Mine exploitation sewage treatment device

Abstract

The present application belongs to the technical field of sewage treatment, and in particular to a mine exploitation sewage treatment device, which comprises an equalization water storage tank, a coagulation reaction tank, a sedimentation tank, a clean water temporary storage tank and a chemical adding device, and further comprises a source water comprehensive impact assessment module, a pretreatment matching degree assessment module and a sludge health degree assessment module; the source water comprehensive impact assessment module is used for acquiring the oxidation-reduction potential, pH value, mining blasting daily equivalent nitrogen load and water-air temperature difference data of wastewater in real time, constructing a source water comprehensive impact model and calculating a source water comprehensive impact coefficient; the pretreatment matching degree assessment module is used for acquiring the gypsum scaling saturation index, adjusting pool turbidity fluctuation data and lime digestion residue yield coefficient in real time, constructing a pretreatment matching degree model and calculating a pretreatment matching degree; and the sludge health degree assessment module is used for monitoring the sludge backflow marginal gain decay rate in real time; effectively solving the problem of over-dosing or untimely response caused by the inability of traditional fixed time delay control to adapt to the dramatic fluctuation of source water quality.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, specifically a wastewater treatment device for mining operations. Background Technology

[0002] In the field of modern industrial and mining wastewater treatment, the lime dosing system is a core process for adjusting the pH value of wastewater, promoting the precipitation of heavy metal ions, and assisting in coagulation and turbidity removal. However, the transportation, dissolution, digestion, and mixing and diffusion processes of lime powder in the reaction tank naturally involve a significant "pure time delay." Traditional lime dosing control systems often employ fixed-time-delay PID control or simple flow feedforward control.

[0003] The existing technology has the following significant drawbacks: First, the source water quality of mining wastewater is extremely unstable. Random nitrogen load impacts from blasting operations, changes in the thermal inertia of the water body caused by diurnal temperature differences, and drastic fluctuations in oxidation-reduction potential and pH value can cause the buffering capacity of the system to change constantly. Controllers with fixed parameters are prone to over-dosing (leading to lime waste and scaling) or insufficient response (leading to effluent exceeding standards). Secondly, existing systems often only focus on macroscopic water quality and flow rate, lacking the ability to perceive microscopic physicochemical flow patterns. In fact, the tendency of gypsum scaling in the pretreatment process, the yield of lime digestion residue, and the characteristics of microscopic current spikes and ultrasonic attenuation of flocs during flocculation and stirring all directly affect the actual dosing requirements and reaction rate. Finally, the health status of the biological sludge system is coupled with the physicochemical dosing system, and traditional isolated dosing control cannot provide dynamic feedforward compensation based on the decline in the activity of the bottom sludge.

[0004] Therefore, the present invention provides a wastewater treatment device for mining operations. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a mining wastewater treatment device according to this invention, comprising a balanced water storage tank, a coagulation reaction tank, a sedimentation tank, a clear water temporary storage tank, and a dosing device; and: The comprehensive impact assessment module for source water is used to acquire data on the oxidation-reduction potential, pH value, daily equivalent nitrogen load from mining blasting, and water-temperature difference of wastewater in real time, construct a comprehensive impact model for source water, and calculate the comprehensive impact coefficient of source water. The pretreatment matching degree evaluation module is used to acquire gypsum scaling saturation index, turbidity fluctuation data of equalization tank and lime digestion residue yield coefficient in real time, construct pretreatment matching degree model and calculate and obtain pretreatment matching degree. The sludge health assessment module is used to monitor the marginal gain decay rate of sludge recirculation in real time, collect ultrasonic echo spectrum data of sludge layer to extract the centroid frequency, construct a sludge health model, and calculate and obtain the sludge health. The comprehensive adaptability assessment module is used to construct an adaptability model based on the comprehensive impact coefficient of the source water, the pretreatment matching degree, the real-time collected floc acoustic attenuation density index, and the real-time current kurtosis coefficient of the flocculation agitator, and to calculate and obtain the comprehensive adaptability of the system. The lag time optimization feedforward module is used to construct a lag time optimization model based on comprehensive adaptability and sludge health, output the pure lag time of the target lime addition response, and send this time parameter to the feedforward controller to execute the adaptive delayed lime addition action.

[0007] The beneficial effects of this invention are as follows: 1. This invention establishes a comprehensive source water impact assessment module and a lag time optimization feedforward module to acquire real-time data on oxidation-reduction potential, pH value, daily equivalent nitrogen load from mining blasting, and water and air temperature differences. It constructs a comprehensive source water impact coefficient and, combined with comprehensive adaptability and sludge health, dynamically outputs the pure lag time of the target lime addition response based on a rational algebraic variable-scale model. This timeframe is then sent to the feedforward controller for adaptive delayed addition, effectively solving the problem of over-dosing or untimely response caused by the inability of traditional fixed-delay control to adapt to drastic fluctuations in source water quality.

[0008] 2. This invention establishes a pretreatment matching degree evaluation module and a comprehensive adaptability evaluation module to acquire in real time the gypsum scaling saturation index, turbidity fluctuation data of the equalization tank, the lime digestion residue yield coefficient, as well as the floc acoustic attenuation density index and the real-time current kurtosis coefficient of the flocculation agitator. These are calculated using a multidimensional negative exponential attenuation joint function and a hyperbolic secant-asymptotic mixing attenuation model, respectively, to accurately quantify the pretreatment matching degree and the system comprehensive adaptability, thus overcoming the deficiency of traditional systems in lacking the ability to perceive microscopic physicochemical fluid states.

[0009] 3. This invention incorporates a sludge health assessment module to monitor the sludge return marginal gain decay rate in real time and collect the centroid frequency of the ultrasonic echo spectrum of the sludge layer. It calculates the sludge health using a composite function of rational fractional attenuation and a symmetrical Gaussian bell curve, and uses this as the denominator reduction coefficient in the lag time optimization model. This achieves coupled feedforward control of the biological sludge system and the physicochemical dosing system, solving the problem that traditional isolated dosing cannot dynamically compensate for the decline in sludge activity. Attached Figure Description

[0010] The invention will now be further described with reference to the accompanying drawings.

[0011] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a flowchart of the entire invention; Figure 3 This is a flowchart of the comprehensive impact assessment module for source water in this invention; Figure 4 This is a flowchart of the preprocessing matching degree evaluation module in this invention; Figure 5 This is a flowchart of the sludge health assessment module in this invention; Figure 6 This is a flowchart of the feedforward module for optimizing the overall adaptability and lag time in this invention.

[0012] In the diagram: 1. Balanced water storage tank; 2. Coagulation reaction tank; 3. Sedimentation tank; 4. Clear water temporary storage tank; 5. Dosing device. Detailed Implementation

[0013] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0014] like Figures 1 to 6 As shown, the mining wastewater treatment device of the present invention includes a leveling tank 1, a coagulation reaction tank 2, a sedimentation tank 3, a clear water storage tank 4, and a dosing device 5. The leveling tank 1 can be configured as a buffer tank with a fixed volume for receiving and initially homogenizing mining wastewater. The inlet and outlet water are regulated by a simple level control valve. The coagulation reaction tank 2 can be configured as a mixing container with a mechanical stirrer for mixing the coagulant added by the dosing device 5 with the wastewater. The sedimentation tank 3 can be designed as a gravity sedimentation tank, and flocs are separated by adjusting the tank size and residence time. The clear water storage tank 4 can be configured to store the treated water, and the start and stop of the effluent pump are monitored and controlled by a level sensor. The dosing device 5 can be implemented as a system consisting of a metering pump and a storage tank, and lime is added by manually setting the flow rate of the metering pump.

[0015] The comprehensive source water impact assessment module is used to acquire real-time data on wastewater oxidation-reduction potential, pH value, daily equivalent nitrogen load from mining blasting, and water-temperature temperature difference. It also constructs a comprehensive source water impact model and calculates the comprehensive source water impact coefficient. The above parameters are measured using independent sensors, and the measured values ​​are directly input into a preset linear weighted model. The comprehensive impact coefficient of the source water is obtained by simply weighting and summing the parameters. .

[0016] The pretreatment matching degree evaluation module is used to acquire gypsum scaling saturation index, turbidity fluctuation data in the equalization tank, and lime digestion residue yield coefficient in real time, and to construct a pretreatment matching degree model to calculate the pretreatment matching degree. The gypsum scaling saturation index was analyzed by manual periodic sampling. The turbidity of the equalization tank was monitored by a turbidity meter and its maximum difference was calculated. The lime digestion residue yield coefficient was obtained by empirical value or periodic weighing method. These data were then input into a logic model based on threshold judgment to determine the degree of matching of pretreatment.

