Petrochemical sewage up-to-standard discharge advanced treatment method and system

Abstract

The invention relates to the technical field of petrochemical sewage treatment, and discloses a petrochemical sewage up-to-standard discharge advanced treatment method and system, and the method comprises the steps: obtaining weather forecast data, and generating an environmental influence prediction vector; generating a microbial community metabolic capability vector through 16SrRNA high-throughput sequencing; carrying out overlay analysis on the two, and outputting a community structure change risk assessment result; predicting an unconventional metabolic pathway by using the constraint optimization model, and generating a metabolic toxicity risk early warning list in combination with the molecular structure-toxicity relation model; generating a preventive deep processing regulation and control scheme; executing real-time metabolism monitoring and response control; and after the environmental disturbance is finished, performing community recovery evaluation and stabilization regulation and control. According to the method, prospective assessment can be carried out on microbial metabolism level risks under extreme weather environment disturbance, and preventive regulation and control can be implemented.

Description

Technical Field

[0001] This invention relates to the field of petrochemical wastewater treatment technology, and more specifically, to a method and system for deep treatment of petrochemical wastewater to achieve compliant discharge. Background Technology

[0002] Petrochemical wastewater contains characteristic pollutants such as phenols and benzene compounds. Biological treatment units rely on the metabolic activity of microbial communities to biodegrade these pollutants, which is a crucial step in ensuring effluent meets discharge standards. Existing advanced treatment methods maintain stable operation of biological treatment units by monitoring conventional water quality indicators, adjusting operating parameters such as dissolved oxygen and sludge age, and verifying degradation pathways based on chemical reaction knowledge graphs.

[0003] However, existing advanced treatment methods rely on the assumption of a steady-state community for pathway verification and parameter optimization, failing to consider the dynamic impact of environmental disturbances caused by extreme weather events such as severe cold, torrential rain, and high temperatures on the structure and metabolic functions of microbial communities. Dramatic changes in environmental parameters can suppress the activity of some functional microorganisms and lead to abnormal succession of community structure, thereby causing metabolic pathway disorders and the generation of unconventional toxic intermediates. These unconventional toxic intermediates are not covered by conventional water quality indicators, potentially resulting in excessive toxicity in the effluent from advanced treatment, affecting compliance with discharge standards. Fixed chemical reaction knowledge graphs cannot reflect the metabolic specificity under dynamic community changes, and existing methods lack the ability to prospectively assess and preventatively regulate risks at the microbial metabolic level. Summary of the Invention

[0004] This invention provides a method and system for advanced treatment of petrochemical wastewater to achieve compliant discharge, which solves the technical problems in related technologies such as the inability to predict the metabolic risks of microbial communities in advance and the lack of early warning and preventive control measures for toxic intermediates of unconventional metabolic pathways during the advanced treatment of petrochemical wastewater.

[0005] This invention discloses a method for advanced treatment of petrochemical wastewater to achieve standard discharge, comprising: acquiring meteorological forecast data within a future preset time window, standardizing the meteorological parameters in the meteorological forecast data, inputting the standardized meteorological parameters into a meteorological-biochemical impact prediction model, and generating an environmental impact prediction vector for the advanced treatment biochemical treatment unit; High-throughput sequencing was performed on activated sludge samples from the advanced biochemical treatment unit to generate a relative abundance matrix of microbial community composition. The relative abundance matrix was then mapped to a microbial metabolic function database to generate a microbial community metabolic capacity vector. By overlaying the microbial community metabolic capacity vector with the environmental impact prediction vector, and based on the environmental tolerance parameters of each functional microbial community, we can identify microbial communities that may be suppressed or over-proliferated under predicted environmental conditions, and output the risk assessment results of community structure change. Based on the risk assessment results of community structure change, unconventional metabolic pathways are predicted using a constrained optimization model. Molecular structure-toxicity relationship model is used to predict intermediates in unconventional metabolic pathways, identify highly toxic intermediates whose predicted toxicity values ​​exceed the toxicity threshold, and generate a list of metabolic toxicity risk warnings. For each risk item in the metabolic toxicity risk warning list, a matching control strategy is generated to produce a preventive in-depth treatment control plan; Metabolic indicator parameters are collected in real time and compared with the warning indicators in the metabolic toxicity risk warning list. When an abnormality is detected in the warning indicator, the downgrade operation mode is activated and a targeted regulatory response is triggered. After the environmental disturbance ends, the community composition data during the recovery period is compared with the community baseline to perform a difference analysis and calculate the community deviation. If the community deviation exceeds a preset threshold, a metabolic pathway stabilization regulation scheme is generated.

[0006] Furthermore, the meteorological-biochemical impact prediction model is a nonlinear regression model trained based on historical operational data. The input of the meteorological-biochemical impact prediction model is a standardized meteorological parameter sequence, and the output is the time-series predicted values ​​of environmental parameters of the biochemical treatment unit. The environmental impact prediction vector includes time-series predicted data of water temperature change curves, dissolved oxygen change trends, and influent dilution ratios.

[0007] Furthermore, the microbial community metabolic capacity vector is calculated by multiplying the metabolic function matrix and the relative abundance matrix; the elements in the metabolic function matrix represent the contribution coefficients of each species to various metabolic functions, and the contribution coefficients are determined based on the functional gene annotation information in the microbial metabolic function database. If a species carries a functional gene corresponding to a metabolic function, a quantitative value is assigned based on the expression intensity data of the functional gene of the corresponding species; otherwise, the value is zero.

[0008] Furthermore, the overlay analysis includes: for each metabolic function component in the microbial community metabolic capacity vector, calculating the activity decay coefficient of the key contributing microbial community corresponding to the metabolic function under predicted environmental conditions; the activity decay coefficient is calculated based on the deviation between the predicted value of each environmental parameter and the optimal value of the corresponding functional microbial community, as well as the tolerance width parameter of the functional microbial community to each environmental parameter; when the activity decay coefficient is lower than a preset threshold, it is determined that the corresponding metabolic function is at risk of damage.

[0009] Furthermore, the constrained optimization model employs a mixed-integer linear programming method, defining the activation state of metabolic pathways as binary variables. The constraints include: the precursors of metabolic pathways are present in the influent, the enzyme functions required by metabolic pathways are present in functionally enhanced bacterial communities, and the inhibitory factors of metabolic pathways are reduced in functionally impaired bacterial communities. The objective function is to maximize the total activation probability of unconventional metabolic pathways. The set of unconventional metabolic pathways that satisfy the constraints and have the highest activation probability is obtained by solving the branch-and-bound method.

[0010] Furthermore, the molecular structure-toxicity relationship model is a quantitative structure-activity relationship model based on molecular descriptors. The input is the molecular structure characteristics of the intermediate product, and the output is the predicted toxicity equivalent value. The molecular structure characteristics include molecular weight, octanol-water partition coefficient, polar surface area, and functional group type. The toxicity threshold is set as a preset proportion of the acute toxicity equivalent limit specified in the effluent discharge standard.

