A Smart Optimization Method for Biological Oxidation Processes Based on Control Models

Abstract

This invention discloses an intelligent optimization method for a bio-oxidation process based on a control model, relating to the field of bio-oxidation process control technology. The method includes: constructing a bio-oxidation coupling deviation operator and a liquid level overflow risk operator based on a set of smooth monitoring sequences of the bio-oxidation process, and generating a control response factor set to form a bio-oxidation process control model; performing stability statistical processing on the bio-oxidation coupling deviation operator and the liquid level overflow risk operator to form a bio-oxidation coupling deviation operating threshold set and a liquid level operating state threshold set, classifying the operating states, and generating intelligent optimization control commands for the bio-oxidation process in conjunction with the bio-oxidation process control model; issuing the intelligent optimization control commands to the actuators to complete the adjustment, and re-collecting bio-oxidation process monitoring data to generate new bio-oxidation coupling deviation operators and liquid level overflow risk operators to correct the bio-oxidation process control model.

Description

Technical Field

[0001] This invention relates to the field of biological oxidation process control technology, and in particular to an intelligent optimization method for biological oxidation processes based on a control model. Background Technology

[0002] Biological oxidation technology is widely used in the pretreatment of refractory gold-bearing sulfide ores and other raw materials. By controlling slurry temperature, redox potential, liquid level, ventilation volume, and feed-discharge balance, it maintains microbial activity and the oxidation reaction rate. Industrial sites typically employ multi-source online measurement devices for temperature, potential, liquid level, and flow rate, which, combined with the process control system, implement ventilation, feeding, and discharge adjustment strategies to achieve stable maintenance and reaction efficiency management of the biological oxidation process. With changes in slurry properties, load fluctuations, and microbial metabolic conditions, the biological oxidation process exhibits significant dynamism and coupling, making operational status identification and adjustment intensity determination crucial aspects of process control.

[0003] However, existing methods still have two limitations: First, conventional control strategies rely on discrete monitoring and interval judgment of single variables such as temperature, potential, and liquid level, making it difficult to form a unified quantitative structure that can simultaneously characterize multi-source coupling deviations and liquid level safety risks; Second, the triggering and intensity of adjustment actions are often based on fixed intervals or empirical thresholds, making it difficult to continuously and model-based inference of response intensity based on the evolution trend, accumulation degree, and multi-dimensional characteristics of the deviation, thus limiting the efficiency of coordinated adjustment under complex fluctuating conditions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent optimization method for biological oxidation processes based on control models, which solves the problems of unified quantification of multi-source coupling deviation and liquid level risk in biological oxidation processes, as well as the difficulty in achieving continuous and model-based generation of adjustment intensity according to the deviation evolution trend.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an intelligent optimization method for biological oxidation processes based on a control model, which includes collecting biological oxidation process monitoring data and performing quality verification and double-layer smoothing processing to obtain a set of smoothed monitoring sequences for the biological oxidation process;

[0008] Based on the set of smooth monitoring sequences of the biological oxidation process, a biological oxidation coupling deviation operator and a liquid level overflow risk operator are constructed, and a control response factor set is generated to form a biological oxidation process control model;

[0009] Stability statistics are performed on the biological oxidation coupling deviation operator and the liquid level overflow risk operator to form a biological oxidation coupling deviation operating threshold group and a liquid level operating state threshold group, the operating state is divided, and intelligent optimization control instructions for the biological oxidation process are generated in combination with the biological oxidation process control model.

[0010] The intelligent optimization control command for the biological oxidation process is sent to the actuator to complete the adjustment, and the monitoring data of the biological oxidation process is re-collected to generate new biological oxidation coupling deviation operators and liquid level overflow risk operators to correct the biological oxidation process control model.

[0011] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the step of collecting biological oxidation process monitoring data and performing quality verification includes collecting temperature measurement data, oxidation-reduction potential measurement data, liquid level measurement data, ventilation volume measurement data, upstream feed flow measurement data, and total discharge flow measurement data of this tank, and attaching a unified time stamp to form a biological oxidation process monitoring sequence set;

[0012] The set of monitoring sequences for biological oxidation processes is subjected to validity verification based on the physical allowable range and continuity verification based on the rate of change limit. Abnormal measurement data that exceed the physical allowable range or do not meet the rate of change limit are removed to form a set of valid monitoring sequences for biological oxidation processes.

[0013] As a preferred embodiment of the intelligent optimization method for biological oxidation process based on control model described in this invention, the double-layer smoothing process to obtain a set of smoothed monitoring sequences for biological oxidation process includes: performing median smoothing processing based on a first sliding window on each effective measurement data sequence based on the set of effective monitoring sequences for biological oxidation process to form a median smoothed measurement data sequence.

[0014] The median smoothed measurement data sequence set is subjected to arithmetic mean smoothing based on the second sliding window to generate smoothed measurement data sequences for temperature, redox potential, liquid level, ventilation, upstream feed flow, and total discharge flow of the tank. All smoothed measurement data sequences are then combined into a smoothed monitoring sequence set for the biological oxidation process.

[0015] As a preferred embodiment of the intelligent optimization method for biological oxidation process based on control model described in this invention, the construction of the biological oxidation coupling deviation operator includes, based on the set of smooth monitoring sequences of biological oxidation process, identifying temperature-stable biological oxidation intervals, potential-stable biological oxidation intervals, and ventilation-stable biological oxidation intervals in temperature-stable measurement data sequences, redox potential-stable measurement data sequences, and ventilation volume-stable measurement data sequences.

[0016] Within the temperature-stable biological oxidation range, the target temperature center value and the allowable temperature deviation amplitude are calculated. Within the potential-stable biological oxidation range, the target potential center value and the allowable potential deviation amplitude are calculated. Within the ventilation-stable biological oxidation range, the ventilation volume reference value is calculated. Based on the time difference between the changes in ventilation volume smoothed measurement data and temperature smoothed measurement data, as well as the time difference between the changes in ventilation volume smoothed measurement data and redox potential smoothed measurement data, the ventilation deviation response duration period is calculated.

[0017] A biological oxidation coupling deviation operator is formed by normalizing and combining the deviations of temperature smoothing measurement data from the target temperature center value, the deviations of potential smoothing measurement data from the target potential center value, and the historical deviations of ventilation volume smoothing measurement data within the ventilation deviation response duration period.

[0018] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the liquid level overflow risk operator includes identifying the stable operating range of the liquid level in the liquid level smoothing measurement data sequence based on the set of smooth monitoring sequences of the biological oxidation process.

