A control system and method for optimizing high salinity mine water deep well injection parameters

Abstract

The application discloses a kind of control system and method for optimizing high salinity mine water deep well injection parameters, the system includes data acquisition unit, control unit, execution unit and environmental monitoring module;The method is through data acquisition, data processing and analysis, optimization and calculation, execution and adjustment, environmental monitoring and protection and remote monitoring and management etc., real-time monitoring and dynamic adjustment injection parameters.The application combines real-time data analysis, dynamic optimization and remote monitoring function, significantly improves the efficiency and safety of mine water treatment, and reduces the negative impact on the environment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of water resources management, and relates to a control system and method for optimizing the parameters of deep well injection of high salinity mine water. In particular, it relates to an adaptive control system and method for optimizing the parameters of deep well injection of high salinity mine water and mitigating environmental impact. BACKGROUND

[0002] With the increase of mining activities, the discharge of high salinity mine water has become an increasingly serious environmental problem. These mine waters not only contain a large amount of salt, but also may contain other harmful substances, and direct discharge will cause serious pollution to surface water and soil. Traditional mine water treatment methods such as evaporation ponds, surface discharge, etc. have problems such as low treatment efficiency, large land occupation and high environmental risk. Deep well injection as an effective mine water treatment method has the advantages of small land occupation, large treatment capacity and small environmental impact. However, how to optimize the parameters of deep well injection to ensure efficient operation of the system and mitigate environmental impact is still a problem to be solved. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application aims to provide a control system and method for optimizing the parameters of deep well injection of high salinity mine water, which can effectively improve the efficiency of deep well injection and reduce the potential harm to the environment by real-time monitoring and dynamic adjustment of injection parameters.

[0004] To solve the above technical problems, the present application adopts the following technical solutions:

[0005] A control system for optimizing the parameters of deep well injection of high salinity mine water, comprising:

[0006] A data acquisition unit comprising a plurality of sensors and detection devices for real-time monitoring of various parameters of mine water;

[0007] A control unit capable of receiving real-time data from the data acquisition unit, analyzing and processing the data, and calculating the optimal injection parameters according to a pre-set optimization algorithm;

[0008] An execution unit comprising a plurality of actuators and control devices capable of automatically adjusting the operating state of the injection equipment according to the instructions of the control unit to ensure that the mine water is injected into the ground in the optimal state;

[0009] An environmental monitoring module comprising groundwater quality monitoring instruments and data transmission devices capable of monitoring the groundwater quality in the injection area to detect and prevent possible pollution problems in a timely manner.

[0010] The present application also includes the following technical features:

[0011] Specifically, the plurality of sensors and detection devices include salinity sensors, temperature sensors, pressure sensors, and flow meters.

[0012] The plurality of actuators and control devices include variable frequency pumps, regulating valves, and injection equipment.

[0013] A method for optimizing the injection parameters of high-salinity mine water into deep wells, which is based on the control system for optimizing the injection parameters of high-salinity mine water into deep wells; including the following steps:

[0014] Step one, data collection:

[0015] Real-time collection of salinity, temperature, pressure, and flow data of mine water through salinity sensors, temperature sensors, pressure sensors, and flow meters, and transmission of the collected data to the control unit;

[0016] Step two, data processing and analysis:

[0017] Data preprocessing: after the control unit receives the data, it performs data cleaning and normalization processing to ensure data accuracy; real-time data analysis: multi-dimensional analysis of data to identify key factors affecting injection effectiveness;

[0018] Step three, optimization and calculation:

[0019] Based on the key variables affecting the current injection effect obtained in step two, enter the optimization stage, dynamically adjust the injection parameters to ensure that the system can balance between injection efficiency, environmental protection, and safety;

[0020] Step four, execution and adjustment:

[0021] Send instructions: the control unit sends control signals to the execution unit according to the optimization results in step three; adjust the equipment: after receiving the instructions, the execution unit dynamically adjusts the injection pressure and flow through the variable frequency pump and regulating valve to achieve the best state after optimization;

[0022] Step five, environmental monitoring and protection:

[0023] Monitor groundwater quality: the environmental monitoring module monitors the pH value and heavy metal content of groundwater in real time; pollution warning: when the groundwater quality is abnormal, the system automatically adjusts the injection parameters to reduce the risk of pollution, and notifies the management personnel through the alarm system;

[0024] Step six, remote monitoring and management:

[0025] Real-time monitoring platform: provides a user interface to display the system's running status, historical data, and prediction results; remote operation: users can remotely view the system status through the network, make necessary parameter adjustments, and handle emergencies.

[0026] Specifically, in step two, data processing and analysis is carried out using LASSO regression for preliminary feature selection to screen out key parameters that have the most significant impact on injection effect; principal component analysis is used for dimensionality reduction, combining two or more related variables into principal components; then random forest regression is used to analyze the nonlinear relationship between the principal components to obtain the contribution of each principal component to the injection effect.

[0027] Specifically, the data processing and analysis includes:

[0028] Step 2.2.1, feature selection using LASSO regression, the objective function of LASSO regression is:

[0029]

[0030] where X ij represents the jth feature value of the ith sample, including salinity, temperature, pressure; y i is the injection effect; β j is the regression coefficient of each parameter; λ is the regularization parameter;

[0031] In the regression process, by adjusting λ, parameters with small impact on injection effect are automatically screened out and their coefficients are sparsified to zero, obtaining key parameters that have the most significant impact on injection effect among the parameters of mine water;

[0032] Step 2.2.2, dimensionality reduction using principal component analysis: using principal component analysis to identify the correlation between the key parameters obtained in step 2.2.1, generating multiple new principal components to represent the main trend of the key parameter variables;

[0033] Step 2.2.3, random forest regression analysis: using a random forest model to perform regression analysis on the principal components after PCA dimensionality reduction to obtain the contribution of each principal component to the injection effect.

