Waste hydrofluoric acid and leachate synergistic treatment system

Abstract

The invention discloses a waste hydrofluoric acid and leachate co-processing system, and relates to the technical field of industrial waste liquid treatment, a heat release rate calculation module, a cooling system heat conduction efficiency monitoring module, an efficiency anomaly index calculation module and a risk grade division and processing module are introduced, and a high-precision sensor and a real-time data analysis technology are utilized to analyze the risk of waste hydrofluoric acid and leachate. The heat change in the waste hydrofluoric acid and leachate co-processing process and the heat dissipation capacity of a cooling system are accurately monitored, the system abnormity is recognized in time and an early warning signal is generated by calculating and analyzing the temperature data weight coefficient in each time period, and finally the risk level is divided into different states, so that efficient and stable temperature control is realized, and the safety of the system is improved. Safe operation of the system under high heat load is ensured, and accidents of temperature out-of-control and equipment damage are prevented.

Description

Technical Field

[0001] The invention relates to the technical field of industrial waste liquid treatment, and in particular to a system for coordinated treatment of waste hydrofluoric acid and leachate. Background Art

[0002] In the industrial production process, hydrofluoric acid is widely used in metallurgy, chemical industry, glass manufacturing and electronics industry as an important chemical raw material. However, the use of hydrofluoric acid will produce a large amount of waste hydrofluoric acid waste liquid, which contains high concentrations of fluoride and other harmful substances. If not properly treated, it will seriously harm the ecosystem and human health if discharged into the environment. Traditional methods for treating waste hydrofluoric acid mostly use lime neutralization precipitation method, calcium salt precipitation method, etc. Although some fluoride ions can be removed, there are problems such as large sediment production, high treatment cost and secondary pollution. In addition, leachate is a kind of wastewater containing complex organic matter and inorganic salts produced by landfills. Its composition is complex and toxic, and it is difficult to treat. The conventional treatment process has low efficiency in removing fluoride, and it is difficult to meet the increasingly stringent emission standards. Therefore, it is particularly urgent to develop an efficient technology for the coordinated treatment of waste hydrofluoric acid and leachate.

[0003] The synergistic treatment system of waste hydrofluoric acid and leachate is designed to treat two highly polluted wastewaters at the same time through resource-based and harmless means, thereby achieving efficient removal of pollutants and resource recycling. This technology can convert fluorides in waste hydrofluoric acid into usable fluorine resources, reduce secondary pollution during the treatment process, and reduce the total treatment cost. At the same time, by optimizing the reaction conditions and process flow, and utilizing the interaction between the organic and inorganic components in the leachate and the hydrofluoric acid waste liquid, the system's treatment efficiency and fluoride removal effect can be further improved.

[0004] The prior art has the following deficiencies: During the coordinated treatment of waste hydrofluoric acid and leachate, certain chemical components in the leachate (such as organic matter, metal ions, etc.) may react with the waste hydrofluoric acid liquid with high exothermicity, especially under high concentration or large-scale treatment conditions. If the cooling system in the treatment system fails, the accumulation of reaction heat may cause local overtemperature, leading to thermal runaway. Thermal runaway may also cause rapid thermal expansion or rupture of system equipment materials (such as reactors, pipelines, etc.), and even cause explosion accidents. Summary of the invention

