A method of bioreactor treatment of gas well produced water

Abstract

The application relates to a bioreactor treatment method for gas well effluent water and relates to the technical field of water treatment. The method comprises the following steps: introducing the gas well effluent water into a bioreactor loaded with a microbial flora, monitoring the chemical oxygen demand concentration, the pH value and the salinity of the effluent water in real time, calculating a comprehensive treatment effect index E based on the monitoring parameters, dividing three treatment effect intervals according to the E value and executing corresponding regulation strategies: maintaining the operation parameters in the qualified interval, dynamically adjusting the hydraulic retention time according to the E value decrease rate in the early warning interval, and adding an exogenous carbon and nitrogen source and increasing the aeration amount in the abnormal interval. The application can solve the problem of unstable treatment efficiency caused by water quality fluctuation in the treatment process of the gas well effluent water.

Description

Technical Field

[0001] This application relates to the technical field of water treatment, and particularly to a biological reactor treatment method for gas well produced water. Background Art

[0002] The produced water generated during gas well production contains high concentrations of salts and organic pollutants, and its effective treatment is of great significance for environmental protection and resource recovery. Due to advantages such as low operating cost and environmental friendliness, biological treatment technology has become a common method for treating gas well produced water. In the prior art, gas well produced water is usually treated by a biological reactor with fixed operating parameters. Microbial flora degrades organic matter in the reactor, and at the same time, key water quality indicators such as chemical oxygen demand (COD) and pH value of the effluent are regularly monitored to evaluate the treatment effect, and the system simply adjusts the operating parameters according to preset thresholds.

[0003] However, under the condition of large fluctuations in the water quality of gas well produced water, it is difficult for the prior art to maintain stable treatment performance, and phenomena such as fluctuating treatment efficiency or unstable system operating status often occur. Summary of the Invention

[0004] [[ID=1?6]]This application provides a biological reactor treatment method for gas well produced water, which can solve the problem of unstable treatment efficiency caused by water quality fluctuations during the treatment of gas well produced water. To achieve the above objective, this application provides the following technical solutions: This application provides a biological reactor treatment method for gas well produced water, including the following steps: S1. Introduce the gas well produced water into a biological reactor, which is loaded with microbial flora; S2. Real-time monitor the key water quality parameters of the effluent from the biological reactor. The key water quality parameters at least include chemical oxygen demand concentration, pH value, and salinity; S3. Based on the key water quality parameters monitored in step S2, calculate the current comprehensive treatment effect index E. The comprehensive treatment effect index E is calculated by the following formula: ; where C0 is the initial concentration of influent chemical oxygen demand, C is the real-time concentration of effluent chemical oxygen demand, pH is the pH value of the effluent, S is the real-time concentration of effluent salinity, S0 is the preset salinity threshold, α, β, and γ are weight coefficients, and α + β + γ = 1; S4. According to the value of the comprehensive treatment effect index E, divide it into three treatment effect intervals, and execute the corresponding regulation strategy for this interval: when E ≥ Eth1, it is determined as the qualified interval, and the current operating parameters are maintained; when Eth2 ≤ E < Eth1, it is determined as the warning interval, and the first-level regulation strategy is executed; when E < Eth2, it is determined as the abnormal interval, and the second-level regulation strategy is executed; where Eth1 and Eth2 are preset thresholds, and Eth1 > Eth2.

[0005] In one optional embodiment, the preset salinity threshold S0 is set to satisfy: Where Savg is the historical average salinity of the influent within the preset period, and K is a coefficient with a value range of 1.1 to 1.5.

[0006] In one optional embodiment, the weighting coefficients α, β, and γ take values ​​in the following ranges: α ∈ [0.5, 0.7], β ∈ [0.2, 0.3], and γ ∈ [0.1, 0.2].

[0007] In one optional embodiment, the first-level control strategy includes: dynamically adjusting the hydraulic retention time (HRT) of the bioreactor based on the rate of decrease of the E value within the warning interval, dE / dt.

[0008] In one optional embodiment, the hydraulic residence time HRT is dynamically adjusted specifically as follows: when dE / dt < When HRT is increased by 25%-40% from the baseline value HRT0; when When ≤ dE / dt<0, increase HRT by 10%-20% based on the baseline value HRT0.

[0009] In one optional embodiment, the reference hydraulic residence time HRT0 ranges from 24 to 36 hours.

[0010] In one optional embodiment, the second-stage control strategy includes the following steps: S71, adding exogenous carbon and nitrogen sources to the bioreactor and adjusting the influent carbon-nitrogen ratio C / N to (20~30):1; S72, simultaneously increasing the aeration rate of the bioreactor to maintain the dissolved oxygen concentration DO at 4.0 mg / L ~ 6.0 mg / L.

[0011] In one optional embodiment, after the second-level control strategy is executed, if the duration of continuous monitoring that the E value is still lower than Eth2 exceeds the dynamic threshold T hours, then the third-level control strategy is executed: some of the old microbial community in the bioreactor is discharged and an equal amount of new microbial community is added.

[0012] In an alternative embodiment, the dynamic threshold T is determined by the formula... It is determined that Emin is the lowest E value monitored during the implementation of the second-level control strategy, and the maximum value of T does not exceed 36 hours.

[0013] In one optional embodiment, in the third-level control strategy, the proportion of microbial communities discharged and replenished is 10% to 30% of the total microbial community in the bioreactor.

[0014] The present application provides a biological reactor treatment method for gas well produced water. This solution achieves the biochemical degradation of pollutants by introducing gas well produced water into a biological reactor loaded with a microbial community. By means of real-time monitoring of key water quality parameters such as the chemical oxygen demand concentration, pH value, and salinity of the effluent, a comprehensive treatment effect index E is constructed. This index avoids misjudgment by a single index through weighted fusion of pollutant removal rate, pH stability, and salinity impact. Based on the E value, three treatment effect intervals of compliance, early warning, and anomaly are divided. When E ≥ Eth1, the operating parameters are maintained to ensure the stable operation of the system. When Eth2 ≤ E < Eth1, the hydraulic retention time HRT is dynamically adjusted according to the rate of decrease of the E value dE / dt to achieve early intervention. When E < Eth2, the microbial activity is restored by adding exogenous carbon and nitrogen sources and increasing the aeration volume, thus solving the problem of unstable treatment efficiency caused by water quality fluctuations. This design improves the adaptability of the system to high-salt and high-organic matter loads, and responds in a timely manner at the initial stage of water quality deterioration through a hierarchical control mechanism, avoiding system instability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of a biological reactor treatment method for gas well produced water provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The present invention will be further described in detail below with reference to embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention.

[0017] Gas well produced water has characteristics such as high salinity, high organic matter load, and severe water quality fluctuations. Traditional biological treatment processes often result in unstable treatment efficiency due to the inhibition of microbial activity, weak shock resistance of the system, and lack of quantitative control basis. Especially when the influent COD concentration suddenly increases, the pH deviates, or the salinity rises, it is easy to occur that the effluent does not meet the standards or even the microbial community is inactivated. Existing technologies mostly rely on a single parameter (such as COD removal rate) for operation judgment, which is difficult to comprehensively reflect the true treatment state under the coupling effect of multiple factors, and also lacks a matching hierarchical response mechanism, resulting in lagged regulation, extensive intervention, and a long system recovery period.

[0018] Based on the above problems, please refer to Figure 1 As shown, a biological reactor treatment method for gas well produced water provided by an embodiment of the present application includes the following steps: S1: Introduce gas well produced water into a biological reactor, and the biological reactor is loaded with a microbial community; The bioreactor can be one of the following: sequencing batch reactor (SBR), upflow anaerobic sludge blanket reactor (UASB), membrane bioreactor (MBR), or moving bed biofilm reactor (MBBR). Its structure includes an influent unit, a reaction zone, a three-phase separation zone, and an effluent unit. The microbial community can refer to a complex functional microbial community that has been domesticated and screened and has the ability to tolerate salt and degrade organic matter. It may include halophilic Pseudomonas, marine Bacillus, and denitrifying Paracoccus, etc., which exist in the reactor in the form of suspension, attached state, or biofilm.