[0034] Specifically, the step 2.2.3 includes:

[0035] Input variables: principal components generated by principal component analysis as input variables; model training: training the principal components generated by PCA at each time with the injection effect, and building multiple decision trees using random forest to capture the complex nonlinear relationship between the principal components and the injection effect; prediction output: after training the model, predicting new injection operations by inputting new principal component values in real time, and the random forest regression model can accurately evaluate whether the current injection conditions are optimized and whether the injection parameters need to be adjusted; variable importance analysis: random forest can also output the importance score of each principal component to obtain the contribution of each variable to the injection effect.

[0036] Specifically, the step three includes:

[0037] Step 3.1, optimization objective function setting:

[0038] Based on the contribution of each variable to the injection effect obtained in step two, and to ensure a balance between injection efficiency, environmental protection and safety, the objective function is defined as follows:

[0039] f(P,Q) = α(t)·E(P,Q) - β(t)·G(S,T) - γ(t)·S(P,Q)

[0040] Where, injection efficiency E(P,Q) = Q: indicates that injection efficiency is proportional to flow Q; environmental impact G(S,T) = k1S + k2T: indicates the impact of salinity S and temperature T on the environment, k1 represents the weight of salinity on environmental impact, k2 represents the weight of temperature on environmental impact; system safety S(P,Q) = (P / Pmax) 2 +(Q / Qmax) 2 : ensures that injection pressure P and flow Q are within the safe range, Pmax and Qmax are the safety thresholds of pressure and flow respectively; α(t) is the weight of injection efficiency, β(t) is the weight of environmental impact, γ(t) is the weight of system safety;

[0041] Step 3.2, dynamic adjustment and feedback mechanism:

[0042] Using Bayesian optimization algorithm, combined with real-time data feedback, dynamically adjust the injection parameters to continuously optimize the injection process; the following steps are realized: real-time feedback and iterative adjustment: real-time monitoring of pressure, flow, salinity and temperature through sensors, and comparing the actual injection effect with the objective function; dynamic adjustment of weight coefficients: through real-time feedback, the optimization algorithm adjusts the weight coefficients α(t), β(t), γ(t) in the objective function.

[0043] Specifically, in step five, when the environmental monitoring module detects that the groundwater quality index exceeds the standard, feedback and adjustment are carried out through the following steps:

[0044] Step 5.1, monitoring and alarm: the environmental monitoring module monitors the groundwater quality in real time; if it detects that a certain index exceeds the set threshold, the system triggers an alarm;

[0045] Step 5.2, data feedback: the environmental monitoring module transmits the exceeding data and alarm information to the control unit; after receiving the feedback, the control unit re-evaluates the current injection strategy;

[0046] Step 5.3, adjust the objective function: adjust the weight coefficients in the objective function, introduce the environmental penalty term F env , and update the objective function as:

[0047] f'(P,Q)=a(t)E(P,Q)-b(t)G(S,T)-g(t)S(P,Q)-d(t)F env

[0048] where F env represents the environmental over-standard penalty, and d is the weight coefficient of environmental over-standard;

[0049] Step 5.4, re-optimizing injection parameters: the control unit uses the updated objective function f'(P,Q) to recalculate the optimal injection pressure P and injection flow Q; through an optimization algorithm, new P and Q are found to maximize the objective function f'(P,Q) while reducing the impact of environmental over-standard;

[0050] Step 5.5, implementing adjustment: apply the new injection pressure and flow parameters to the injection system to adjust the operation of the injection equipment in real time; continue to monitor the groundwater quality to ensure that the adjustment measures are effective until the environmental indicators return to the normal range.

[0051] Specifically, when the environmental monitoring module detects that the pH value is over-standard, the environmental penalty term F env is defined as:

[0052] F env = k pH ·(pH current -pH target ) 2

[0053] where k pH is the penalty coefficient of pH over-standard, pH current is the current monitored pH value, and pH target is the target pH value.

[0054] Specifically, when the environmental monitoring module detects that the heavy metal is over-standard, the environmental penalty term F env is defined as:

[0055] F env = k HM ·(HM current -HM target ) 2

[0056] where k HM is the penalty coefficient of heavy metal over-standard, HM current is the current monitored heavy metal content, and HM target is the target heavy metal content.

[0057] Compared with the prior art, the present application has the following technical effects:

[0058] The application provides an intelligent control system for deep well injection of mine water, which combines real-time data analysis, dynamic optimization and remote monitoring functions, significantly improving the efficiency and safety of mine water treatment, and reducing the negative impact on the environment.

[0059] The application uses LASSO regression, PCA dimensionality reduction and random forest regression to analyze real-time monitoring data in multiple dimensions, screen out key variables affecting injection effect, and dynamically adjust injection parameters through Bayesian optimization algorithm. This optimization process ensures that the system can flexibly respond under different working conditions, always maintains maximum injection efficiency, while controlling environmental impact and ensuring system safety. Compared with the traditional fixed injection parameter method, the intelligent optimization control of the application greatly improves the accuracy and adaptability of the injection process.