[0005] The object of the present invention is to provide a system for the coordinated treatment of waste hydrofluoric acid and leachate to overcome the deficiencies in the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a waste hydrofluoric acid and leachate coordinated treatment system, including a heat release rate calculation module, a cooling system heat conduction efficiency monitoring module, an efficiency abnormality index calculation module and a risk level classification and processing module; Heat release rate calculation module: A high-precision heat flow sensor is installed in the reactor for the coordinated treatment of waste hydrofluoric acid and leachate to monitor the heat changes generated by the reaction in real time, obtain the heat output data in the reactor through the heat flow sensor, calculate the heat release rate per unit time, transmit the monitored heat release rate data to the control system, and predict the change trend of the heat release rate; Cooling system heat conduction efficiency monitoring module: Install temperature sensors and flow meters in different links of the cooling system to monitor the temperature change and flow of the cooling medium in real time. Combined with the heat dissipation calculation formula, the real-time heat conduction efficiency of the cooling system is obtained. After analyzing the fluctuation of the heat conduction efficiency within a fixed time period, it is evaluated whether the cooling system can operate effectively under the heat load; Efficiency abnormality index calculation module: the waste hydrofluoric acid and leachate collaborative treatment process is divided into several time periods, and the weight coefficients of the temperature data in different time periods during the waste hydrofluoric acid and leachate collaborative treatment process are determined according to the changing trend of the heat release rate and the fluctuation of the heat conduction efficiency in each time period, and the weight coefficients of the temperature data in different time periods are calculated by weighted average calculation to obtain the abnormality index of the cooling system; Risk level classification and processing module: After comparing the calculated abnormal index of the cooling system with the gradient standard threshold, the risk of temperature runaway in the coordinated treatment of waste hydrofluoric acid and leachate is divided into different levels, which are divided into normal operation state, possible normal operation state and overheating operation state, and corresponding processing is performed. The gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold.

[0007] Preferably, in the heat release rate calculation module, a prediction curve of the heat release rate is generated based on the result of the model fitting, and a heat release rate anomaly index is generated by analyzing the change trend of the heat release rate in different time periods. The method for obtaining the heat release rate anomaly index is: Mark the change points of the predicted curve of heat release rate to obtain the model of heat release rate changing with time. If the change of heat release rate conforms to the normal distribution, the expression is: ; Where HGR(t) is the heat release rate at time point t, is the expected value of the heat release rate at time t, The variance of the heat release rate is used to construct a Bayesian model, and the probability of abnormal changes in the heat release rate is calculated according to the change points of the prediction curve. The initial prior probability P(θ) is: ; Where T is the total length of the time series; Calculate the likelihood function , the expression is: ; is the expected value of the heat release rate, is the variance of the heat release rate; according to Bayes' theorem, combined with the prior probability and likelihood function, the posterior probability of the system state change at time point t is calculated, the expression is: ; In the formula, is the normalized constant; the heat release rate anomaly index is calculated using the expression: ; Where FD is the abnormal heat release rate index.

[0008] Preferably, in the cooling system heat conduction efficiency monitoring module, the heat conduction efficiency fluctuation index is generated after analyzing the fluctuation of the heat conduction efficiency within a fixed time period. The heat conduction efficiency fluctuation index is obtained by: In a fixed time period T, the thermal conductivity efficiency CTE of the cooling system is monitored in real time through temperature sensors and flow meters to generate a time series data set: ;in, Represents the heat conduction efficiency at the nth time point, selects the attenuation factor λ, and calculates the smoothed value at each time point according to the formula of exponentially weighted moving average , which is the weighted average of the current heat transfer efficiency, the formula is: ; For time point The exponentially weighted moving average of ; Calculate the actual heat transfer efficiency at each time point With exponentially weighted moving average The residual between , the residual indicates the degree to which the actual value deviates from the predicted smoothed value, and the expression is: ; Calculate the heat conduction efficiency fluctuation index based on the residual, the expression is: ; Where n is the number of data points in the analysis time period, and ED is the thermal conduction efficiency fluctuation index.

[0009] Preferably, in the efficiency anomaly index calculation module, the heat release rate anomaly index and the heat conduction efficiency fluctuation index obtained in each time period are converted into feature vectors, and the feature vectors are used as inputs of the machine learning model. The machine learning model uses each group of feature vectors to predict the weight coefficient value label of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the prediction target, and takes minimizing the sum of the prediction errors of the weight coefficient value labels of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The weight coefficient value of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate is determined according to the model output results, wherein the machine learning model is a polynomial regression model; and the weight coefficients of the temperature data in different time periods are weighted averaged to obtain the anomaly index of the cooling system.

