HPAM microbial degradation intelligent monitoring method for coal slime water treatment

Abstract

The invention relates to the technical field of coal wastewater treatment, in particular to an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment. The method comprises the steps that index data in coal slime water are collected and preprocessed in real time, the preprocessed index data are intelligently processed and analyzed through a hybrid dynamics model and an adaptive optimization algorithm, and an analysis result is obtained; according to an analysis result, automatically adjusting reaction conditions and operation parameters through a multi-dimensional time sequence feedback and nonlinear adaptive learning algorithm; meanwhile, detecting the degradation process to obtain detection data; and performing anomaly analysis based on the detection data to obtain an anomaly analysis result, and performing anomaly diagnosis and alarm according to the anomaly analysis result. The problems that in the coal slime water treatment process, the analysis accuracy of the microbiological degradation process is low, and the self-adaptability is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal wastewater treatment, and in particular to an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment. Background Art

[0002] Coal slime water treatment has always been a challenge in the coal industry. Slurry water is a coal- and water-containing waste generated during coal mining and coal preparation. It typically contains high levels of organic and inorganic matter. Due to its high polluting potential, improper treatment can have serious environmental impacts. Therefore, an efficient and green technical solution for treating coal slime water is urgently needed.

[0003] At present, the main methods for treating coal sludge water include chemical precipitation, physical adsorption, and microbial degradation. Chemical precipitation precipitates harmful substances in water by adding chemical agents. Although it can effectively remove pollutants, its operation process is relatively complicated and can cause secondary pollution. Physical adsorption uses adsorbent materials to adsorb harmful components in water, but this method requires a large amount of adsorbent materials and is less environmentally friendly. Microbial degradation, as a green and economical treatment method, has received widespread attention in recent years. Microbial degradation decomposes organic matter in coal sludge water into harmless substances by utilizing the metabolic function of microorganisms, making it an environmentally friendly method. However, microbial degradation faces some challenges in practical application, mainly including the control of microbial populations, the improvement of degradation efficiency, and the optimization of reaction conditions. Therefore, an intelligent monitoring method for HPAM microbial degradation for coal sludge water treatment is proposed. Summary of the Invention

[0004] The present invention provides an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment, so as to solve the problems of low accuracy and poor adaptability in analyzing the microbial degradation process during coal slime water treatment.

[0005] An intelligent monitoring method for HPAM microbial degradation for coal slime water treatment comprises the following steps: S1. Real-time collection and preprocessing of index data from coal slime water. Intelligent processing and analysis of the preprocessed index data using a hybrid dynamics model and adaptive optimization algorithm to obtain analysis results. The hybrid dynamics model and adaptive optimization algorithm combine a microbial degradation model, nonlinear dynamic characteristics, and an adaptive gain optimization algorithm. During implementation, a microbial degradation model is constructed based on the preprocessed index data. The microbial degradation model calculates the microbial degradation rate based on biochemical kinetics and nonlinear dynamic characteristics. S2. Based on the analysis results, the system automatically adjusts reaction conditions and operating parameters through multi-dimensional time-series feedback and a nonlinear adaptive learning algorithm. Simultaneously, the degradation process is monitored to obtain test data. Anomaly analysis is performed based on the test data to obtain anomaly analysis results, and anomaly diagnosis and alarms are performed based on the anomaly analysis results.

[0006] Preferably, the S1 specifically includes: In the implementation of the hybrid dynamics model and adaptive optimization algorithm, the change in the HPAM concentration after pretreatment was analyzed based on the degradation rate of microorganisms. The differential equation of HPAM concentration was introduced to calculate the rate of change of the HPAM concentration after pretreatment over time. The HPAM concentration at the current moment was then calculated based on the differential equation of HPAM concentration. The differential equation of HPAM concentration is as follows: , in, is the rate of change of HPAM concentration after pretreatment; is the degradation rate of microorganisms; It is at the moment Next, the concentration of HPAM after pretreatment; is the correction coefficient of dissolved oxygen concentration after pretreatment on the degradation rate of microorganisms; It is at the moment Dissolved oxygen concentration after pretreatment; is the maximum concentration of dissolved oxygen; It is a correction factor used to describe the effect of dissolved oxygen concentration on the degradation rate of microorganisms.