[0017] The sludge health assessment module is used to monitor the marginal gain decay rate of sludge recirculation in real time, and to extract the centroid frequency from the ultrasonic echo spectrum data of the sludge layer, construct a sludge health model, and calculate the sludge health. The marginal gain decay rate was estimated by periodically measuring the sludge return ratio manually and combining it with historical data. At the same time, the sludge layer thickness was measured by a simple ultrasonic probe, and the sludge health was estimated based on empirical formulas.

[0018] The comprehensive adaptability assessment module is used based on the comprehensive impact coefficient of source water. Preprocessing matching degree Combined with the real-time collected floc sound attenuation density index and the real-time current peak coefficient of the flocculation agitator Construct an adaptation model and calculate the overall adaptation degree of the system. For example, this module can easily calculate the comprehensive impact coefficient of the source water. Matching degree with preprocessing The product operation is performed, and the floc acoustic attenuation density index is estimated by visually observing the floc settling velocity. And the real-time current kurtosis coefficient of the flocculation agitator, determined by monitoring the average current of the agitator motor. The overall adaptability of the system is determined by looking up a table. .

[0019] The lag time optimization feedforward module was used based on comprehensive fitness. With sludge health Construct a time lag optimization model and output the pure time lag of the target lime addition response. The time parameter is then sent to the feedforward controller to execute an adaptively delayed lime addition action, using a preset fixed pure time delay. And based on overall adaptability and sludge health A simple linear combination of these, fine-tuned for this fixed time, such as when or When it falls below a certain threshold, The time parameter is extended by a fixed small value, or conversely, shortened by a fixed small value, and then the adjusted time parameter is sent to the feedforward controller.

[0020] In use, the mine wastewater is first introduced into the equalization storage tank 1 for preliminary homogenization, and then enters the coagulation reaction tank 2 to be mixed with lime and coagulant added by the dosing device 5. After forming flocs, solid-liquid separation is carried out in the sedimentation tank 3, and finally the clarified effluent is sent to the clear water storage tank 4.

[0021] During operation, the comprehensive impact assessment module for raw water continuously works. For example, when mining blasting operations lead to nitrogen load in wastewater... A sudden increase, at the same time, the oxidation-reduction potential of the wastewater and pH value When drastic fluctuations occur and the temperature difference between water and air is large, this module will acquire this data in real time and calculate a high comprehensive impact coefficient of the source water based on its internal comprehensive source water impact model. This indicates that the current source water is having a significant impact on the treatment system.

[0022] Meanwhile, the pretreatment matching degree evaluation module is also monitoring the operation status of the pretreatment stage in real time. For example, when the gypsum scaling saturation index is detected... The turbidity continued to rise, reaching its maximum difference in the equalization tank. The fluctuation range is outside the normal range, and the yield coefficient of lime digestion residue is also outside the normal range. If the value is too high, the module will calculate a lower preprocessing matching degree based on its internal preprocessing matching degree model. This indicates a poor match between the pretreatment process and the actual treatment process, which may lead to scaling risks or reduced treatment efficiency.

[0023] In addition, the sludge health assessment module monitors the biological sludge system. For example, it measures the rate of marginal gain decay during sludge recirculation. Accelerate, and the centroid frequency of the ultrasonic echo spectrum of the sludge layer When the sludge deviates from the optimal range, the module calculates a lower sludge health level based on its internal sludge health model. This indicates a decrease in sludge activity and a reduction in treatment capacity.

[0024] Subsequently, the comprehensive fit assessment module integrates the above assessment results. This module receives the comprehensive impact coefficient of the source water. Matching degree with preprocessing Combined with the real-time collected floc acoustic attenuation density index (For example, measuring the degree of sound wave attenuation by flocs using ultrasonic sensors) and the real-time current kurtosis coefficient of the flocculation agitator. (For example, by analyzing the current waveform of the stirring motor), the overall adaptability of the system can be calculated based on its internal adaptability model. For example, in cases where the source water has a large impact, the pretreatment matching degree is low, and the flocculation effect is poor (manifested as...) abnormal, Under conditions of large fluctuations, the calculated overall fitness score This will be a low value, reflecting the system's overall weak ability to cope with the current operating conditions.

[0025] Finally, the lag time optimization feedforward module receiver overall adaptability and sludge health Based on its internal lag time optimization model, this module will adjust the current low lag time accordingly. and Dynamically calculate and output an optimized target lime addition response pure time delay. For example, to cope with high impact and low adaptability, the system may... The time can be appropriately extended to allow the lime more sufficient reaction time and avoid insufficient dosing; or, under certain specific operating conditions, the time may be shortened for a rapid response. The optimization The data is then sent to the feedforward controller, which executes the adaptive time-delay lime dosing action. Thus, the dosing device 5 no longer adds lime with a fixed time delay, but dynamically adjusts the dosing response time according to the real-time changing operating conditions, ensuring the accuracy and timeliness of lime dosing.

[0026] Through the aforementioned collaborative operation, the device can adaptively adjust the lime dosing strategy according to the complex and variable working conditions of mine wastewater treatment, thereby effectively solving the problems of over-dosing or insufficient response faced by traditional fixed-delay control.

[0027] Based on the above examples, the overall technical concept of this application demonstrates a significant technical contribution. Traditional existing wastewater treatment systems for mining operations generally employ fixed time delays or simple flow feedforward control for lime dosing. Their main drawbacks are their inability to adapt to drastic fluctuations in source water quality, lack of awareness of microscopic physicochemical flow patterns, and failure to consider the health status of the sludge system. This leads to frequent problems of over-flushing and insufficient response during actual operation, resulting in resource waste and environmental risks.

[0028] This application effectively overcomes the aforementioned technical challenges by introducing a multi-dimensional, hierarchical operational condition assessment system and combining it with dynamically optimized lag time feedforward control. For example, when the source water quality fluctuates drastically, traditional systems can only make lag adjustments through simple pH or turbidity monitoring, while the source water comprehensive impact assessment module of this application can acquire multi-dimensional data such as oxidation-reduction potential, pH value, daily equivalent nitrogen load from mining blasting, and water and air temperature differences in real time, and calculate the comprehensive impact coefficient of the source water. This comprehensive quantitative assessment, compared to traditional single-parameter monitoring, can more accurately capture the intensity and nature of source water impacts, providing a more solid foundation for subsequent control decisions.

[0029] Furthermore, the pretreatment matching degree assessment module and sludge health assessment module of this application conduct in-depth evaluations of the physicochemical properties of the pretreatment stage (such as gypsum scaling saturation index, turbidity fluctuation data in the equalization tank, and lime digestion residue yield coefficient) and the activity status of the biological sludge system (such as the sludge return marginal gain decay rate and the centroid frequency of the ultrasonic echo spectrum of the sludge layer). These parameters are often overlooked in traditional systems, but they have a significant impact on the lime dosing effect and the overall stability of the system. For example, when the pretreatment matching degree... Low or sludge health When performance is poor, traditional systems cannot detect and adjust accordingly, while this application can integrate this information to provide a key basis for subsequent lag time optimization.

[0030] Most importantly, the comprehensive compatibility assessment module integrates source water impact, pretreatment matching degree, and micro-fluidic parameters during the flocculation process (floc acoustic attenuation density index). Real-time current peak coefficient of flocculation agitator ), calculate the overall adaptability of the system .

[0031] Ultimately, the lag time optimization feedforward module is based on comprehensive adaptability. and sludge health Dynamically calculate and output the pure time delay of the target lime addition response. This stands in stark contrast to traditional fixed-delay control methods.

[0032] In the comprehensive impact assessment module for source water, the comprehensive impact model for source water uses a dimensionless hyperbolic tangent mapping function with smooth saturation characteristics for calculation. The independent variables of the mapping function include a first impact term normalized from the redox potential and pH value using an engineering reference ratio, a second impact term normalized from the daily nitrogen load converted from mining blasting using an upper limit reference value, and a thermal inertia term composed of the daily temperature range of water and the daily temperature range of air. By weighted summing of each normalized term and processing it using the hyperbolic tangent mapping function, the comprehensive impact coefficient of source water is obtained. The output range is strictly constrained by Within the bounded saturation interval.

[0033] In the wastewater treatment system for mining operations, the comprehensive impact assessment module for raw water uses a hyperbolic tangent function for fitting and calculation. The specific model formula is as follows:

[0034] in, The comprehensive impact coefficient of the source water; The redox potential of the wastewater in real time; This refers to the real-time pH value of the wastewater. This is the engineering reference ratio for ORP / pH; Nitrogen load converted to daily values ​​for mining blasting; The upper limit benchmark value for the system's resistance to impact nitrogen load; This refers to the daily variation in water temperature. The daily temperature range is represented by α, β, and γ, which are dimensionless sensitivity weighting coefficients. The output range of this model is constrained to [missing information]. Within the range.