[0010] Preferably, in the risk level classification and processing module, the acquired abnormal index of the cooling system is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the abnormal index of the cooling system is compared with the first standard threshold and the second standard threshold respectively; If the abnormal index of the cooling system is greater than the second standard threshold, it indicates that the temperature control effect is poor during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, a first-level warning signal is generated, and it is classified as an overheating operation state, and emergency measures are taken immediately; If the abnormal index of the cooling system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the temperature control effect in the coordinated treatment of waste hydrofluoric acid and leachate is general. At this time, a secondary warning signal is generated, and it is classified as a possible normal operation state, and preventive intervention is carried out; If the abnormal index of the cooling system is less than the first standard threshold, it means that the temperature control effect is good during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, no warning signal is generated and it is classified as a normal operating state, indicating that the cooling system is running smoothly and no intervention is required.

[0011] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes accurate monitoring and intelligent management in the coordinated treatment process of waste hydrofluoric acid and leachate by integrating a heat release rate calculation module, a cooling system heat conduction efficiency monitoring module, an efficiency anomaly index calculation module, and a risk level classification and processing module. By real-time monitoring of the heat release rate and the heat conduction efficiency of the cooling system, the weight coefficients of the temperature data in each time period are predicted and optimized in combination with a machine learning model, the system operation status is effectively evaluated, and an anomaly index is generated. By analyzing these key data, the system can predict the risk of temperature runaway, and take targeted preventive and emergency measures according to the risk level (normal operation, possible normal operation, overheating operation) to ensure the safety and efficiency of the system.

[0012] 2. The present invention not only improves the temperature control accuracy during the treatment of waste hydrofluoric acid and leachate, but also significantly reduces the risk of equipment damage or safety accidents caused by temperature runaway. Through the automated early warning system and multi-level risk management, operators can obtain system status in a timely manner and make corresponding interventions to ensure the stable operation of the cooling system under different loads, thereby improving treatment efficiency, extending equipment life, and reducing energy consumption and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 The present invention is a flow chart of the method.

[0015] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For examples, see Figure 1 and 2 As shown, the waste hydrofluoric acid and leachate coordinated treatment system described in this embodiment includes a heat release rate calculation module, a cooling system heat conduction efficiency monitoring module, an efficiency abnormality index calculation module, and a risk level classification and processing module; Heat release rate calculation module: A high-precision heat flow sensor is installed in the reactor for the coordinated treatment of waste hydrofluoric acid and leachate to monitor the heat changes generated by the reaction in real time, obtain the heat output data in the reactor through the heat flow sensor, calculate the heat release rate per unit time, transmit the monitored heat release rate data to the control system, and predict the change trend of the heat release rate; Cooling system heat conduction efficiency monitoring module: Install temperature sensors and flow meters in different links of the cooling system to monitor the temperature change and flow of the cooling medium in real time. Combined with the heat dissipation calculation formula, the real-time heat conduction efficiency of the cooling system is obtained. After analyzing the fluctuation of the heat conduction efficiency within a fixed time period, it is evaluated whether the cooling system can operate effectively under the heat load; Efficiency abnormality index calculation module: the waste hydrofluoric acid and leachate collaborative treatment process is divided into several time periods, and the weight coefficients of the temperature data in different time periods during the waste hydrofluoric acid and leachate collaborative treatment process are determined according to the changing trend of the heat release rate and the fluctuation of the heat conduction efficiency in each time period, and the weight coefficients of the temperature data in different time periods are calculated by weighted average calculation to obtain the abnormality index of the cooling system; Risk level classification and processing module: After comparing the calculated abnormal index of the cooling system with the gradient standard threshold, the risk of temperature runaway in the coordinated treatment of waste hydrofluoric acid and leachate is divided into different levels, which are divided into normal operation state, possible normal operation state and overheating operation state, and corresponding processing is performed. The gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold.

[0018] In the reaction heat rate calculation module, select a suitable heat flow sensor according to the operating conditions of the reactor (such as temperature range, chemical environment, reactor material, etc.). High temperature resistant and corrosion resistant sensor types are usually required to adapt to the chemical environment of hydrofluoric acid and leachate. Make sure that the selected sensor has sufficient accuracy to detect subtle heat changes, and the sensor is usually required to have high sensitivity and fast response capabilities. Confirm that the sensor size matches the reserved installation interface of the reactor to ensure that it can be safely and securely installed in the reactor. Select a key location that can accurately reflect the temperature and heat changes in the reactor to install the sensor, usually in the most active area of ​​the reactor's heat flow. Avoid installing the sensor near the cooling wall or dead corner to avoid affecting the accuracy of the data. For large reactors, it may be necessary to install heat flow sensors at multiple locations to obtain more comprehensive heat flow data and achieve multi-point monitoring.