[0007] Preferably, the S1 specifically includes: In the process of implementing the hybrid dynamics model and the adaptive optimization algorithm, an adaptive gain optimization algorithm is introduced to optimize the degradation rate of microorganisms.

[0008] Preferably, the S1 specifically includes: In the process of implementing the adaptive gain optimization algorithm, the adaptive gain coefficient is introduced to correct the degradation rate of the microorganisms and obtain the corrected degradation rate of the microorganisms. Based on the corrected degradation rate of the microorganisms, the HPAM concentration at the current moment and the microbial activity in the pretreated coal slime water, the specific calculation formula is: , in, From the beginning to the present moment The cumulative degradation amount represents the analysis result; is the corrected microbial degradation rate; Indicates the HPAM concentration at the current moment; is the adaptive gain coefficient.

[0009] Preferably, the S2 specifically includes: The goal of the multidimensional temporal feedback and nonlinear adaptive learning algorithm is to optimize the reaction conditions and operating parameters during the HPAM degradation process.

[0010] Preferably, the S2 specifically includes: In the implementation process of the multi-dimensional time series feedback and nonlinear adaptive learning algorithm, error calculation is performed based on the analysis results, including the cumulative degradation amount error, dissolved oxygen concentration error, reaction temperature error, and pH value error; and the adjustment amount of the reaction conditions is calculated based on the errors to obtain the adjusted reaction conditions; the calculation formula for the adjustment amount of the reaction conditions is: , in, It is the current moment No. The amount of adjustment for each reaction condition; is the feedback coefficient; It is the current moment No. The error of the reaction conditions; is the feedback coefficient of the historical error term; It is The historical error integral of each reaction condition; is the integration variable; It's time No. The error of the reaction conditions; is the feedback coefficient of the error change rate; It is The error rate of change of each reaction condition; is the nonlinear function feedback coefficient; It is a nonlinear function, which indicates the combined effect of two or more reaction conditions on the The influence of reaction conditions; It's time The current state of each reaction condition.

[0011] Preferably, the S2 specifically includes: In the implementation process of the multi-dimensional time series feedback and nonlinear adaptive learning algorithm, the error is combined with the weight coefficient, and the adjustment amount of the operating parameter is calculated through the feedback mechanism to generate the adjusted operating parameter. The calculation formula of the adjustment amount of the operating parameter is as follows: , in, It is the current moment No. The adjustment amount of an operating parameter; It is Adjustment factors for operating parameters; is the weight of the error in the cumulative degradation amount; It is The weight of the error for each reaction condition; is the error in the cumulative degradation amount; is the adjusted current moment No. The value of the reaction condition.

[0012] The beneficial effects of the technical solution of the present invention are: 1. Utilizing a hybrid dynamics model and adaptive optimization algorithm, combined with the nonlinear dynamics and biochemical kinetics of the microbial degradation process, the degradation rate of microorganisms is intelligently adjusted, thereby improving degradation efficiency. By constructing a microbial degradation model, the degradation of pollutants in coal slime water can be more accurately predicted, thereby improving the overall treatment effect.

[0013] 2. By introducing an adaptive gain optimization algorithm, the degradation rate of microorganisms is adjusted according to indicator data, and the degradation rate is modified to respond to dynamically changing environmental conditions. By dynamically modifying the microbial degradation rate, the adaptability and flexibility of the treatment process are improved.