[0035] Among them, the source water comprehensive impact model is calculated by fitting the hyperbolic tangent function. The hyperbolic tangent function is an S-shaped function whose output value is restricted to a specific range. It is used to map any real number input to a bounded output interval to achieve nonlinear normalization. This function can be implemented by software programming.

[0036] The redox potential of wastewater is measured in real time. It characterizes the redox state of the water body and has a direct impact on the valence state and precipitation effect of heavy metal ions. This parameter is continuously monitored in real time by an online redox potential sensor, which consists of a platinum electrode and a reference electrode. The sensor converts the potential difference into an electrical signal and outputs it to the control system.

[0037] The pH value of wastewater is measured in real time. It is a key indicator for measuring the acidity or alkalinity of water bodies and directly affects the solubility of lime, the precipitation efficiency of heavy metals, and the activity of subsequent biological treatment. The parameter is continuously monitored in real time by an online pH sensor, which is usually composed of a glass electrode and a reference electrode. The sensor converts the hydrogen ion concentration into an electrical signal and outputs it to the control system.

[0038] The ORP / pH ratio is an engineering benchmark reference value, which is a preset ratio of redox potential to pH value determined based on engineering experience or historical operating data. It is used to standardize these two interrelated parameters in the model. This ratio is determined by statistical analysis and regression modeling of historical operating data of specific mine wastewater under ideal treatment effects.

[0039] The daily nitrogen load for mining blasting refers to the pollution load on wastewater quality caused by nitrogen-containing compounds (such as nitrates, nitrites, and ammonia nitrogen) generated during mining blasting operations. This load is calculated as a daily average by combining mining blasting operation plans, blasting intensity, and wastewater nitrogen content data from online or offline monitoring.

[0040] The upper limit benchmark value for the shock nitrogen load of the system design is the highest nitrogen load that the wastewater treatment system can stably operate and effectively treat during the design phase. Exceeding this value may lead to a significant decrease in treatment efficiency. This benchmark value can be set according to the design specifications of the treatment process, the performance parameters provided by the equipment manufacturer, and historical operating experience.

[0041] The daily water temperature difference refers to the difference between the highest and lowest water temperature in a day. It reflects the change in the thermal inertia of the water body. Water temperature fluctuations can affect the chemical reaction rate, microbial activity, and dissolved oxygen content. This parameter can be recorded and calculated in real time by installing online temperature sensors in the wastewater inlet pipeline or equalization tank to measure the daily highest and lowest water temperature differences.

[0042] The daily temperature range is the difference between the highest and lowest ambient temperatures during the day. It serves as a reference for the daily water temperature range and is used to assess the impact of temperature changes on the thermal inertia of water bodies. This parameter is obtained by installing weather stations or temperature sensors in the treatment plant area to record and calculate the daily difference between the highest and lowest temperatures in real time.

[0043] α, β, and γ are all dimensionless sensitivity weighting coefficients, which are used to adjust the contribution of each impact item to the final comprehensive impact coefficient of the source water. This allows the model to assign different importance to different impact factors based on actual operating conditions and experience. The weighting coefficients are obtained by regression analysis or machine learning algorithms (such as genetic algorithms and neural networks) through historical operating data.

[0044] By measuring the oxidation-reduction potential of wastewater in real time pH value Daily equivalent nitrogen load from mining blasting and water temperature difference data and Multiple impact parameters from different sources and with different dimensions were calculated using hyperbolic tangent function fitting and integrated into a unified comprehensive impact coefficient of source water. The model first considers the redox potential With pH value Combined with engineering benchmark reference ratio Treatment was carried out to reflect the combined impact of the water's acid-base and redox properties. Then, the daily nitrogen load from mining blasting was calculated. Compared with the upper limit benchmark value of the system design for shock nitrogen load Ratio processing was performed to quantify the random nitrogen load impact caused by blasting operations, while the daily water temperature difference was also considered. Daily temperature range Ratio processing is performed to characterize the changes in water thermal inertia caused by diurnal temperature variations. These processed impact terms are then weighted and summed using dimensionless sensitivity weighting coefficients α, β, and γ, and finally input into the hyperbolic tangent function. The accumulated result is nonlinearly mapped and constrained to... Within the interval, a normalized comprehensive impact coefficient of source water with uniform dimensions is output. It is used to reflect the actual impact state of the source water and serves as a key input parameter, providing a reliable basis for the aforementioned lag time optimization feedforward module, thereby realizing adaptive delay control of lime dosing action and effectively improving the intelligence and robustness of the entire mine wastewater treatment device.

[0045] This model effectively integrates impact parameters from different sources and with different dimensions, such as redox potential, pH value, daily equivalent nitrogen load from mining blasting, and water and air temperature differences. It normalizes the calculation results to a unified standard using a hyperbolic tangent function. Within the range, the degree of influence of different impact factors on the system can be reasonably reflected, so that the comprehensive impact coefficient of the source water can accurately reflect the actual impact state of the source water.

[0046] In the pretreatment matching degree evaluation module, the pretreatment matching degree model is calculated using a multidimensional negative exponential decay joint function. The exponential term of the negative exponential decay joint function includes the square of the ratio of the real-time saturation index of gypsum scaling to the critical scaling saturation index benchmark, the ratio of the maximum difference in turbidity in the equalization tank to the tolerance benchmark, and the lime digestion residue yield coefficient. When any evaluation dimension in the exponential term deviates positively, the pretreatment matching degree... The overall behavior exhibits a non-linear, continuously exponential decay, and the output range is constrained. Within the range.

[0047] The preprocessing matching degree model is calculated using a combination function of multidimensional Gaussian and exponential decay. The specific model formula is as follows:

[0048] in, To preprocess the matching degree; The real-time saturation index for gypsum scaling; The critical scaling saturation index of gypsum serves as the benchmark. This represents the maximum difference in turbidity in the equalization tank. This serves as the tolerance benchmark for the maximum turbidity fluctuations allowed by the process. The yield coefficient of lime digestion residue; and The empirical penalty factor is used; the output range of this model is constrained to... Within the range.

[0049] The pretreatment matching degree model is used to quantify the adaptability of the current pretreatment process to changes in wastewater quality. Employing a combination function of multidimensional Gaussian and exponential decay, it comprehensively considers multiple interrelated or independently influential parameters affecting pretreatment effectiveness. Through nonlinear functional relationships, it effectively maps and normalizes the impact of these parameters on the pretreatment matching degree, thus providing an intuitive and physically meaningful matching degree index. This combination function can be implemented through software programming. Pretreatment Matching Degree It is a dimensionless index used to characterize the adaptability or treatment effect of the current pretreatment stage to changes in influent water quality. Its value is usually constrained within a specific range. The higher the value, the better the pretreatment effect and the stronger the adaptability to subsequent treatment. The calculation result of this index can be directly displayed on the operator interface, allowing operators to monitor the operation of the pretreatment system in real time. It can also be used as an input parameter to be passed to higher-level control algorithms.

[0050] Real-time saturation index of gypsum scale It is a real-time indicator for measuring the scaling tendency of calcium sulfate (gypsum) in wastewater. A value greater than 1 indicates that the wastewater is in a supersaturated state and poses a risk of scaling. This index is calculated by real-time monitoring of the calcium ion concentration, sulfate ion concentration, temperature, and pH value of the wastewater using online sensors, combined with the thermodynamic balance equation. This is the benchmark for the critical scaling saturation index of gypsum. It is a preset, acceptable risk threshold for gypsum scaling, which can be set based on historical operating data, equipment manufacturer recommendations, or industry standards, and stored in the parameter database of the control system.

[0051] Maximum difference in turbidity in equalization tank This reflects the fluctuation range of turbidity in the effluent from the equalization tank within a certain time window. By installing an online turbidity sensor at the outlet of the equalization tank to collect turbidity data in real time, and calculating the difference between the maximum and minimum values ​​within a sliding time window, the maximum allowable turbidity fluctuation tolerance benchmark is obtained. This is the maximum range of turbidity fluctuation allowed in the effluent from the equalization tank, set during the pretreatment process design. It can be set based on the design load of subsequent treatment units, effluent quality requirements, and historical operating experience. (The lime digestion residue yield coefficient is also mentioned.) This indicates the proportion of unreacted or undissolved solid residue to the total amount of lime added during the lime slaking process. The empirical penalty factor can be calculated by periodically weighing and analyzing the residue after lime slaking, combined with the amount of lime added. and It is a weighting coefficient used to adjust the influence of the maximum difference in turbidity in the equalization tank and the yield coefficient of lime digestion residue on the pretreatment matching degree. It is trained and optimized through historical data analysis, expert experience or machine learning algorithms to make the matching degree output by the model best match the actual pretreatment effect.