[0019] Calculate the lower limit, upper limit, and reference rate of liquid level change within the stable operating range of liquid level;

[0020] Based on the smoothed measurement data sequence of upstream feed flow and the smoothed measurement data sequence of total discharge flow in this tank, the duration of the inlet and outlet flow deviation response is obtained by calculating the statistical amplitude of the change in upstream feed flow and the statistical amplitude of the change in total discharge flow in this tank.

[0021] The liquid level overflow risk operator is formed by normalizing and combining the positional deviation of the smoothed liquid level measurement data between the lower and upper limits of the liquid level, the deviation of the liquid level change rate from the reference rate of liquid level change, and the historical imbalance of the smoothed upstream feed flow rate measurement data and the smoothed total discharge flow rate measurement data of this tank within the duration of the inlet and outlet flow rate deviation response.

[0022] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the step of generating a control response factor group to form a biological oxidation process control model includes using the biological oxidation coupling deviation operator and the liquid level overflow risk operator as independent variables, and generating ventilation control response factors, feeding control response factors and emission control response factors respectively through exponential amplification, and forming a control response factor group.

[0023] A biological oxidation process control model is formed by jointly expressing the biological oxidation coupling deviation operator, the liquid level overflow risk operator, and the control response factor group.

[0024] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the step of performing stability statistical processing to form a biological oxidation coupling deviation operating threshold group and a liquid level operating state threshold group includes calculating the stability statistical average value and stability statistical deviation of the biological oxidation coupling deviation operator and the stability statistical average value and stability statistical deviation of the liquid level overflow risk operator based on the continuous historical values ​​of the biological oxidation coupling deviation operator and the liquid level overflow risk operator, respectively.

[0025] By using the stability statistical average and stability statistical deviation of the biological oxidation coupling deviation operator, a set of operating thresholds for biological oxidation coupling deviation is constructed. Similarly, by using the stability statistical average and stability statistical deviation of the liquid level overflow risk operator, a set of operating thresholds for liquid level is constructed.

[0026] As a preferred embodiment of the intelligent optimization method for biological oxidation process based on control model described in this invention, the division of operating states includes dividing the operating states of the biological oxidation process into a stable operating range, a slightly deviated operating range, a severely deviated operating range, and a protective operating range, according to the relationship between the biological oxidation coupling deviation operator and the biological oxidation coupling deviation operating threshold group.

[0027] Based on the relationship between the liquid level overflow risk operator and the liquid level operating status threshold group, the liquid level operating status is divided into the normal operating range, the liquid level warning range, the liquid level severe warning range, and the liquid level protection range.

[0028] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the step of generating intelligent optimization control instructions for the biological oxidation process by combining the biological oxidation process control model includes: based on the biological oxidation process operating status belonging to the stable operating range, the slightly deviated operating range, the severely deviated operating range, or the protective operating range, and the liquid level operating status belonging to the normal operating range, the early warning operating range, the severe early warning operating range, or the protective operating range, calling ventilation control response factors, feeding control response factors, and emission control response factors from the biological oxidation process control model, determining control actions such as increasing ventilation volume, decreasing feeding volume, increasing emission volume, and suspending feeding, and forming intelligent optimization control instructions for the biological oxidation process.

[0029] As a preferred embodiment of the intelligent optimization method for the biological oxidation process based on the control model described in this invention, the step of issuing the intelligent optimization control command for the biological oxidation process to the actuators includes applying the intelligent optimization control command for the biological oxidation process to the ventilation actuator, the feeding actuator, and the emission actuator, and adjusting the ventilation volume, the feeding volume, and the emission volume.

[0030] After adjustment, the monitoring data of the biological oxidation process are collected again, new biological oxidation coupling deviation operators and liquid level overflow risk operators are generated, and the adjustment results are recorded to correct the biological oxidation process control model.

[0031] The beneficial effects of this invention are as follows: by constructing a biological oxidation coupling deviation operator and a liquid level overflow risk operator, a continuous quantitative expression of the degree of reaction deviation and the safe offset of liquid level is realized, so that multi-source monitoring quantities can form a calculable deviation characterization under a unified structure; by generating a control response factor group and forming a biological oxidation process control model, a model mapping between deviation state and ventilation regulation, feeding regulation and emission regulation is realized, so that the control action exhibits continuous adjustment characteristics as the deviation changes. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of an intelligent optimization method for biological oxidation processes based on a control model.

[0034] Figure 2 This is a flowchart for data acquisition and double-layer smoothing of biological oxidation process monitoring data.

[0035] Figure 3 A flowchart for developing a control model of the biological oxidation process.

[0036] Figure 4 A flowchart for generating state division and intelligent optimization control instructions.

[0037] Figure 5 This is a schematic diagram showing the time-varying curve of the biological oxidation coupling deviation operator and the operating threshold group of the biological oxidation coupling deviation.

[0038] Figure 6 This is a schematic diagram of the time-varying curve of the liquid level overflow risk operator and the threshold group of liquid level operating status. Detailed Implementation

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0042] Reference Figures 1-6 This is one embodiment of the present invention, which provides an intelligent optimization method for a biological oxidation process based on a control model, comprising the following steps:

[0043] S1. Collect monitoring data of biological oxidation process and perform quality verification and double-layer smoothing to obtain a set of smoothed monitoring sequences of biological oxidation process.

[0044] Furthermore, based on the real-time monitoring requirements of temperature, oxidation-reduction potential, liquid level, ventilation volume, and feed-discharge balance during the bio-oxidation process, temperature sensors are installed in each bio-oxidation tank to form temperature measurement points, oxidation-reduction potential sensors to form oxidation-reduction potential measurement points, liquid level gauges to form liquid level measurement points, and ventilation volume measurement devices to form ventilation volume measurement points. An upstream feed flow meter is installed in the feed pipeline of each bio-oxidation tank to form an upstream feed flow measurement point, and a total flow meter is installed in the discharge pipeline of each bio-oxidation tank to form a total discharge flow measurement point. During operation, all these measurement points continuously output temperature measurement data, oxidation-reduction potential measurement data, liquid level measurement data, ventilation volume measurement data, upstream feed flow measurement data, and total discharge flow measurement data. All these measurement data collectively constitute the bio-oxidation process monitoring data.

[0045] For each collected temperature measurement data, redox potential measurement data, liquid level measurement data, ventilation volume measurement data, upstream feed flow measurement data, and total discharge flow measurement data of this tank, a unified time stamp is added to form a temperature measurement data sequence with a unified time stamp, a redox potential measurement data sequence with a unified time stamp, a liquid level measurement data sequence with a unified time stamp, a ventilation volume measurement data sequence with a unified time stamp, an upstream feed flow measurement data sequence with a unified time stamp, and a total discharge flow measurement data sequence of this tank with a unified time stamp. All measurement data sequences are then combined into a biological oxidation process monitoring sequence set.