[0019] In this embodiment, the microbial community was obtained by inoculating activated sludge from a mature oilfield wastewater treatment system and acclimating it to a gradient salinity of 30 days (5 g / L → 25 g / L). The initial inoculation concentration was 3.5 g / L MLSS. The purpose of this step is to provide a stable biocatalytic carrier for subsequent biochemical reactions, so that organic pollutants in the gas well produced water can undergo oxidative decomposition and assimilation under the action of microbial metabolism.

[0020] This application can, for example, continuously pump gas well produced water into the bottom of the bioreactor reaction zone using an inlet pump, achieving uniform distribution via a water distributor; alternatively, it can inject gas well produced water into the bioreactor inlet zone via gravity flow from an elevated water tank, and then buffer it through a baffle plate before entering the reaction zone; furthermore, it can employ a pulse inlet mode, quantitatively injecting gas well produced water at a set cycle (once every 2 hours) to alleviate high-concentration shock loads. This application achieves a stable introduction of gas well produced water based on any of the above methods, providing a continuous and controllable substrate supply for the microbial community.

[0021] For example, this application may involve approximately 48 m³ of produced water per day from a gas well at a gas field gathering station. After preliminary sand settling and oil removal pretreatment, the COD concentration is 850 mg / L, the pH is 6.2, and the salinity is 18.3 g / L. This produced water is continuously fed into a 48 m³ MBR reactor (HRT=24 h) at a flow rate of 2.0 m³ / h via a variable frequency influent pump. The reactor has a stable enrichment of a complex of salt-tolerant denitrifying bacteria and aerobic degrading bacteria, with an MLSS concentration of 4.2 g / L. The influent undergoes mixing and reaction below the membrane module, and is discharged after microorganisms are removed by an ultrafiltration membrane.

[0022] S2: Real-time monitoring of key water quality parameters in the bioreactor effluent. Key water quality parameters include at least chemical oxygen demand (COD) concentration, pH value, and salinity. Among them, COD concentration can refer to the oxygen equivalent concentration consumed by the oxidation of reducing substances in water under the action of strong oxidants, with the unit being mg / L; pH value is a dimensionless parameter characterizing the acidity or alkalinity of a solution; salinity can refer to the comprehensive concentration of major ions (Na⁺, Cl⁻, Ca²⁺, Mg²⁺, etc.) in total dissolved solids (TDS) in water, with the unit being g / L or ‰.

[0023] In this embodiment, COD is detected in real time on the effluent pipe using an online COD analyzer with a response time of ≤5 min; pH is continuously measured using an industrial-grade pH electrode sensor (accuracy ±0.05); salinity is indirectly measured using a conductivity-salinity conversion module, with the conductivity probe installed on the effluent return pipe; all three parameters are uploaded to the central control system at a sampling frequency of 1 minute to form a time-series data stream; the purpose of this step is to provide real-time, synchronous, and multi-dimensional input data for calculating the comprehensive treatment effect index E, ensuring the timeliness and completeness of the evaluation criteria.

[0024] This application can, for example, obtain real-time COD values ​​by converting the 4–20 mA current signal output by the online COD analyzer into analog-to-digital values ​​using a PLC; it can also calculate the pH value in real time by combining the mV voltage signal output by the pH sensor with a temperature compensation algorithm; furthermore, it can obtain the corresponding salinity S by looking up the built-in salinity-conductivity mapping relationship database based on the conductivity value measured by the conductivity sensor; this application obtains synchronous, continuous, and high signal-to-noise ratio monitoring results of three key water quality parameters—COD, pH, and salinity—based on any of the above methods.

[0025] For example, this application may integrate a three-in-one water quality monitoring unit into the effluent pipeline of the aforementioned MBR system. The COD analyzer automatically samples and digests every 2 minutes and outputs the current COD value (C=126 mg / L), the pH electrode provides real-time feedback that the effluent pH is 6.8, the conductivity sensor measures the conductivity to be 28.4 mS / cm, and the current salinity S=19.1 g / L is calculated. All data is uploaded to the DCS system via an RS485 interface, with a timestamp alignment error of less than 100 ms.

[0026] S3: Based on the key water quality parameters monitored above, calculate the current comprehensive treatment effect index E; the comprehensive treatment effect index E is calculated using the following formula: Where C0 is the initial concentration of influent chemical oxygen demand, C is the real-time concentration of effluent chemical oxygen demand, pH is the pH value of effluent, S is the real-time concentration of effluent salinity, S0 is the preset salinity threshold, α, β, and γ are weighting coefficients, and α + β + γ = 1. Among them, the comprehensive treatment effect index E is a dimensionless normalized index used to quantitatively characterize the current overall treatment performance of the bioreactor; C0 is the influent COD concentration measured before the start of this operation cycle, which serves as the benchmark value for calculating the pollutant removal rate. Indicates COD removal rate, reflecting the efficiency of organic matter degradation; This is a pH stability normalization term. When pH∈[4,10], the value of this term is in the range of [0,1]. The closer the pH is to neutral, the larger this term becomes.

[0027] S / S0 is the salinity stress normalization term, reflecting the degree of inhibition of microbial activity by salinity; α, β, and γ are empirical weighting coefficients, which assign different decision priorities to COD removal, pH stability, and salinity tolerance, respectively; in this embodiment, CO is set to 850 mg / L (consistent with the example above), S0 is set to 20.0 g / L, α=0.6, β=0.25, and γ=0.15 (satisfying α+β+γ=1); the purpose of this step is to integrate multi-source heterogeneous water quality parameters into a single discriminant index, providing a unified and comparable quantitative basis for the above interval division and strategy execution.

[0028] This application, for example, can call an embedded computing module to perform formula calculations based on the CO value stored in the DCS system and the real-time updated C / pH / S values, refreshing the E value every 5 minutes; this application can also collect multi-channel sensor data through an edge computing gateway, use a lightweight Python script to parse the formula in real time and output E; further, this application can also break down each sub-calculation into independent sub-tasks: first, the COD module outputs the removal rate term, then the pH module outputs the stability term, and finally the salinity module outputs the stress term, and the final E value is obtained after weighted summation; this application obtains the comprehensive treatment effect index E at the current moment based on any of the above methods, realizing dynamic quantitative evaluation of the treatment status.

[0029] For example, this application may use the aforementioned operating conditions. =850 mg / L, C=126 mg / L, pH=6.8, S=19.1 g / L, =20.0 g / L, then: ; ; ; Substituting, we get: ; This E value will serve as the direct input for the aforementioned interval determination.

[0030] S4: Based on the value of the comprehensive treatment effect index E, divide it into three treatment effect intervals and implement the corresponding control strategy for each interval: when When it is determined to be within the acceptable range, the current operating parameters are maintained; when When it is determined to be a warning zone, the first-level control strategy is implemented; when When the condition is deemed to be in an abnormal range, a second-level control strategy is implemented; among which, and The preset threshold, and .

[0031] in, and It is a decision threshold determined through engineering calibration; The minimum acceptable E value representing the system's efficient and stable operating state is set to 0.65 in this embodiment; This represents a critical point indicating significant degradation in system performance. A value below this indicates a risk of multiple stresses compounding; in this embodiment, it is set to 0.45. Both conditions must be met. And the difference To reserve a reasonable buffer zone for early warning response.

[0032] Maintaining the current operating parameters can mean keeping all process parameters such as influent flow rate, HRT, aeration rate, and carbon source dosage unchanged; the first-level control strategy and the second-level control strategy correspond to the contents defined in Example 4 and Example 7, respectively, and will be elaborated in subsequent examples; the purpose of this step is to establish a closed-loop logic chain from quantitative evaluation to engineering response, so that the system has the ability to autonomously identify the operating status and trigger adaptive control actions based on the E value, avoiding reliance on human experience and response delay.