[0052] Constrain the model output range to Within the range, the preprocessing matching degree was ensured. It is a standardized, easy-to-understand, and comparable indicator; normalization processing makes it... It can be easily integrated with other indicators through exponential functions. The form is naturally realized.

[0053] The preprocessing matching degree model integrates the real-time saturation index of gypsum scaling. Maximum difference in turbidity in the equalization tank and the yield coefficient of lime digestion residue These three key parameters, combined with the corresponding benchmark values , and experience penalty factor , A comprehensive evaluation system was constructed, and the model will... and The square of the ratio, and The ratio term (multiplied by) )as well as Item (multiplied by) The factors are accumulated to form a comprehensive penalty term, which is then used as the independent variable of a negative exponential function to calculate the preprocessing matching degree through exponential decay. This means that when any adverse factor (such as increased scaling risk, intensified turbidity fluctuations, or increased residue yield) occurs, the penalty term will increase, leading to... The value decreases, thus accurately reflecting the deterioration of the preprocessing effect; conversely, when the preprocessing runs well, the penalty term decreases. The value approaches 1.

[0054] Real-time saturation index of gypsum scale Compared with the critical scaling saturation index benchmark of gypsum The squared term of the ratio can amplify the penalty effect when scaling approaches the critical value, more sensitively reflecting the negative impact of scaling on pretreatment efficiency, and the maximum difference in turbidity in the equalization tank. With respect to the maximum turbidity fluctuation tolerance standard allowed by the process The ratio, combined with the empirical penalty factor This quantifies the pretreatment's buffering capacity against fluctuations in source water turbidity, and the lime digestion residue yield coefficient. With experience penalty factor The introduction of this parameter directly reflects the effectiveness and utilization rate of lime addition. Through multi-parameter, nonlinear combination calculations, the preprocessing matching degree evaluation module can provide a comprehensive, accurate, and real-time updated preprocessing status evaluation result.

[0055] By introducing a preprocessing matching degree model, which uses a combination function of multidimensional Gaussian and exponential decay to calculate the real-time saturation index of gypsum scaling, the model can be optimized. Maximum difference in turbidity in the equalization tank and lime digestion residue yield coefficient By integrating key parameters and combining them with corresponding benchmark values ​​and empirical penalty factors for nonlinear processing, the degree of matching between the current preprocessing state and the ideal processing state can be accurately quantified.

[0056] In the sludge health assessment module, the sludge health model is calculated using a continuous smooth function combining rational fractional attenuation and a symmetrical Gaussian bell curve. Specifically, the rational fractional attenuation function applies a nonlinear penalty to the attenuation rate of the sludge recirculation marginal gain, which incorporates a time constant weight. The symmetrical Gaussian bell curve function applies a probability density-based attenuation constraint to the fluctuation of the centroid frequency of the real-time sludge layer's ultrasonic echo spectrum from the optimal reference frequency. The product of these two factors outputs the sludge health score. And the output range is constrained to Within the range.

[0057] In the sludge health assessment module, the sludge health model is calculated using a rational fractional decay and a Gaussian bell-shaped composite function. The specific model formula is as follows:

[0058] in, For sludge health; The rate of decay of the marginal gain of sludge recirculation; The centroid frequency of the real-time ultrasonic echo spectrum of the sludge layer; The centroid frequency of the reference ultrasonic echo spectrum under optimal flocculation and compaction conditions; The standard deviation of health tolerance for the frequency distribution of the spectrum; The time constant weights are used to represent the decay rate; the output range of this model is constrained to... Within the range.

[0059] The sludge health model is calculated using rational fractional decay and a Gaussian bell-shaped composite function, aiming to comprehensively evaluate the dynamic operating performance and microstructural state of the sludge. The rational fractional decay component is primarily used to quantify the decay rate of the marginal gain from sludge recirculation. Impact on sludge health, sludge recirculation marginal gain decay rate This is an important indicator for measuring the dynamic changes in sludge activity and settling performance. A higher value generally indicates decreased sludge activity and reduced treatment capacity. This rate is obtained by continuously monitoring the relationship between the sludge return ratio and effluent quality (such as turbidity and COD) and calculating the decay trend of its marginal benefit. The Gaussian bell function component is used to evaluate the centroid frequency of the real-time sludge layer ultrasonic echo spectrum. Centroid frequency of the reference ultrasonic echo spectrum under optimal flocculation and compaction conditions The degree of deviation between them, the centroid frequency of the real-time ultrasonic echo spectrum of the sludge layer It reflects the particle size distribution, density and structural stability of sludge flocs. By installing ultrasonic sensors in sedimentation tanks or sludge thickening tanks, ultrasonic waves are emitted and their echo signals in the sludge layer are received. The center of gravity frequency is extracted by spectrum analysis technology.

[0060] Reference ultrasonic echo spectrum centroid frequency The ideal center-of-gravity frequency value, determined through experimental calibration or historical data analysis, represents the optimal flocculation and compaction state of sludge flocs under optimal system operating conditions. It also represents the health tolerance standard deviation of the spectral frequency distribution. The real-time centroid frequency is then defined. Deviation from reference frequency The acceptable range, the value of which can be set according to process requirements and experience. Weight of the time constant for the decay rate. It is an adjustment parameter used to adjust the rate of decay of the marginal gain of sludge recirculation. Overall sludge health The sensitivity to the impact can be calibrated based on actual operational experience or optimization algorithms. Ultimately, the model will determine the sludge health. The output range is constrained by Within the timeframe, the standardization and comparability of the evaluation results were ensured.

[0061] By reducing the marginal gain rate of sludge recirculation With the centroid frequency of the real-time ultrasonic echo spectrum of the sludge layer The organic combination of these two different dimensions of parameters constructs the sludge health index. The composite evaluation model, in which the rational fractional attenuation term Capable of capturing the degradation of dynamic operating performance of sludge systems, as the marginal gain of sludge recirculation decays. As the value increases, this term decreases, reflecting a decline in sludge health and quantifying the macroscopic dynamic performance of the sludge system. Simultaneously, the Gaussian bell-shaped function term... This focuses on assessing the microstructural state of sludge flocs, when the real-time center of gravity frequency... Deviation from optimal reference frequency At that time, regardless of the direction of deviation, this value will decrease, thereby reducing sludge health. This ensures accurate perception of the sludge flocculation and compaction state. These two parts are combined through a multiplicative operation, resulting in a comprehensive assessment of sludge health. It can be affected by both the dynamic properties and microstructure of sludge; deterioration in either dimension will lead to a decline in overall health. The decline.

[0062] Through the above technical solution, this application can accurately quantify the current health status of sludge, providing reliable input parameters for optimizing the subsequent lime addition lag time. The model structure, employing a combination of rational fractional decay and Gaussian bell function, can adapt to the influence of different dimensional parameters on sludge health, avoiding the problem of insufficient adaptability of a single model structure, and reducing the marginal gain decay rate of sludge recirculation. Incorporating this into the rational fractional attenuation term calculation allows for a reasonable quantification of the negative impact of this parameter on sludge health, demonstrating the advantage of combining sludge dynamic performance evaluation with health assessment. The centroid frequency of the real-time sludge layer ultrasonic echo spectrum is then used. By incorporating the Gaussian bell-shaped term into the calculation, the actual flocculation and compaction structure of the sludge layer can be accurately reflected, thus compensating for the shortcomings of traditional assessments that cannot reflect the microstructure of the sludge.

[0063] In the comprehensive fitness evaluation module, the fitness model is calculated using a hyperbolic secant pulse decay and asymptotically approaching saturation product model; the fitness model uses preprocessed matching degree... As a forward dynamic reference gain, it is obtained by considering the comprehensive impact coefficient of the source water. Real-time current peak coefficient of flocculation agitator The hyperbolic secant function is penalized with multidimensional fluctuations and multiplied by the density exponent of floc acoustic attenuation. The asymptotically exponentially increasing term, normalized to a reference benchmark, is used to output the overall fit. And the output range is constrained to Within the range.

[0064] In the comprehensive fitness evaluation module, the fitness model is calculated using a hyperbolic secant and asymptotic hybrid attenuation model. The specific model formula is as follows:

[0065] in, For overall compatibility; To preprocess the matching degree; The comprehensive impact coefficient of the source water; This refers to the real-time current peak value coefficient of the flocculation agitator. The density index for sound attenuation of flocs; This serves as a reference for sound attenuation corresponding to an ideal dense floc; , and All parameters are dimensionless adjustment parameters; the output range of this model is constrained to... Within the range.