[0046] Furthermore, when performing quality verification on the monitoring sequence set of biological oxidation processes, the quality verification is divided into validity verification based on the physical allowable range and continuity verification based on the rate of change limit.

[0047] Based on the temperature limit, potential limit, liquid level limit, ventilation limit, upstream feed flow limit, and total discharge flow limit of the bio-oxidation process, the physical allowable range of the monitoring data for each physical quantity is determined, and temperature measurement data, oxidation-reduction potential measurement data, liquid level measurement data, ventilation flow measurement data, upstream feed flow measurement data, and total discharge flow measurement data of the tank that exceed the physical allowable range are excluded.

[0048] Based on the following criteria: the rate of temperature change is limited by the thermal inertia of the heating and cooling processes; the rate of change of redox potential is limited by the reaction rate and the metabolic regulation capacity of the microbial community; the rate of change of liquid level is limited by the difference between the upstream feed flow rate and the total discharge flow rate of the tank; the rate of change of ventilation volume is limited by the response speed of the ventilation regulation device; the rate of change of upstream feed flow rate is limited by the regulation capacity of the upstream conveying mechanism; and the rate of change of total discharge flow rate of the tank is limited by the regulation capacity of the discharge pump group, abrupt increases in liquid level measurement data, abrupt changes in temperature measurement data, abrupt jumps in redox potential measurement data, abnormal transitions in ventilation volume measurement data, abnormal jumps in upstream feed flow rate measurement data, and abnormal jumps in total discharge flow rate measurement data of the tank are identified between adjacent time markers. Abnormal abrupt changes that do not meet the rate of change limits between adjacent time markers are then eliminated.

[0049] After completing the validity verification based on the physical allowable range and the continuity verification based on the change rate limit, the effective measurement data sequences of temperature, oxidation-reduction potential, liquid level, ventilation volume, upstream feed flow rate and total discharge flow rate of this tank are obtained from the biological oxidation process monitoring sequence set and are continuous and meet the process dynamic constraints, forming the effective monitoring sequence set of the biological oxidation process.

[0050] Furthermore, for each valid measurement data sequence in the effective monitoring sequence set of biological oxidation processes, a two-layer smoothing process based on a combination of moving median and moving average operations is performed.

[0051] Specifically, a first sliding window covering continuous sampling points of time is set along the time mark direction of each valid measurement data sequence. The first sliding window covers several adjacent valid measurement data points each time it slides. By performing median value calculation on the valid measurement data covered by the first sliding window, median smoothed measurement data corresponding to the center time mark of the first sliding window is generated. As the first sliding window slides point by point along the time mark direction, a corresponding median smoothed measurement data sequence is formed. The median smoothed measurement data sequence is used to reduce isolated spike interference.

[0052] After forming the median smoothed measurement data sequence, a second sliding window covering continuous sampling points of time is set along the time mark direction of the median smoothed measurement data sequence. The second sliding window covers several adjacent median smoothed measurement data points each time it slides. By performing an arithmetic mean operation on the median smoothed measurement data covered by the second sliding window, smoothed measurement data corresponding to the center time mark of the second sliding window is generated. As the second sliding window slides point by point along the time mark direction, a corresponding smoothed measurement data sequence is formed. The second sliding window is used to suppress high-frequency fluctuations in the median smoothed measurement data sequence.

[0053] After performing double-layer smoothing on each effective measurement data sequence in the effective monitoring sequence set of the biological oxidation process, the following smoothed measurement data sequences are obtained: temperature smoothed measurement data sequence, redox potential smoothed measurement data sequence, liquid level smoothed measurement data sequence, ventilation smoothed measurement data sequence, upstream feed flow smoothed measurement data sequence, and total discharge flow smoothed measurement data sequence of this tank. All smoothed measurement data sequences are then combined into a smoothed monitoring sequence set for the biological oxidation process.

[0054] It should be noted that the window lengths of the first and second sliding windows can be adaptively set according to the sampling frequency, signal noise level, and process fluctuation characteristics. The windows move point by point along the time mark direction, and each move covers a new set of continuous measurement data.

[0055] S2. Based on the set of smooth monitoring sequences of the biological oxidation process, construct the biological oxidation coupling deviation operator and the liquid level overflow risk operator, and generate the control response factor group to form a biological oxidation process control model.

[0056] Furthermore, in the set of smoothed monitoring sequences for biological oxidation processes, based on temperature smoothed measurement data sequences, redox potential smoothed measurement data sequences, and ventilation volume smoothed measurement data sequences, at a time marker of Temperature smoothing measurement data, redox potential smoothing measurement data, and ventilation volume smoothing measurement data were read from the respective locations.

[0057] In the temperature smoothing measurement data sequence and the redox potential smoothing measurement data sequence, along the time marker direction, the changes in temperature smoothing measurement data and redox potential smoothing measurement data between adjacent time markers are calculated respectively. The continuous time period in the temperature smoothing measurement data sequence where the absolute value of all changes is less than the statistical amplitude of temperature change is identified as the temperature-stable biological oxidation interval, and the continuous time period in the redox potential smoothing measurement data sequence where the absolute value of all changes is less than the statistical amplitude of potential change is identified as the potential-stable biological oxidation interval.

[0058] Among them, the statistical amplitude of temperature change is determined by the arithmetic mean of the absolute values ​​of adjacent differences of the temperature smoothed measurement data sequence over the entire time-marked range; the statistical amplitude of potential change is determined by the arithmetic mean of the absolute values ​​of adjacent differences of the redox potential smoothed measurement data sequence; the temperature-stable biological oxidation range and the potential-stable biological oxidation range are used to characterize the time range in which temperature and redox potential are in natural equilibrium.

[0059] An arithmetic mean was calculated for all temperature-smoothed measurement data within the stable biological oxidation range. The arithmetic mean of all temperature-smoothed measurement data within the stable biological oxidation range was recorded as the target temperature center value. Extreme value analysis was performed on all temperature-smoothed measurement data within the stable biological oxidation range. The half-range between the maximum and minimum temperature-smoothed measurement data was recorded as the temperature tolerance deviation amplitude, which was used to characterize the acceptable range of temperature variation within the stable biological oxidation range.