[0011] Furthermore, the proposed preventative deep treatment control scheme includes: using a genetic algorithm to collaboratively optimize control parameters, including the carbon-to-nitrogen ratio, trace element dosage, and sludge return ratio; a fitness function that comprehensively considers the normalized value of the predicted generation of highly toxic intermediate products, the predicted activity retention rate of the target functional microbial community, and the normalized value of control costs; and optimization constraints that include the adjustable range of each control parameter and the compliance requirements of conventional effluent water quality indicators.

[0012] Furthermore, the downgraded operation mode includes: reducing the influent load to alleviate the metabolic pressure on the community, extending the hydraulic retention time to compensate for the decrease in degradation efficiency, and activating the auxiliary physicochemical treatment unit to share part of the pollutant removal task; the metabolic indicator parameters include redox potential, characteristic band ultraviolet absorbance, and fluorescence characteristics of dissolved organic matter.

[0013] Furthermore, the community deviation is calculated by measuring the similarity between the relative abundance of the community during the recovery period and the relative abundance of the community baseline; the metabolic pathway stabilization regulation scheme includes progressive parameter adjustment, gradually restoring the operating parameters from the degraded operating mode to the normal operating state according to a preset adjustment step size; the metabolic pathway stabilization regulation scheme also includes a selective enhancement strategy, adjusting growth-promoting factors for functional microbial communities with delayed recovery.

[0014] This invention provides a deep treatment system for petrochemical wastewater to meet discharge standards, comprising: The environmental impact prediction module is used to acquire meteorological forecast data, standardize the meteorological parameters, and input them into the meteorological-biochemical impact prediction model to generate environmental impact prediction vectors. The community metabolic capacity analysis module is used to perform high-throughput sequencing on activated sludge samples, generate a relative abundance matrix, and map it with a microbial metabolic function database to generate a microbial community metabolic capacity vector. The community risk assessment module is used to overlay and analyze the microbial community metabolic capacity vector with the environmental impact prediction vector to output the community structure change risk assessment results. The metabolic toxicity early warning module is used to predict unconventional metabolic pathways using a constrained optimization model, perform molecular structure-toxicity relationship model prediction, and generate a list of metabolic toxicity risk warnings. The preventive regulation module is used to match regulatory strategies to the list of metabolic toxicity risk warnings and generate preventive in-depth treatment regulation plans. The real-time monitoring and response module is used to collect metabolic indicator parameters in real time and compare them with early warning indicators. When an abnormality is detected, a degraded operation mode is activated. The community recovery assessment module is used to calculate the community deviation and generate a metabolic pathway stabilization regulation scheme when the deviation exceeds a preset threshold.

[0015] This invention couples meteorological impact prediction with microbial community metabolic capacity vector analysis to predict the possible direction of community structure changes before environmental disturbances actually occur. This solves the technical problem that existing methods, which are based on the assumption of a steady-state community, cannot cope with the dynamic impact of drastic changes in environmental parameters on the community. It achieves the technical effect of prospectively assessing the risk of community structure changes and metabolic disorders under environmental disturbances.

[0016] This invention uses a constrained optimization model to predict unconventional metabolic pathways and combines a molecular structure-toxicity relationship model to conduct toxicity assessment. This solves the technical problem that conventional water quality indicators cannot cover unconventional toxic intermediates, and achieves the technical effect of extending the scope of risk monitoring to unconventional toxic intermediates and identifying risk factors that may lead to excessive toxicity in effluent.

[0017] This invention forms a control system covering the entire cycle before, during, and after environmental disturbances through preventive deep treatment control schemes, downgraded operation modes, and gradual parameter adjustments. It solves the technical problem of existing methods lacking preventive control capabilities and achieves the technical effect of maintaining the ability to continuously meet emission standards for deep treatment of petrochemical wastewater under extreme environmental conditions. Attached Figure Description

[0018] Figure 1 This is a flowchart of the deep treatment method for petrochemical wastewater provided in an embodiment of the present invention; Figure 2 This is a graph showing the results of standardized meteorological parameter processing provided in an embodiment of the present invention; Figure 3 This is a prediction diagram of environmental parameters for a biochemical treatment unit provided in an embodiment of the present invention; Figure 4 This is a microbial community metabolic capacity assessment diagram provided in an embodiment of the present invention; Figure 5 This is a key functional microbial community activity attenuation assessment diagram provided in the embodiments of the present invention; Figure 6 This is a graph showing the changes in real-time metabolic monitoring parameters provided in an embodiment of the present invention; Figure 7 This is a parameter optimization diagram of the preventive control scheme provided in the embodiments of the present invention. Detailed Implementation

[0019] At least one embodiment of the present invention discloses a method for advanced treatment of petrochemical wastewater to achieve discharge standards, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain the environmental impact prediction vector; Meteorological forecast data for a predetermined future time window is acquired, and the meteorological parameters in the forecast data are preprocessed. Data preprocessing includes Z-score standardization of meteorological parameters such as temperature, precipitation, and relative humidity to eliminate dimensional and numerical range differences between different meteorological parameters. The preprocessed meteorological parameters are then input into a meteorological-biochemical impact prediction model to calculate the expected environmental parameter changes for the deep-processing biochemical treatment unit, generating an environmental impact prediction vector. The expected environmental parameter changes include water temperature change curves, dissolved oxygen change trends, and influent dilution ratios.

[0020] It should be noted that the aforementioned preset time window is 72 hours. This preset time window covers the duration of typical extreme weather events, providing sufficient preparation time for preventive regulation.

[0021] It should be noted that the aforementioned meteorological-biochemical impact prediction model is a nonlinear regression model trained based on historical operational data. The input to the meteorological-biochemical impact prediction model is a standardized sequence of meteorological parameters, including time-series data of temperature, precipitation, and relative humidity; the output is the time-series predicted values ​​of environmental parameters for the biochemical treatment unit, including predicted sequences of water temperature, dissolved oxygen, and influent dilution factor. The meteorological-biochemical impact prediction model learns the mapping relationship between meteorological conditions and the actual environmental parameters of the biochemical treatment unit, converting external meteorological changes into predictions of the environmental response within the biochemical treatment unit. The nonlinear regression model employs a multilayer perceptron neural network architecture, containing an input layer, two hidden layers, and an output layer. The number of nodes in the input layer equals the number of meteorological parameter features. The first hidden layer contains 64 neurons, and the second hidden layer contains 32 neurons. The number of nodes in the output layer equals the number of environmental parameters to be predicted. The output layer uses a linear activation function to achieve continuous value prediction, while the hidden layers use the ReLU activation function. The training process uses mean squared error as the loss function, and the parameters are updated using the Adam optimizer.

[0022] Furthermore, the aforementioned environmental impact prediction vector includes time dimension information, specifically represented as follows: ,in This represents a time within a preset time window, with a time resolution of 1 hour. For a preset time window of 72 hours, the environmental impact prediction vector contains predicted environmental parameter values ​​for 72 time points. The predicted values ​​for each time point include three parameters: water temperature, dissolved oxygen, and influent dilution factor, thus forming a vector with a dimension of [missing information]. The time series prediction matrix.