[0066] The hyperbolic secant and asymptotic hybrid decay model can effectively capture the nonlinear influence of different input parameters on the system fit. The hyperbolic secant function has the characteristic of changing slowly when the input value is small and decaying rapidly when the input value increases. It is suitable for describing the inhibitory effect of negative factors (such as shocks and fluctuations) on the system fit. The asymptotic hybrid decay model can describe the contribution of positive factors (such as floc density) to the system fit, and its contribution has a saturation upper limit.

[0067] The specific model formula will preprocess the matching degree. Comprehensive impact coefficient of source water Real-time current peak coefficient of flocculation agitator and the density index of floc sound attenuation Multiple key parameters are organically integrated and their synergistic impact on overall fit is reflected through a product, thus preprocessing the matching degree. It reflects the fit status of the pretreatment process (such as gypsum scaling, turbidity fluctuations, and lime digestion residue yield). As the basic product term for the overall fit calculation, its value directly affects the final overall fit.

[0068] When the pretreatment matching degree is low, even if other factors perform well, the overall fit will be significantly limited, and the comprehensive impact coefficient of the source water will be affected. The volatility and impact intensity of the source water quality were quantified. A higher value indicates greater instability in the source water quality and a greater impact on system operation. In the model, the hyperbolic secant function negatively influences the overall fit, and the real-time current kurtosis coefficient of the flocculation agitator is also considered. The kurtosis coefficient characterizes the intensity of current fluctuations during flocculation and stirring, indirectly reflecting the stability and uniformity of floc formation. A high kurtosis coefficient usually indicates abnormalities in the stirring process or unstable floc formation, negatively impacting overall suitability. The floc acoustic attenuation density index... It is an indicator for measuring the density and structural stability of flocs. A higher value generally indicates denser flocs and better flocculation. In the model, it contributes positively to the overall fit through an asymptotic mixing attenuation term, referencing the acoustic attenuation benchmark. It is a preset floc acoustic attenuation density index corresponding to the ideal flocculation state, used for real-time measurement. Normalization and comparison are performed, and dimensionless adjustment parameters are used. , and These are the model's weights or sensitivity factors, used to adjust the relative strength of the influence of different input parameters on the overall fit. They are calibrated through historical data analysis, expert experience, or optimization algorithms to better adapt the model to specific process conditions and wastewater characteristics, thus improving the overall fit. The output range is limited to Within the range, the results are standardized and normalized, which not only facilitates comparison and integration with other parameters, but also avoids problems such as numerical overflow or dimensional mismatch, ensuring the stability and effectiveness of subsequent control algorithms.

[0069] The comprehensive fit evaluation module preprocesses the matching degree. As a fundamental product term, it directly reflects the fit status of the pretreatment stage, ensuring that the overall fit degree fully considers the basic impact of pretreatment from the beginning of the calculation. Based on this, the model introduces a hyperbolic secant function term, which incorporates the comprehensive impact coefficient of the source water. Real-time current peak coefficient of flocculation agitator Weighted combination, comprehensive impact coefficient of source water This characterizes the intensity of external disturbances to the source water quality, while the real-time current kurtosis coefficient of the flocculation agitator... This reflects the stability of the internal flocculation process. The hyperbolic secant function can effectively transform these two negative factors into a suppressive effect on the overall fit, that is, the greater the impact and the greater the agitation fluctuation, the lower the overall fit. At the same time, the model also introduces an asymptotic mixing attenuation term, which combines the floc acoustic attenuation density index. Compared with the reference sound attenuation standard of the corresponding ideal dense floc Flocculation sound attenuation density index It is a direct reflection of the microscopic flocculation effect. The closer its value is to the ideal state, the better the flocculation effect. The asymptotic mixing attenuation term can integrate this positive contribution into the overall fit in a nonlinear way, so that the better the flocculation effect and the higher the overall fit, but its improvement effect has a saturation trend.

[0070] By using the pretreatment matching degree as the basic product term, the fundamental impact of the pretreatment stage on the overall adaptability is ensured. By integrating the comprehensive impact coefficient of the source water and the real-time current kurtosis coefficient of the flocculation agitator through the hyperbolic secant function term, the negative impact of external impact and internal agitation fluctuations on the system adaptability is effectively quantified. By combining the asymptotic mixing attenuation term with the floc acoustic attenuation density index, the positive contribution of the flocculation effect to the system adaptability is accurately reflected.

[0071] In the lag time optimization feedforward module, the lag time optimization model is calculated using a rational algebraic variable-scale stepless scaling model; the lag time optimization model uses the system-calibrated baseline physical pure lag time. As the algebraic base, based on the comprehensive fitness The degree of reverse offset degradation is used as a molecular amplification factor, based on sludge health. The positive gain is used as the denominator reduction factor. Through nonlinear algebraic scaling of the numerator and denominator, the pure time delay of the target lime addition response with time dimension is dynamically output. .

[0072] The lag time optimization model is calculated using a rational algebraic variable metric model. The specific model formula is as follows:

[0073] in, The pure time delay of the target lime addition response; The reference physical pure time delay for system calibration; For overall compatibility; For sludge health; The delay dynamic scaling factor is used to control the conservative expansion of response time when operating conditions deteriorate; The time delay dynamic scaling factor is used to radically shorten the response time when the control system is in good health.

[0074] This rational algebraic variable scaling model is a mathematical expression characterized by using rational fractions to represent multiple input variables (such as overall fit). and sludge health Nonlinearly mapped to an output variable (target lime addition response pure time delay). Its advantage lies in its ability to flexibly capture the complex effects of different input variables on output variables, and to adjust the parameters in the model (such as...). and This allows for refined control of the system's dynamic response, with the target lime addition response having a pure time delay. In the process of treating wastewater from mining operations, this refers to the shortest time required from the initiation of lime addition to the desired effect of the lime in the reaction tank. This time is dynamic and needs to be adjusted according to real-time operating conditions. Besides serving as the response time for lime addition, It can also be used as a reference lag time for the addition of other chemical agents (such as coagulants and coagulant aids) to achieve synergistic optimization of multiple agents and the baseline physical pure lag time calibrated by the system. This refers to the inherent physical lag time of lime addition under standard operating conditions, obtained through experimental testing or empirical estimation during the system design or commissioning phase. This baseline time reflects the fixed time delay caused by physical factors such as the length of the lime delivery pipeline, the efficiency of the mixing equipment, and the volume of the reaction tank. This baseline time is determined by conducting a step response experiment under stable operating conditions after the system's initial installation or major overhaul, measuring the time required from the issuance of the addition signal to a stable change in key water quality indicators (such as pH). The overall suitability is also considered. This is an indicator reflecting the overall operational status of the current mine wastewater treatment system; its value typically ranges from 0 to 1, with lower values ​​indicating lower values. The value indicates that the system is facing a significant impact or the pretreatment effect is poor, such as drastic fluctuations in the quality of the source water, high risk of gypsum scaling, and poor flocculation effect.

[0075] sludge health It is an indicator for measuring the activity and treatment capacity of biological sludge in a wastewater treatment system. Its value is usually between 0 and 1, with higher values ​​indicating better performance. A value indicates good sludge activity, strong pollutant removal capacity, and resistance to shock loads; a lower value indicates better sludge activity. A value indicating decreased sludge activity and potentially reduced treatment efficiency suggests a low level of sludge health. The sludge health assessment module calculates the sludge health status by monitoring the marginal gain decay rate of sludge recirculation in real time and extracting the centroid frequency from the ultrasonic echo spectrum data of the sludge layer.

[0076] The time delay dynamic scaling factor for conservatively widening the response time when control conditions deteriorate It is a dimensionless parameter used to adjust the overall system fit. At lower (i.e., worsening operating conditions), the pure lag time of the target lime addition response The extent of expansion, The higher the value, the better the performance under deteriorating operating conditions. The larger the expansion range, the more conservative the system response becomes, avoiding over-application due to an overly fast response. This coefficient is calibrated during the system debugging phase through historical operating data analysis and expert experience. It is a dynamic scaling factor for the delay, representing the extent to which the response time is aggressively shortened when the control system is healthy and in good condition. It is a dimensionless parameter used to adjust the sludge health status. At a relatively high level (i.e., excellent system health), the pure time delay of the target lime addition response. The shortening range, The higher the value, the higher the sludge health. The greater the reduction in amplitude, the more aggressive the system response and the improved processing efficiency. This coefficient was also calibrated through historical operating data analysis and expert experience.

