affect the distribution and diffusion rate of impurities. Therefore, a thermal-mass coupling equation is used to correlate temperature gradient changes with impurity concentration changes. This model, combined with the catalytic effect of metal ions, the rate of concentration change, and the impact of organic matter on stability, constructs a mathematical expression describing the safety boundaries of different zones. This model derives safety constraints involving temperature, concentration, and impurities. Using these constraints, a multidimensional risk assessment function is constructed. This function considers key variables such as temperature, concentration, metal ions, organic matter, and the device status index. The risk impact of each variable is quantified using a weighted approach, resulting in a multidimensional risk quantification value that comprehensively reflects the system's safety status. During the construction of the risk assessment function, weight coefficients are rationally assigned to different variables. The relative impact of each variable on system stability is determined based on historical data and experimental results, ensuring the accuracy of the risk quantification value. By performing real-time calculations of the risk assessment function, the system's safety status is dynamically assessed and potential risk factors are identified.Adaptive adjustments are made to the bounds based on the risk quantification values. By monitoring the changing trends of key parameters in real time, the safe operating boundary is automatically adjusted when the system state approaches or exceeds the warning zone, ensuring that the system always operates within the safe zone. During this adaptive adjustment process, the safe operating boundary is dynamically contracted or expanded based on the rate of change of temperature, concentration, and impurities, enabling flexible adjustment to accommodate varying operating conditions. After the dynamic update, the constraint violation probability is calculated for the updated safe operating boundary. By predicting and simulating the system state for multiple future time steps, the probability of the system violating the safe boundary is quantified, thereby predicting potential system risks in advance. This dynamic calculation of the constraint violation probability generates a hydrogen peroxide decomposition risk warning indicator. This indicator provides real-time risk warning information during system operation and triggers an alarm mechanism when the risk exceeds a preset threshold, ensuring that operators can take effective intervention measures before the risk occurs, such as adjusting control parameters or initiating an emergency shutdown procedure, to prevent the system from entering a dangerous state.

[0031] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0032] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a hydrogen peroxide purification process status monitoring device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0033] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the status of a hydrogen peroxide purification process, characterized in that: include: Conduct multi-point real-time monitoring of the temperature, pressure, conductivity, and organic matter content of hydrogen peroxide purification equipment to obtain equipment operating status data; Based on the equipment operating status data, calculating and analyzing the target risk factors for hydrogen peroxide decomposition in the hydrogen peroxide purification equipment to obtain an assessment result of the impact of temperature gradient and impurity content on hydrogen peroxide stability; Based on the impact assessment results, characteristic signals of metal ions and organic matter are extracted to obtain real-time values ​​of impurity content, and predictive control parameter calculations are performed on the heating power, cooling flow rate, and feed rate of the hydrogen peroxide purification equipment to obtain a safe operating parameter range for the equipment; By utilizing the safe operating parameter range of the equipment, dynamic safety boundary calculations are performed on the temperature distribution and concentration boundary during the purification process to obtain a hydrogen peroxide decomposition risk warning indicator.

2. The method for monitoring the state of a hydrogen peroxide purification process according to claim 1, wherein: The temperature, pressure, conductivity and organic matter content of the hydrogen peroxide purification equipment are monitored in real time at multiple points to obtain equipment operating status data, including: A temperature sensor array is set at the reaction zone, separation zone and discharge port target position of the hydrogen peroxide purification equipment, and the temperature distribution is continuously collected by the temperature sensor array to obtain equipment temperature gradient data; Conductivity sensors are respectively provided at the feed inlet, reaction zone, separation zone and discharge port of the hydrogen peroxide purification equipment, and the conductivity sensors are used to detect the change of solution conductivity in real time to obtain solution conductivity distribution data; Based on the solution conductivity distribution data, the organic matter content in the solution is scanned and analyzed using near-infrared spectroscopy technology to obtain organic matter composition spectrum data; Performing inductively coupled plasma mass spectrometry on a solution sample in the hydrogen peroxide purification device to analyze the type and content of metal ions and obtain metal impurity distribution data; Installing pressure sensors in the high-temperature zone and the high-pressure zone of the hydrogen peroxide purification equipment, monitoring process pressure fluctuations through the pressure sensors, and obtaining pressure change trend data; The device temperature gradient data, the solution conductivity distribution data, the organic matter composition spectrum data, the metal impurity distribution data and the pressure change trend data are subjected to outlier elimination and standardization processing to obtain device operation status data.

3. The method for monitoring the state of the hydrogen peroxide purification process according to claim 2, wherein: The method comprises calculating and analyzing target factors of hydrogen peroxide decomposition risk in the hydrogen peroxide purification equipment based on the equipment operation status data to obtain an evaluation result of the impact of temperature gradient and impurity content on hydrogen peroxide stability, including: Performing temperature and concentration correlation analysis on the equipment operation status data to obtain hydrogen peroxide concentration distribution law data; According to the hydrogen peroxide concentration distribution law data, the reaction rate under different temperature conditions is fitted to obtain a mathematical expression of the reaction rate including the temperature gradient influencing factor; Based on the reaction rate mathematical expression, the hydrogen peroxide purification equipment is divided into multiple control units, and a local state equation matrix is ​​established based on the multiple control units to obtain a spatial distribution model of hydrogen peroxide decomposition; Calculating the catalytic coefficient of the metal ion and the temperature correction function based on the metal impurity distribution data to obtain a quantitative result of the metal ion catalytic effect; Using the organic matter composition spectral data, a mathematical model of the effect of organic matter on the stability of hydrogen peroxide is constructed and an organic matter stability impact index is calculated to obtain an organic matter interference assessment value; Lyapunov stability analysis was performed on the spatial distribution model of hydrogen peroxide decomposition, the quantitative results of the metal ion catalytic effect, and the organic matter interference evaluation value to obtain evaluation results of the effects of temperature gradient and impurity content on hydrogen peroxide stability.