[0019] The high-precision heat flow sensor installed inside the reactor monitors the heat changes generated by the reaction in real time. The sensor converts the heat flow data into an electrical signal, which represents the heat output generated during the reaction. The system should be set to an appropriate sampling frequency to ensure accurate recording of heat changes (such as once per second or once per minute). Calculate the heat release rate per unit time. The heat release rate formula is: ; Where HGR is the heat release rate per unit time (unit: W or J / s), which indicates the heat generated by the reaction per second. ΔQ is the heat change detected by the sensor (unit: J). Δt: time interval (unit: s), which indicates the sampling period of heat change. In each time period (Δt), the corresponding heat release rate HGR is calculated by the collected heat flow data ΔQ. The HGR data at each time point is recorded to form time series data. The heat release rate data calculated in each time period is transmitted to the central control system via wired or wireless means. Ensure stable and delay-free data transmission. In the control system, the heat release rate data is displayed and stored in real time for analysis and trend prediction. The control system interface can dynamically display the curve of heat release rate change over time.

[0020] When the change in heat release rate is relatively stable, linear regression is used to predict future trends.

[0021] Polynomial regression: For complex heat flow variations, quadratic or polynomial fits can be used to more accurately capture the fluctuation characteristics.

[0022] Exponential smoothing method: If there is significant time correlation in the data, exponential smoothing method can be used to predict future heat release trends.

[0023] Machine learning models: For complex reactions, it may be necessary to use machine learning algorithms (such as time series prediction models such as LSTM) for multi-dimensional trend prediction.

[0024] Based on the results of model fitting, a prediction curve of heat release rate is generated. The heat release rate anomaly index is generated by analyzing the change trend of heat release rate in different time periods. The method for obtaining the heat release rate anomaly index is: Mark the change points of the predicted curve of heat release rate to obtain the model of heat release rate changing with time. If the change of heat release rate conforms to the normal distribution, the expression is: ; Where HGR(t) is the heat release rate at time point t, is the expected value of the heat release rate at time t, is the variance of the heat release rate, indicating the fluctuation range. A Bayesian model is constructed to calculate the probability of abnormal changes in the heat release rate based on the change points of the prediction curve; the initial prior probability P(θ) is: ; Where T is the total length of the time series, indicating that each time point has an equal probability of change; Calculate the likelihood function , the likelihood function P(HGR(t) |θ) reflects whether the heat release rate at the current time point conforms to the prediction model under a given state, and the expression is: ; is the expected value of the heat release rate, is the variance of the heat release rate; according to Bayes' theorem, combined with the prior probability and likelihood function, the posterior probability of the system state change at time point t is calculated, the expression is: ; In the formula, is a normalization constant used to ensure that the sum of the posterior probabilities is 1; the heat release rate anomaly index is calculated as follows: ; Where FD is the abnormal heat release rate index.

[0025] The larger the heat release rate anomaly index is, the more unstable the trend of the heat release rate change is, and the higher the possibility of anomalies. This means that there may be sudden and drastic changes in heat release during the reaction, such as increased heat fluctuations caused by intensified reactions or unexpected factors. Increased fluctuations in the heat release rate indicate that the reaction system may be on the verge of losing control, the temperature rises faster, and the cooling system may not be able to respond to heat accumulation in time, posing a risk of temperature runaway. Therefore, the larger the anomaly index is, the worse the temperature stability is, and the safety of the system may also be threatened, and emergency measures need to be taken to stabilize the system operation.

[0026] When the heat release rate anomaly index is smaller, it means that the change trend of the heat release rate is more stable. When the system is in normal operation, the heat release during the reaction process conforms to the expected stable mode. The fluctuation of the heat release rate is small, indicating that the temperature is well controlled, the cooling system can effectively regulate the heat generated during the reaction, and the system is in a state of high temperature stability. At this time, the system runs safely and controllably, the temperature remains within a safe range, the reaction process tends to be stable, and no additional intervention measures are required.