[0060] An arithmetic mean was calculated for all smoothed redox potential measurements within the stable biological oxidation range. The arithmetic mean of all smoothed redox potential measurements within the stable biological oxidation range was recorded as the target potential center value. Extreme value analysis was performed on all smoothed redox potential measurements within the stable biological oxidation range. The half-range between the maximum and minimum values ​​of the smoothed redox potential measurements within the stable biological oxidation range was recorded as the permissible potential deviation amplitude, which is used to characterize the acceptable range of redox potential variation within the stable biological oxidation range.

[0061] In the smoothed ventilation volume measurement data sequence, the change in smoothed ventilation volume measurement data between adjacent time markers is calculated along the time marker direction. The time period in which the absolute value of all changes in a continuous time period is less than the statistical amplitude of ventilation change is identified as the stable biological oxidation interval of ventilation, which is used to characterize the time range in which ventilation volume is in a natural equilibrium state. The statistical amplitude of ventilation change is determined by the arithmetic mean of the absolute values ​​of adjacent differences in the smoothed ventilation volume measurement data sequence over the entire time marker range.

[0062] An arithmetic mean was calculated on all smoothed ventilation volume measurement data within the stable ventilation biological oxidation range. The arithmetic mean of all smoothed ventilation volume measurement data within the stable ventilation biological oxidation range was recorded as the ventilation volume reference value, which was used to characterize the ventilation supply level of the biological oxidation process under natural equilibrium conditions.

[0063] In the smoothed measurement data sequences of ventilation volume and temperature, the difference between the time markers of changes in the smoothed measurement data of ventilation volume and the time markers of responses in the smoothed measurement data of temperature is calculated to obtain the duration of the influence of ventilation volume changes on the smoothed measurement data of temperature. In the smoothed measurement data sequences of ventilation volume and redox potential, the duration of the influence of ventilation volume changes on the smoothed measurement data of redox potential is calculated in the same way. After obtaining the duration of the influence of ventilation volume changes on the smoothed measurement data of temperature and redox potential, the larger of the two durations is recorded as the ventilation deviation response duration. The ventilation deviation response duration is used to characterize the response time required for the ventilation volume change to be transmitted to the smoothed measurement data of temperature and redox potential.

[0064] By normalizing and combining the deviations of temperature smoothing measurement data from the target temperature center value, the deviations of potential smoothing measurement data from the target potential center value, and the historical deviations of ventilation volume smoothing measurement data within the ventilation deviation response duration, a biological oxidation coupling deviation operator is formed, expressed as:

[0065] ;

[0066] in, For time A biological oxidation coupling deviation operator is used to characterize the combined deviation of the biological oxidation tank in terms of temperature, potential, and ventilation conditions. The time variable corresponding to the current control cycle. For time Temperature smoothing measurement data, The target temperature center value, This refers to the allowable temperature deviation range. For time The redox potential smoothing measurement data, The target potential center value, This is the allowable deviation amplitude of the potential. The duration of the ventilation deviation response. Historical time index Smoothed ventilation volume measurement data This is a reference value for ventilation volume. This provides a historical time index for smoothing ventilation volume measurement data within the duration of the ventilation deviation response period.

[0067] Furthermore, in the set of smoothed monitoring sequences for the biological oxidation process, based on the smoothed measurement data sequences of liquid level, upstream feed flow rate, and total discharge flow rate of the tank, at a time marker of The smoothed measurement data of liquid level, smoothed measurement data of upstream feed flow rate, and smoothed measurement data of total discharge flow rate of this tank are read from the respective locations.

[0068] In the liquid level smoothing measurement data sequence, the difference between liquid level smoothing measurement data between adjacent time marks is calculated along the time mark direction. The absolute value of the difference between liquid level smoothing measurement data between each adjacent time mark is recorded as the liquid level change amplitude. An arithmetic mean operation is performed on the liquid level change amplitudes over the entire time mark range, and the arithmetic mean of the liquid level change amplitudes is recorded as the liquid level change statistical amplitude. Along the time mark direction, all continuous time periods where the absolute value of the liquid level change amplitude is less than the liquid level change statistical amplitude are identified as liquid level stable operating intervals. Liquid level stable operating intervals are used to characterize the fluctuation range of liquid level smoothing measurement data under natural equilibrium conditions.

[0069] Extreme value analysis is performed on all smoothed liquid level measurement data within the stable operating range. The minimum value of the smoothed liquid level measurement data within the stable operating range is recorded as the lower limit of the liquid level, and the maximum value of the smoothed liquid level measurement data within the stable operating range is recorded as the upper limit of the liquid level. At the same time, the arithmetic mean of the absolute values ​​of adjacent differences of all smoothed liquid level measurement data within the stable operating range is performed. The arithmetic mean of the absolute values ​​of adjacent differences of the smoothed liquid level measurement data within the stable operating range is recorded as the reference rate of liquid level change, which is used to characterize the typical rate of change of liquid level under natural equilibrium conditions.

[0070] In the upstream feed flow smoothing measurement data sequence, the difference between the upstream feed flow smoothing measurement data between adjacent time markers is calculated along the time marker direction. The absolute value of the difference between the upstream feed flow smoothing measurement data between each adjacent time marker is recorded as the upstream feed flow change amplitude. An arithmetic mean operation is performed on the upstream feed flow change amplitude over the entire time marker range, and the arithmetic mean of the upstream feed flow change amplitude is recorded as the upstream feed flow change statistical amplitude. In the total discharge flow smoothing measurement data sequence of this tank, the absolute value of the adjacent difference of the total discharge flow smoothing measurement data of this tank is calculated in the same way, and the arithmetic mean of the absolute values ​​of the adjacent difference of the total discharge flow smoothing measurement data of this tank is recorded as the total discharge flow change statistical amplitude of this tank.

[0071] In the upstream feed flow rate smoothing measurement data sequence and the liquid level smoothing measurement data sequence, the time points in the upstream feed flow rate smoothing measurement data where the absolute value of the change is greater than the statistical magnitude of the upstream feed flow rate change are identified. Corresponding response times in the liquid level smoothing measurement data sequence where the liquid level change magnitude significantly increases are found. The time difference between each set of upstream feed flow rate change times and liquid level change response times is calculated. The maximum value among all time differences is recorded as the duration of the influence of the upstream feed flow rate change on the liquid level smoothing measurement data. The same method is used to calculate the duration of the influence of the total discharge flow rate change on the liquid level smoothing measurement data in the same way as in the tank discharge total flow rate smoothing measurement data sequence and the liquid level smoothing measurement data sequence. After obtaining the duration of the influence of both the upstream feed flow rate change and the total discharge flow rate change on the liquid level smoothing measurement data, the larger of the two durations is recorded as the inflow / outflow flow deviation response duration, used to characterize the response time required for the upstream feed flow rate balance state and the total discharge flow rate balance state to significantly influence the liquid level smoothing measurement data.