[0060] The application sets a penalty coefficient to effectively control environmental impact factors during the injection process, especially for high salinity, heavy metal exceeding standard and pH value not meeting standard water bodies, the system can automatically adjust parameters to reduce the pollution risk to groundwater and surrounding environment. At the same time, the system adjusts in real time according to the monitored pressure and flow to ensure that the equipment operates within a safe range, effectively preventing system overload or failure.

[0061] The application integrates real-time monitoring platform and remote operation function, users can view the running state, historical data and prediction results of the system through the network platform. The monitoring platform provides an intuitive user interface to facilitate real-time monitoring of system operation. In addition, the system supports remote parameter adjustment, users can perform necessary optimization operations and emergency treatment according to the monitoring results to ensure stable operation of the system under different working conditions. This remote control method significantly improves management efficiency, reduces the need for manual intervention, and maintains efficient and reliable operation during the treatment process.

[0062] In addition to being suitable for the treatment of high salinity mine water, the system architecture and optimization algorithm of the application have strong adaptability and can be extended to other similar industrial wastewater treatment scenarios. By adjusting system parameters, it can effectively cope with complex conditions of different water quality and different treatment requirements, providing an effective solution for other wastewater treatment fields and having wide application prospects.

[0063] The application breaks through the limitations of traditional mine water injection systems by combining intelligent control and optimization algorithms, achieving efficient, environmentally friendly, safe and intelligent injection process, with significant technical advantages and practical application value. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 The flowchart of the method of the application. DETAILED DESCRIPTION

[0065] The application provides a control system for optimizing deep well injection parameters of high salinity mine water, comprising:

[0066] A data acquisition unit comprising a plurality of sensors and detection devices for real-time monitoring of various parameters of mine water; specifically, the plurality of sensors and detection devices comprise salinity sensors, temperature sensors, pressure sensors, and flow meters;

[0067] A control unit, the core of which is a high-performance processor equipped with advanced data processing algorithms and control logic, capable of receiving real-time data from the data acquisition unit, analyzing and processing the data, and calculating the optimal injection parameters according to the preset optimization algorithm;

[0068] An execution unit comprising a variety of actuators and control devices capable of automatically adjusting the operating state of the injection equipment according to the instructions of the control unit to ensure that the mine water is injected into the ground in the optimal state; specifically, the variety of actuators and control devices comprise variable frequency pumps, regulating valves, and injection equipment;

[0069] An environmental monitoring module comprising groundwater quality monitoring instruments and data transmission devices capable of monitoring the groundwater quality in the injection area to promptly discover and prevent possible pollution problems.

[0070] The application also provides a method for optimizing deep well injection parameters of high salinity mine water, which is realized based on the control system for optimizing deep well injection parameters of high salinity mine water; as shown in Figure 1 The method comprises the following steps:

[0071] Step one, data acquisition:

[0072] The salinity, temperature, pressure, and flow data of mine water are collected in real time by salinity sensors, temperature sensors, pressure sensors, and flow meters, and the collected data are transmitted to the control unit;

[0073] Step two, data processing and analysis:

[0074] 2.1 Data preprocessing: after the control unit receives the data, data cleaning and normalization processing are performed to ensure data accuracy;

[0075] 2.2 Real-time data analysis: multi-dimensional analysis of data is performed using big data analysis technology to identify key factors affecting injection effect;

[0076] Specifically, the injection effect of mine water is influenced by multiple factors, such as salinity, temperature, pressure, flow rate, etc. However, different factors have different degrees of influence on the injection effect, and some factors may have less influence or be redundant in certain situations. Therefore, LASSO regression is used for preliminary feature selection, principal component analysis (PCA) is used for dimension reduction, and then random forest regression is used to further analyze the nonlinear relationship between the influencing factors, and finally the contribution of each principal component to the injection effect is obtained; more specifically: LASSO regression selects the most important variables for the injection effect, such as pressure P, salinity S, and temperature T, and the optimization algorithm will adjust the flow rate Q and pressure P based on these variables; after dimension reduction by PCA, some related variables (such as salinity and temperature) may be combined into principal components, and the optimization algorithm will adjust according to these principal components, simplifying the computational complexity of optimization; then, random forest regression provides the nonlinear relationship between variables, which will be used for precise adjustment of parameters in the optimization process.

[0077] Through multiple sensors installed in the mine water treatment system, real-time multi-dimensional data is collected, including: salinity (S) (ppm), temperature (T) (℃), pressure (P) (bar), flow rate (Q) (L / min), injection effect (Y) (score, 0-1, indicating the efficiency of injection); data is collected every minute, generating a multi-dimensional time series dataset, as shown in Table 1;

[0078] Table 1: Example data

[0079] Time Salinity (ppm) Temperature (°C) Pressure (bar) Flow (L / min) Injection effect Y 12:00 10000 25 50 120 0.85 12:01 9800 26 48 118 0.82 12:02 9600 24 49 119 0.83 12:03 10200 27 51 125 0.87 … … … … … …

[0080] 2.2.1 Feature selection using LASSO regression:

[0081] LASSO regression is a regularized regression method for handling high-dimensional data, and is particularly good at selecting the most important features from a large number of variables. The basic idea is to add an L1 regularization term to the regression coefficients, so that some regression coefficients become 0, thereby achieving feature selection. The goal is to find out which parameters of mine water (such as salinity, temperature, pressure, etc.) have the most significant influence on the injection effect (Y). Assume that the data is represented by X1, X2, … X P , where each variable represents salinity, temperature, etc., and the target variable is Y (injection effect).

[0082] Objective function of LASSO regression:

[0083]

[0084] Where:

[0085] Xi,j ij represents the jth feature value of the ith sample, including salinity, temperature, pressure, etc.