[0014] 3. Through multi-dimensional time-series feedback and nonlinear adaptive learning algorithms, reaction conditions and operating parameters are automatically adjusted to optimize degradation efficiency. Multi-dimensional time-series feedback and nonlinear adaptive learning algorithms use real-time error calculation and feedback mechanisms to enable precise adjustment of reaction conditions based on actual conditions, thereby achieving intelligent optimization of the degradation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the intelligent monitoring method for HPAM microbial degradation for coal slime water treatment described in the present invention. DETAILED DESCRIPTION

[0016] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The specific scheme of the intelligent monitoring method for HPAM microbial degradation for coal slime water treatment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] Refer to the attached Figure 1 , which shows a flow chart of an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment provided by one embodiment of the present invention, the method comprising the following steps: S1. Real-time collection and pre-processing of index data in coal slurry water, intelligent processing and analysis of the pre-processed index data using a hybrid dynamics model and adaptive optimization algorithm to obtain analysis results; During the coal slime water treatment process, online sensors such as dissolved oxygen sensors, pH sensors, temperature sensors, ultraviolet spectrometers, etc. are used to collect index data in real time, including HPAM concentration, dissolved oxygen, pH value, temperature, microbial activity, etc.; the index data are preprocessed such as data cleaning, data standardization, and time synchronization to obtain preprocessed index data. The above preprocessing adopts existing technical means and will not be elaborated here.

[0020] Furthermore, the pre-processed index data is intelligently processed and analyzed using a hybrid dynamics model and an adaptive optimization algorithm to obtain analysis results; the hybrid dynamics model and the adaptive optimization algorithm combine the microbial degradation model, nonlinear dynamic characteristics, and an adaptive gain optimization algorithm. The specific implementation process is as follows: First, a microbial degradation model was constructed; the microbial degradation model took into account the pre-treated index data in the coal slime water, including HPAM (high molecular weight compound) concentration, microbial activity, environmental factors (such as dissolved oxygen, temperature, pH value, etc.) and the dynamic relationship between them. The microbial degradation model describes the microbial degradation rate , we must consider both biochemical dynamics and nonlinear dynamic characteristics. The specific expression is: , in, is the degradation rate of microorganisms, which means at time Under the condition of , the rate of microbial degradation of HPAM; is the microbial degradation efficiency constant, which indicates the maximum efficiency or maximum rate of microbial degradation of HPAM; For the moment Microbial activity in pretreated coal slime water; It is at the moment Next, the concentration of HPAM after pretreatment; is the half-saturation constant of HPAM, which represents the half-saturation concentration of the effect of HPAM concentration on the degradation rate of microorganisms; is the Michalis-Menden kinetic coefficient, which indicates the affinity of microorganisms to HPAM and is used to describe the nonlinear effect of HPAM concentration on the degradation rate of microorganisms; It is the environmental fluctuation correction coefficient, which is used to represent the effect of fluctuations caused by environmental factors (such as temperature, dissolved oxygen concentration, etc.) on the degradation rate of microorganisms; is the environmental periodic fluctuation frequency, which indicates the fluctuation frequency of environmental factors; It is the phase shift of the environmental fluctuation, indicating the initial phase of the environmental fluctuation and reflecting the starting time point of the fluctuation; is the periodic environmental fluctuation term, which represents the effect of periodic fluctuations of environmental factors (such as temperature, oxygen concentration, etc.) after pretreatment on the degradation rate of microorganisms. The key to the above formula is to incorporate the dynamic changes of environmental factors (such as temperature, oxygen concentration, etc.) after pretreatment into the microbial degradation model by introducing the sine function. Since environmental fluctuations are periodic and directly affect the growth and degradation activities of microorganisms, this correction term enables the microbial degradation model to flexibly adapt to actual environmental conditions; at the same time, biochemical kinetics is (Michalis-Menden equation); nonlinear dynamic characteristics are reflected by exponential relationships and sinusoidal fluctuations, that is, and .