[0072] By normalizing and combining the positional deviation of the smoothed level measurement data between the lower and upper limits of the level, the deviation of the level change rate from the reference rate of level change, and the historical imbalance between the smoothed upstream feed flow rate measurement data and the smoothed total discharge flow rate measurement data within the duration of the inflow and outflow deviation response, a level overflow risk operator is formed, expressed as:

[0073] ;

[0074] in, For time The liquid level overflow risk operator is used to characterize the comprehensive deviation of the biological oxidation tank from the liquid level position, liquid level change trend, and inflow / outflow balance state. For time Liquid level smoothing measurement data, For time Liquid level smoothing measurement data, This is the lower limit of the liquid level. This is the upper limit of the liquid level. This is the reference rate for liquid level change. The duration of the response to the deviation in inflow and outflow rates. Historical time index Smoothing measurement data of upstream feed flow rate Historical time index The total discharge flow rate of this tank is smoothed from the measurement data. This is a historical time index of the smoothed measurement data of upstream feed flow rate and the smoothed measurement data of total discharge flow rate of this tank within the duration of the inlet and outlet flow rate deviation response.

[0075] Furthermore, based on the physical meaning of the biological oxidation coupling deviation operator and the liquid level overflow risk operator, and using these two operators as independent variables, an exponential amplification method is adopted to define a control response factor group, including ventilation control response factor, feeding control response factor, and emission control response factor, expressed as:

[0076] ;

[0077] ;

[0078] ;

[0079] in, For time The ventilation control response factor is used to characterize the amplification level of the effect of the biological oxidation coupling deviation operator on ventilation regulation. For time The feed control response factor is used to characterize the amplification level of the combined effect of the biological oxidation coupling deviation operator and the liquid level overflow risk operator on the feed regulation. For time The emission control response factor is used to characterize the amplification level of the emission regulation effect of the liquid level overflow risk operator. It is the base of the natural index.

[0080] A biological oxidation process control model is constructed by jointly expressing the biological oxidation coupling deviation operator, the liquid level overflow risk operator, the ventilation control response factor, the feed control response factor, and the emission control response factor, as follows:

[0081] ;

[0082] in, For time A biological oxidation process control model, used to measure time... The ventilation regulation intensity, feed regulation intensity, and emission regulation intensity of the biological oxidation process are given.

[0083] S3. Perform stability statistical processing on the biological oxidation coupling deviation operator and the liquid level overflow risk operator to form a biological oxidation coupling deviation operating threshold group and a liquid level operating state threshold group, classify the operating state, and generate intelligent optimization control instructions for the biological oxidation process in combination with the biological oxidation process control model.

[0084] Furthermore, continuous historical values ​​of the biological oxidation coupling deviation operator and the liquid level overflow risk operator are collected on the time axis, and stability statistical processing is performed on all historical values.

[0085] By performing arithmetic and absolute deviation averaging operations on all historical biological oxidation coupling deviation operators, the stability statistical average and stability statistical deviation of the biological oxidation coupling deviation operators are obtained. Based on the stability statistical average and stability statistical deviation of the biological oxidation coupling deviation operators, a set of biological oxidation coupling deviation operating thresholds is constructed, including biological oxidation stable operating thresholds, biological oxidation deviation operating thresholds, and biological oxidation protection operating thresholds, expressed as:

[0086] ;

[0087] ;

[0088] ;

[0089] in, The stable operating threshold for biological oxidation is used to define the range within which the biological oxidation process is in equilibrium. This refers to deviations from the operating threshold of biological oxidation, used to define the range within which the biological oxidation process deviates but remains adjustable. The biological oxidation protection operating threshold is used to define the range within which the biological oxidation process requires protective control. This represents the statistical average value of the stability of the biological oxidation coupling bias operator. This represents the stability statistical deviation of the biological oxidation coupling bias operator.

[0090] By performing arithmetic and absolute deviation averaging operations on all historical liquid level overflow risk operators, the stability statistical average and stability statistical deviation of the liquid level overflow risk operators are obtained. Based on the stability statistical average and stability statistical deviation of the liquid level overflow risk operators, a set of liquid level operating status thresholds is constructed, including the normal operation threshold, the early warning operation threshold, and the severe early warning operation threshold, expressed as:

[0091] ;

[0092] ;

[0093] ;

[0094] in, This is the normal operating threshold for liquid level, used to define the normal range of liquid level fluctuations. The liquid level warning threshold is used to define the range within which liquid level fluctuations enter an abnormal trend. This is the critical liquid level warning threshold, used to define the range where liquid level fluctuations enter a state of severe risk. This represents the statistical average stability of the liquid level overflow risk operator. This represents the stability statistical deviation of the liquid level overflow risk operator.

[0095] Furthermore, based on the relationship between the biological oxidation coupling deviation operator and the biological oxidation coupling deviation operating threshold group, the operating state of the biological oxidation process is divided into a stable biological oxidation operating range, a slightly deviated biological oxidation operating range, a severely deviated biological oxidation operating range, and a protective biological oxidation operating range.

[0096] The specific rules for classifying the operating states of a biological oxidation process are as follows:

[0097] when At that time, the operating state of the biological oxidation process is divided into the stable operating range of biological oxidation.

[0098] when At that time, the operating status of the biological oxidation process was divided into the biological oxidation slightly deviated from the operating range.

[0099] when At that time, the operating status of the biological oxidation process was divided into the severely deviated operating range of biological oxidation.

[0100] when At that time, the operating status of the biological oxidation process is divided into biological oxidation protection operating intervals.

[0101] Furthermore, based on the relationship between the liquid level overflow risk operator and the liquid level operating status threshold group, the liquid level operating status is divided into the normal operating range, the liquid level warning range, the liquid level severe warning range, and the liquid level protection range.

[0102] The specific rules for classifying liquid level operating states are as follows:

[0103] when At that time, the liquid level operating status is divided into the normal operating range of liquid level.

[0104] when At that time, the liquid level operating status is divided into liquid level early warning operating ranges.

[0105] when At that time, the liquid level operation status is divided into the liquid level severe warning operation range.

[0106] when At that time, the liquid level operating status is divided into liquid level protection operating ranges.

[0107] Furthermore, ventilation control response factors, feeding control response factors, and emission control response factors are obtained from the biological oxidation process control model. Based on the combination relationship between the biological oxidation process operating status and the liquid level operating status, intelligent optimization control commands for the biological oxidation process are generated.