[0023] Step 2: Generate a microbial community metabolic capacity vector; Activated sludge samples from the deep-processing biochemical treatment unit were periodically collected, and 16S rRNA high-throughput sequencing was performed on the activated sludge samples to obtain sequencing data. Sequence alignment and species annotation were performed on the sequencing data to generate a relative abundance matrix of the microbial community composition. The relative abundance matrix was mapped to a microbial metabolic function database, and based on the annotation of known metabolic function genes for each species, the potential metabolic function profile of the current community was inferred, generating a microbial community metabolic capacity vector.

[0024] It should be noted that the above relative abundance matrix The dimension is ,in The number of species detected, elements in the relative abundance matrix Indicates the first The relative abundance value of each species in the community. Number the species.

[0025] It should be noted that the aforementioned microbial metabolic function database is a pre-constructed species-metabolic function mapping library, containing metabolic function gene annotation information for each species, as well as the corresponding metabolic substrate types and metabolite types. The aforementioned microbial community metabolic capacity vector... The generation method is as follows:

[0026] in, This is a metabolic function matrix with dimensions of 1. , The number of metabolic function types. The number of species detected, elements of the metabolic function matrix Indicates the first The species to the first Contribution coefficient of metabolic function Number the species. This is a number representing the type of metabolic function. This is a relative abundance matrix. The dimension is The first of the microbial community metabolic capacity vectors The component represents the current community's relationship to the first... The overall capacity value of metabolic functions.

[0027] Furthermore, the above metabolic function matrix Contribution coefficient The value of is derived from the functional gene annotation information in the microbial metabolic function database. Specifically, if the microbial metabolic function database records the first... The species carries the first For functional genes corresponding to metabolic functions, the expression intensity data of these functional genes in known reference strains of this species are used to assign... The corresponding quantization value; if the first The species does not carry the first Functional genes, then The value is 0.

[0028] It should be noted that the periodic collection period can be adjusted according to the operational stability of the biochemical treatment unit. A longer period can be used during stable operation, while the collection period can be shortened before environmental disturbances are predicted to obtain more timely community status information.

[0029] Step 3: Conduct a risk assessment of changes in community structure; By overlaying the microbial community metabolic capacity vector with the environmental impact prediction vector, and based on the environmental tolerance parameters of each functional microbial community, we can identify microbial communities that may be suppressed or over-proliferated under predicted environmental conditions, and output the risk assessment results of community structure change.

[0030] It should be noted that the environmental tolerance parameters mentioned above are parameters from a pre-established functional microbial community environmental response feature library, including the temperature tolerance range, dissolved oxygen suitability range, and osmotic pressure sensitivity of each functional microbial community. When the predicted value of a certain environmental parameter in the environmental impact prediction vector exceeds the tolerance range of the corresponding functional microbial community, it is determined that the functional microbial community is at risk of activity inhibition.

[0031] It should be noted that the calculation method for the above overlay analysis is as follows: for each metabolic function component in the microbial community metabolic capacity vector, the activity decay coefficient of the main contributing microbial community corresponding to that metabolic function under the predicted environmental conditions is calculated. :

[0032] in, Indicates the time within the preset time window. The number indicating the type of metabolic function. Indicates the number of the environmental parameter. This represents the total number of environmental parameters. For the first An environmental parameter at time The predicted value, For the first Functional microbial communities on the first The optimal value of each environmental parameter For the first Functional microbial communities on the first The tolerance width parameter of each environmental parameter. This represents an exponential function. and All of them are derived from the functional microbial community environmental response feature library, among which The value was determined by the statistical mean of environmental parameters corresponding to the historical optimal growth conditions of this functional microbial community. The activity retention rate of this functional bacterial community decreased under different environmental parameter conditions. The corresponding parameter deviation range is determined.

[0033] Furthermore, the above-mentioned activity decay coefficient The preset threshold is 0.6, which is determined based on the correlation analysis between functional microbial community activity and treatment effect in historical operating data. At any time within the preset time window... satisfy At that time, the judgment of the first There is a risk of impaired metabolic function.

[0034] In this embodiment of the application, to improve the accuracy of risk assessment, the competition and cooperation relationships among bacterial communities are also considered during the overlay analysis. When a functional bacterial community is suppressed, the possibility of its competing bacterial communities over-proliferating and the possibility of its cooperating bacterial communities being simultaneously damaged are assessed, thereby generating a prediction of the overall evolution trend of the community structure.

[0035] Step 4: Generate a list of metabolic toxicity risk warnings; Based on the risk assessment results of community structure changes, a constrained optimization model is used to predict potentially activated unconventional metabolic pathways. Molecular structure-toxicity relationship models are performed on intermediates in these unconventional metabolic pathways to predict their predicted toxicity values. Highly toxic intermediates with predicted toxicity values ​​exceeding a toxicity threshold are identified, and their associated metabolic pathways are traced to generate a metabolic toxicity risk warning list.

[0036] It should be noted that the input to the above constrained optimization model consists of lists of functionally impaired and functionally enhanced bacterial communities from the community structure change risk assessment results, and the output is a set of unconventional metabolic pathways that may be activated under the current community state. The constrained optimization model employs a mixed-integer linear programming method, defining the activation state of metabolic pathways as binary variables. ,in Metabolic pathways are numbered; a binary variable value of 1 indicates a activated metabolic pathway, and a binary variable value of 0 indicates an inactive metabolic pathway. The constraints of the optimization model include: the presence of precursors for the metabolic pathway in the influent; the presence of enzymes required for the metabolic pathway in functionally enhanced bacterial communities; and a reduction in inhibitors of the metabolic pathway in functionally impaired bacterial communities. The objective function is to maximize the total activation probability of unconventional metabolic pathways.

[0037] in, This represents the total number of candidate unconventional metabolic pathways. Numbering metabolic pathways For the first The activation probability of a metabolic pathway. For the first A binary variable representing the activation state of each metabolic pathway. The activation probability of each metabolic pathway. The pathways are calculated by weighting the concentration of their precursors, the relative abundance of the required enzymes, and the degree of reduction in inhibitory factors. Solving this mixed-integer linear programming problem yields the set of unconventional metabolic pathways that satisfy the constraints and have the highest activation probability.

[0038] Furthermore, the above mixed-integer linear programming problem is solved using the branch and bound method. Specifically, firstly, the binary variables are linearly relaxed to find the optimal solution of the continuous relaxation problem; if there are non-integer variables in the relaxed solution, branches are created for these non-integer variables, and constraints on non-integer variables taking the values ​​0 and 1 are added respectively before recursively solving the subproblems; branches that cannot be better than the current optimal integer solution are pruned using an upper bound pruning strategy until all branches obtain integer solutions or are pruned, and the set of unconventional metabolic paths with the highest total activation probability is output as the final result.

[0039] It should be noted that the above molecular structure-toxicity relationship model is a quantitative structure-activity relationship (QSAR) model based on molecular descriptors. The input to the molecular structure-toxicity relationship model is the molecular structural features of the intermediate product, including molecular weight, octanol-water partition coefficient, polar surface area, and functional group type; the output is the predicted toxicity equivalent value. Before inputting the molecular structure-toxicity relationship model, the molecular structural features undergo data preprocessing: continuous features such as molecular weight, octanol-water partition coefficient, and polar surface area are normalized to mean based on range; categorical features such as functional group type are converted to numerical features using one-hot encoding. The QSAR model uses a random forest regression algorithm, integrating the prediction results of multiple decision trees to estimate the toxicity equivalent value. During training, compounds with known toxicity data are used as training samples, the preprocessed molecular descriptors are used as feature inputs, and the measured toxicity equivalent values ​​are used as label outputs.