[0086] y i is the injection effect (target variable);

[0087] β j are regression coefficients of individual parameters;

[0088] λ is a regularization parameter to control the sparsity of coefficients.

[0089] In the regression process, by adjusting λ, the parameters that have little effect on the injection effect are automatically screened out and their coefficients are sparsified to zero, obtaining the key parameters that have the most significant impact on the injection effect (Y) among the various parameters of the mine water (such as salinity, temperature, pressure, flow, etc.). "Little effect" here means that the regression coefficient of a certain parameter is reduced to be small enough in the LASSO model calculation process, so that it can be ignored in prediction and optimization. When the regularization parameter λ increases, LASSO will impose greater penalties on unimportant parameters, sparsifying their coefficients to 0. At this time, these parameters are considered to have a small effect on the injection effect. For example, the results show that the flow (Q) has a small effect on the injection effect, and the model sets the coefficient β Q of the flow to 0, finally only retaining the three key parameters of salinity, temperature and pressure.

[0090] 2.2.2, Principal Component Analysis (PCA) for dimensionality reduction: After using LASSO regression to screen the most important variables, the factors that have a greater impact on the injection effect (such as salinity, temperature and pressure) are retained. Next, Principal Component Analysis (PCA) is used to process these variables.

[0091] Using Principal Component Analysis (PCA) to identify the correlation between the key parameters obtained in step 2.2.1, generate multiple new principal components to represent the main trend of the key parameter variables; although the number of variables has been reduced at this time, PCA generates several new "principal components" to represent the main trend of these variables by identifying the correlation between these variables. For example, salinity and temperature may have similar trends, and PCA will combine them into a new variable (principal component), so that fewer variables (principal components) can be used to represent most of the information of the original data, thus simplifying the structure of the model and reducing the computational complexity.

[0092] For example, assume we have the following data in Table 2. Through LASSO, we retain these three variables. However, we find that the trends of salinity and temperature are very similar: when salinity increases, temperature also has a similar trend. PCA will generate a principal component based on this similarity (which may be a weighted combination of "salinity + temperature"), and this principal component can explain the main changes in salinity and temperature. If PCA yields the following principal components: the first principal component is 80% salinity and 20% temperature, and the second principal component is mainly pressure; in this way, the three variables that need to be considered can now be explained by two principal components. The first principal component explains the main changes in salinity and temperature, and the second principal component explains the changes in pressure.

[0093] Table 2 example data

[0094] Salinity (ppm) Temperature (°C) Pressure (bar) 10000 25 50 9800 26 48 9600 24 49 10200 27 51

[0095] 2.2.3 Random Forest Regression Analysis: An ensemble learning method that integrates the results of multiple decision trees. It can not only handle linear problems, but also complex nonlinear relationships, and can give the contribution of each feature. Random Forest builds multiple decision trees, each tree is trained on a different random subset, and finally the prediction results of each tree are averaged to get the final prediction value. This method is very suitable for processing nonlinear data, so it can further help analyze the nonlinear impact of each feature on the injection effect.

[0096] After LASSO regression and PCA, key variables that have a greater impact on "injection effect" have been screened out, and the data structure has been further simplified through PCA, reducing redundant information. However, even after simplifying these variables or principal components, the relationship between them and "injection effect" is often not a simple linear relationship. Therefore, the next step of random forest regression is to deal with the nonlinear relationship between these key variables or principal components and "injection effect", and further improve the prediction accuracy.

[0097] Using the random forest model to regress the principal components after PCA dimensionality reduction, we get the contribution of each principal component to the injection effect;

[0098] After completing LASSO and PCA, we have obtained the simplified main variables (or principal components). Random forest regression will be further used for modeling between these variables and injection effect. The specific steps are as follows:

[0099] 1. Input variables: The principal components generated by PCA (e.g., principal component 1 representing salinity and temperature, and principal component 2 representing pressure) are used as input variables. Through the random forest model, the nonlinear impact of these principal components on the injection effect is predicted.

[0100] 2. Model Training: The principal components generated by PCA at each time point are trained with the injection effects. Random Forest constructs multiple decision trees to capture the complex non-linear relationship between principal components and injection effects.

[0101] 3. Prediction Output: After the model is trained, new injection operations are predicted. By inputting new principal component values in real-time, the Random Forest regression model can accurately assess whether the current injection conditions are optimized and whether injection parameters need to be adjusted.

[0102] 4. Variable Importance Analysis: Random Forest can also output the importance score of each principal component to determine the contribution of each variable to the injection effect. For example, the Random Forest model may find that Principal Component 1 (mainly salinity and temperature) has the greatest impact on injection effect, accounting for 60%, while Principal Component 2 (pressure) accounts for 40%. This helps further optimize injection strategies, such as focusing on controlling the fluctuation range of salinity and temperature.

[0103] Example:

[0104] Suppose two main principal components are obtained through LASSO and PCA:

[0105] Principal Component 1: mainly composed of salinity and temperature, explaining most of the variance in the data;

[0106] Principal Component 2: mainly composed of pressure, explaining the remaining variance information.

[0107] These principal components are used as input to train the Random Forest model. The model constructs 100 decision trees, each of which predicts the injection effect (Y) based on different values of Principal Component 1 and Principal Component 2.