[0033] This application could be, for example, a DCS system that compares the real-time E value with a pre-stored value. =0.65、 The comparison is performed. When E≥0.65, a normal operating status code is output and all actuator outputs are locked. Alternatively, when 0.45≤E<0.65, the system automatically activates the warning flag, pushes a prompt message to the operation terminal, and prepares to call the first-level control strategy module.

[0034] Furthermore, this application can also involve the system immediately closing the inlet valve, starting the backup aeration branch, and sending an abnormal alarm event packet to the central control platform when E < 0.45. This application achieves three-level state recognition and strategy triggering driven by the E value based on any of the above methods, ensuring that the control actions and system states are strictly matched.

[0035] For example, in the aforementioned continuous operation scenario, during a certain period, a sudden increase in the oil content of the upstream gas well's produced fluid causes the influent COD to jump to 1200 mg / L. Although buffered by the reactor, the effluent C rises to 210 mg / L, the pH drops to 6.1, and the S rises to 21.5 g / L; recalculated, E≈0.41 (< The system determined that it had entered an abnormal range and immediately implemented a second-level control strategy, including adding sodium acetate as an exogenous carbon source, adjusting the urea dosage to increase the C / N ratio to 25:1, and raising the DO control target from 3.0 mg / L to 5.2 mg / L; 2 hours later, E rose back to 0.48, entering the warning range, and switched to the first-level control.

[0036] This application establishes a biochemical treatment foundation by introducing gas well produced water into a bioreactor loaded with microbial communities; it acquires multidimensional dynamic data reflecting the system status by real-time monitoring of effluent COD, pH, and salinity; it constructs a comprehensive treatment effect index E that integrates removal rate, stability, and stress level to achieve a unified quantitative characterization of treatment efficiency under complex water quality influences; and it forms a closed-loop feedback control mechanism of perception-evaluation-decision-execution through E-value-driven three-level interval division and strategy matching. This effectively overcomes the risks of misjudgment, response lag, and inaccurate control caused by single-parameter monitoring in traditional methods, and significantly improves the robustness, adaptability, and operational stability of the biological treatment system in the face of drastic fluctuations in gas well produced water quality.

[0037] In one optional implementation, this application also provides a preset salinity threshold. The settings satisfy: ,in The historical average salinity of the influent within the preset period is denoted by K, which is a coefficient ranging from 1.1 to 1.5.

[0038] Step 1: The preset salinity threshold S0 is set to satisfy the following: ,in The historical average salinity of the influent within the preset period is denoted by K, which is a coefficient ranging from 1.1 to 1.5.

[0039] in, This is the benchmark reference value used in the formula for calculating the comprehensive treatment effect index E to normalize the effluent salinity S. The arithmetic mean of the measured salinity data of gas well produced water intake collected and statistically analyzed continuously for no less than 30 days after the system was put into operation; this value reflects the current salinity distribution center trend of the gas field water source under typical operating conditions, and has time representativeness and regional stability. K is used in A dimensionless adjustment parameter with a safety margin is introduced on the basis, with a value range of 1.1 to 1.5, which means that the value is increased by 10% to 50% on the basis of the historical average, in order to cover the uncertainty caused by short-term salinity fluctuations and avoid misjudging the abnormality by triggering a significant drop in E value due to a slight increase in instantaneous salinity. and The linear proportional relationship between them allows S0 to be dynamically updated with the long-term evolution trend of the influent water quality, rather than using a fixed empirical value (such as a constant setting of 5000 mg / L), thereby ensuring that the normalization benchmark of the salinity term in the E calculation always has realistic adaptability.

[0040] This application obtains the data by performing sliding window mean statistics on the influent salinity sequence within a preset period. This application also obtains the salinity data by weighted moving average of the influent salinity sequence within a preset period. Recent data is given higher weight to enhance response sensitivity; furthermore, this application also obtains the arithmetic mean by removing outliers from the influent salinity sequence within a preset period. This is to suppress deviations caused by abnormal sampling or instrument drift. This application obtains a solution based on any of the above methods that matches the current influent water quality characteristics. And based on this, determine those with adaptive capabilities. This ensures that the comprehensive treatment effect index E is neither too sensitive nor too insensitive to salinity disturbances.

[0041] For example, in this application: After 45 days of continuous operation, 45 sets of daily influent salinity data were collected for a gas field block. After data cleaning and removal of two significantly outlier values ​​(such as low-salinity samples due to pipeline flushing), the arithmetic mean of the remaining 43 sets of data was 4280 mg / L; taking K = 1.3, then... = 1.3 × 4280 mg / L = 5564 mg / L; This S0 value is written into the system parameter table and used for salinity normalization in all subsequent E value calculations; when the influent salinity rises to 5400 mg / L on a subsequent day, The value remains within a reasonable range and will not cause a sharp drop in the E value due to this single contribution, thus avoiding unnecessary early warning actions.

[0042] Step 2: Preset the period for calculation The time span can be dynamically configured based on the stability of the gas well's produced water quality, and can be 7 days, 15 days, 30 days, or 90 days.

[0043] The preset period is a continuous time window that the system automatically maintains on a rolling basis. Its starting time moves forward synchronously with time to ensure S a ᵥg always reflects the latest water quality characteristics; The choice of cycle length needs to balance data representativeness and timeliness of response: a cycle of less than 7 days is easily affected by a single abnormal discharge, leading to... Distortion; cycles exceeding 90 days are difficult to adapt to the slow evolution of water quality caused by seasonality or development phases. In this embodiment, the preset period is 30 days. This length can cover the typical production fluctuation cycle in the actual operation of most onshore gas fields and has sufficient statistical robustness.

[0044] This application updates automatically on a daily basis according to the system's built-in clock. Each update is based on the effective salinity monitoring values ​​for the most recent 30 consecutive calendar days; this application also has an update mechanism triggered by the influent salinity change rate, which is activated in advance when the absolute value of the salinity change rate exceeds 5% / d for three consecutive days. The calculation process is recalculated; furthermore, this application also incorporates manually set water quality event markers (such as fracturing flowback period, after acidizing operations, etc.) to perform segmented weighted processing on the preset cycle, so that... It more closely reflects the actual working conditions. This application is based on any of the above methods. The dynamic evolution, thereby supporting Continuous adaptation to the time-varying characteristics of water quality.

[0045] For example, in this application: the system records that a gas well completes acidizing operations on day 0, and then within 5 days, the influent salinity rapidly increases from 3800 mg / L to 6200 mg / L; at this time, the system identifies this period as an unsteady-state condition and calculates... The system automatically marks the 5-day data as a transient region, excludes it from mean calculation, and instead uses the previous 30-day stable period data for updates. This ensures that S0 is not distorted by short-term disturbances.

[0046] The synergistic effect of these technical features is as follows: This application will be approved Constructed into The product form with coefficient K enables dual flexible control of the salinity benchmark value—on the one hand, Extract statistically representative influent salinity center values ​​from historical data, so that... It possesses water quality self-sensing capabilities; on the other hand, K serves as an adjustable safety factor. By adding a controllable margin on top of this, S0 possesses both disturbance rejection and responsiveness; the synergy between the two reduces the salinity penalty term in the overall treatment effect index E. The quantitative scale is always anchored to the actual influent level, which not only prevents false alarms caused by small fluctuations in salinity, but also reflects the decline in E value in a timely manner when salinity deteriorates substantially, thereby improving the judgment accuracy and operational robustness of the entire control logic.

[0047] This application also provides the value ranges of the weighting coefficients α, β, and γ, specifically: α ∈ [0.5, 0.7], β ∈ [0.2, 0.3], and γ ∈ [0.1, 0.2].

[0048] Step 1: The weighting coefficients α, β, and γ have the following ranges: α ∈ [0.5, 0.7], β ∈ [0.2, 0.3], and γ ∈ [0.1, 0.2].