[0040] Furthermore, the aforementioned toxicity thresholds are determined based on the comprehensive toxicity limits in the effluent discharge standards. Specifically, the toxicity threshold is set at 50% of the acute toxicity equivalent limit specified in the effluent discharge standards to provide a sufficient safety margin. When the predicted toxicity value exceeds the toxicity threshold, the corresponding intermediate product is marked as a highly toxic intermediate product.

[0041] It should be noted that the above list of metabolic toxicity risk warnings includes the following information: chemical structure identification of highly toxic intermediates, predicted toxicity level, identification of associated metabolic pathways, environmental condition thresholds that trigger the associated metabolic pathways, and metabolic indicator parameters for real-time monitoring.

[0042] In this embodiment of the application, to improve the coverage of unconventional metabolic pathway prediction, metabolic network topology analysis is introduced into the constrained optimization model. Specifically, based on the known topology of the metabolic network, bypass metabolic nodes that are dormant under normal conditions but may be activated under specific environmental perturbations are identified, and these bypass metabolic nodes are included in the prediction range of unconventional metabolic pathways.

[0043] Step 5: Generate a preventative deep treatment control plan; For each risk item in the metabolic toxicity risk warning list, corresponding control strategies are matched to generate a preventive deep treatment control plan. The control strategies include: adjusting the carbon-nitrogen ratio to maintain the competitive advantage of the target functional bacteria, pre-adding trace elements to supplement the nutritional needs of sensitive bacteria, and adjusting the sludge return ratio to slow down the rate of community disturbance.

[0044] It should be noted that the adjustment range of the carbon-nitrogen ratio mentioned above is determined based on the nutritional requirements of the suppressed functional bacterial community. When there is a risk of suppression of the functional bacterial community that degrades a specific pollutant, the amount of carbon source added to the influent is adjusted to maintain the carbon-nitrogen ratio within the optimal growth range of that functional bacterial community.

[0045] It should be noted that the above-mentioned micronutrient pre-addition strategy is determined based on the cofactor requirements of sensitive bacterial communities. For metabolic enzyme systems that rely on specific metal ions as cofactors, the corresponding micronutrients are pre-added before environmental disturbances to maintain the stability of the metabolic enzyme activity.

[0046] It should be noted that the purpose of adjusting the sludge return ratio is to control the community turnover rate. By increasing the sludge return ratio, the residence time of microorganisms in the system is increased, the impact of environmental disturbances on the community structure is slowed down, and a buffer time is provided for the adaptive adjustment of functional microbial communities.

[0047] In this embodiment, to achieve quantitative optimization of the preventive deep treatment control scheme, a genetic algorithm is used to collaboratively optimize each control parameter. The input of the genetic algorithm is the adjustable range and constraints of the control parameters, and the output is the optimized combination of control parameters. Real number encoding is used, directly encoding control parameters such as carbon-nitrogen ratio, trace element dosage, and sludge return ratio into chromosomes; the population size is set to 100 individuals, and the number of generations is set to 200; the selection operation uses roulette wheel selection, determining the probability of an individual being selected based on its fitness value; the crossover operation uses arithmetic crossover with a crossover probability of 0.8, linearly combining the corresponding control parameters of two parent individuals to generate offspring; the mutation operation uses Gaussian mutation with a mutation probability of 0.1, adding a normally distributed random perturbation to the individual control parameters. Before calculating the fitness function, the predicted generation amount of highly toxic intermediate products and the control cost are normalized using mean normalization based on the range to eliminate dimensional differences between different indicators. The fitness function is calculated as follows:

[0048] in, For the fitness function value, This represents the normalized value of the predicted amount of highly toxic intermediate products. To predict the retention rate of the target functional microbial community. To adjust the normalized value of costs, , , These are the weighting coefficients. The calculation method is as follows: the activity attenuation coefficient of each target functional bacterial community in step 3 is... After reassessment under the current combination of control parameters, a weighted average is taken, with the weights allocated according to the contribution ratio of each target functional microbial community to the degradation of key pollutants.

[0049] Furthermore, the above The calculation method is as follows: Under the current combination of regulatory parameters, the constrained optimization model in step 4 is re-executed to predict the activation of unconventional metabolic pathways. The theoretical amounts of highly toxic intermediates in each predicted activated unconventional metabolic pathway are accumulated, and then the accumulated values ​​are normalized to the mean based on the range to obtain the result. The above. The calculation involves a time dimension, specifically: calculating each moment within a preset time window. Activity decay coefficient The average value over time is then used to calculate a weighted average. The formula is as follows:

[0050] in, The number of metabolic function types. This is a number representing the type of metabolic function. The total number of moments within the preset time window. For the time within the preset time window, For the first Functional microbial communities at all times The activity decay coefficient, For the first Contribution weights of functional microbial communities.

[0051] Furthermore, the values ​​of the aforementioned weighting coefficients are... , , The allocation of these weighting coefficients reflects the regulatory principles of prioritizing toxicity risk control, ensuring functional stability, and taking into account economic efficiency. Optimization constraints include: the adjustable range of each regulation parameter and the compliance requirements for conventional effluent water quality indicators.

[0052] In this embodiment of the application, the preventive deep treatment control scheme also includes a control timing arrangement, that is, based on the timing of changes in environmental parameters in the environmental impact prediction vector, the start time nodes of various control measures are determined so that the control response matches the arrival time of environmental disturbances.

[0053] Step 6: Perform real-time metabolic monitoring and response control; The metabolic indicator parameters of the deep-processing biochemical treatment unit are collected in real time and compared with the warning indicators in the metabolic toxicity risk warning list. When an abnormality is detected in the warning indicator, a downgraded operation mode is activated and a targeted control response is triggered to prioritize ensuring that the key pollutant indicators meet the standards.

[0054] It should be noted that the above-mentioned metabolic indicator parameters are online monitoring parameters that can indirectly reflect the active state of metabolic pathways, including redox potential, characteristic ultraviolet absorbance, and fluorescence characteristics of dissolved organic matter.

[0055] Furthermore, the criteria for determining abnormalities in the aforementioned warning indicators are as follows: the real-time measured value of the metabolic indicator parameter is compared with the trigger threshold of the corresponding risk item in the metabolic toxicity risk warning list. When the real-time measured value deviates from the normal range by more than the trigger threshold, the warning indicator is determined to be abnormal. The trigger threshold is determined based on the statistical correlation between the metabolic indicator parameter and the actual occurrence of highly toxic intermediate products in historical data.

[0056] It should be noted that the above-mentioned downgraded operation mode is an emergency operation strategy to maintain basic treatment capacity during the period when community function is impaired. It includes: reducing the influent load to alleviate the metabolic pressure on the community, extending the hydraulic retention time to compensate for the decline in degradation efficiency, and activating auxiliary physicochemical treatment units to share some of the pollutant removal tasks.