[0108] In practical applications, when new real-time data is collected (including salinity, temperature, pressure), new Principal Component 1 and Principal Component 2 are obtained after PCA dimensionality reduction. Based on these inputs, the Random Forest model obtains the importance scores of Principal Component 1 and Principal Component 2, for example:

[0109] Principal Component 1 (salinity and temperature) contributes 60% to the injection effect;

[0110] Principal Component 2 (pressure) contributes 40% to the injection effect.

[0111] This information can help further optimize the injection process and determine which variables to control and adjust more.

[0112] Through multidimensional analysis of known real-time data, the system identifies the most significant variables affecting injection effect (step two), and then adjusts injection parameters based on these analysis results to optimize future injection operations (step three).

[0113] Step three, optimization and calculation:

[0114] After completing the data processing and analysis in step two, the system has identified the key variables (such as pressure P, salinity S, temperature T, etc.) that have the most significant impact on the current injection effect Y through LASSO regression, PCA dimensionality reduction, and random forest regression. Through the analysis of these variables, the relationship between them and the injection effect is clarified.

[0115] Based on the key variables obtained in step two that have the most significant impact on the current injection effect, the optimization phase is entered to dynamically adjust the injection parameters to ensure that the system can balance between injection efficiency, environmental protection, and safety.

[0116] 3.1. Optimization objective function setting

[0117] The optimization objective is to dynamically adjust the pressure P and flow rate Q while optimizing injection efficiency, environmental impact, and system safety. Based on the contribution of each variable to the injection effect obtained in step two, and to ensure a balance between injection efficiency, environmental protection, and safety, the objective function is defined as follows:

[0118] f(P,Q) = α(t)·E(P,Q) - β(t)·G(S,T) - γ(t)·S(P,Q)

[0119] Where, injection efficiency E(P,Q) = Q: indicates that the injection efficiency is proportional to the flow rate Q;

[0120] Environmental impact G(S,T) = k1S + k2T: indicates the impact of salinity S and temperature T on the environment, k1 and k2 are constant coefficients representing the degree of impact of salinity and temperature on the environment;

[0121] System safety S(P,Q) = (P / Pmax) 2 +(Q / Qmax) 2 : ensures that the injection pressure and flow rate are within the safe range, Pmax and Qmax are the safety thresholds for pressure and flow rate, respectively.

[0122] α(t): weight of injection efficiency, indicating the priority of the system in terms of efficiency under current conditions.

[0123] β(t): weight of environmental impact, indicating the priority of the system in terms of environmental protection.

[0124] γ(t): weight of system safety, ensuring that the system can operate within the safety range at any time.

[0125] k1 represents the weight of salinity on environmental impact. Higher salinity can lead to groundwater pollution, decreased formation permeability, or other negative environmental impacts. Therefore, the value of k1 is determined based on the sensitivity of salinity to the environment during the injection of mine water, through experimental data and industry standards.

[0126] k2 represents the weight of temperature on environmental impact. Changes in temperature can cause changes in the rate of chemical reactions between mine water and groundwater, affecting the biological environment of the water body. The value of k2 is also determined by experimental data and industry standards.

[0127] 3.2. Dynamic adjustment and feedback mechanism

[0128] Using the Bayesian optimization algorithm, combined with real-time data feedback, the injection parameters are dynamically adjusted to continuously optimize the injection process. The optimization process is achieved through the following steps:

[0129] ① Real-time feedback and iterative adjustment: Real-time monitoring of pressure, flow rate, salinity and temperature through sensors, and comparison of actual injection effect with target function. According to the feedback results, the system will dynamically adjust the flow rate Q and pressure P.

[0130] ② Dynamic adjustment of weight coefficients: Through real-time feedback, the optimization algorithm adjusts the weight coefficients α(t), β(t), γ(t) in the target function. For example, when the salinity is too high, the system automatically increases the weight of β(t) to prioritize reducing environmental impact; if the pressure is close to the safety threshold, the system will increase the weight of γ(t) to prioritize system safety.

[0131] Specific optimization example:

[0132] Suppose through step two: data processing and analysis, the system identifies that salinity S, pressure P and temperature T are the main factors affecting the injection effect. The current flow rate Q = 100 L / min, pressure P = 45 bar, salinity S = 10000 ppm, temperature T = 25℃.

[0133] The initial target function of the optimization algorithm is:

[0134]

[0135] The system initially sets α(t) = 1, β(t) = 0.5, γ(t) = 0.8, and dynamically adjusts according to real-time feedback. If the salinity is found to be too high, the system may increase β(t) to reduce the flow rate and control the impact of salinity on the environment.

[0136] Through this feedback and adjustment mechanism, the system can achieve a balance and optimization between injection efficiency, environmental protection and system safety.

[0137] Step four, execution and adjustment:

[0138] 4.1. Sending instructions: The control unit sends control signals to the execution unit based on the optimization results from step three, containing adjustment instructions for injection pressure P and flow rate Q.

[0139] 4.2. Adjusting equipment: After receiving the instructions, the execution unit adjusts the variable frequency pump and regulating valve to dynamically adjust the injection pressure and flow rate, making the system reach the optimal state after optimization.

[0140] Step five, environmental monitoring and protection:

[0141] 1. Monitoring groundwater quality: The environmental monitoring module monitors the pH value, heavy metal content, and other indicators of groundwater in real time. 2. Pollution early warning: When the groundwater quality is detected to be abnormal, the system automatically adjusts the injection parameters to reduce the pollution risk and notifies the management personnel through the alarm system.