[0027] Cooling system heat conduction efficiency monitoring module: Install temperature sensors and flow meters in different links of the cooling system to monitor the temperature changes and flow of the cooling medium in real time. Combined with the heat dissipation calculation formula, the real-time heat conduction efficiency of the cooling system is obtained. After analyzing the fluctuation of the heat conduction efficiency within a fixed time period, it is evaluated whether the cooling system can operate effectively under the heat load.

[0028] Select appropriate temperature sensors and flow meters to ensure that their accuracy and response speed meet the requirements of the cooling system. Temperature sensors should be installed at the water inlet and outlet of the cooling system to measure the temperature difference of the cooling medium when it enters and leaves the cooling system. They should be installed in the circulation pipeline of the coolant to monitor the flow rate of the cooling medium and ensure accurate flow data.

[0029] The temperature sensors should be installed at the inlet and outlet of the cooling medium to accurately measure the inlet and outlet temperature difference of the coolant. The flow meter is installed in the main circulation pipeline of the cooling medium to ensure that the flow rate can be obtained in real time.

[0030] The temperature sensor monitors the inlet temperature Tin and outlet temperature Tout of the cooling medium in real time. The system needs to continuously record these data to ensure that the heat absorbed or released by the coolant in the cooling system can be understood in real time. The flow meter monitors the flow rate of the cooling medium in real time (unit: kg / s) and records the flow rate of the coolant. The flow data is used in combination with the temperature data to calculate the heat dissipation of the cooling system.

[0031] Heat transfer efficiency represents the amount of heat removed by the cooling system per unit time and can usually be calculated using the mass flow rate, specific heat capacity, and temperature change of the coolant.

[0032] The basic formula for thermal conductivity CTE is: ; In the formula, The flow rate of the cooling medium, in kg / s, represents the mass of coolant flowing through the cooling system per second. is the specific heat capacity of the cooling medium, in J / (kg·K), which indicates the amount of heat required to raise the temperature of a unit mass of the cooling fluid by 1 degree Celsius. The specific heat capacity of common cooling fluids (such as water) is about 4184 J / (kg·K). ΔT=Tin−Tout is the inlet and outlet temperature difference of the cooling medium, in K (or °C), which indicates the amount of heat absorbed by the cooling fluid in the cooling system. CTE unit: Through the above formula, the CTE (thermal conduction efficiency of the cooling system) obtained is in Watts (W), which indicates the amount of heat removed from the system per second.

[0033] After analyzing the fluctuation of heat transfer efficiency within a fixed time period, a heat transfer efficiency fluctuation index is generated to evaluate whether the cooling system can operate effectively under heat load. The method for obtaining the heat transfer efficiency fluctuation index is as follows: In a fixed time period T (for example, every second or every minute), the thermal conductivity efficiency CTE of the cooling system is monitored in real time through temperature sensors and flow meters to generate a time series data set: ;in, Indicates the heat conduction efficiency at the nth time point. Select the attenuation factor λ. The λ in the exponentially weighted moving average is the attenuation coefficient, which determines the degree of influence of the data at each time point on the overall fluctuation. The value range of λ is (0, 1): Larger λ values ​​(such as 0.7 or 0.8) make the most recent data have a greater impact on the average, which is suitable for capturing short-term fluctuations. Smaller λ values ​​(such as 0.1 or 0.2) make the historical data more weighted, which is suitable for analyzing long-term trends. According to the formula of the exponentially weighted moving average, calculate the smoothed value at each time point , which is the weighted average of the current heat transfer efficiency, the formula is: ; For time point The exponentially weighted moving average (smoothed value of the previous time point) of . Calculate the actual heat transfer efficiency at each time point With exponentially weighted moving average The residual between , the residual indicates the degree to which the actual value deviates from the predicted smooth value, reflecting the magnitude of the fluctuation. The expression is: ; Calculate the heat conduction efficiency fluctuation index based on the residual, the expression is: ; Where n is the number of data points in the analysis time period, and ED is the thermal conduction efficiency fluctuation index.