[0108] When the bio-oxidation process is in a stable operating range and the liquid level is in a normal operating range, the ventilation volume, feed rate, and discharge rate remain unchanged. No amplification adjustment operations are performed. This is to maintain the baseline control output of the bio-oxidation process under the current stable operating conditions.

[0109] When the bio-oxidation process is in a state of slight deviation from the operating range and the liquid level is in the normal operating range, the ventilation volume is increased according to the ventilation control response factor, the ventilation volume increase command is executed, and the feed rate and discharge rate are kept unchanged. This is used to correct the bio-oxidation coupling deviation first without changing the load conditions.

[0110] When the biological oxidation process is operating in a severely deviated operating range or a protected operating range, and the liquid level is operating within the normal operating range, the ventilation volume is increased according to the ventilation control response factor. Based on the relative position of the biological oxidation coupling deviation operator within the range of the biological oxidation deviation threshold and the biological oxidation protected operating threshold, the biological oxidation deviation grading ratio coefficient is calculated, expressed as:

[0111] ;

[0112] in, For time The proportionality coefficient of biological oxidation deviation classification, This is used to limit the bio-oxidation deviation classification ratio coefficient to 1 when the bio-oxidation coupling deviation operator exceeds the bio-oxidation protection operation threshold.

[0113] The feed reduction range is determined by multiplying the bio-oxidation deviation grading ratio coefficient and the feed control response factor, and the feed rate reduction command is executed. This is used to accelerate the restoration of bio-oxidation reaction stability under safe liquid level conditions through enhanced ventilation and load reduction.

[0114] When the biological oxidation operation is in the stable operating range or slightly deviates from the operating range, and the liquid level is in the liquid level warning range, the ventilation volume is increased according to the ventilation control response factor. Based on the position of the liquid level overflow risk operator between the normal operating threshold and the liquid level warning threshold, the liquid level warning deviation ratio coefficient is calculated, expressed as:

[0115] ;

[0116] in, For time The liquid level warning deviation ratio coefficient.

[0117] The feed reduction range is determined by multiplying the feed control response factor and the liquid level warning deviation ratio coefficient, and the feed rate reduction command is executed. This is used to gently limit the liquid level rise when the liquid level is in an early risk state, while avoiding excessive load reduction on the bio-oxidation reaction.

[0118] When the bio-oxidation operation is in the severely deviated bio-oxidation operation range or the bio-oxidation protection operation range, and the liquid level operation is in the liquid level warning operation range, the ventilation volume is increased according to the ventilation control response factor, and the feed reduction is determined according to the product of the liquid level warning deviation ratio coefficient, the bio-oxidation deviation classification ratio coefficient, and the feed control response factor. The feed volume is decreased, and the discharge is increased according to the adjustment intensity corresponding to the discharge control response factor. This is used to simultaneously strengthen ventilation adjustment, load reduction, and discharge diversion when the bio-oxidation conditions deteriorate significantly and the liquid level has entered the warning state, so as to limit the continued accumulation of deviation and inhibit the liquid level from developing into the severe risk range.

[0119] When the liquid level operation is within the critical warning range, regardless of whether the biological oxidation operation is within the stable, slightly deviated, or severely deviated range, the emission control response factor will be used to increase the emission amount. Furthermore, based on the position of the liquid level overflow risk operator between the critical warning threshold and the severe warning threshold, the severe deviation proportionality coefficient will be calculated, expressed as:

[0120] ;

[0121] in, For time The proportional coefficient for severe deviation of liquid level.

[0122] The reduction range is determined by multiplying the severe level deviation ratio coefficient with the feed control response factor, and the feed rate reduction command is executed to quickly suppress the upward trend of the liquid level and prevent the liquid level from approaching the liquid level protection operating range.

[0123] When the liquid level is in the liquid level protection operating range, regardless of whether the biological oxidation is in the stable biological oxidation operating range, the slightly deviated biological oxidation operating range, the severely deviated biological oxidation operating range, or the biological oxidation protection operating range, the emission volume will be further amplified according to the adjustment intensity corresponding to the emission control response factor, an emergency emission adjustment operation will be executed, and feeding will be suspended. This is used to suppress the further rise of the liquid level by rapidly reducing the liquid level and cutting off the feed load when the liquid level enters the liquid level protection operating range.

[0124] It should be noted that through the intelligent optimization control decision of the biological oxidation process control model, the biological oxidation process can form an integrated linkage control strategy under operating conditions such as increased deviation, abnormal liquid level, feed imbalance and insufficient ventilation, thereby achieving dynamic stability maintenance and risk suppression of the biological oxidation process.

[0125] It should also be noted that, in this embodiment, to verify the response characteristics and interval discrimination capabilities of the biological oxidation coupling deviation operator and the liquid level overflow risk operator under different disturbance conditions, different operating states, and different deviation levels, and thus to verify the effectiveness of the biological oxidation coupling deviation operator, the liquid level overflow risk operator, and the biological oxidation process control model in expressing the degree of reaction deviation and the safe liquid level offset, a verification experiment was conducted on a test platform simulating biological oxidation reaction conditions. The test platform includes a biological oxidation reactor with constant temperature control, aeration regulation, and online liquid level monitoring capabilities. The effective volume of the reactor is 20~50L, and it is equipped with a variable frequency blower, a constant flow feed pump, and a drain pump to construct ventilation disturbance, feeding impact, and drain regulation scenarios. The monitoring unit includes a PT100 temperature probe, an online oxidation-reduction potential electrode, and a pressure or ultrasonic liquid level sensor. The sampling period is set to 1s~5s, and the monitoring data is continuously input into the host computer via a PLC or data acquisition card. The host computer runs a Linux operating system, is configured with a multi-core general-purpose processor and 32GB of memory, and uses a Python program to calculate the biological oxidation coupling deviation operator, the liquid level overflow risk operator, and their corresponding operating threshold groups. The experiment constructed 400 continuous sampling points, covering the stable operation phase, the disturbance loading phase, and the system recovery phase, and superimposed slight random noise to simulate on-site measurement fluctuations, obtaining the following results: Figure 5 The curve of the biological oxidation coupling deviation operator changing over time and the schematic diagram of the biological oxidation coupling deviation operating threshold group are shown below. Figure 6 The diagram shows the time-varying curve of the liquid level overflow risk operator and the schematic diagram of the liquid level operating status threshold group.