[0142] The objective function f(P,Q) = α(t)·E(P,Q) - β(t)·G(S,T) - γ(t)·S(P,Q) mainly considers injection efficiency, salinity, temperature, and system safety, without directly considering environmental indicators such as pH value and heavy metals. However, when the environmental monitoring link exceeds the standard alarm, the system can still indirectly affect these environmental indicators by adjusting the injection pressure and flow rate.

[0143] The following is a detailed explanation and flow of how to reduce the negative impact on the environment by adjusting the injection parameters when the environmental monitoring link exceeds the standard alarm.

[0144] Feedback mechanism and adjustment logic: When the environmental monitoring module detects that the groundwater quality indicators (such as pH value and heavy metals) exceed the standard, the system will perform feedback and adjustment through the following steps:

[0145] 5.1 Monitoring and alarm:

[0146] The environmental monitoring module monitors the groundwater quality in real time; if it detects that a certain indicator (such as pH value or heavy metal content) exceeds the set threshold, the system triggers an alarm.

[0147] 5.2 Data feedback:

[0148] The environmental monitoring module transmits the exceeding data and alarm information to the control unit; after receiving the feedback, the control unit re-evaluates the current injection strategy.

[0149] 5.3 Adjusting the objective function:

[0150] The control unit temporarily adjusts the weight coefficients in the objective function to prioritize solving the environmental exceeding problem.

[0151] Specifically, a new environmental penalty term F env is introduced, and the objective function is updated:

[0152] f′(P,Q)=α(t)·E(P,Q)-β(t)·G(S,T)-γ(t)·S(P,Q)-δ(t)·F env

[0153] Among them, F env The term F represents the penalty for exceeding environmental standards, where δ is the weighting coefficient for exceeding environmental standards; the penalty term for exceeding environmental standards is... env Definition: When the environmental monitoring module detects that the pH value exceeds the standard, F env Defined as:

[0154] F env =k pH ·(pH current -pH target ) 2

[0155] Where, k pH This is the penalty coefficient for exceeding pH limits. When the pH value is too high or too low, it may cause changes in the acidity or alkalinity of groundwater, affecting the chemical balance and ecological environment of the water body. Therefore, the value of kpH is determined based on experimental data and industry standards, according to the degree of environmental impact caused by pH exceeding limits during mine water injection. current This is the currently monitored pH value. target It is the target pH value.

[0156] Similarly, when heavy metals exceed the standard, F env Defined as:

[0157] F env =k HM ·(HM current -HM target ) 2

[0158] Where, k HM This is the penalty coefficient for heavy metal contamination. Excessive heavy metal levels can lead to water pollution, soil degradation, and toxic effects on organisms. Therefore, k... HM The values ​​are determined based on the environmental impact of excessive heavy metal content, using experimental data and industry standards. HM current This refers to the currently monitored heavy metal content, HM target It refers to the target heavy metal content.

[0159] 5.4 Re-optimize injection parameters:

[0160] The control unit recalculates the optimal injection pressure P and flow rate Q using the updated objective function f'(P, Q). Through an optimization algorithm, new P and Q are found that maximize the objective function f'(P, Q) while minimizing the impact of environmental exceedances.

[0161] 5.5 Implementation of adjustments:

[0162] Apply the new injection pressure and flow rate parameters to the injection system, adjust the operation of the injection equipment in real time; continue to monitor the groundwater quality, ensure that the adjustment measures are effective, until the environmental indicators return to the normal range.

[0163] Step six, remote monitoring and management:

[0164] 6.1. Real-time monitoring platform: provides a friendly user interface to display the system's running status, historical data and prediction results;

[0165] 6.2. Remote operation: users can remotely view the system status through the network, make necessary parameter adjustments and emergency treatment.

[0166] Through the above technical solutions, the present application provides a high-efficiency and reliable mine water deep well injection control system, which can significantly improve the treatment efficiency and reduce the impact on the environment, and has a wide application prospect. The adaptive control system of the present application can not only be used for the treatment of high-salinity mine water, but also can be applied to other similar industrial wastewater treatment fields, providing strong support for environmental protection and resource utilization.

[0167] The following gives a specific embodiment of the present application, it should be noted that the present application is not limited to the following specific embodiments, any equivalent transformation based on the technical solutions of the present application falls within the protection scope of the present application.

[0168] Embodiment:

[0169] A mining company generated a large amount of high-salinity mine water during the mining process, which posed a serious pollution risk to the environment. Therefore, the company decided to implement a deep well injection system to optimize the treatment process of mine water and reduce the impact on the environment.

[0170] Step one, data collection:

[0171] 1.1. Install sensors: install salinity sensors, temperature sensors, pressure sensors and flow meters at key locations of the mine water treatment system (such as the injection wellhead), a total of 10 sensors.

[0172] 1.2. Real-time monitoring: sensors collect real-time salinity, temperature, pressure and flow data of mine water every minute. For example, the results of a data collection are as follows:

[0173] Time: 12:00, Salinity: 10000 ppm, Temperature: 25°C, Pressure: 50 bar, Flow: 120 L / min.

[0174] 1.3. Data Transmission: The collected data is transmitted to the control unit through the wireless module for subsequent data processing.

[0175] Step Two, Data Processing and Analysis:

[0176] 2.1. Data Preprocessing: The control unit cleans and normalizes the received data, removes outliers, and ensures data accuracy.

[0177] 2.2. Real-time Data Analysis: Using big data analysis techniques, key factors affecting injection effectiveness are identified. Through LASSO regression, principal component analysis (PCA), and random forest regression, it is determined that salinity, temperature, and pressure are the main factors affecting injection efficiency.