[0021] Furthermore, the changes in HPAM concentration after pretreatment were analyzed. The change in HPAM concentration over time is determined by the degradation rate of microorganisms and the effects of environmental conditions. The differential equation for HPAM concentration is introduced: , in, is the rate of change of HPAM concentration after pretreatment, which indicates the change in HPAM concentration per unit time; It is the correction coefficient of dissolved oxygen concentration after pretreatment on the degradation rate of microorganisms, which describes the influence of the change of dissolved oxygen concentration on the degradation rate of microorganisms; It is at the moment The dissolved oxygen concentration after pretreatment is the concentration of dissolved oxygen in water; It is the maximum concentration of dissolved oxygen, representing the maximum tolerable oxygen concentration of microorganisms; It is a correction factor used to describe the effect of dissolved oxygen concentration on the degradation rate of microorganisms.

[0022] The above equation shows that the change of HPAM concentration is not only directly affected by the degradation rate of microorganisms, but also has a nonlinear relationship with the dissolved oxygen concentration. The change of dissolved oxygen concentration directly affects the degradation rate of microorganisms. When the dissolved oxygen concentration is low, the degradation efficiency of microorganisms decreases. Therefore, a correction factor is introduced to consider the dynamic effect of dissolved oxygen concentration on the degradation process of microorganisms. Based on the differential equation of HPAM concentration, the existing Euler method is used to obtain the HPAM concentration at the current moment. .

[0023] Furthermore, to enhance the adaptability and intelligent regulation of the microbial degradation process, an adaptive gain optimization algorithm was introduced to optimize the changes in microbial degradation rate. The core purpose of the adaptive gain optimization algorithm is to optimize the microbial growth environment by adjusting microbial activity, HPAM concentration, and environmental factors in real time, thereby improving degradation efficiency.

[0024] Adaptive gain coefficient The degradation rate of microorganisms can be dynamically changed by adjusting the deviation between environmental factors and microorganisms. The specific formula is: , in, is the adaptive gain coefficient, which is used to adjust the degradation rate of microorganisms. It varies between 0 and 1 and represents the degree of correction of the degradation rate of microorganisms, reflecting whether the microbial growth environment is close to the optimal conditions. It is the optimal value of microbial activity and the degradation rate of microorganisms at a certain moment. Maximized ideal microbial activity, at which concentration microorganisms can participate in the degradation of HPAM with optimal efficiency, obtained based on expert experience; It is the optimal concentration of dissolved oxygen and the best oxygen concentration for microbial degradation. When the dissolved oxygen concentration is close to this optimal value, the microbial degradation efficiency reaches its maximum. is the gain coefficient adjustment factor, which is used to control the sensitivity of the relationship between microbial activity and dissolved oxygen concentration and the adaptive gain coefficient; It is the weight factor of the influence of HPAM concentration on the adaptive gain coefficient, which determines the degree of influence of HPAM concentration on the gain coefficient; It is the optimal HPAM concentration, which means the ideal HPAM concentration and the concentration at which microorganisms can degrade most effectively.

[0025] Through adaptive gain coefficient , further affecting the degradation rate of microorganisms The modified microbial degradation rate formula is as follows: , in, is the corrected microbial degradation rate; is the renewal step, which is used to control the renewal rate of microbial degradation rate; Indicates at time Under the action of the microorganisms, the degradation rate of the

[0026] After the above-mentioned microbial degradation model and adaptive gain optimization algorithm, the comprehensive effect of HPAM degradation in coal slime water was finally obtained. It can be calculated by the following integral formula: , in, It represents the cumulative degradation amount from the beginning to the current moment, that is, the analysis result; the integral operation accumulates the degradation process from the beginning to the current moment, taking into account the effects of the corrected microbial degradation rate, HPAM concentration and adaptive gain coefficient.

[0027] S2. Based on the analysis results, the reaction conditions and operating parameters are automatically adjusted through multi-dimensional time series feedback and nonlinear adaptive learning algorithms. At the same time, the degradation process is detected to obtain detection data. Based on the detection data, anomaly analysis is performed to obtain anomaly analysis results, and anomaly diagnosis and alarm are performed based on the anomaly analysis results.