[0126] Figure 5 This is used to demonstrate the response behavior of the biological oxidation coupling bias operator during the entire experiment and its relationship with the biological oxidation coupling bias running threshold set. Figure 5In the figure above, the thin blue solid line represents the time series of the bio-oxidation coupling deviation operator after adding slight noise, reflecting the continuous changes in the combined deviation of temperature, potential, and ventilation volume. The green dotted line, orange dashed line, and red dashed line represent the stable operation threshold, the deviation threshold, and the protection threshold of bio-oxidation, respectively, used to divide the value of the bio-oxidation coupling deviation operator into the stable operation range, the slightly deviation range, the severely deviation range, and the protection range. With the addition of feed impact, the bio-oxidation coupling deviation operator rises significantly in the middle and later stages. Figure 5 The red dashed rectangle in the image above marks the magnified area of ​​sampling points 200-260. Figure 5 The figure below is a magnified view, which shows that after the disturbance, the biological oxidation coupling deviation operator quickly crosses the biological oxidation deviation operating threshold and the biological oxidation protection operating threshold, and a biological oxidation protection peak point appears. This indicates that the reaction conditions have entered the deviation range from the stable biological oxidation operating range and further into the biological oxidation protection operating range, and control needs to be restored through ventilation adjustment, feed adjustment and emission adjustment. Figure 5 The study verified that the biological oxidation coupling deviation operator can continuously characterize the degree of deviation under perturbation scenarios, and achieves quantitative differentiation of deviation levels through the biological oxidation coupling deviation operation threshold group, providing a reliable deviation input for the biological oxidation process control model.

[0127] Figure 6 This is used to demonstrate the response characteristics of the liquid level overflow risk operator to the liquid level change process, and the correspondence between it and the liquid level operating state threshold group. Figure 6 In the figure above, the thin orange solid line represents the time series of the liquid level overflow risk operator after adding slight noise, which comprehensively considers the positional deviation of the liquid level relative to the stable operating range, the rate of liquid level change, and the degree of imbalance between the upstream feed flow and the total discharge flow. The green dotted line, the blue dashed line, and the red dashed line represent the normal operating threshold, the liquid level warning operating threshold, and the liquid level severe warning operating threshold, respectively, which are used to divide the value of the liquid level overflow risk operator into the normal operating range, the liquid level warning operating range, the liquid level severe warning operating range, and the liquid level protection operating range. Figure 6 The image above also uses a red dashed rectangle to mark the magnified area of ​​the 200-260 sampling points. Figure 6The image below is a magnified view, showing that during the short-term increase in feed and the subsequent lag in discharge adjustment, the liquid level overflow risk operator curve exhibits a significant peak exceeding the severe liquid level warning threshold. The peak point of the severe liquid level warning is marked with an arrow, indicating that the liquid level is approaching the overflow risk boundary. Overall, the results demonstrate that the liquid level overflow risk operator maintains a high sensitivity response to rapid liquid level increases and flow imbalances. The liquid level operating state threshold set can intuitively provide changes in risk level, offering a quantitative and calculable risk assessment basis for liquid level safety control in bio-oxidation processes.

[0128] S4. Send the intelligent optimization control command of the biological oxidation process to the actuator to complete the adjustment, and re-collect the monitoring data of the biological oxidation process to generate a new biological oxidation coupling deviation operator and a liquid level overflow risk operator to correct the biological oxidation process control model.

[0129] Furthermore, the intelligent optimization control commands for the bio-oxidation process are applied to the ventilation actuator, feeding actuator, and emission actuator, enabling real-time adjustment and change of ventilation volume, feeding volume, and emission volume at the execution level.

[0130] The ventilation actuator includes a blower unit or a blower regulating valve, used to increase the ventilation supply according to the ventilation control response factor; the feeding actuator includes a feeding pump unit or a feeding control valve, used to reduce the feeding amount according to the reduction range; the emission actuator includes a discharge pump unit or a discharge valve, used to increase the emission amount according to the emission control response factor.

[0131] Furthermore, after adjusting the ventilation volume, feed rate, and discharge rate, the temperature measurement data, oxidation-reduction potential measurement data, liquid level measurement data, ventilation volume measurement data, upstream feed flow rate measurement data, and total discharge flow rate measurement data of this tank are collected again in the next control cycle. Based on the new biological oxidation process monitoring data, smoothed measurement data of temperature, oxidation-reduction potential, liquid level, ventilation volume, upstream feed flow rate, and total discharge flow rate of this tank are generated for the next cycle.

[0132] Based on the new smoothed measurement data, the bio-oxidation coupling deviation operator and the liquid level overflow risk operator for the next cycle are generated, and new ventilation control response factors, feeding control response factors and emission control response factors are determined accordingly, so as to realize the real-time updating of control commands in continuous cycles.

[0133] Furthermore, at the execution level, the adjustment results of each intelligent optimization control command for the bio-oxidation process are recorded to form a sequence of bio-oxidation process adjustment results.

[0134] The sequence of biological oxidation process regulation results is compared with the historical trends of the biological oxidation coupling deviation operator and the liquid level overflow risk operator to automatically identify the degree of delay or insufficient regulation effect.

[0135] When ventilation adjustment delay, insufficient feeding response, or insufficient discharge guidance is detected, adaptive amplification is applied to the ventilation control response factor, feeding control response factor, or discharge control response factor to enhance the adjustment intensity in the next cycle, realize the dynamic self-optimization of the biological oxidation process control model, and enable the biological oxidation process to maintain stable operation under operating conditions such as increased deviation, abnormal liquid level, or imbalance between feed and discharge.