[0049] Wherein, α (alpha, chemical oxygen demand contribution weighting coefficient) is a dimensionless coefficient used in the calculation formula of the comprehensive treatment effect index E for the weighted COD removal rate term; β (beta, pH stability contribution weighting coefficient) is a dimensionless coefficient used in the weighted pH normalization term; and γ (gamma, salinity inhibition contribution weighting coefficient) is a dimensionless coefficient used in the weighted salinity influence term.

[0050] In the field of water treatment and bioreactor control technology, the above three types of weighting coefficients together constitute a linear weighted fusion mechanism for multidimensional water quality response. Its function is to uniformly map water quality parameters with different physical meanings, dimensions, and sensitivities to the same evaluation scale, thereby supporting quantitative decision-making.

[0051] In this embodiment, α is limited to the range of [0.5, 0.7], indicating that COD removal efficiency plays a dominant role in the overall evaluation. Its lower limit ensures that organic pollutant reduction is always the core assessment target, while its upper limit prevents the excessive weakening of the influence of environmental constraints such as pH and salinity. β is limited to the range of [0.2, 0.3], reflecting a moderate level of concern for the pH stability of the effluent. This range covers the normalized score of 0.67 to 1.0 corresponding to pH = 6.0 to 8.0, which is sufficient to ensure the activity window of most aerobic microbial communities. γ is limited to the range of [0.1, 0.2], reflecting a moderately suppressive weighting of the salinity stress effect, avoiding false alarms caused by a systematically low E value due to high salinity in the effluent, while retaining its ability to identify treatment efficiency degradation.

[0052] This application, for example, determines the weighted combination range that shows the highest correlation between the E-value change trend and the actual microbial activity decay, COD removal rate fluctuation, and system recovery capacity based on long-term operational data statistics and multi-condition calibration experiments. This application, for example, selects a weighted configuration that makes the E-value more sensitive to COD changes than to single-factor disturbances in pH and salinity based on the typical gas well produced water quality distribution characteristics (COD fluctuation range 500–3000 mg / L, pH concentrated in 6.2–7.8, salinity between 8000–25000 mg / L) through sensitivity analysis. Furthermore, this application, for example, verifies historical anomalies by ensuring that in failure scenarios where COD rebounds but pH and salinity do not exceed limits, the E-value decrease is ≥15% to trigger an early warning, while in scenarios where only salinity increases but COD and pH remain stable, the E-value decrease is ≤5%, thereby maintaining the robustness of the control strategy. This application obtains a reasonable weighted expression of the comprehensive treatment effect index E based on any of the above methods, so that it can truly characterize the actual treatment efficiency of the bioreactor under the complex water quality disturbance of gas well produced water.

[0053] This application constructs a three-layer weighted structure with organic matter removal as the core, pH stability as the support, and salinity tolerance as the correction, by setting α to [0.5, 0.7], β to [0.2, 0.3], and γ to [0.1, 0.2], under the premise that α+β+γ=1. Based on this, the comprehensive treatment effect index E can prominently reflect the fundamental treatment goal of COD reduction, while not ignoring the basic constraint of pH on microbial activity, and appropriately suppressing the systematic suppression of evaluation results by high salt background values. This makes the division of the three treatment effect intervals in step S4 more engineering reasonable, ensuring that the triggering of the warning interval and the abnormal interval is neither too sensitive nor too delayed, thereby supporting the precise execution of subsequent graded control strategies.

[0054] This application also provides the rate of decrease of the E value within the warning interval. Dynamically adjusting the hydraulic retention time (HRT) of a bioreactor, including: Step 1: Based on the rate of decrease of the E value within the warning interval The hydraulic retention time (HRT) of the bioreactor is dynamically adjusted.

[0055] The warning range can refer to the comprehensive treatment effect index. satisfy The numerical range, which is defined by two preset thresholds. and The common definition is used to characterize a state where current processing performance is at a critical point of degradation; its function is to trigger a proactive intervention mechanism to prevent the system from slipping into an abnormal range; the rate of decrease of the E value. It is the comprehensive processing efficiency index per unit time. The change in the rate reflects the dynamic evolution trend of the system's processing performance; its inherent function is to quantitatively assess the stability and deterioration acceleration of the current operating state; in this embodiment, the rate is used as the input criterion of the first-level control strategy to distinguish different degrees of deterioration and drive differentiated responses.

[0056] Hydraulic retention time (HRT) is the average residence time of gas well produced water in a bioreactor. Its inherent function is to regulate the sufficiency of contact between microorganisms and pollutants and the degree of completion of biochemical reactions. In this embodiment, HRT is an adjustable operating parameter. Its adjustment directly changes the substrate degradation kinetics in the reactor, thereby producing a reverse correction effect on the E-value change trend.

[0057] This application, for example, can calculate the slope by performing a sliding window linear fit on a continuously acquired E sequence to obtain... This application may use methods to determine the intensity of the current deterioration trend; for example, it may also approximate the calculation by dividing the difference of E values ​​at adjacent time points by the corresponding time interval using a central difference scheme. This application uses various methods to determine the strength of the current deteriorating trend; furthermore, it can also construct a first-order autoregressive model based on the E-value time series, and derive the trend term coefficients through model residual analysis to estimate... The methods described above are used to determine the intensity of the current deterioration trend. This application obtains a quantitative characterization of the dynamic change trend of the E value based on any of the above methods, providing a reliable basis for the subsequent graded response of HRT.

[0058] For example, this application could involve the system continuously monitoring during a particular run. The value slowly decreased from 0.68 to 0.63 (within the warning range) over a period of 2 hours. Therefore, according to the central difference method, we get... ,fall into Based on this range, a slight upward adjustment of HRT is initiated; if the E value drops sharply from 0.63 to 0.57 within the following hour, then... This triggers a more significant HRT extension. The process does not rely on human experience or judgment; it is entirely data-driven and executed in a closed loop.

[0059] This application uses the dynamic change rate of the E value within the warning interval. As a trigger for regulation, and mapped to a gradient adjustment mechanism of hydraulic retention time (HRT), the bioreactor can identify accelerating deterioration trends before its treatment performance significantly deteriorates. By flexibly adjusting this core hydraulic parameter, HRT, the contact time between pollutants and microorganisms is enhanced without introducing exogenous agents, altering the microbial community composition, or increasing aeration energy consumption. This improves organic matter degradation efficiency and pH buffering capacity, delaying the inhibitory effect of salinity accumulation on microbial activity. Ultimately, this maintains the potential for the overall treatment efficiency index (E) to recover within the warning range, preventing it from falling below the warning level. However, it has entered an abnormal range that requires strong intervention.

[0060] This application also provides for dynamically adjusting the hydraulic residence time (HRT), specifically: when At that time, HRT will be set at the reference value. Increase by 25%–40% on the basis; when At that time, increase HRT by 10%–20% from the baseline value HRT0.

[0061] Step 1: When At that time, HRT will be set at the reference value. Increase by 25%–40% on the basis; Wherein, the derivative of the comprehensive treatment effect index E with respect to time is the instantaneous rate of decrease of the E value over time, with units of... This technical feature is used to characterize the urgency of the current deterioration in processing effectiveness; The HRT0 reference hydraulic retention time is the initial operating retention time set for the bioreactor under standard operating conditions, and its value ranges from 24 to 36 hours. In this embodiment, its value will not be explained again. An increase of 25%–40% could refer to Using the base as the base, the incremental value is calculated proportionally, and the final adjusted value is obtained. , where δ ∈ [0.25, 0.40]; this ratio range is used to ensure sufficient reaction time for microorganisms while avoiding the risk of sludge aging or effluent retention caused by excessive retention time.