4. The method for monitoring the state of a hydrogen peroxide purification process according to claim 1, wherein: According to the impact assessment results, the metal ion and organic matter characteristic signals are extracted to obtain the real-time value of the impurity content, and the heating power, cooling flow rate and feed rate of the hydrogen peroxide purification equipment are predictively calculated to obtain the equipment safe operating parameter range, including: Constructing a basic signal separation model including a hydrogen peroxide main body signal, an impurity signal, and a noise signal, and performing wavelet decomposition processing on the basic signal separation model to obtain characteristic wavelet coefficients of different frequency bands; Based on the characteristic wavelet coefficients, a characteristic mapping matrix for metal ion impurities is constructed, and characteristic response patterns of various metal ions in the sensor network are recorded according to the characteristic mapping matrix to obtain a metal ion characteristic recognition standard; Creating a state equation and an observation equation based on the impact assessment results, establishing an impurity concentration dynamic filtering model, and using the impurity concentration dynamic filtering model to perform principal component analysis on the spectral data to obtain an organic impurity feature vector; Inputting the metal ion characteristic identification standard and the organic impurity characteristic vector into a neural network mapping function to calculate the impurity concentration value and its confidence interval to obtain a real-time numerical value of the impurity content; According to the real-time numerical value of the impurity content, predictive control parameter calculations are performed on the heating power, cooling flow rate and feed rate of the hydrogen peroxide purification equipment to obtain a safe operating parameter range of the equipment.

5. The method for monitoring the state of a hydrogen peroxide purification process according to claim 4, wherein: The predictive control parameter calculation of the heating power, cooling flow rate and feed rate of the hydrogen peroxide purification equipment is performed based on the real-time value of the impurity content to obtain the equipment safe operating parameter range, including: Constructing a state space model including hydrogen peroxide concentration, temperature distribution, impurity concentration and control variables based on the real-time value of the impurity content; Setting prediction time domain and control time domain parameters based on the state space model, and performing state prediction according to the prediction time domain and control time domain parameters to obtain a system behavior prediction sequence; Designing a control objective function for the system behavior prediction sequence, introducing weight matrices and constraint slack variables of heating power, cooling flow rate, and feed rate, and obtaining an optimized solution objective function; According to the real-time value of the impurity content and the impact assessment result, the physical limitations and safety boundaries of the equipment are set as hard constraints, and the product quality indicators and efficiency targets are set as soft constraints to obtain a hierarchical constraint condition set; A two-layer optimization algorithm is executed using the optimization solution objective function and the hierarchical constraint condition set to first solve the optimal solution under the hard constraints and then optimize the soft constraints to obtain the optimal control instruction sequence; The optimal control instruction sequence is processed through a fast channel and a slow channel to obtain a safe operating parameter range for the equipment. The fast channel directly responds to temperature anomalies and pressure fluctuations, and the slow channel handles concentration adjustment and impurity control.

6. The method for monitoring the state of the hydrogen peroxide purification process according to claim 5, wherein: The method of performing a two-layer optimization algorithm using the optimization solution objective function and the hierarchical constraint condition set, first solving the optimal solution under the hard constraints, and then optimizing the soft constraints to obtain the optimal control instruction sequence, includes: Decomposing the control performance index and constraint violation penalty term of the optimization objective function to obtain a hierarchical objective function structure; Converting the hard constraints in the hierarchical constraint set into a linear matrix inequality form, processing the physical limitations of the equipment and the safety boundary conditions, and obtaining a constraint matrix for the first-level optimization problem; Based on the hierarchical objective function structure and the constraint matrix of the first-level optimization problem, an interior point method is used to solve the first-level optimization problem to obtain an initial feasible solution that satisfies the hard constraints; Using the initial feasible solution that satisfies the hard constraints, a soft constraint violation metric function is constructed, product quality indicators and efficiency targets are numerically quantified, and a soft constraint optimization space is obtained; In the soft constraint optimization space, the hierarchical objective function structure is iteratively optimized by a sequential quadratic programming algorithm, and soft constraint indicators are analyzed while hard constraints are satisfied, so as to obtain an optimal control instruction sequence.

7. The method for monitoring the state of a hydrogen peroxide purification process according to claim 1, wherein: The safe operating parameter range of the equipment is used to dynamically calculate the temperature distribution and concentration boundary during the purification process to obtain a hydrogen peroxide decomposition risk warning indicator, including: Performing stability boundary calculation based on the safe operating parameter range of the equipment to obtain a state boundary equation that characterizes the stability of the system; According to the state boundary equation, the state space of the hydrogen peroxide purification process is divided into three areas: safe operation area, warning area and danger area, and the hierarchical safety area division result is obtained; Based on the hierarchical safety zone division results, a temperature gradient-guided concentration boundary constraint mathematical model is established to set differentiated impurity content limits for different temperature intervals, thereby obtaining a safety constraint relationship involving temperature, concentration, and impurities; Using the safety constraint relationship, constructing a risk assessment function including temperature, concentration, metal ions, organic matter and equipment status index, and calculating a multi-dimensional risk quantification value based on the risk assessment function; Boundary constraint adaptive adjustment is performed according to the multi-dimensional risk quantification value to obtain a dynamically updated safe operation boundary, and constraint violation probability calculation is performed on the dynamically updated safe operation boundary to obtain a hydrogen peroxide decomposition risk warning indicator.

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