[0136] In summary, this invention achieves continuous quantitative expression of reaction deviation and liquid level safety offset by constructing a biological oxidation coupling deviation operator and a liquid level overflow risk operator, enabling multi-source monitoring quantities to form a calculable deviation characterization under a unified structure; and by generating a control response factor set and forming a biological oxidation process control model, it realizes a model-based mapping between deviation state and ventilation regulation, feeding regulation and emission regulation, so that the control action exhibits continuous adjustment characteristics as the deviation changes.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart optimization method for biological oxidation processes based on a control model, characterized in that: include, Data on biological oxidation processes were collected, and quality verification and double-layer smoothing were performed to obtain a set of smoothed monitoring sequences for biological oxidation processes. Based on the set of smooth monitoring sequences of the biological oxidation process, a biological oxidation coupling deviation operator and a liquid level overflow risk operator are constructed, and a control response factor set is generated to form a biological oxidation process control model; The construction of the biological oxidation coupling deviation operator includes, based on the set of smoothed monitoring sequences of the biological oxidation process, identifying temperature-stable biological oxidation intervals, potential-stable biological oxidation intervals, and ventilation-stable biological oxidation intervals in temperature-stable measurement data sequences, redox potential-stable measurement data sequences, and ventilation volume-stable measurement data sequences. Within the temperature-stable biological oxidation range, the target temperature center value and the allowable temperature deviation amplitude are calculated. Within the potential-stable biological oxidation range, the target potential center value and the allowable potential deviation amplitude are calculated. Within the ventilation-stable biological oxidation range, the ventilation volume reference value is calculated. Based on the time difference between the changes in ventilation volume smoothed measurement data and temperature smoothed measurement data, as well as the time difference between the changes in ventilation volume smoothed measurement data and redox potential smoothed measurement data, the ventilation deviation response duration period is calculated. By normalizing and combining the deviations of temperature smoothing measurement data from the target temperature center value, the deviations of potential smoothing measurement data from the target potential center value, and the historical deviations of ventilation volume smoothing measurement data within the ventilation deviation response duration period, a biological oxidation coupling deviation operator is formed. The liquid level overflow risk operator includes identifying the stable operating range of the liquid level in the liquid level smoothing measurement data sequence based on the set of biological oxidation process smoothing monitoring sequences; Calculate the lower limit, upper limit, and reference rate of liquid level change within the stable operating range of liquid level; Based on the smoothed measurement data sequence of upstream feed flow and the smoothed measurement data sequence of total discharge flow in this tank, the duration of the inlet and outlet flow deviation response is obtained by calculating the statistical amplitude of the change in upstream feed flow and the statistical amplitude of the change in total discharge flow in this tank. The liquid level overflow risk operator is formed by normalizing and combining the positional deviation of the smoothed liquid level measurement data between the lower and upper limits of the liquid level, the deviation of the trend of the liquid level change relative to the reference rate of the liquid level change, and the historical imbalance of the smoothed upstream feed flow rate measurement data and the smoothed total discharge flow rate measurement data of this tank within the duration of the inlet and outlet flow rate deviation response. Stability statistics are performed on the biological oxidation coupling deviation operator and the liquid level overflow risk operator to form a biological oxidation coupling deviation operating threshold group and a liquid level operating state threshold group, the operating state is divided, and intelligent optimization control instructions for the biological oxidation process are generated in combination with the biological oxidation process control model. The intelligent optimization control command for the biological oxidation process is sent to the actuator to complete the adjustment, and the monitoring data of the biological oxidation process is re-collected to generate new biological oxidation coupling deviation operators and liquid level overflow risk operators to correct the biological oxidation process control model.

2. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 1, characterized in that: The collection and quality verification of biological oxidation process monitoring data includes collecting temperature measurement data, oxidation-reduction potential measurement data, liquid level measurement data, ventilation volume measurement data, upstream feed flow rate measurement data, and total discharge flow rate measurement data of this tank, and adding a unified time stamp to form a biological oxidation process monitoring sequence set; The set of monitoring sequences for biological oxidation processes is subjected to validity verification based on the physical allowable range and continuity verification based on the rate of change limit. Abnormal measurement data that exceed the physical allowable range or do not meet the rate of change limit are removed to form a set of valid monitoring sequences for biological oxidation processes.

3. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 2, characterized in that: The double-layer smoothing process, which yields a set of smoothed monitoring sequences for the biological oxidation process, includes performing median smoothing on each effective measurement data sequence based on a first sliding window, based on the set of effective monitoring sequences for the biological oxidation process, to form a median smoothed measurement data sequence. The median smoothed measurement data sequence set is subjected to arithmetic mean smoothing based on the second sliding window to generate smoothed measurement data sequences for temperature, redox potential, liquid level, ventilation, upstream feed flow, and total discharge flow of the tank. All smoothed measurement data sequences are then combined into a smoothed monitoring sequence set for the biological oxidation process.

4. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 3, characterized in that: The generation of the control response factor group to form the biological oxidation process control model includes using the biological oxidation coupling deviation operator and the liquid level overflow risk operator as independent variables, and generating ventilation control response factors, feeding control response factors and emission control response factors respectively through exponential amplification, and forming a control response factor group. A biological oxidation process control model is formed by jointly expressing the biological oxidation coupling deviation operator, the liquid level overflow risk operator, and the control response factor group.

5. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 4, characterized in that: The execution of stability statistical processing to form the biological oxidation coupling deviation operation threshold group and the liquid level operation status threshold group includes, based on the continuous historical values ​​of the biological oxidation coupling deviation operator and the liquid level overflow risk operator, calculating the stability statistical average value and stability statistical deviation of the biological oxidation coupling deviation operator, and the stability statistical average value and stability statistical deviation of the liquid level overflow risk operator, respectively. By using the stability statistical average and stability statistical deviation of the biological oxidation coupling deviation operator, a set of operating thresholds for biological oxidation coupling deviation is constructed. Similarly, by using the stability statistical average and stability statistical deviation of the liquid level overflow risk operator, a set of operating thresholds for liquid level is constructed.

6. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 5, characterized in that: The division of operating states includes dividing the operating states of the biological oxidation process into a stable operating range, a slightly deviated operating range, a severely deviated operating range, and a protective operating range, based on the relationship between the biological oxidation coupling deviation operator and the biological oxidation coupling deviation operating threshold group. Based on the relationship between the liquid level overflow risk operator and the liquid level operating status threshold group, the liquid level operating status is divided into the normal operating range, the liquid level warning range, the liquid level severe warning range, and the liquid level protection range.

7. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 6, characterized in that: The process of generating intelligent optimization control instructions for the bio-oxidation process by combining the bio-oxidation process control model includes: based on the bio-oxidation process operating status (which is in the stable operating range, slightly deviated operating range, severely deviated operating range, or protective operating range), and the liquid level operating status (which is in the normal operating range, early warning operating range, severe early warning operating range, or protective operating range), calling ventilation control response factors, feeding control response factors, and emission control response factors from the bio-oxidation process control model, determining control actions such as increasing ventilation volume, decreasing feeding volume, increasing emission volume, and suspending feeding, and forming intelligent optimization control instructions for the bio-oxidation process.

8. The intelligent optimization method for biological oxidation processes based on a control model as described in claim 7, characterized in that: The step of sending the intelligent optimization control command for the bio-oxidation process to the actuators includes applying the intelligent optimization control command for the bio-oxidation process to the ventilation actuator, the feeding actuator, and the emission actuator, and adjusting the ventilation volume, feeding volume, and emission volume. After adjustment, the monitoring data of the biological oxidation process are collected again, new biological oxidation coupling deviation operators and liquid level overflow risk operators are generated, and the adjustment results are recorded to correct the biological oxidation process control model.

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