[0077] When overall fit A lower value indicates that the source water impact is large, the pretreatment effect is poor, or the flocculation state is not ideal, resulting in a lower value in the model. The value will increase by multiplying it by a time delay dynamic scaling factor. This increases the numerator term, thereby widening the pure time delay of the target lime addition response. This conservative response strategy helps avoid excessive lime addition due to overly rapid response under harsh operating conditions, thereby reducing reagent waste and scaling risks.

[0078] At the same time, when the sludge health A higher value indicates that the biological treatment system has good activity and strong response capability, which will increase the denominator term in the model. This can be increased by multiplying by the time delay dynamic scaling factor. This increases the denominator, thereby shortening the pure time delay of the target lime addition response. This aggressive response strategy can fully utilize the rapid processing capacity of sludge, ensuring that the system can quickly respond to changes in water quality when in a healthy state, and avoid effluent exceeding standards due to response lag. In this way, the model can adaptively adjust the pure lag time of lime addition based on the overall operating conditions of the system and the health status of the sludge, so that the lag time matches the actual operating state of the system.

[0079] During the initial commissioning phase of the wastewater treatment device for mining operations, simulated wastewater can be injected into the equalization tank 1, and a lime step-dosing experiment can be conducted. The time required from the time the dosing device 5 issues the dosing command to the time when the pH value of the effluent from the sedimentation tank 3 stabilizes and reaches the target range can be measured to determine the baseline physical pure lag time. For example, it can be set to 15 minutes, and then the latency can be dynamically scaled by analyzing historical operating data and expert experience. and Perform calibration. For example, when the overall system adaptability... When the lag time is below 0.5, it is necessary to significantly widen the lag time to avoid overshoot. It can be set to 2.0; when the sludge health level When the value is above 0.8, the system has a strong response capability, and the lag time can be aggressively shortened to improve efficiency. It can be set to 1.5. In actual operation, assuming that at a certain moment the source water comprehensive impact assessment module, pretreatment matching degree assessment module, sludge health assessment module, and comprehensive suitability assessment module calculate the current comprehensive suitability, The sludge health score is 0.4. The value is 0.9. Substituting these values ​​into the rational algebraic variable scaling model:

[0080] At this point, the pure time delay of the target lime addition response The time was calculated to be approximately 14.04 minutes. The feedforward controller will execute the lime dosing action based on this dynamically adjusted lag time. If subsequent operating conditions worsen, for example... It dropped to 0.2, while If it remains unchanged, then:

[0081] At this point, the lag time is extended to approximately 16.59 minutes to address worse operating conditions. Conversely, if operating conditions improve, Increase, or sludge health Further improvement, This will be shortened accordingly. This dynamic adjustment mechanism ensures that the response time for lime addition always matches the current actual operating state of the system.

[0082] Through the above technical solution, this application can dynamically adjust the pure lag time of lime addition based on the overall operating conditions and sludge health status of the system. Specifically, using a rational algebraic variable-scaling model, based on the system-calibrated baseline physical pure lag time, and combined with real-time acquired overall fit and sludge health, the target lime addition response pure lag time is dynamically calculated and output. This dynamic adjustment mechanism effectively solves the problems of over-dosing or untimely response that are prone to occur in traditional fixed lag time control. When the system operating conditions deteriorate, the lag time can be conservatively widened to avoid waste and scaling caused by excessive lime addition; when the system is in good health, the lag time can be aggressively shortened to ensure rapid response and avoid effluent exceeding standards. This allows the lime addition action to better adapt to the control needs of large fluctuations in the source water quality of mining wastewater and dynamic changes in system operating status, significantly improving the accuracy, stability, and economy of lime addition, thereby ensuring the overall operating efficiency of the wastewater treatment device and the compliance of effluent quality.

[0083] The comprehensive compatibility evaluation module involves the real-time current kurtosis coefficient of the flocculation agitator. The calculation is performed using a fourth-order standard moment statistical model based on a continuous-time sliding window. Specifically, within a set real-time sliding time window, the deviation between the instantaneous stator current measurement value of the stirrer and the expected mean value of the current within the window is calculated by the time integral mean of the fourth-order central moment. Then, the deviation is divided by the square of the time integral mean of the second-order central moment of the deviation. The absolute dimension of the current is completely eliminated by algebraic division of higher-order moments, and the dimensionless morphological scalar that characterizes the abnormal peak characteristics of the flow field floc collision resistance is extracted.

[0084] Real-time current peak coefficient of flocculation agitator The fourth standard moment is obtained based on a continuous-time sliding window calculation. The specific calculation model is as follows:

[0085] in, The width of the real-time sliding time window; This is the current calculation time; For integration time; In order to be in The instantaneous stator current measurement value of the stirrer at any given moment; In order to be in The average current over time within the time window.

[0086] The real-time current peak coefficient of the flocculation agitator Kurtosis is a statistical measure used to quantify the "kurtosis" or "peak morphology" of the probability distribution of random variables, describing the tail characteristics and peak concentration of the data distribution. In the flocculation and stirring process, the kurtosis coefficient of the current signal can reflect the drastic instantaneous change in the agitator load, especially when flocs form, agglomerate, or break up, the current will produce spikes or fluctuations. The kurtosis coefficient can sensitively capture these micro-fluid state changes. Continuous-time sliding window is a data processing technique. Its core idea is to define a fixed-length time interval on a continuous data stream. This interval "slides" over time, so that data samples from the most recent period can be obtained at each moment. This method can ensure that the calculation results always reflect the latest process state, while containing enough data for statistical analysis, avoiding the random errors that may be caused by instantaneous sampling.

[0087] The window can slide forward a preset time step at a fixed frequency (e.g., per second) or be updated each time a new data point arrives. The fourth standard moment, also known as kurtosis, is one of the indicators used in statistics to measure the shape of data distribution. It is used to characterize the thickness of the tails and the sharpness of the peaks of the data distribution by calculating the fourth power of the difference between the data and the mean and then standardizing it. Compared with the second moment (variance), which only reflects the dispersion of the data, the fourth standard moment can capture the frequency and intensity of outliers or peaks in the data distribution more precisely. It is of great significance for identifying instantaneous impacts or abnormal fluctuations in stirring current.

[0088] Width of the real-time sliding time window The data sampling time length for calculating the kurtosis coefficient is defined. Its selection needs to take into account the dynamic response characteristics of the process and the real-time requirements of the calculation. It can be set according to the typical time scale of the flocculation reaction to ensure that the window can contain a sufficient number of stirring cycles or floc formation / breakup events.

[0089] Current calculation time This refers to the current point in time when the kurtosis coefficient is calculated. All data sampling and integration end at this point and trace back to the previous point. Time length, integration time variable In mathematical integrals, this represents the time window during which time is slid. arrive A point in time between which the instantaneous stator current of the stirrer is measured. The instantaneous stator current measurement value of the stirrer is obtained by continuous summation. It is the instantaneous value of the stator current of the flocculation agitator motor collected in real time by the sensor. This current value directly reflects the load borne by the agitator at a specific moment. The change of load is closely related to the micro-flow state such as the viscosity of the liquid in the flocculation tank, the size and density of the flocs, and the interaction between the agitator and the flocs. It is obtained by high-frequency sampling through Hall effect current sensors or current transformers.

[0090] exist Average current over time within the time window In the current sliding time window Inner, instantaneous stator current measurement value of the stirrer The average value is used as a benchmark to calculate the deviation of the current from its average level, thereby eliminating the influence of the current baseline offset on the calculation of the kurtosis coefficient under different operating conditions, so that the kurtosis coefficient can more accurately reflect the morphological characteristics of the current fluctuation.

[0091] By introducing a continuous-time sliding window to calculate the real-time current kurtosis coefficient of the flocculation agitator. The method aims to provide more accurate input parameters for the comprehensive fit evaluation module. Specifically, it first uses a high-frequency sensor to collect the instantaneous stator current measurement value of the flocculation agitator motor in real time. Subsequently, at each current calculation time The system will define a fixed-width continuous time sliding window. The window covers from arrive Within this time window, the system calculates the average current value. Next, using this average value as a benchmark, the deviation of the current from its average value is calculated, and then the fourth power average of these deviations and the square average of the deviations are calculated. Finally, the real-time current kurtosis coefficient is obtained by dividing the fourth power average by the square of the square average. The fourth-order standard moment (kurtosis) is used as the core indicator, which enables the coefficient to sensitively reflect the "kurtosis" of the stirring current distribution, that is, whether there are frequent or violent peak fluctuations. These peak fluctuations are often a direct manifestation of the micro-flow regime changes such as floc formation, aggregation, and breakage during the flocculation process.