[0057] In this embodiment, to improve the timeliness of response control, a real-time mapping relationship between metabolic indicator parameters and early warning indicators is established. When the changing trend of the metabolic indicator parameter matches the triggering characteristics of a certain risk item in the metabolic toxicity risk early warning list, the corresponding regulatory response is initiated in advance before the risk item actually occurs.

[0058] Step 7: Perform community recovery assessment and stabilization regulation; After the environmental disturbance ends, activated sludge samples are collected for rapid community detection to obtain community composition data during the recovery period. The community composition data during the recovery period is then compared with the pre-disturbance community baseline to calculate the community deviation. If the community deviation exceeds a preset threshold, a metabolic pathway stabilization and regulation scheme is generated, guiding the community structure towards steady-state recovery through gradual parameter adjustments.

[0059] It should be noted that the above community baseline is the relative abundance matrix obtained from the most recent community detection before the environmental disturbance occurred, and it serves as a reference standard for community recovery.

[0060] It should be noted that the above-mentioned community deviation is calculated as follows:

[0061] in, The total number of species, Number the species. For the recovery period The relative abundance of each species, The first community baseline The relative abundance of each species.

[0062] Furthermore, the preset threshold for the aforementioned community deviation is 0.3, which is determined based on the correlation analysis between community similarity and processing performance stability. When When the community structure deviates significantly from the community baseline, stabilization measures need to be initiated to accelerate community recovery.

[0063] It should be noted that the aforementioned gradual parameter adjustment refers to gradually restoring the operating parameters from the degraded operating mode to the normal operating state according to the preset adjustment step size during the community recovery period, so as to avoid the sudden change of operating parameters causing secondary impact on the recovering community.

[0064] In this embodiment of the application, to accelerate the community recovery process, the metabolic pathway stabilization regulation scheme further includes a selective enhancement strategy. Based on the community deviation analysis results, functional microbial communities with lagging recovery are identified, and the growth-promoting factors of these functional microbial communities are adjusted in a targeted manner, including adjusting the carbon source type to match the substrate preferences of the functional microbial communities and adjusting the dissolved oxygen level to adapt to the respiratory characteristics of the functional microbial communities.

[0065] This implementation method couples meteorological impact prediction with microbial community metabolic capacity vector analysis to predict the possible direction of community structure changes before environmental disturbances actually occur, thereby achieving a forward-looking assessment of the risk of community structure changes and metabolic disorders under environmental disturbances. Because this forward-looking assessment is based on dynamic inference of the community's real-time metabolic functional state and predicted changes in environmental parameters, rather than relying on fixed steady-state community assumptions, it can cope with the dynamic shocks to the community caused by drastic changes in environmental parameters.

[0066] This implementation method uses a constrained optimization model to predict unconventional metabolic pathways and combines this with a molecular structure-toxicity relationship model for toxicity assessment, expanding the scope of risk monitoring from conventional water quality indicators to unconventional toxic intermediates. Because the metabolic toxicity risk warning list includes information on unconventional toxic intermediates not covered by conventional water quality indicators, it can identify risk factors that may lead to excessive effluent toxicity but are not detected by routine monitoring.

[0067] This implementation method proactively adjusts operating parameters before environmental disturbances through a preventative deep treatment control scheme, maintains basic treatment capacity during periods of impaired community function through a downgraded operation mode, and accelerates community recovery through gradual parameter adjustments. This forms a control system covering the entire cycle before, during, and after environmental disturbances, thereby ensuring the continuous compliance of petrochemical wastewater deep treatment with discharge standards under extreme environmental conditions.

[0068] The following is a specific application example of this invention: Step 1: Obtain the environmental impact prediction vector; Meteorological forecast data for a predetermined future time window is acquired, and the meteorological parameters in the forecast data are preprocessed. Data preprocessing includes Z-score standardization of meteorological parameters such as temperature, precipitation, and relative humidity to eliminate dimensional and numerical range differences between different meteorological parameters. The preprocessed meteorological parameters are then input into a meteorological-biochemical impact prediction model to calculate the expected environmental parameter changes for the deep-processing biochemical treatment unit, generating an environmental impact prediction vector. The expected environmental parameter changes include water temperature change curves, dissolved oxygen change trends, and influent dilution ratios.

[0069] On July 15, 20XX, a petrochemical company A received a 72-hour weather forecast for its wastewater advanced treatment system. The forecast indicated heavy rainfall expected 48 hours later, with a cumulative precipitation of 120 mm. This advanced treatment system has a daily processing capacity of 8,000 cubic meters and primarily treats mixed wastewater from oil refining and chemical plants. The influent contains characteristic pollutants such as phenol, benzene, and toluene. System operators obtained hourly weather forecast data for the next 72 hours from the meteorological department, including parameters such as temperature, precipitation, and relative humidity, and needed to predict the impact of the heavy rainfall on the biological treatment unit.

[0070] The meteorological forecast data was Z-score standardized. The original temperature data ranged from 26 to 32 degrees Celsius, precipitation from 0 to 15 mm per hour, and relative humidity from 65% to 95%. The standardized data was then input into a meteorological-biochemical impact prediction model. This model was trained based on the company's historical operating data over the past three years, including 1095 days of meteorological data and the correspondence between actual environmental parameters of the biochemical treatment unit. The model predicted that within 12 hours after the onset of the rainstorm, the influent dilution ratio would rapidly increase from the normal 1.0 to 2.8, the water temperature would decrease from 29 degrees Celsius to 23 degrees Celsius, and dissolved oxygen would fluctuate from 4.2 mg / L to 5.8 mg / L due to the decrease in water temperature and the dilution effect.

[0071] Table 1. Raw data and standardized results of weather forecasts (at some times):

[0072] Table 2. Prediction results of environmental parameters for the biochemical treatment unit (critical moments):

[0073] Figure 2 It displays the trends of standardized values ​​of temperature, precipitation, and relative humidity during a rainstorm.

[0074] Figure 3 The predicted changes in water temperature, dissolved oxygen, and influent dilution ratio during heavy rainfall events are shown.

[0075] Step 2: Generate a microbial community metabolic capacity vector; Activated sludge samples from the deep-processing biochemical treatment unit were periodically collected, and 16S rRNA high-throughput sequencing was performed on the activated sludge samples to obtain sequencing data. Sequence alignment and species annotation were performed on the sequencing data to generate a relative abundance matrix of microbial community composition. The relative abundance matrix was mapped to a microbial metabolic function database, and based on the annotation of known metabolic function genes of each species, the potential metabolic function profile of the current community was inferred, generating a microbial community metabolic capacity vector.

[0076] Upon receiving the weather forecast, system operators immediately collected 500 ml of activated sludge sample from the aeration zone of the biological treatment tank and sent it to a cooperating third-party testing institution for 16S rRNA high-throughput sequencing. Sequencing was completed within 24 hours, yielding 87,536 valid sequences with a sequencing depth coverage of 97.8%. Sequence alignment and species annotation identified 152 microbial species, of which 23 were dominant species with a relative abundance greater than 1%.