[0178] Step Three, Optimization and Calculation:

[0179] 3.1. Construction of Adaptive Optimization Model: Based on real-time data, an adaptive optimization model is established to dynamically adjust injection parameters (pressure and flow).

[0180] 3.2. Definition of Optimization Objective Function: The system defines the objective function as:

[0181] f(P,Q) = α(t)·E(P,Q) - β(t)·G(S,T) - γ(t)S(P,Q)

[0182] 3.3. Dynamic Adjustment of Weight Coefficients: According to real-time data and environmental monitoring feedback, dynamically adjust weight coefficients α(t), β(t), γ(t) to ensure that when environmental indicators exceed, injection efficiency is reduced first.

[0183] 3.4. Evaluation and Adjustment of Optimization Process: The system monitors that the salinity is high, automatically triggers the optimization algorithm, and the adjusted injection parameters are:

[0184] Injection pressure: 45 bar;

[0185] Injection flow: 110 L / min.

[0186] Step Four, Execution and Adjustment:

[0187] 4.1. Execute injection operation: According to the optimized parameters, the control unit instructs the execution unit to adjust the frequency pump and regulating valve to start injection.

[0188] 4.2. Dynamic adjustment process: During the injection process, parameters are monitored in real-time and feedback adjustments are made. When the pressure approaches the upper limit, the flow rate is automatically reduced to ensure the safety of the injection process.

[0189] Step five, environmental monitoring and protection:

[0190] 5.1. Monitoring groundwater quality: The environmental monitoring module monitors key indicators of groundwater in real-time, including pH value, heavy metal content, etc. The monitoring data is sent to the control unit through the data transmission device. pH value: 9.0 (exceeds the standard, normal range is 6.5-8.5), heavy metal content: 0.02 mg / L (normal range);

[0191] 5.2. Pollution warning: If the pH value or heavy metal content exceeds the standard, the system will immediately trigger an alarm and adjust the injection parameters to reduce environmental impact. Due to the pH value exceeding the standard, the system will trigger the alarm mechanism;

[0192] 5.3. Introduce environmental over-standard penalty term: Due to the detection of pH value exceeding the standard, the system will introduce an environmental over-standard penalty term F env ;

[0193] 5.4. Adjust the objective function: f'(P,Q) = α(t)·E(P,Q) - β(t)·G(S,T) - γ(t)·S(P,Q) - δ(t)·F env

[0194] 5.5. Re-optimize injection parameters: The control unit uses the updated objective function f'(P,Q) to recalculate the optimal injection pressure P and flow rate Q. The new parameters obtained in the optimization process are: new injection pressure: 30 bar; new injection flow rate: 85 L / min.

[0195] 5.6. Implement adjustments: Apply the new injection pressure and flow rate parameters to the injection system, adjust the operation of the injection equipment in real-time, and the system will continue to monitor the groundwater quality to ensure that the adjustment measures are effective until the pH value returns to the normal range (between 6.5 and 8.5).

Claims

1. A method of optimizing high salinity mine water deep well injection parameters, characterized in that, The method is realized based on an optimized control system for high-salinity mine water deep well injection parameters; the optimized control system for high-salinity mine water deep well injection parameters comprises: a data acquisition unit comprising a plurality of sensors and detection devices for real-time monitoring of various parameters of mine water; a control unit capable of receiving real-time data from the data acquisition unit, analyzing and processing the data, and calculating the optimal injection parameters according to a preset optimization algorithm; an execution unit comprising a plurality of actuators and control devices capable of automatically adjusting the operating state of the injection equipment according to the instructions of the control unit to ensure that the mine water is injected into the ground in the optimal state; an environmental monitoring module comprising groundwater quality monitoring instruments and data transmission devices capable of monitoring the groundwater quality in the injection area to promptly discover and prevent possible pollution problems; the plurality of sensors and detection devices include salinity sensors, temperature sensors, pressure sensors, and flow meters; the plurality of actuators and control devices include variable frequency pumps, regulating valves, and injection equipment; the method comprises the following steps: Step 1: Data acquisition: Real-time collection of salinity, temperature, pressure, and flow data of mine water through salinity sensors, temperature sensors, pressure sensors, and flow meters, and transmission of the collected data to the control unit; Step 2: Data processing and analysis: Data preprocessing: the control unit receives data, performs data cleaning and normalization processing to ensure data accuracy; real-time data analysis: multi-dimensional analysis of data to identify key factors affecting injection effect; Step 3: Optimization and calculation: Based on the key variables affecting the current injection effect obtained in Step 2, enter the optimization stage, dynamically adjust the injection parameters to ensure that the system can balance between injection efficiency, environmental protection, and safety; Step 4: Execution and adjustment: Send instructions: the control unit sends control signals to the execution unit according to the optimization results in Step 3; adjust the equipment: the execution unit receives the instructions, adjusts the injection pressure and flow through the variable frequency pump and regulating valve to reach the optimized best state; Step 5: Environmental monitoring and protection: Monitor groundwater quality: the environmental monitoring module monitors the pH value and heavy metal content of groundwater in real time; pollution warning: when the groundwater quality is abnormal, the system automatically adjusts the injection parameters to reduce the pollution risk and notifies the management personnel through the alarm system; Step 6: Remote monitoring and management: Real-time monitoring platform: provides a user interface to display the system's operating status, historical data, and prediction results; remote operation: users can remotely view the system status through the network and make necessary parameter adjustments and emergency treatment; In Step 2, data processing and analysis are performed using LASSO regression for preliminary feature selection to screen out key parameters that have the most significant impact on injection effect; principal component analysis is used for dimensionality reduction to combine two or more related variables into principal components; then, random forest regression is used to analyze the nonlinear relationship between principal components to obtain the contribution of each principal component to injection effect.