[0092] When the size or density of the flocs increases, the load on the agitator changes instantaneously, causing current spikes. By precisely quantifying these spike characteristics, this method overcomes the limitations of traditional methods that only focus on macroscopic water quality parameters while ignoring microscopic flow regime perception. This precise calculation yields the real-time current kurtosis coefficient of the flocculation agitator. The input is fed into the comprehensive fit evaluation module, along with the comprehensive impact coefficient of the source water. Preprocessing matching degree and the density index of floc sound attenuation By jointly constructing an adaptability model, the overall adaptability of the system to the current operating conditions can be significantly improved. The accuracy of the assessment.

[0093] The real-time current kurtosis coefficient of the flocculation agitator was calculated based on a continuous-time sliding window. The system can accurately capture the peak fluctuations of the stirring current. These characteristics directly reflect the microscopic physical processes such as floc formation, agglomeration, or breakup. As a key input to the comprehensive fit evaluation module, this allows for a precise assessment of the overall fit. The calculation results are closer to actual working conditions. Therefore, when the comprehensive adaptability evaluation module receives more accurate results... When the value is given, its overall fit is determined by the output. This will allow for a more accurate reflection of the overall operational status of current mine wastewater treatment facilities and the demand for lime addition. This further ensures that the time lag optimization feedforward module can be based on more reliable... Value, combined with sludge health Outputs more precise target lime addition response time pure time delay. .

[0094] The overall fit evaluation module involves the floc sound attenuation density index. A complementary correction model combining acoustic energy absorption and dissipation with fluid physical dynamic viscosity is employed for calculation. Specifically, the background total decibel attenuation rate is calculated based on the logarithmic ratio of the initial excitation voltage of the ultrasonic transmitting transducer to the capture voltage of the receiving transducer, combined with the physical sound path straight distance. After algebraically subtracting the inherent background acoustic attenuation coefficient of pure water at the corresponding temperature, the result is multiplied by a negative exponential compensation correction factor constructed from the deviation ratio of real-time dynamic viscosity to reference dynamic viscosity. The final output is the floc acoustic attenuation density index in decibels per meter. .

[0095] In the comprehensive fit evaluation module, the floc acoustic attenuation density index is involved. The calculation model includes a pure water background attenuation deduction term and a wastewater dynamic viscosity index compensation term.

[0096] in, The density index for sound attenuation of flocs; The physical acoustic path distance between the ultrasonic transmitting probe and the receiving probe; This represents the peak value of the initial excitation voltage of the ultrasonic transducer. The attenuated peak voltage captured by the ultrasonic receiving transducer; This is the inherent background acoustic attenuation coefficient of pure water at the current temperature; For real-time monitoring of wastewater dynamic viscosity; The reference dynamic viscosity of wastewater under standard operating conditions; This is an empirical constant for viscosity compensation.

[0097] floc sound attenuation density index The system includes a pure water background attenuation subtraction term and a wastewater dynamic viscosity index compensation term. This index is a key indicator for measuring the density and quantity of flocs in wastewater, indirectly reflected by the attenuation characteristics of ultrasound waves propagating in the medium. The pure water background attenuation subtraction term aims to eliminate the inherent attenuation effect of pure water on ultrasound waves. This can be achieved by subtracting a pure water attenuation coefficient related to the current water temperature after measuring the total attenuation. A database of pure water acoustic attenuation coefficients at different temperatures can be pre-established, and the corresponding coefficient can be obtained by looking up the table based on the water temperature during real-time measurement. The value is deducted, and the wastewater dynamic viscosity index compensation term is used to correct the effect of wastewater viscosity changes on ultrasonic attenuation. This is achieved by measuring wastewater viscosity in real time and comparing it to a standard viscosity, then compensating using an exponential function. The dynamic viscosity of the wastewater can be measured in real time using a vibratory viscometer or a capillary viscometer. And substitute it into the compensation model.

[0098] The specific calculation model is a mathematical expression used to convert ultrasonic measurement data and wastewater physicochemical parameters into a floc acoustic attenuation density index. This model provides computation The specific algorithm ensures the standardization and repeatability of the calculation process and includes corrections for pure water background attenuation and wastewater dynamic viscosity changes. It can be embedded into a dedicated signal processing chip (such as a DSP or FPGA) to achieve high-speed real-time calculation, or implemented in an industrial control computer (IPC) or programmable logic controller (PLC) using a programming language.

[0099] The physical acoustic path distance between the ultrasonic transmitting and receiving probes refers to the actual physical distance that an ultrasonic wave travels from the transmitting transducer to the receiving transducer. It is a fundamental parameter for calculating the ultrasonic wave attenuation rate and directly affects the accuracy of the attenuation coefficient. This distance can be determined during device design and installation by accurately measuring and fixing the relative position of the ultrasonic probes.

[0100] The initial excitation voltage peak of the ultrasonic transducer refers to the peak voltage of the electrical pulse that drives the ultrasonic transducer to generate the ultrasonic signal. It represents the initial intensity of the ultrasonic signal and is an important reference for calculating the attenuation. This peak value is provided with a stable excitation voltage through a high-precision voltage source or signal generator and is monitored or preset in real time. For example, the excitation voltage is controlled by a digital-to-analog converter (DAC) and calibrated by a voltage sensor.

[0101] The peak voltage captured by the ultrasonic receiving transducer after attenuation refers to the peak voltage of the electrical signal converted back by the receiving transducer after the ultrasonic signal has propagated and attenuated through the wastewater medium. It directly reflects the energy loss of the ultrasonic wave after propagation in the medium and is the core data for calculating the attenuation. After the receiving transducer converts the acoustic signal into an electrical signal, it can be amplified by a high-gain, low-noise amplifier circuit, and then its peak voltage can be captured by a peak detection circuit or a high-speed analog-to-digital converter (ADC).

[0102] This refers to the intrinsic background acoustic attenuation coefficient of pure water at the current temperature. It indicates the degree of attenuation caused by the inherent absorption and scattering of ultrasonic waves by pure water at a specific temperature. It is used to subtract the attenuation contribution of pure water itself from the total attenuation, thereby separating the attenuation caused by flocs. This coefficient is obtained through experimental measurement or by consulting physics handbooks. A database of acoustic attenuation coefficients of pure water at different temperatures has been established. In practical applications, the wastewater temperature is measured in real time using a temperature sensor, and the corresponding value is retrieved from the database based on the temperature value. .

[0103] The dynamic viscosity of wastewater for real-time monitoring refers to the ability of wastewater to resist shear deformation when flowing. It is a measure of the internal friction of the fluid and is used to compensate for the influence of changes in wastewater viscosity on ultrasonic attenuation, ensuring the accuracy of the floc acoustic attenuation density index. This viscosity is measured in real time using an online viscometer, such as a vibratory viscometer, by measuring the damping of the probe when it vibrates in the fluid.

[0104] The reference dynamic viscosity of wastewater under standard operating conditions refers to the dynamic viscosity value of wastewater that is determined during system design or calibration and represents ideal or typical operating conditions. It serves as a reference for viscosity compensation and is used to quantify the deviation between real-time viscosity and standard viscosity. During the system commissioning phase, this reference value is determined by laboratory measurements on typical wastewater samples to obtain an average or representative viscosity value.

[0105] The viscosity compensation empirical constant is a dimensionless constant used to adjust the influence of the viscosity compensation term on the floc acoustic attenuation density index. It allows the system to adjust the sensitivity of viscosity compensation based on actual operating conditions and experience to optimize model accuracy. This constant is determined through experimental calibration and optimization, such as by measuring ultrasonic attenuation under different viscosity conditions and comparing the results with actual floc density, and then determining the optimal value through regression analysis. value.

[0106] The comprehensive fit evaluation module obtains the floc sound attenuation density index. A calculation model was adopted that includes a pure water background attenuation subtraction term and a wastewater dynamic viscosity index compensation term. This model first uses the physical acoustic path distance between the ultrasonic transmitting probe and the receiving probe. Peak initial excitation voltage of the ultrasonic transducer and the attenuated voltage peak captured by the ultrasonic receiving transducer The model calculates the total attenuation of ultrasound in wastewater. Furthermore, to eliminate the inherent attenuation effect of pure water on ultrasound, the model subtracts the inherent background acoustic attenuation coefficient of pure water at the current temperature. This deduction operation ensures that the remaining attenuation amount more accurately reflects the attenuation caused by flocs in the wastewater, and avoids interference from fluctuations in pure water attenuation due to changes in water temperature on the calculation of floc attenuation.