[0062] This application, for example, can adjust the HRT by changing the water flow velocity and total retention volume within the system based on whether the real-time calculated dE / dt value falls within a certain threshold range, combined with a preset proportional mapping relationship, and by adjusting the inlet pump frequency or the outlet valve opening. Alternatively, this application can also adjust the HRT by ensuring that dE / dt is below a certain threshold for three consecutive sampling periods. The determination result triggers the PLC controller to automatically call the HRT gain parameter group and synchronously update the HRT setting value in the DCS system to complete the closed-loop control.

[0063] Furthermore, this application can also select a corresponding proportional increase from a pre-stored control strategy library based on the similarity matching between the dE / dt trend slope and the historical decay curve, and then superimpose it onto... A new set value is formed on top.

[0064] This application achieves rapid, graded response capability to situations where treatment effectiveness deteriorates drastically based on any of the above methods, ensuring that the bioreactor maintains effective degradation function even when water quality fluctuations intensify.

[0065] For example, this application could involve monitoring at a gas well produced water treatment site that the E value continuously decreased from 0.82 to 0.71 (over a period of 2 hours), and calculating... The system automatically increased the original HRT0 = 30 hours to 30 × 1.32 = 39.6 hours (an increase of 32%), and by reducing the influent flow rate by 12% and fine-tuning the overflow weir height, the actual hydraulic retention time stabilized and approached the target value. During this process, the downward trend of effluent COD concentration slowed significantly, and the pH fluctuation range decreased, indicating that the metabolic activity of microorganisms was effectively maintained.

[0066] Step Two: When At that time, HRT will be set at the reference value. Increase by 10%–20% on the basis; in, This indicates that the E value is in a slow decline and has not yet shown a sudden deterioration, but it still suggests that there is a risk of marginal weakening of the processing efficiency; this conditional feature, together with the above, constitutes a two-level quantitative criterion for the decline trend of the E value. The two are mutually exclusive and cover all possible negative change scenarios within the warning interval. An increase of 10%–20% could refer to Using the base as the base, the incremental value is calculated proportionally, and the final adjusted value is obtained. , where δ ∈ [0.10, 0.20]; this range of proportions is used to moderately extend the microbial contact reaction time without significantly disturbing the hydraulic balance of the system, so as to suppress potential performance decline.

[0067] For example, this application may drive the SCADA system to call a lightweight HRT compensation module based on whether the average value of dE / dt within the most recent sliding time window (e.g., 60 minutes) falls within the range, so as to only slightly adjust the reflux ratio without changing the main water intake path and achieve gentle intervention; this application may also combine the current water intake load fluctuation coefficient λ (defined as the ratio of real-time water intake COD load to 72-hour moving average load).

[0068] When λ ∈ [0.85, 1.15] and dE / dt meets the conditions of this step, a 15% increase is used as the default compensation value to balance robustness and response sensitivity. Furthermore, this application can also jointly judge dE / dt with the change in E value ΔE at the previous moment. When |ΔE|<0.03 and dE / dt ∈ [-0.05, 0), the minimum increase (10%) response is activated to avoid redundant control actions.

[0069] This application obtains the ability to proactively intervene in early performance degradation signals based on any of the above methods, thereby improving system operational stability and anti-interference margin.

[0070] For example, this application could be based on another operating condition where the E value slowly decreases from 0.78 to 0.76 (over a period of 4 hours), and the calculated value is... It falls within the second threshold range; the control system did not trigger a significant parameter change, but only... The time was increased to 28 × 1.15 = 32.2 hours, and the internal mass transfer efficiency of the reactor was enhanced by increasing the speed of the internal circulation pump by 5%. In the following two hours, the E value rose back to 0.77, confirming that the flexible control strategy effectively blocked the chain reaction of performance degradation.

[0071] This application divides the time-varying decay rate dE / dt of the E value into two gradient intervals with clear physical meaning and engineering boundaries, and configures differentiated and quantifiable HRT amplification rules for each interval, thus establishing the first-level control strategy on observable, calculable, and executable data-driven logic; based on this, combined with By determining the reasonable range of values ​​(24–36 hours) and the inherent kinetic characteristics of the bioreactor, it is ensured that each adjustment is sufficient to improve the treatment effect without causing microbial community instability or system hydraulic shock due to sudden changes in residence time. Ultimately, it achieves accurate identification and graded response to different deterioration intensities within the warning range, significantly improving the automation level, operational reliability and long-term adaptability of the gas well produced water biological treatment process.

[0072] This application also provides that the reference hydraulic residence time HRT0 ranges from 24 to 36 hours.

[0073] Step 1: Reference hydraulic residence time The value range is 24 to 36 hours.

[0074] in, It can refer to the average residence time of gas well produced water in a bioreactor, which is a key operating parameter reflecting the reactor's treatment capacity and the metabolic response of microorganisms.

[0075] The initial set value can be determined comprehensively based on the characteristics of the gas well produced water, the degradation kinetics of the microbial community, and the engineering economics.

[0076] In this embodiment, As the benchmark value for dynamic adjustment in the first-level control strategy, its value directly affects the rationality of the starting point and adjustment range of the subsequent HRT incremental adjustment based on the rate of decrease of E value dE / dt.

[0077] when When set to 24 hours, it is suitable for influent chemical oxygen demand. Operating conditions with relatively low (e.g., below 800 mg / L), small salinity fluctuations, and stable microbial activity; when When set to 36 hours, it is applicable Complex operating conditions with high concentrations (e.g., above 1500 mg / L), significant pH shifts, or salinity close to the S0 threshold are required to ensure sufficient degradation of organic matter and stability of microbial function.

[0078] This application can be based on the statistical analysis results of historical water quality data of gas well produced water, selecting the minimum HRT value within a 24-36 hour interval that achieves a COD compliance rate of ≥90%, pH fluctuation range of ≤0.5, and no significant salinity inhibition effect as the minimum HRT value. .

[0079] This application can also be based on the steady-state operation test results of a pilot-scale bioreactor under simulated real influent load, ensuring the continuous E value. Under the premise of this, the shortest effective HRT value is calculated by reverse calculation and rounded up to the range of 24-36 hours as the minimum effective HRT value. .

[0080] This application obtains a stable, reproducible, and engineering-adaptable reference hydraulic residence time based on any of the above methods. This provides a reliable starting point for the dynamic control mechanisms in Examples 4 and 5.

[0081] For example, this application could be based on actual measured water intake at a gas well production water treatment site. The concentration was 1250 mg / L, the sulfur concentration was 18600 mg / L, and the pH was 6.3; after 7 days of continuous monitoring, historical... The concentration is 17200 mg / L. Taking K=1.3, we get... =22360 mg / L; set α=0.6, β=0.25, γ=0.15, , Before dynamic control is initiated, Set it to 30 hours and run stably for 48 hours. During this period, the average value of E is 0.76 and the standard deviation is 0.02. Subsequently, the influent load is artificially increased to make the value of E enter the warning range. At this time, based on this 30-hour benchmark, the adjustment amount of HRT is calculated in real time according to dE / dt, and the value of E is successfully maintained at for up to 16 hours, verifying the rationality of the value and the effectiveness of the regulation response.

[0082] In this application, by setting the reference hydraulic retention time to be 24 - 36 hours, combined with the technical characteristics of high concentration of organic matter, limited biodegradability, and significant salinity fluctuation in the produced water of gas wells, it is ensured that the bioreactor has sufficient microbial metabolic response time in the initial operation stage. This range can avoid insufficient degradation caused by being too short and the value of E frequently falling into the warning or abnormal range, and prevent sludge aging, decline in dissolved oxygen mass transfer efficiency, and redundant equipment investment caused by being too long. As the common reference benchmark for all dynamic adjustment operations in the first-level regulation strategy, the reasonable value of

[0083] This application also provides a second-level regulation strategy, including: Step S71: Add external carbon source and nitrogen source to the bioreactor, and adjust the influent carbon-nitrogen ratio C / N to (20 - 30):1; Among them, the carbon-nitrogen ratio C / N can refer to the mass ratio of the total amount of biodegradable organic carbon to the total nitrogen content in the influent water. The carbon-nitrogen ratio C / N is the ratio of the total biodegradable carbon source characterized by chemical oxygen demand COD equivalent to Kjeldahl nitrogen TKN. The external carbon source is at least one of sodium acetate, glucose or methanol, and its purpose of addition is to supplement the shortage of natural carbon source in the produced water of gas wells, so that heterotrophic microorganisms can obtain sufficient electron donors. The external nitrogen source is at least one of ammonium chloride, ammonium sulfate or urea, and its purpose of addition is to balance the influent nitrogen nutrition level and avoid the inhibition of the activity of nitrifying bacteria due to nitrogen limitation.