2. The method of optimizing high salinity mine water deep well injection parameters of claim 1, wherein, The data processing and analysis include: Step 2.2.1, feature selection using LASSO regression, the objective function of LASSO regression is: Wherein, X ij represents the jth feature value of the ith sample, including salinity, temperature, pressure; y i is the injection effect; β j is the regression coefficient of each parameter; λ is the regularization parameter; In the regression process, by adjusting λ, the parameters with little effect on injection effect are automatically screened out and their coefficients are sparsified to zero, obtaining the key parameters in each parameter of mine water that have the most significant impact on injection effect; Step 2.2.2, principal component analysis for dimension reduction: using principal component analysis to identify the correlation between the key parameters obtained in step 2.2.1, generating multiple new principal components to represent the main trend of the key parameter variables; Step 2.2.3, random forest regression analysis: using random forest model to conduct regression analysis on the principal components after PCA dimension reduction, to obtain the contribution of each principal component to the injection effect.

3. The method of optimizing high salinity mine water deep well injection parameters of claim 2, wherein, The step 2.2.3 includes: Input variables: using the principal components generated by principal component analysis as input variables; model training: training the principal components generated by PCA at each time with the injection effect, and building multiple decision trees by random forest to capture the complex nonlinear relationship between the principal components and the injection effect; prediction output: after training the model, predicting the new injection operation, and through real-time input of new principal component values, the random forest regression model can accurately evaluate whether the current injection condition is optimized and whether the injection parameters need to be adjusted; variable importance analysis: random forest can also output the importance score of each principal component to obtain the contribution of each variable to the injection effect.

4. The method for optimizing high salinity mine water deep well injection parameters of claim 1, wherein, The step three includes: Step 3.1, optimization objective function setting: Based on the contribution of each variable to the injection effect obtained in step two, and to ensure a balance between injection efficiency, environmental protection and safety, the objective function is defined as follows: ƒ(P,Q)=α(t)·E(P,Q)-β(t)·G(S,T)-γ(t)·S(P,Q) wherein, injection efficiency E(P, Q) = Q: indicates that the injection efficiency is proportional to the flow rate Q; environmental impact G(S, T) = k1·S + k2·T: indicates the impact of salinity S and temperature T on the environment, k1 represents the weight of salinity on the environmental impact, and k2 represents the weight of temperature on the environmental impact; system safety S(P, Q) = (P / Pmax) 2 +(Q / Qmax) 2 : ensures that the injection pressure P and the flow rate Q are within a safe range, Pmax and Qmax are the safety thresholds of pressure and flow rate, respectively; α(t) is the weight of injection efficiency, β(t) is the weight of environmental impact, and γ(t) is the weight of system safety; Step 3.2, dynamic adjustment and feedback mechanism: Using Bayesian optimization algorithm, combined with real-time data feedback, dynamically adjusting the injection parameters to continuously optimize the injection process; the following steps are realized: real-time feedback and iterative adjustment: real-time monitoring of pressure, flow, salinity and temperature by sensors, and comparing the actual injection effect with the objective function; dynamic adjustment of weight coefficients: through real-time feedback, the optimization algorithm adjusts the weight coefficients α(t), β(t), γ(t) in the objective function.

5. The method of optimizing high salinity mine water deep well injection parameters of claim 4, wherein, In the step five, when the environmental monitoring module detects that the groundwater quality index exceeds the standard, feedback and adjustment are carried out through the following steps: Step 5.1, monitoring and alarm: the environmental monitoring module monitors the groundwater quality in real time; if it detects that a certain index exceeds the set threshold, the system triggers an alarm; Step 5.2, data feedback: the environmental monitoring module transmits the exceeding data and alarm information to the control unit; after receiving the feedback, the control unit reevaluates the current injection strategy; Step 5.3, Adjust the objective function: adjust the weight coefficients in the objective function, introduce the environmental penalty term F env and update the objective function as: f'(P,Q) = a(t) - E(P,Q) - β(t) - G(S,T) - γ(t) - S(P,Q) - δ(t) - F env where F env represents the environmental over-standard penalty, and δ is the weight coefficient of environmental over-standard. Step 5.4, re-optimizing injection parameters: the control unit uses the updated objective function ƒ'(P,Q) to recalculate the optimal injection pressure P and injection flow Q; through the optimization algorithm, new P and Q are found to maximize the objective function ƒ'(P,Q) while reducing the impact of environmental over-standard; Step 5.5, Implement adjustments: Apply the new injection pressure and flow parameters to the injection system, adjusting the operation of the injection equipment in real time; continue to monitor the groundwater quality, ensuring that the adjustment measures are effective until the environmental indicators return to the normal range.

6. The method of optimizing high salinity mine water deep well injection parameters of claim 5, wherein, When the environmental monitoring module detects that the pH value exceeds the standard, the environmental penalty term F env is defined as: wherein k pH is a penalty coefficient for pH values that are outside the target range, pH current is the current monitored pH value, pH target is the target pH value.

7. The method of optimizing high salinity mine water deep well injection parameters of claim 5, wherein, When the environment monitoring module detects that heavy metals exceed the standard, the environment penalty term F env is defined as: wherein, k HM is a penalty coefficient for exceeding the heavy metal limit, HM current is the current monitored heavy metal content, HM target is the target heavy metal content.

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