[0107] Subsequently, to compensate for the impact of changes in wastewater dynamic viscosity on ultrasonic wave propagation attenuation, the model introduced a wastewater dynamic viscosity index compensation term, which is based on the real-time monitored wastewater dynamic viscosity. Wastewater reference dynamic viscosity under standard operating conditions The difference between them, and compensated by the empirical constant of viscosity. Adjustments are made to the result after background attenuation is subtracted in an exponential manner.

[0108] Since the viscosity of wastewater changes in real time with factors such as temperature and impurity content, directly affecting the propagation characteristics of ultrasound, introducing this compensation term can effectively offset the measurement deviations caused by these changes, thus improving the final calculated floc acoustic attenuation density index. It can more realistically and accurately reflect the density and state of the flocs themselves, without being affected by the additional interference of fluctuations in the physicochemical properties of the wastewater medium. Through the above two corrections, it can provide a highly accurate floc sound attenuation density index. , The value is used as input and sent to the fitness model in the above-mentioned comprehensive fitness evaluation module.

[0109] The fitness model utilizes this Combined with preprocessing matching degree Comprehensive impact coefficient of source water and the real-time current peak coefficient of the flocculation agitator Calculate the overall adaptability of the system. Compared to the uncorrected version The revised Improved overall compatibility The calculation accuracy and overall adaptability This time delay is then used by the aforementioned lag time optimization feedforward module to construct a lag time optimization model, thereby outputting a more accurate pure lag time for the target lime addition response. Ultimately, this enables the feedforward controller to perform more adaptive and precise lime dosing actions, effectively avoiding problems such as over-dosing or insufficient response, and improving the stability and effluent quality of the mining wastewater treatment device.

[0110] By introducing a pure water background attenuation subtraction term, the calculation results can accurately isolate the attenuation contribution of pure water itself, avoiding measurement deviations caused by water temperature fluctuations. Simultaneously, the addition of a wastewater dynamic viscosity index compensation term effectively corrects the influence of wastewater viscosity changes on ultrasonic wave propagation attenuation, ensuring the accuracy of the floc acoustic attenuation density index under different water quality conditions. This precisely corrected floc acoustic attenuation density index... As a key input to the aforementioned comprehensive fit evaluation module, it improved the overall fit. The calculation accuracy, given the overall adaptability It is a core parameter of the subsequent lag time optimization model, and its improved accuracy directly guarantees the pure lag time of the target lime addition response. The optimized accuracy of this solution enables the feedforward controller to perform more adaptive and precise lime dosing actions, thereby effectively avoiding resource waste and scaling risks caused by excessive dosing, as well as substandard effluent quality caused by insufficient dosing. Ultimately, this improves the operational stability, treatment efficiency, and effluent quality compliance rate of the wastewater treatment device for mining operations.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A mine exploitation sewage treatment device, characterized in that: It includes a balanced water storage tank (1), a coagulation reaction tank (2), a sedimentation tank (3), a clear water temporary storage tank (4), and a dosing device (5); And: a comprehensive source water impact assessment module, used to acquire in real time the oxidation-reduction potential, pH value, daily equivalent nitrogen load of mining blasting, and water and air temperature difference data of wastewater, to construct a comprehensive source water impact model, and to calculate and obtain the comprehensive source water impact coefficient; The pretreatment matching degree evaluation module is used to acquire gypsum scaling saturation index, turbidity fluctuation data of equalization tank and lime digestion residue yield coefficient in real time, construct pretreatment matching degree model and calculate and obtain pretreatment matching degree. The sludge health assessment module is used to monitor the marginal gain decay rate of sludge recirculation in real time, collect ultrasonic echo spectrum data of sludge layer to extract the centroid frequency, construct a sludge health model, and calculate and obtain the sludge health. The comprehensive adaptability evaluation module is used to construct an adaptability model based on the comprehensive impact coefficient of the source water, the pretreatment matching degree, the real-time collected floc acoustic attenuation density index, and the real-time current kurtosis coefficient of the flocculation agitator, and to calculate and obtain the comprehensive adaptability of the system. The lag time optimization feedforward module is used to construct a lag time optimization model based on the comprehensive adaptability and sludge health, output the pure lag time of the target lime addition response, and send the time parameter to the feedforward controller to execute the adaptive delayed lime addition action.

2. A mine exploitation sewage treatment device according to claim 1, characterized in that: In the source water comprehensive impact assessment module, the source water comprehensive impact model is calculated using a dimensionless hyperbolic tangent mapping function with smooth saturation characteristics; The independent variables of the mapping function include a first impact term normalized by the ratio of the redox potential to the pH value through an engineering reference, a second impact term normalized by the daily nitrogen load converted from mining blasting through an upper limit reference, and a thermal inertia term composed of the daily water temperature difference and the daily air temperature difference. By weighted summing the normalized terms and processing them through the hyperbolic tangent mapping function, the output range of the source water comprehensive impact coefficient is strictly constrained within the bounded saturation interval of .

3. The mine exploitation sewage treatment device according to claim 1, characterized in that: In the preprocessing matching degree evaluation module, the preprocessing matching degree model is calculated using a multidimensional negative exponential decay joint function; The exponential term of the negative exponential decay joint function includes the square of the ratio of the real-time saturation index of gypsum scaling to the critical scaling saturation index benchmark, the ratio of the maximum difference in turbidity in the equalization tank to the tolerance benchmark, and the yield coefficient of the lime digestion residue. when any of the evaluation dimensions in the exponential term positively deviates, the pre-processing matching degree exponentially decays continuously and non-linearly as a whole, and the output range is constrained to the interval.

4. The mine exploitation sewage treatment device according to claim 1, characterized in that: In the sludge health assessment module, the sludge health model is calculated using a continuous smooth function that combines rational fractional decay and a symmetrical Gaussian bell curve. wherein the sludge health degree is outputted by multiplying the rational fraction decay function which non-linearly punishes the sludge return marginal gain decay rate combined with time constant weight, and the symmetric Gaussian bell curve function which attenuates the fluctuation of the real-time sludge blanket ultrasonic echo spectrum barycenter frequency deviating from the optimal reference frequency in the form of probability density, and the output range is constrained within and .

5. A wastewater treatment device for mining operations according to claim 1, characterized in that: In the comprehensive fitness evaluation module, the fitness model is calculated using a hyperbolic secant and asymptotic hybrid attenuation model, and the specific model formula is as follows: ； in, For overall compatibility, To preprocess the matching degree, The comprehensive impact coefficient of the source water. This refers to the real-time current peak value coefficient of the flocculation agitator. The density index for sound attenuation in flocs. As a reference sound attenuation standard corresponding to an ideal dense floc, , and All parameters are dimensionless adjustment parameters, and the output range of this model is constrained to... Within the range.

6. The wastewater treatment device for mining operations according to claim 1, characterized in that: In the lag time optimization feedforward module, the lag time optimization model is calculated using a rational algebraic variable-scale stepless scaling model. The lag time optimization model uses the baseline physical pure lag time calibrated by the system. As the algebraic base, based on the aforementioned comprehensive fitness degree The degree of reverse offset degradation is used as a molecular amplification factor, based on the sludge health. The positive gain is used as a denominator reduction factor, and through nonlinear algebraic scaling of the numerator and denominator, the pure time delay of the target lime addition response, with time dimensions, is dynamically output. .

7. A wastewater treatment device for mining operations according to claim 5, characterized in that: The comprehensive adaptability evaluation module involves the real-time current kurtosis coefficient of the flocculation agitator. The calculation is performed using a fourth-order standard moment statistical model based on a continuous-time sliding window. Specifically, within a set real-time sliding time window, the deviation between the instantaneous stator current measurement value of the stirrer and the expected mean value of the current within the window is calculated by the time integral mean of the fourth-order central moment. Then, the deviation is divided by the square of the time integral mean of the second-order central moment of the deviation. The absolute dimension of the current is completely eliminated by algebraic division of higher-order moments, and the dimensionless morphological scalar that characterizes the abnormal peak characteristics of the flow field floc collision resistance is extracted.

8. A wastewater treatment device for mining operations according to claim 5, characterized in that: The comprehensive fit evaluation module involves the floc acoustic attenuation density index. The calculation was performed using a complementary correction model of acoustic energy absorption and dissipation and fluid physical dynamic viscosity; Specifically, the background total decibel attenuation rate is calculated based on the logarithmic ratio of the voltage amplitudes of the initial excitation voltage of the ultrasonic transmitting transducer and the capture voltage of the receiving transducer, combined with the physical sound path straight distance. After algebraically subtracting the inherent background sound attenuation coefficient of pure water at the corresponding temperature, it is multiplied by a negative exponential compensation correction factor constructed from the deviation ratio of real-time dynamic viscosity and reference dynamic viscosity. Finally, the floc sound attenuation density index with decibels per meter is output. .

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