[0084] In this embodiment, when the comprehensive treatment effect index E < Eth2, it indicates that the system has entered the abnormal range and the microbial metabolic ability has decreased significantly. At this time, by adding external carbon source and nitrogen source and dynamically adjusting the influent C / N to (20 - 30):1, the dual nutritional requirements of heterotrophic bacteria for degrading organic matter and nitrifying bacteria for oxidizing ammonia nitrogen can be satisfied simultaneously, thus reconstructing the cooperative metabolic basis of the microbial functional community.

[0085] This application calculates the required carbon and nitrogen source dosages based on the real-time COD and TKN concentrations of the influent, combined with the target C / N range, and injects them proportionally through a metering pump. This application establishes a C / N-DO-E response relationship table based on historical operating data. When E is continuously lower than Eth2 for 15 minutes, the corresponding C / N adjustment level is obtained by looking up the table, and the opening of the outlet valve of the carbon source / nitrogen source storage tank is controlled in conjunction with the adjustment. Furthermore, this application uses feedforward feedback composite control logic, with the influent COD and TKN measured by the pre-positioned water quality sensor as feedforward input, and the trend of the change in the effluent E value as feedback correction signal, to correct the carbon and nitrogen addition ratio in real time.

[0086] This application achieves precise, dynamic, and closed-loop control of the influent carbon-nitrogen ratio based on any of the above methods, ensuring that the C / N ratio is always stably maintained within the range of (20~30):1, providing suitable nutrient structure support for the microbial community.

[0087] For example, at a gas well produced water treatment site, when the E value is monitored to be continuously lower than Eth2 for 20 minutes, and the effluent COD removal rate drops below 65% and the ammonia nitrogen residual concentration rises above 8 mg / L, the control system automatically starts step S71. Based on the online COD analyzer measuring the influent COD as 420 mg / L and TKN as 18 mg / L, and calculating back according to the target value of C / N=25:1, it is necessary to supplement sodium acetate (calculated as COD) 310 mg / L and ammonium chloride (calculated as N) 12.6 mg / L. After being injected by the metering pump, the influent C / N rises to 24.8:1 within 1 hour, and the effluent E value rises back to above Eth2 after 2 hours, and the system gradually gets out of the abnormal state.

[0088] Step S72: Simultaneously increase the aeration rate of the bioreactor to maintain the dissolved oxygen concentration (DO) at 4.0 mg / L ~ 6.0 mg / L; where dissolved oxygen (DO) can refer to the concentration of free oxygen dissolved in the aqueous phase in molecular form; the aeration rate is the volume of air supplied per unit time set by the blower airflow regulating valve or the microporous aeration disc airflow controller.

[0089] The aeration rate is increased synchronously within 30 seconds after the start of step S71, or triggered by the same control command as the carbon and nitrogen source addition action, to achieve spatiotemporal coupling between aeration intensity and nutrient supply.

[0090] In this embodiment, DO is maintained at 4.0 mg / L to 6.0 mg / L, which can fully meet the maximum respiratory rate requirements of aerobic heterotrophic bacteria and nitrifying bacteria, while avoiding excessive secretion of extracellular polymers or oxidative damage to the microorganisms caused by excessive DO, thereby enhancing metabolic driving force while ensuring the stability of the microbial community structure.

[0091] Based on the real-time readings of the DO online sensor, this application uses a PID controller to dynamically adjust the output frequency of the blower inverter, so that the DO concentration is stabilized within the set range of 5.0 mg / L ± 0.5 mg / L. Based on the trend of oxidation-reduction potential (ORP) in the bioreactor, this application increases the aeration rate in advance when the ORP is continuously below +150 mV to prevent the formation of local hypoxia zones. Furthermore, this application combines HRT changes and influent load fluctuations to establish a DO demand prediction model, using the influent COD load rate and the current HRT as input variables, preset the DO target value and adjust the aeration intensity in advance.

[0092] This application achieves active, responsive, and adaptive regulation of dissolved oxygen concentration based on any of the above methods, ensuring that DO is always within the effective range of 4.0 mg / L to 6.0 mg / L, thus providing a continuous and stable supply of electron acceptors for microbial aerobic metabolism.

[0093] For example, at the same treatment site, after step S71 is started, the PLC system simultaneously turns on the high-power aeration mode, increasing the blower frequency from 35 Hz to 48 Hz, corresponding to an increase in airflow from 85 m³ / h to 132 m³ / h; 5 minutes later, the DO sensor shows that DO has increased from 3.2 mg / L to 4.7 mg / L, and then stabilized between 4.5 and 5.3 mg / L for the next 90 minutes; at the same time, a 42% increase in nitrification rate was observed, and the phenomenon of nitrite accumulation disappeared, confirming that enhanced aeration effectively activated the activity of nitrifying bacteria.

[0094] This application constructs a dual-factor driven mechanism of nutrient supply and energy supply by directional regulation of the influent C / N ratio in step S71 and synergistic enhancement of dissolved oxygen (DO) in step S72. On the one hand, the C / N ratio is precisely adjusted to (20~30):1 to provide a balanced substrate ratio for heterotrophic and nitrifying bacteria, alleviating metabolic imbalance caused by carbon source deficiency under high salt stress. On the other hand, DO is stably maintained at 4.0~6.0 mg / L to ensure the efficient operation of the aerobic respiratory chain and inhibit facultative / anaerobic side reaction pathways. This allows for rapid restoration of the overall metabolic activity and pollutant removal efficiency of the microbial community within abnormal ranges, significantly shortening the system's self-healing cycle and enhancing the robustness of the bioreactor to sudden changes in the quality of gas well produced water.

[0095] This application also provides a third-level control strategy if, after implementing the second-level control strategy, the duration of continuous monitoring showing that the E value remains below Eth2 exceeds the dynamic threshold T hours: a portion of the existing microbial community within the bioreactor is discharged, and an equal amount of new microbial community is replenished, including: Step 1: After the second-level control strategy is executed, if the duration of continuous monitoring of the E value still being lower than Eth2 exceeds the dynamic threshold T hours, the third-level control strategy is executed. The dynamic threshold T (hours) is a time window adaptively determined based on the current operating status, used to distinguish between short-term disturbances and systemic functional degradation.

[0096] The dynamic threshold T (in hours) is a delay judgment parameter determined based on the relative deviation between Eth2 and the lowest E value Emin monitored during the execution of the second-level control strategy. In this embodiment, the dynamic threshold T is determined by the formula... The calculation shows that the closer Emin is to Eth2 (i.e., the smaller the deviation), the less the system performance degradation, allowing for a longer observation waiting time; conversely, if Emin is significantly lower than Eth2 (i.e., the larger the deviation), it indicates that the system has become deeply unstable and the response delay needs to be shortened to accelerate intervention. This dynamic setting mechanism allows T to be continuously adjusted between 12 hours (when Emin = Eth2) and 36 hours, thereby avoiding response lag or false triggering caused by fixed delay.

[0097] For example, this application may use a sliding time window to count the duration of consecutive values ​​below Eth2 based on the real-time collected E value sequence, and update Emin synchronously; this application may also cache the E value sampling data in a circular buffer, and after each new data is written, traverse the buffer to determine whether all samples are <Eth2 and the cumulative duration is ≥T.