[0034] When the heat transfer efficiency fluctuation index is larger, it means that the heat transfer efficiency of the cooling system has fluctuated greatly in different time periods, indicating that the system is unstable when dealing with heat load. This instability may mean that the heat dissipation capacity of the cooling system cannot match the heat load, resulting in inconsistent heat dissipation effects of the system at different time points. As the fluctuation index increases, the system may not be able to effectively remove the generated heat in a timely manner, resulting in temperature fluctuations, reduced cooling efficiency, or equipment overheating. Therefore, a larger fluctuation index usually means that the cooling system does not operate ideally under heat load, with potential risks of inefficiency and temperature runaway.

[0035] The smaller the thermal conductivity efficiency fluctuation index is, the more stable and consistent the thermal conductivity efficiency of the cooling system is, indicating that the system can maintain a stable heat dissipation effect when facing a heat load. A smaller fluctuation index means that the cooling system can continue to effectively remove heat, and even if the heat load changes, the system can still respond in time and maintain temperature stability. Therefore, a smaller fluctuation index usually indicates that the cooling system operates well under heat load, has high heat dissipation efficiency and a stable system, which can effectively avoid the risk of drastic temperature fluctuations and equipment overheating.

[0036] Efficiency anomaly index calculation module: The coordinated treatment process of waste hydrofluoric acid and leachate is divided into several time periods. According to the changing trend of the heat release rate and the fluctuation of the heat conduction efficiency in each time period, the weight coefficient of the temperature data in different time periods during the coordinated treatment process of waste hydrofluoric acid and leachate is determined, and the weight coefficient of the temperature data in different time periods is weighted averaged to calculate the anomaly index of the cooling system.

[0037] In the process of synergistic treatment of waste hydrofluoric acid and leachate, it is necessary to divide the entire process into several time periods in order to better monitor and optimize the treatment effect. Each time period represents a different reaction stage or operating condition, such as the addition, reaction, precipitation, separation and other links of the waste liquid. During these time periods, key parameters such as reaction temperature, chemical reaction rate, and heat dissipation efficiency of the cooling system can be monitored and adjusted in real time. By segmenting the treatment process, the system performance of each stage can be accurately analyzed, potential problems can be identified, and optimization measures can be taken according to the different working conditions of each time period, thereby ensuring the stability and efficiency of the synergistic treatment process.

[0038] The heat release rate anomaly index and heat conduction efficiency fluctuation index obtained in each time period are converted into feature vectors, and the feature vectors are used as the input of the machine learning model. The machine learning model uses each group of feature vectors to predict the weight coefficient value label of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the prediction target, and takes minimizing the sum of prediction errors of the weight coefficient value labels of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the training target. The machine learning model is trained until the sum of prediction errors converges and the model training is stopped. The weight coefficient value of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate is determined according to the model output results, wherein the machine learning model is a polynomial regression model; and the weight coefficients of the temperature data in different time periods are weighted averaged to calculate the anomaly index of the cooling system.

[0039] The method for obtaining the weight coefficient value of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate is as follows: from the first eigenvector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, FD is the heat release rate anomaly index, ED is the heat conduction efficiency fluctuation index, It is the weight coefficient value of temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate.

[0040] Risk level classification and processing module: After comparing the calculated abnormal index of the cooling system with the gradient standard threshold, the risk of temperature runaway in the coordinated treatment of waste hydrofluoric acid and leachate is divided into different levels, which are divided into normal operation state, possible normal operation state and overheating operation state, and corresponding processing is performed. The gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold.

[0041] The acquired abnormal index of the cooling system is compared with a gradient standard threshold, wherein the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the abnormal index of the cooling system is compared with the first standard threshold and the second standard threshold respectively; If the abnormal index of the cooling system is greater than the second standard threshold, it means that the temperature control effect is poor during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, a first-level warning signal is generated, and it is classified as an overheating operation state. Emergency measures should be taken immediately, such as increasing the flow rate of the cooling system, reducing the reaction rate, or turning on the backup cooling equipment. The monitoring system may be on the verge of temperature out of control, and timely intervention is required to prevent system overload or equipment damage; If the abnormal index of the cooling system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the temperature control effect in the coordinated treatment of waste hydrofluoric acid and leachate is general. At this time, a secondary warning signal is generated, and it is classified as a possible normal operation state. The system shows certain fluctuations, but has not reached the critical point of overheating. The system should be monitored and the temperature and other key parameters should be closely observed. At the same time, preventive adjustments can be made, such as slightly adjusting the coolant flow rate or reaction conditions to prevent the system from entering an overheating state. If the abnormal index of the cooling system is less than the first standard threshold, it means that the temperature control effect is good during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, no warning signal is generated, and it is classified as a normal operating state, indicating that the cooling system is running smoothly and can cope with the current heat load, and no additional intervention measures are required. Continue routine monitoring to ensure that the system is within the safe operating range.