[0077] The relative abundance matrix was mapped to a pre-constructed microbial metabolic function database. This database contains metabolic function gene annotations for 458 species commonly found in petrochemical wastewater treatment systems, covering 18 metabolic functions including phenol degradation, benzene degradation, toluene degradation, nitrification, and denitrification. Based on the relative abundance of 152 detected species and their corresponding metabolic function contribution coefficients, a microbial community metabolic capacity vector was calculated.

[0078] For phenol degradation function, the main contributing bacterial groups were strain 01 (relative abundance 18.5%) and strain 02 (relative abundance 12.3%), with functional gene contribution coefficients of 0.85 and 0.72, respectively. The overall capacity value for phenol degradation function was calculated as follows:

[0079] The first two terms represent the contributions of the major contributing bacterial groups, while the subsequent summation term includes minor contributions from 150 other species.

[0080] Table 3. Relative abundance of dominant species in the microbial community:

[0081] Table 4. Microbial community metabolic capacity vectors (partial functions):

[0082] Figure 4It demonstrates the comprehensive metabolic capacity value of different types of metabolic functions in the microbial community.

[0083] Step 3: Conduct a risk assessment of changes in community structure; By overlaying the microbial community metabolic capacity vector with the environmental impact prediction vector, and based on the environmental tolerance parameters of each functional microbial community, we can identify microbial communities that may be suppressed or over-proliferated under predicted environmental conditions, and output the risk assessment results of community structure change.

[0084] Based on data from the functional microbial community environmental response characteristic library, strain groups 01 and 02, the main contributing microbial communities to phenol degradation, are sensitive to changes in temperature and osmotic pressure. The optimum temperature for strain group 01 is 28 degrees Celsius, with a temperature tolerance width parameter of 2.5 degrees Celsius; strain group 02 is sensitive to osmotic pressure, with an optimum influent dilution factor of 1.0 and an osmotic pressure tolerance width parameter of 0.4.

[0085] At the peak of the rainstorm (60-hour time), the predicted water temperature was 23 degrees Celsius, and the predicted influent dilution factor was 2.8. Calculate the activity attenuation coefficient of strain group 01 at this time:

[0086] Calculate the activity decay coefficient of strain group 02 at this moment (considering only the effect of osmotic pressure):

[0087] The activity attenuation coefficient of strain O2 was far below the preset threshold of 0.6, indicating a serious risk of inhibition. Considering the activity attenuation of the two main contributing bacterial groups, the overall activity attenuation coefficient of phenol degradation function during the peak of the rainstorm was approximately 0.068, far below the preset threshold, indicating a serious risk of damage to phenol degradation function.

[0088] Simultaneously, the evaluation revealed that the environmentally tolerant bacterial strain group 08 was not significantly inhibited when the substrate concentration decreased due to influent dilution. However, due to the decreased activity of its competitors, strain groups 01 and 02, strain group 08 may gain a competitive advantage and proliferate relatively.

[0089] Table 5 Assessment of the decline in the activity of key functional microbiota (at 60 hours):

[0090] Table 6. Results of the risk assessment for impaired metabolic function:

[0091] Figure 5 The comparison of the decay coefficients of key functional bacterial communities is presented.

[0092] Step 4: Generate a list of metabolic toxicity risk warnings; Based on the risk assessment results of community structure changes, a constrained optimization model is used to predict potentially activated unconventional metabolic pathways. Molecular structure-toxicity relationship models are performed on intermediates in these unconventional metabolic pathways to predict their predicted toxicity values. Highly toxic intermediates with predicted toxicity values ​​exceeding a toxicity threshold are identified, and their associated metabolic pathways are traced to generate a metabolic toxicity risk warning list.

[0093] Based on the risk assessment results of community structure changes, the main contributing bacterial groups, strains 01 and 02, for phenol degradation were severely suppressed, while the environmentally tolerant bacterial group, strain 08, may have relatively proliferated. This information was input into a constrained optimization model, which includes 67 known metabolic pathways in petrochemical wastewater treatment systems, 12 of which are unconventional metabolic pathways.

[0094] The constraints of the constrained optimization model were set as follows: the concentration of phenol in the influent was 85 mg / L (precursor substances were present); the enzyme function carried by strain group 08 included monooxygenase but lacked catechol dioxygenase required for the complete phenol degradation pathway (partial enzyme function was present); the inhibition of strain groups 01 and 02 led to a decrease in the activity of catechol dioxygenase in the normal phenol degradation pathway to 6.8% of the normal level (reduction of inhibitory factors).

[0095] Solve a mixed-integer linear programming problem with the objective function of maximizing the total activation probability of unconventional metabolic pathways. Using a branch-and-bound method, three potentially activated unconventional metabolic pathways were identified: pathway NCP07 (phenol is oxidized to catechol by monooxygenases, then accumulates due to dioxygenase deficiency), pathway NCP12 (catechol undergoes reduction in an anaerobic microenvironment to generate catechol monoanions), and pathway NCP19 (catechol undergoes non-specific oxidation to o-benzoquinone).

[0096] Intermediates from these three unconventional metabolic pathways were input into a molecular structure-toxicity model. Catechol, with a molecular weight of 110, an octanol-water partition coefficient of 0.88, and a polar surface area of ​​40.5 Ų, was predicted by the model to have a toxicity equivalent of 1.2 (the acute toxicity equivalent limit for wastewater discharge is 2.0, and the toxicity threshold is set at 1.0). O-benzoquinone, with a molecular weight of 108, an octanol-water partition coefficient of 0.20, and a polar surface area of ​​34.1 Ų, was predicted to have a toxicity equivalent of 3.8, exceeding the toxicity threshold.

[0097] The associated metabolic pathway of o-benzoquinone is traced to pathway NCP19. This pathway is activated when the water temperature is below 24 degrees Celsius and the dissolved oxygen is above 5.5 mg / L. The triggering environmental threshold is a water temperature of 23 degrees Celsius and a dissolved oxygen of 5.8 mg / L. The corresponding metabolic indicator parameter is an increase in redox potential to above +180 mV.

[0098] Table 7. Prediction results of activation of unconventional metabolic pathways:

[0099] Table 8. List of Identification and Early Warning Measures for Highly Toxic Intermediate Products:

[0100] Step 5: Generate a preventative deep treatment control plan; For each risk item in the metabolic toxicity risk warning list, corresponding control strategies are matched to generate a preventive deep treatment control plan. The control strategies include: adjusting the carbon-nitrogen ratio to maintain the competitive advantage of the target functional bacteria, pre-adding trace elements to supplement the nutritional needs of sensitive bacteria, and adjusting the sludge return ratio to slow down the rate of community disturbance.

[0101] To address the severely impaired phenol degradation function and the high toxicity risk of o-benzoquinone, the system needs to initiate preventative regulation 48 hours before the heavy rainfall (i.e., at the current time). Based on the nutritional requirements of strains 01 and 02, these two bacterial groups require a high carbon-to-nitrogen ratio (optimal range 15-20) to maintain metabolic activity during phenol degradation. The current influent carbon-to-nitrogen ratio is 12, and it needs to be increased to 18 by supplementing the carbon source.