[0098] Furthermore, this application can also be based on an event-driven architecture, starting a countdown timer when E < Eth2 is first detected, and determining whether E remains below Eth2 in each subsequent sampling period. If E ≥ Eth2 in any period, the timer is reset. This application obtains reliable identification of the system's functional degradation trend based on any of the above methods, supporting the accurate triggering of the third-level control strategy.

[0099] For example, in a gas well produced water treatment site, after implementing a second-stage control strategy (i.e., adding carbon and nitrogen sources and increasing aeration), the comprehensive treatment effect index E initially rises slightly to 0.51 (Eth2 is set to 0.55), but then continues to decline, with Emin = 0.42 monitored at the 18th hour; substituting into the formula yields... The system has been continuously recording E < 0.55 for 14.2 hours, exceeding the dynamic threshold T, and thus automatically triggers the third-level control command.

[0100] Step 2: Implement the third-level control strategy: discharge some of the existing microbial community in the bioreactor and replenish an equal amount of new microbial community. The existing microbial community can refer to a collection of microorganisms that have been operating in the current bioreactor for a long time, whose activity has decreased, whose population structure is unbalanced, or whose functional gene expression has declined.

[0101] The new microbial community is a compound functional bacterial agent that has been activated and cultured in the laboratory and has high COD degradation activity and salt tolerance. Its composition includes at least two of the genera Pseudomonas, Bacillus, and Halomonas.

[0102] In this embodiment, discharging and replenishing an equal amount can refer to replacing the sludge according to the principle of equivalent volume or sludge concentration, ensuring that the total microbial biomass in the bioreactor remains basically stable, and only completing the functional population renewal.

[0103] For example, this application can be controlled by linking a sludge pump located at the bottom of the bioreactor with the upper inlet pipe, so as to complete the discharge of old bacteria and the injection of new bacteria in coordination according to a preset flow rate and time; this application can also be used to connect the sludge discharge pipeline and the inlet pipeline respectively using a three-way switching valve, and execute the sludge discharge, flushing and inlet process in stages under the timing logic of the PLC controller.

[0104] Furthermore, this application can also be based on feedback from online sludge concentration sensors to adjust the sludge discharge and bacterial replenishment rates in a closed loop, so that MLSS fluctuations are controlled within ±5%. Based on any of the above methods, this application can obtain controllable and low-disturbance updates to the microbial community structure, ensuring the rapid reconstruction of the system's biological treatment capacity.

[0105] For example, this application may involve the control system receiving a third-level control command, first opening the bottom sludge discharge valve to discharge 15% of the volume of the mixed liquid in the reactor at a flow rate of 1.2 m³ / h for 1.8 hours; then closing the sludge discharge valve and starting the top bacterial replenishment pump to inject the activated bacterial suspension at the same flow rate. bacterial count The process lasted for 1.8 hours. Throughout the process, the reactor liquid level was kept constant, and the DO concentration was dynamically maintained at 5.2±0.3 mg / L by the PID controller. After 24 hours, the E value rose back to 0.63 and tended to stabilize.

[0106] This application achieves a quantitative assessment of the degree of system functional degradation by coupling the determination of the time when the E value is continuously lower than Eth2 with a dynamic threshold T; by leveraging the dependence of T on Emin, the response delay is adaptively scaled according to the severity of the fault; furthermore, through precisely triggered microbial replacement operations, the function of the core biological unit is reset without interrupting operation; and finally, while ensuring process continuity, the robustness and resilience of the bioreactor to high salinity and highly fluctuating water quality of gas well produced water are significantly improved.

[0107] This application also provides a dynamic threshold T via a formula. Confirmed, among which The lowest E value monitored during the implementation of the second-level control strategy, with the maximum value of T not exceeding 36 hours.

[0108] Step 1: Dynamic threshold T is determined using the formula... Confirmed, among which The lowest E value monitored during the implementation of the second-level control strategy, with the maximum value of T not exceeding 36 hours.

[0109] The dynamic threshold T can refer to the time criterion used when determining whether to activate the third-level control strategy. Its value is not fixed, but is calculated in real time based on the actual deterioration of the system. This technical feature is a new limitation added to this application compared to the above embodiments, which is different from the case of a fixed T value or an undefined T value method implied in this application. It can refer to the minimum value recorded by the comprehensive treatment effect index E between the start time of the second-level control strategy and the current time; this technical feature is an independent data object introduced for the first time in this application, used to characterize the worst performance of the system in the second-level intervention process.

[0110] The preset threshold defined in this application is the lower boundary for dividing the abnormal interval and the warning interval. In this step, it is used as a normalization benchmark in the calculation of T. Its meaning and function have been explained in the corresponding implementation of this application, and are only referred to here naturally. The maximum value of T not exceeding 36 hours is a hard upper limit constraint imposed on the dynamic calculation result. It belongs to the state condition feature and is bound to the dynamic threshold T to prevent [further issues]. Extremely low values ​​lead to excessively high theoretical values ​​for T, resulting in a loss of timeliness in emergency response.

[0111] This application may, for example, determine the method based on the sliding window statistics of historical E-sequences. This application could also determine the value of E by continuously collecting the value of E and updating the minimum value in real time from the triggering time of the second-level control strategy. Furthermore, this application can also determine the minimum value by storing the E value in a circular buffer and synchronously comparing and refreshing the current minimum value after each new E value is written. This application obtains a quantitative characterization of the system's deterioration level based on any of the above methods, thereby supporting the adaptive generation of the T value.

[0112] For example, this application could involve the system collecting the E value every 5 minutes after the second-level control strategy is activated at t=0 h; at t=2.5 h, E=0.21 (the current lowest), at t=4.2 h, E=0.18 (updated to the new lowest), at t=7.8 h, E=0.15 (updated again); until t=10.0 h, If at this time Substituting into the formula, we get The T value represents the maximum waiting time allowed for continued observation. If E remains below 0.20 for 13.5 consecutive hours thereafter, the third-level control strategy will be automatically triggered at t=23.5 h. If the calculated T>36, then T=36 hours will be used directly.

[0113] Step 2: The calculation result of the dynamic threshold T is used to determine whether to execute the third-level control strategy. Specifically, after executing the second-level control strategy, if the E value is continuously monitored to be lower than... If the duration exceeds T hours, then part of the original microbial community in the bioreactor will be discharged, and an equal amount of new microbial community will be added.

[0114] Among them, E values ​​were continuously monitored to be lower than The duration can refer to whether E is continuously satisfied, starting from the initiation time of the second-level control strategy. The length of time accumulated during uninterrupted logical judgment; this technical feature is a newly added state judgment condition in this application, and its judgment logic depends on the T generated above; Emission... and supplementation of an equal amount of new microbial community are technical actions already defined in this application, and their structure, object and purpose have been explained in the corresponding implementation of this application, and are only naturally quoted here without being elaborated again.

[0115] This application may, for example, use a timer module in conjunction with an E-comparison unit to determine the duration: whenever... When established, start or keep the timer running; once The timer is immediately cleared; when the timer reading is ≥ T, a trigger signal is output; this application can also construct a Boolean status register, for example. The method employs a set condition and a timer overflow as a reset condition, driving the microbial community replacement instruction through state toggle edges. Further, this application can also input the E sequence into a finite state machine, defining two states: an inefficient state and a timeout state. When in the inefficient state and the dwell time is ≥ T, it transitions to the timeout state and activates the third-level regulation. Based on any of the above methods, this application achieves accurate identification of the regulation timing, ensuring that microbial community replacement neither prematurely disturbs the system nor delays repair.

[0116] For example, this application may involve the system continuously comparing real-time E with [the target value] after the second-level control strategy is activated. If a sample finds E=0.19 (<0.20), the timer starts. In the subsequent 12 samples (5-minute intervals), E remains between 0.16 and 0.19, accumulating to 60 minutes, and the timer displays 1.0 h. At this time, T=13.5 h, and the timer has not yet been triggered. By the 27th sample (t=13.5 h), the timer reaches the threshold, and the system automatically sends a coordinated control command to the dosing pump and the sludge discharge valve to perform the microbial replacement operation.