[0042] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal. Relevant personnel can take corresponding handling measures according to different warning signal levels.

[0043] In this application, the implementation of risk level division and early warning system can significantly improve the safety and efficiency of the coordinated treatment process of waste hydrofluoric acid and leachate. By comparing the abnormal index of the cooling system with the gradient standard threshold, the system can monitor the risk of temperature runaway in real time and automatically generate early warning signals. The first-level early warning signal indicates that the temperature control effect is poor, requiring immediate emergency measures to prevent system overheating or equipment damage; the second-level early warning signal indicates that the temperature control effect is general and requires close monitoring and preventive adjustments; if there is no early warning signal, it indicates that the system is running smoothly and no additional intervention is required. This hierarchical processing mechanism can effectively avoid temperature runaway and ensure the stable operation of the system under different heat loads, while optimizing operating efficiency and reducing maintenance costs and accident risks.

[0044] In this embodiment, during the coordinated treatment of waste hydrofluoric acid and leachate, a high-precision heat flow sensor is first installed in the reactor to monitor the heat changes generated by the reaction in real time, calculate the heat release rate per unit time, and transmit the data to the control system to predict the change trend of the heat release rate. At the same time, temperature sensors and flow meters are installed in different links of the cooling system to monitor the temperature and flow of the cooling medium in real time, and the real-time heat conduction efficiency is obtained by combining the heat dissipation calculation formula, and the fluctuations in a fixed time period are analyzed to evaluate the operating efficiency of the cooling system under heat load. The system divides the treatment process into several time periods, determines the weight coefficient of the temperature data in each time period according to the trend of the heat release rate and the fluctuation of the heat conduction efficiency, and obtains the abnormal index of the cooling system by weighted average calculation. Finally, the abnormal index is compared with the gradient standard threshold (including the first and second standard thresholds), and the system risk is divided into three states: normal operation, possible normal operation, and overheating operation, and corresponding treatment measures are taken.

[0045] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0046] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0047] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. Waste hydrofluoric acid and leachate coordinated treatment system, characterized by: It includes a heat release rate calculation module, a cooling system heat conduction efficiency monitoring module, an efficiency abnormality index calculation module, and a risk level classification and processing module; Heat release rate calculation module: A high-precision heat flow sensor is installed in the reactor for the coordinated treatment of waste hydrofluoric acid and leachate to monitor the heat changes generated by the reaction in real time, obtain the heat output data in the reactor through the heat flow sensor, calculate the heat release rate per unit time, transmit the monitored heat release rate data to the control system, and predict the change trend of the heat release rate; Cooling system heat conduction efficiency monitoring module: Install temperature sensors and flow meters in different links of the cooling system to monitor the temperature change and flow of the cooling medium in real time. Combined with the heat dissipation calculation formula, the real-time heat conduction efficiency of the cooling system is obtained. After analyzing the fluctuation of the heat conduction efficiency within a fixed time period, it is evaluated whether the cooling system can operate effectively under the heat load; Efficiency abnormality index calculation module: the waste hydrofluoric acid and leachate collaborative treatment process is divided into several time periods, and the weight coefficients of the temperature data in different time periods during the waste hydrofluoric acid and leachate collaborative treatment process are determined according to the changing trend of the heat release rate and the fluctuation of the heat conduction efficiency in each time period, and the weight coefficients of the temperature data in different time periods are calculated by weighted average calculation to obtain the abnormality index of the cooling system; Risk level classification and processing module: After comparing the calculated abnormal index of the cooling system with the gradient standard threshold, the risk of temperature runaway in the coordinated treatment of waste hydrofluoric acid and leachate is divided into different levels, which are divided into normal operation state, possible normal operation state and overheating operation state, and corresponding processing is performed. The gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold.