[0102] Meanwhile, the phenol monooxygenase of strain 01 requires iron ions as a cofactor, while the catechol dioxygenase of strain 02 requires manganese ions as a cofactor. Ferrous sulfate (5 mg / L) and manganese sulfate (2 mg / L) were pre-added before environmental disturbance to maintain the activity of the metabolic enzymes.

[0103] To slow down the rate of community disturbance, the sludge return ratio will be increased from the current 50% to 80%, extending the retention time of microorganisms in the system from 12 days to 18 days, providing a buffer time for functional microbial communities to adapt to environmental changes.

[0104] A genetic algorithm was used to collaboratively optimize the control parameters. The adjustable ranges were set as follows: carbon-nitrogen ratio 12-25, micronutrient dosage 0-10 mg / L for iron ions and 0-5 mg / L for manganese ions, and sludge return ratio 50%-100%. After 200 generations of evolution, with a population size of 100 individuals, the optimal combination of control parameters was obtained.

[0105] Under the optimal combination of control parameters, the components of the fitness function were recalculated: the predicted generation of o-benzoquinone decreased from a peak of 18 mg / L in the uncontrolled case to 4 mg / L, and the normalized risk index... The predicted activity retention rate of phenol degradation function improved from 0.068 to 0.58, and nitration function improved from 0.007 to 0.35. The weighted average functional activity index... The control costs include the cost of carbon source addition, the cost of trace element addition, and the energy consumption cost of sludge recirculation. The normalized cost indicators are... .

[0106] Fitness function calculation:

[0107] Table 9. Results of parameter optimization for the preventive control program:

[0108] Figure 7 The display shows a comparison of the adjustment range of each control parameter from its current value to the target value.

[0109] Step 6: Perform real-time metabolic monitoring and response control; The metabolic indicator parameters of the deep-processing biochemical treatment unit are collected in real time and compared with the warning indicators in the metabolic toxicity risk warning list. When an abnormality is detected in the warning indicator, a downgraded operation mode is activated and a targeted control response is triggered to prioritize ensuring that the key pollutant indicators meet the standards.

[0110] During the rainstorm (starting 48 hours later), the system's online monitoring equipment collected metabolic indicator parameters hourly, including redox potential, UV 254 nm absorbance, and three-dimensional fluorescence spectral characteristics. Ten hours after the start of the rainstorm (out of a total of 58 hours), the redox potential increased from the normal +120 mV to +165 mV, approaching the monitoring threshold of +180 mV for o-benzoquinone in the metabolic toxicity risk warning list.

[0111] Twelve hours after the start of the rainstorm (out of a total of 60 hours), the redox potential reached +188 mV, exceeding the warning threshold. Simultaneously, the ultraviolet absorbance at 254 nm increased from 0.32 to 0.52, also exceeding the catechol monitoring threshold of 0.45. The system determined this to be an abnormal warning indicator and automatically activated a downgraded operation mode.

[0112] The specific measures for the downgraded operation mode include: reducing the influent flow rate from the normal 333 cubic meters per hour to 250 cubic meters per hour by adjusting the frequency of the influent pump, thus reducing the influent load by 25%; increasing the aeration rate from 6,000 cubic meters per hour to 7,200 cubic meters per hour to increase the dissolved oxygen supply and inhibit the formation of an anaerobic microenvironment; and activating the pre-set auxiliary physicochemical treatment unit by adding 50 mg / L of powdered activated carbon to adsorb intermediate products such as o-benzoquinone.

[0113] Two hours after the degraded operation mode was activated, the redox potential began to drop to +172 mV, and the UV 254 nm absorbance decreased to 0.41. The system continued to maintain the degraded operation mode until 6 hours after the rainstorm ended (total time 72 hours), at which point the redox potential recovered to +135 mV, and the UV 254 nm absorbance recovered to 0.36, below the warning threshold.

[0114] Throughout the rainstorm, the phenol concentration in the effluent remained between 0.08 and 0.15 mg / L, not exceeding the discharge standard of 0.5 mg / L; the COD in the effluent remained between 42 and 58 mg / L, not exceeding the discharge standard of 60 mg / L; the ammonia nitrogen in the effluent fluctuated due to impaired nitrification, with a peak value of 6.8 mg / L, close to but not exceeding the discharge standard of 8 mg / L.

[0115] Table 10 Real-time metabolic monitoring and response control execution record:

[0116] Figure 6 It displays real-time monitoring data of redox potential and ultraviolet absorbance during heavy rain.

[0117] Step 7: Perform community recovery assessment and stabilization regulation; After the environmental disturbance ends, activated sludge samples are collected for rapid community detection to obtain community composition data during the recovery period. The community composition data during the recovery period is then compared with the pre-disturbance community baseline to calculate the community deviation. If the community deviation exceeds a preset threshold, a metabolic pathway stabilization and regulation scheme is generated, guiding the community structure towards steady-state recovery through gradual parameter adjustments.

[0118] Two days after the rainstorm ended (96 hours in total), system operators collected activated sludge samples again for rapid community analysis. Quantitative real-time PCR was used to quantitatively analyze key functional microbial communities. This method can obtain community composition data within 8 hours, significantly shortening the detection time compared to complete high-throughput sequencing.

[0119] The results showed that the relative abundance of strain group 01 decreased from 18.5% before environmental disturbance to 9.2%, strain group 02 decreased from 12.3% to 5.6%, while the abundance of the environmentally tolerant bacterial group 08 increased from 4.8% to 14.3%. Difference analysis was performed between the community composition data during the recovery period and the community baseline before environmental disturbance to calculate the community deviation.

[0120] The community deviation of 0.377 exceeds the preset threshold of 0.3, indicating that the community structure deviates significantly from the baseline state and stabilization regulation needs to be initiated.

[0121] A metabolic pathway stabilization and regulation scheme was developed to selectively enhance the recovery of phenol-degrading bacterial strains, specifically strains 01 and 02, whose function was lagging. The carbon source type was adjusted from the common sodium acetate source to a mixed carbon source of glucose and sodium acetate (mass ratio 3:1), preferred by the phenol-degrading bacteria, while maintaining a carbon-to-nitrogen ratio of 18. Dissolved oxygen was gradually reduced from 5.8 mg / L during the heavy rain to 4.5 mg / L to match the respiratory characteristics of strains 01 and 02.

[0122] A gradual parameter adjustment strategy was adopted, gradually restoring the operating parameters to normal levels within a 7-day recovery period according to the preset adjustment steps. The sludge return ratio was reduced by 5 percentage points per day from 80%, and restored to 50% on the 6th day; the influent flow rate was increased by 15 cubic meters per hour per day from 250 cubic meters per hour, and restored to 333 cubic meters per hour on the 6th day; the carbon source dosage was gradually reduced from a high concentration, and restored to a normal concentration on the 7th day.

[0123] On day 7 of the recovery period (total time 264 hours), activated sludge samples were collected again for community analysis. Results showed that the relative abundance of strain group 01 recovered to 16.8%, strain group 02 recovered to 11.2%, and strain group 08 decreased to 6.1%. The community deviation decreased to 0.18, below the preset threshold of 0.3, indicating that the community structure had essentially returned to a steady state. At this point, all effluent indicators were stable and met standards: phenol concentration 0.06 mg / L, COD 38 mg / L, and ammonia nitrogen 2.1 mg / L. The system resumed normal operation.