[0028] Based on the analysis results, the reaction conditions and operating parameters are automatically adjusted through a multi-dimensional time series feedback and nonlinear adaptive learning algorithm to optimize the degradation efficiency. The goal of the multi-dimensional time series feedback and nonlinear adaptive learning algorithm is to optimize the reaction conditions and operating parameters during the HPAM degradation process to achieve higher degradation efficiency. Reaction conditions include dissolved oxygen, temperature, pH value, etc.; operating parameters include feed amount, reaction time, etc. The specific implementation process is as follows: First, based on the analysis results and combined with the target values ​​preset according to the expert experience method, the error calculation is performed, including the cumulative degradation amount error, dissolved oxygen concentration error, reaction temperature error, and pH value error; the calculation formula is as follows: , in, It is the current moment No. The error of the reaction conditions; It is the current moment No. The ideal value of each reaction condition; It is the current moment No. The actual value of each reaction condition; the cumulative degradation error is also calculated using the above error formula; Furthermore, the adjustment amount of the reaction conditions is calculated based on the error, and the adjustment amount calculation formula is: , in, It is the current moment No. The amount of adjustment for each reaction condition; is the feedback coefficient, which indicates the degree of response to the current error; is the feedback coefficient of the historical error term, which indicates the degree of response to the historical error; It is The historical error integral of each reaction condition; is the integration variable; It's time No. The error of the reaction conditions; is the feedback coefficient of the error change rate, which indicates the degree of response to the error change rate; It is The error rate of change of each reaction condition; is the nonlinear function feedback coefficient, which indicates the degree of response to the influence of the overall state, that is, the degree of response to the influence of the combination of multiple reaction conditions; is a nonlinear function, which indicates the combined effect of multiple reaction conditions on the The effect of multiple reaction conditions at the same time The joint state of The adjustment amount of each reaction condition is obtained using the existing support vector regression machine learning model; It's time The current state of each reaction condition.

[0029] The adjusted reaction conditions are further obtained: , in, is the adjusted current moment No. The value of the reaction condition.

[0030] Furthermore, the error is combined with the weight coefficient and a feedback mechanism is used to calculate the adjustment amount of the operating parameters. The calculation formula of the adjustment amount is as follows: , in, It is the current moment No. The adjustment amount of an operating parameter; It is An adjustment factor for an operating parameter, used to control the amplitude of the adjustment; is the weight of the error in the cumulative degradation amount; It is The weight of the error for each reaction condition; is the error in the cumulative degradation amount.

[0031] Further get at the current moment Adjusted operating parameters : , in, Indicates the current time No. operation parameters.

[0032] The degradation process is detected to obtain detection data. Based on the detection data, an abnormal analysis is performed using the existing deviation analysis method to obtain an abnormal analysis result. When abnormal data is shown in the abnormal analysis result, an abnormal model pre-defined according to the expert experience method is used to diagnose the possible cause based on the detected abnormal data. The abnormal data and the diagnosis cause are sent to the staff via SMS or email for alarm to achieve intelligent monitoring.

[0033] In summary, an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment was completed.

[0034] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0035] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An intelligent monitoring method for HPAM microbial degradation for coal slime water treatment, characterized in that: The following steps are involved: S1. Real-time collection and pre-processing of index data in coal slime water, intelligent processing and analysis of the pre-processed index data using a hybrid dynamics model and an adaptive optimization algorithm to obtain analysis results; the hybrid dynamics model and the adaptive optimization algorithm combine the microbial degradation model, nonlinear dynamic characteristics and an adaptive gain optimization algorithm; In the process of implementing the hybrid dynamics model and adaptive optimization algorithm, a microbial degradation model is constructed based on the preprocessed index data; The microbial degradation model calculates the degradation rate of microorganisms based on biochemical kinetics and nonlinear dynamic characteristics; S2. According to the analysis results, the reaction conditions and operating parameters are automatically adjusted through multi-dimensional time series feedback and nonlinear adaptive learning algorithms; at the same time, the degradation process is detected to obtain detection data; based on the detection data, anomaly analysis is performed to obtain abnormal analysis results, and abnormal diagnosis and alarm are performed based on the abnormal analysis results.

2. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 1, characterized in that: The S1 specifically includes: In the process of implementing the hybrid dynamics model and the adaptive optimization algorithm, the change of the HPAM concentration after pretreatment is analyzed based on the degradation rate of microorganisms; the differential equation of HPAM concentration is introduced to calculate the change rate of the HPAM concentration after pretreatment over time, and then the HPAM concentration at the current moment is calculated based on the differential equation of HPAM concentration; the differential equation of HPAM concentration is as follows: ， in, is the rate of change of HPAM concentration after pretreatment; is the degradation rate of microorganisms; It is at the moment Next, the concentration of HPAM after pretreatment; It is the correction factor of dissolved oxygen concentration after pretreatment on the degradation rate of microorganisms; It is at the moment Dissolved oxygen concentration after pretreatment; is the maximum concentration of dissolved oxygen; It is a correction factor used to describe the effect of dissolved oxygen concentration on the degradation rate of microorganisms.

3. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 1, characterized in that: The S1 specifically includes: In the process of realizing the hybrid dynamics model and the adaptive optimization algorithm, an adaptive gain optimization algorithm is introduced to optimize the degradation rate of microorganisms.

4. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 3, characterized in that: The S1 specifically includes: In the process of implementing the adaptive gain optimization algorithm, an adaptive gain coefficient is introduced to correct the degradation rate of the microorganisms to obtain the corrected degradation rate of the microorganisms; based on the corrected degradation rate of the microorganisms, the HPAM concentration at the current moment and the microbial activity calculation and analysis results in the pretreated coal slime water, the specific calculation formula is: ， in, From the beginning to the present moment The cumulative degradation amount represents the analysis result; is the corrected microbial degradation rate; Indicates the HPAM concentration at the current moment; is the adaptive gain coefficient.

5. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 1, characterized in that: The S2 specifically includes: The goal of the multidimensional temporal feedback and nonlinear adaptive learning algorithm is to optimize the reaction conditions and operating parameters in the HPAM degradation process.

6. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 1, characterized in that: The S2 specifically includes: In the process of implementing the multi-dimensional time series feedback and nonlinear adaptive learning algorithm, based on the analysis results, error calculation is performed, including cumulative degradation error, dissolved oxygen concentration error, reaction temperature error, and pH value error; and the adjustment amount of the reaction conditions is calculated according to the error to obtain the adjusted reaction conditions; the calculation formula for the adjustment amount of the reaction conditions is: ， in, It is the current moment No. The amount of adjustment of each reaction condition; is the feedback coefficient; It is the current moment No. The error of the reaction conditions; is the feedback coefficient of the historical error term; It is The historical error integral of each reaction condition; is the integration variable; It's time No. The error of the reaction conditions; is the feedback coefficient of the error change rate; It is The error rate of change of each reaction condition; is the nonlinear function feedback coefficient; is a nonlinear function, indicating the combined effect of two or more reaction conditions on the The influence of reaction conditions; It's time The current state of each reaction condition.

7. The intelligent monitoring method for HPAM microbial degradation for coal slime water treatment according to claim 6, characterized in that: The S2 specifically includes: In the process of implementing the multi-dimensional time series feedback and nonlinear adaptive learning algorithm, the error is combined with the weight coefficient, and the adjustment amount of the operating parameter is calculated through the feedback mechanism to generate the adjusted operating parameter; the calculation formula of the adjustment amount of the operating parameter is as follows: ， in, It is the current moment No. The adjustment amount of an operating parameter; It is Adjustment factors for operating parameters; is the weight of the error in the cumulative degradation amount; It is The weight of the error for each reaction condition; is the error in the cumulative degradation amount; is the adjusted current moment No. The value of a reaction condition.

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