2. The waste hydrofluoric acid and leachate coordinated treatment system according to claim 1, characterized in that: In the heat release rate calculation module, a prediction curve of the heat release rate is generated based on the results of model fitting. The heat release rate anomaly index is generated by analyzing the change trend of the heat release rate in different time periods. The method for obtaining the heat release rate anomaly index is as follows: Mark the change points of the predicted curve of heat release rate to obtain the model of heat release rate changing with time. If the change of heat release rate conforms to the normal distribution, the expression is: ; Where HGR(t) is the heat release rate at time point t, is the expected value of the heat release rate at time t, The variance of the heat release rate is used to construct a Bayesian model, and the probability of abnormal changes in the heat release rate is calculated according to the change points of the prediction curve. The initial prior probability P(θ) is: ; Where T is the total length of the time series; Calculate the likelihood function , the expression is: ; is the expected value of the heat release rate, is the variance of the heat release rate; according to Bayes' theorem, combined with the prior probability and likelihood function, the posterior probability of the system state change at time point t is calculated, the expression is: ; In the formula, is the normalized constant; the heat release rate anomaly index is calculated using the expression: ; Where FD is the abnormal heat release rate index.

3. The waste hydrofluoric acid and leachate coordinated treatment system according to claim 2, characterized in that: In the cooling system heat conduction efficiency monitoring module, the heat conduction efficiency fluctuation index is generated after analyzing the fluctuation of the heat conduction efficiency within a fixed time period. The method for obtaining the heat conduction efficiency fluctuation index is as follows: In a fixed time period T, the thermal conductivity efficiency CTE of the cooling system is monitored in real time through temperature sensors and flow meters to generate a time series data set: ;in, Represents the heat conduction efficiency at the nth time point, selects the attenuation factor λ, and calculates the smoothed value at each time point according to the formula of exponentially weighted moving average , which is the weighted average of the current heat transfer efficiency, the formula is: ; For time point The exponentially weighted moving average of ; Calculate the actual heat transfer efficiency at each time point With exponentially weighted moving average The residual between , the residual indicates the degree to which the actual value deviates from the predicted smoothed value, and the expression is: ; Calculate the heat conduction efficiency fluctuation index based on the residual, the expression is: ; Where n is the number of data points in the analysis time period, and ED is the thermal conduction efficiency fluctuation index.

4. The waste hydrofluoric acid and leachate coordinated treatment system according to claim 3, characterized in that: In the efficiency anomaly index calculation module, the heat release rate anomaly index and the heat conduction efficiency fluctuation index obtained in each time period are converted into feature vectors, and the feature vectors are used as the input of the machine learning model. The machine learning model uses each group of feature vectors to predict the weight coefficient value label of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the prediction target, and takes minimizing the sum of the prediction errors of the weight coefficient value labels of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The weight coefficient value of the temperature data in each time period during the coordinated treatment of waste hydrofluoric acid and leachate is determined according to the model output results, wherein the machine learning model is a polynomial regression model; and the weight coefficients of the temperature data in different time periods are weighted averaged to calculate the anomaly index of the cooling system.

5. The waste hydrofluoric acid and leachate coordinated treatment system according to claim 4, characterized in that: In the risk level classification and processing module, the acquired abnormal index of the cooling system is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the abnormal index of the cooling system is compared with the first standard threshold and the second standard threshold respectively; If the abnormal index of the cooling system is greater than the second standard threshold, it indicates that the temperature control effect is poor during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, a first-level warning signal is generated, and it is classified as an overheating operation state, and emergency measures are taken immediately; If the abnormal index of the cooling system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the temperature control effect in the coordinated treatment of waste hydrofluoric acid and leachate is general. At this time, a secondary warning signal is generated, and it is classified as a possible normal operation state, and preventive intervention is carried out; If the abnormal index of the cooling system is less than the first standard threshold, it means that the temperature control effect is good during the coordinated treatment of waste hydrofluoric acid and leachate. At this time, no warning signal is generated and it is classified as a normal operating state, indicating that the cooling system is running smoothly and no intervention is required.

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