[0124] Table 11 Changes in the relative abundance of key bacterial communities during the community recovery period:

[0125] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for advanced treatment of petrochemical wastewater to achieve compliant discharge, characterized in that, Includes the following steps: Acquire meteorological forecast data within a future preset time window, standardize the meteorological parameters in the meteorological forecast data, input the standardized meteorological parameters into the meteorological-biochemical impact prediction model, and generate environmental impact prediction vectors for the deep-processed biochemical treatment unit; High-throughput sequencing was performed on activated sludge samples from the advanced biochemical treatment unit to generate a relative abundance matrix of microbial community composition. The relative abundance matrix was then mapped to a microbial metabolic function database to generate a microbial community metabolic capacity vector. By overlaying the microbial community metabolic capacity vector with the environmental impact prediction vector, and based on the environmental tolerance parameters of each functional microbial community, we can identify microbial communities that may be suppressed or over-proliferated under predicted environmental conditions, and output the risk assessment results of community structure change. Based on the risk assessment results of community structure change, unconventional metabolic pathways are predicted using a constrained optimization model. Molecular structure-toxicity relationship model is used to predict intermediates in unconventional metabolic pathways, identify highly toxic intermediates whose predicted toxicity values ​​exceed the toxicity threshold, and generate a list of metabolic toxicity risk warnings. For each risk item in the metabolic toxicity risk warning list, a matching control strategy is generated to produce a preventive in-depth treatment control plan; Metabolic indicator parameters are collected in real time and compared with the warning indicators in the metabolic toxicity risk warning list. When an abnormality is detected in the warning indicator, the downgrade operation mode is activated and a targeted regulatory response is triggered. After the environmental disturbance ends, the community composition data during the recovery period is compared with the community baseline to perform a difference analysis and calculate the community deviation. If the community deviation exceeds a preset threshold, a metabolic pathway stabilization regulation scheme is generated.

2. The method according to claim 1, characterized in that, The meteorological-biochemical impact prediction model is a nonlinear regression model trained based on historical operational data. The input of the meteorological-biochemical impact prediction model is a standardized meteorological parameter sequence, and the output is the time-series predicted values ​​of environmental parameters of the biochemical treatment unit. The environmental impact prediction vector includes time-series predicted data of water temperature change curves, dissolved oxygen change trends, and influent dilution ratios.

3. The method according to claim 1, characterized in that, The microbial community metabolic capacity vector is calculated by multiplying the metabolic function matrix and the relative abundance matrix. The elements in the metabolic function matrix represent the contribution coefficients of each species to various metabolic functions. The contribution coefficients are determined based on the functional gene annotation information in the microbial metabolic function database. If a species carries a functional gene corresponding to a metabolic function, a quantitative value is assigned based on the expression intensity data of the functional gene of the corresponding species; otherwise, the value is zero.

4. The method according to claim 1, characterized in that, The overlay analysis includes: for each metabolic function component in the microbial community metabolic capacity vector, calculating the activity decay coefficient of the key contributing microbial community corresponding to the metabolic function under predicted environmental conditions; the activity decay coefficient is calculated based on the deviation between the predicted value of each environmental parameter and the optimal value of the corresponding functional microbial community, as well as the tolerance width parameter of the functional microbial community to each environmental parameter; when the activity decay coefficient is lower than a preset threshold, it is determined that the corresponding metabolic function is at risk of damage.

5. The method according to claim 1, characterized in that, The constrained optimization model employs a mixed-integer linear programming method, defining the activation state of metabolic pathways as binary variables. The constraints include: the presence of precursors of metabolic pathways in the influent, the presence of enzyme functions required by metabolic pathways in functionally enhanced bacterial communities, and the reduction of inhibitory factors of metabolic pathways in functionally impaired bacterial communities. The objective function is to maximize the total activation probability of unconventional metabolic pathways. The set of unconventional metabolic pathways with the highest activation probability that satisfy the constraints is obtained by using the branch and bound method.

6. The method according to claim 1, characterized in that, The molecular structure-toxicity relationship model is a quantitative structure-activity relationship model based on molecular descriptors. The input is the molecular structure characteristics of the intermediate product, and the output is the predicted toxicity equivalent value. The molecular structure characteristics include molecular weight, octanol-water partition coefficient, polar surface area, and functional group type. The toxicity threshold is set as a preset proportion of the acute toxicity equivalent limit specified in the effluent discharge standard.

7. The method according to claim 1, characterized in that, The proposed preventative deep treatment control scheme includes: using a genetic algorithm to collaboratively optimize control parameters, which include the carbon-to-nitrogen ratio, trace element dosage, and sludge return ratio; the fitness function comprehensively considers the normalized value of the predicted generation of highly toxic intermediate products, the predicted activity retention rate of the target functional microbial community, and the normalized value of control costs; the optimization constraints include the adjustable range of each control parameter and the compliance requirements of conventional effluent water quality indicators.

8. The method according to claim 1, characterized in that, The downgraded operation mode includes: reducing the influent load to alleviate the metabolic pressure on the community, extending the hydraulic retention time to compensate for the decrease in degradation efficiency, and activating the auxiliary physicochemical treatment unit to share part of the pollutant removal task; the metabolic indicator parameters include redox potential, characteristic band ultraviolet absorbance, and fluorescence characteristics of dissolved organic matter.

9. The method according to claim 1, characterized in that, The community deviation is calculated by measuring the similarity between the relative abundance of the community during the recovery period and the relative abundance of the community baseline; the metabolic pathway stabilization regulation scheme includes progressive parameter adjustment, which gradually restores the operating parameters from the degraded operating mode to the normal operating state according to a preset adjustment step size; the metabolic pathway stabilization regulation scheme also includes a selective enhancement strategy, which adjusts growth-promoting factors for functional microbial communities with delayed recovery.

10. A deep treatment system for petrochemical wastewater to achieve compliant discharge, used to execute the method according to any one of claims 1 to 9, characterized in that, include: The environmental impact prediction module is used to acquire meteorological forecast data, standardize the meteorological parameters, and input them into the meteorological-biochemical impact prediction model to generate environmental impact prediction vectors. The community metabolic capacity analysis module is used to perform high-throughput sequencing on activated sludge samples, generate a relative abundance matrix, and map it with a microbial metabolic function database to generate a microbial community metabolic capacity vector. The community risk assessment module is used to overlay and analyze the microbial community metabolic capacity vector and the environmental impact prediction vector to output the community structure change risk assessment results. The metabolic toxicity early warning module is used to predict unconventional metabolic pathways using a constrained optimization model, perform molecular structure-toxicity relationship model prediction, and generate a list of metabolic toxicity risk warnings. The preventive regulation module is used to match regulatory strategies to the list of metabolic toxicity risk warnings and generate preventive in-depth treatment regulation plans. The real-time monitoring and response module is used to collect metabolic indicator parameters in real time and compare them with early warning indicators. When an abnormality is detected, a degraded operation mode is activated. The community recovery assessment module is used to calculate the community deviation and generate a metabolic pathway stabilization regulation scheme when the deviation exceeds a preset threshold.

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