[0117] This application establishes a negative correlation between the dynamic threshold T and the lowest measured E value during the second-level regulation period, causing T to shorten as the system deteriorates. Utilizing this mapping mechanism, combined with a 36-hour hard upper limit constraint on T, it achieves dual protection for the timing of microbial replacement: on the one hand, it extends the observation period to avoid over-intervention when the system still has some recovery potential; on the other hand, it significantly compresses the response delay when the system is on the verge of functional collapse, improving the sensitivity and adaptability of emergency response. Thus, without increasing hardware investment, it enhances the robustness and adaptive regulation capability of the bioreactor to sudden changes in the quality of gas well produced water.

[0118] This application also provides the proportion of microbial communities discharged and replenished in the third-level control strategy, which is 10% to 30% of the total microbial community in the bioreactor.

[0119] Step 1: In the third-level control strategy, the proportion of microbial communities discharged and replenished is 10% to 30% of the total microbial community in the bioreactor.

[0120] The proportion of discharged and replenished microbial communities can refer to the percentage of the total mass (or volume equivalent) of the original microbial community removed from the bioreactor and the mass (or volume equivalent) of the new microbial community added simultaneously when implementing the third-level control strategy, relative to the total mass (or total volume equivalent) of all microbial communities currently loaded on the bioreactor; the numerical range of this proportion is limited to 10% to 30%, excluding the endpoint extension, that is, the minimum is not less than 10% and the maximum is not more than 30%.

[0121] In terms of technical function, this ratio represents a quantitative constraint on the intensity of microbial population replacement: its lower limit of 10% ensures that each operation has a measurable and cumulative ecological disturbance effect, avoiding the inability to effectively improve system activity due to insufficient replacement volume; its upper limit of 30% prevents the removal of too many native dominant strains that have adapted to the gas well's produced water quality at one time, thereby maintaining the basic stability and functional continuity of the microbial community structure in the reactor.

[0122] This application, for example, can calculate the volume of sludge to be discharged based on the measured value of the suspended solids concentration in the mixed liquor of the bioreactor and the sampling volume, and then prepare an equal amount of fresh bacterial suspension for replenishment. Alternatively, it can estimate the total bacterial count based on the effective volume of the bioreactor and empirical values ​​of typical microbial density, and then set the replenishment mass within the range of 10% to 30%. Furthermore, this application can dynamically adjust the replenishment ratio in real time based on feedback from online monitoring of microbial activity indicators, selecting the optimal value within the range of 10% to 30%. Based on any of the above methods, this application achieves controllable regulation of the intensity of microbial community renewal, enabling the system to maintain a gradual recovery capability of biodegradation function even after severe treatment efficiency degradation.

[0123] For example, this application can be conducted at a gas well produced water treatment site, where the effective volume of the bioreactor is 50 m³, the current MLSS concentration is 8000 mg / L, and the estimated total dry weight of the microbial community is approximately 400 kg. When the third-level control strategy is triggered, it is implemented at a rate of 20%, resulting in the discharge and replenishment of 80 kg of microbial community (by dry weight). The discharged portion is pumped out through a bottom sludge pump, and the replenished portion is a suspension of activated and cultured compound functional microbial agents, whose microbial composition is functionally complementary to the original microbial community, and whose activity is not less than 1.5 × 10¹¹ CFU / kg. The entire discharge and replenishment process is completed within 2 hours, during which aeration and influent flow rates are maintained constant to ensure the continuous stability of the reactor's hydraulic state and dissolved oxygen level.

[0124] This application strictly controls the microbial community replenishment ratio in the third-level control strategy within the range of 10% to 30%. On the one hand, it preserves the main body of the original functional microbial community in the reactor that is adapted to the high-salt, low-carbon-nitrogen ratio environment, maintaining the system's basic degradation capacity for characteristic pollutants (such as organic acids, benzene compounds, and trace heavy metals) in the gas well produced water. On the other hand, it introduces an appropriate amount of highly active new microbial community to compensate for the decline in population diversity and metabolic inertia caused by long-term operation. Based on this, multiple replenishment operations that meet this ratio can gradually achieve functional optimization and structural renewal of the microbial community without interrupting process operation, thereby addressing the problem of E value being consistently lower than 10%. In case of abnormal operating conditions, it avoids the risk of start-up delay caused by full replacement and avoids the low repair efficiency caused by sporadic replenishment, ultimately ensuring the robustness and adaptability of the bioreactor in long-term operation.

[0125] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A bioreactor treatment method for produced water from a gas well, characterized in that, Includes the following steps: S1: Introduce gas well produced water into a bioreactor, which is loaded with microbial communities; S2: Real-time monitoring of key water quality parameters of the effluent from the bioreactor, including at least chemical oxygen demand concentration, pH value and salinity; S3: Based on the key water quality parameters monitored in step S2, calculate the current comprehensive treatment effect index E; the comprehensive treatment effect index E is calculated using the following formula: Where C0 is the initial concentration of influent chemical oxygen demand, C is the real-time concentration of effluent chemical oxygen demand, pH is the pH value of effluent, S is the real-time concentration of effluent salinity, S0 is the preset salinity threshold, α, β, and γ are weighting coefficients, and α+β+γ=1. S4: Based on the value of the comprehensive processing effect index E, divide it into three processing effect intervals and execute the corresponding control strategy for each interval: when E ≥ Eth1, it is determined to be a qualified interval, and the current operating parameters are maintained; when Eth2 ≤ E < Eth1, it is determined to be a warning interval, and the first-level control strategy is executed; when E < Eth2, it is determined to be an abnormal interval, and the second-level control strategy is executed; where Eth1 and Eth2 are preset thresholds, and Eth1 > Eth2.

2. The method according to claim 1, characterized in that, The preset salinity threshold S0 is set to satisfy the following: Where Savg is the historical average salinity of the influent within the preset period, and K is a coefficient with a value range of 1.1 to 1.

5.

3. The method according to claim 1, characterized in that, The weighting coefficients α, β, and γ have the following ranges: α ∈ [0.5, 0.7], β ∈ [0.2, 0.3], and γ ∈ [0.1, 0.2].

4. The method according to claim 1, characterized in that, The first-level control strategy includes: dynamically adjusting the hydraulic retention time (HRT) of the bioreactor based on the rate of decrease of the E value (dE / dt) within the warning interval.

5. The method according to claim 4, characterized in that, The dynamic adjustment of the hydraulic residence time (HRT) specifically refers to: when... When HRT is increased by 25%-40% from the baseline value HRT0; when At that time, increase HRT by 10%-20% from the baseline value HRT0.

6. The method according to claim 5, characterized in that, The reference hydraulic residence time The value range is 24 to 36 hours.

7. The method according to claim 1, characterized in that, The second-level regulation strategy includes the following steps: S71, adding exogenous carbon and nitrogen sources to the bioreactor and adjusting the influent carbon-nitrogen ratio C / N to (20~30):1; S72. Simultaneously increase the aeration rate of the bioreactor to maintain the dissolved oxygen concentration (DO) at 4.0 mg / L ~ 6.0 mg / L.

8. The method according to claim 7, characterized in that, If, after the second-level control strategy is implemented, the duration of the continuous monitoring of the E value remaining below Eth2 exceeds the dynamic threshold T hours, then the third-level control strategy is implemented: discharging part of the existing microbial community in the bioreactor and replenishing it with an equal amount of new microbial community.

9. The method according to claim 8, characterized in that, The dynamic threshold T is determined by the formula: It is determined that Emin is the lowest E value monitored during the implementation of the second-level control strategy, and the maximum value of T does not exceed 36 hours.

10. The method according to claim 8, characterized in that, In the third-level control strategy, the proportion of microbial communities discharged and replenished is 10% to 30% of the total microbial community in the bioreactor.

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