Supergravity waste gas oil removal method

Abstract

The invention relates to the technical field of waste gas oil removal, in particular to a supergravity waste gas oil removal method which comprises the following steps: a working condition sensing step for acquiring a real-time operation state parameter set of a supergravity rotor in real time, a load quantification step for determining the oil content of the supergravity rotor on the basis of the real-time operation state parameter set and a preset reference parameter set, generating a comprehensive load index for representing the real-time oil mist load of the waste gas; a target rotating speed optimization step, which is used for determining the emission compliance critical rotating speed according to the comprehensive load index, and setting the rotating speed with the dynamic safety margin as the optimal target rotating speed in combination with the current rotating speed of the motor; the closed-loop speed regulation execution step is used for generating a rotating speed adjusting instruction according to the deviation between the optimal target rotating speed and the current rotating speed of the motor and issuing the rotating speed adjusting instruction to the frequency converter so as to drive the rotating speed of the motor to converge to the optimal target rotating speed, the waste gas oil removal treatment efficiency and the energy-saving effect are improved, and the self-adaptive capacity and the operation stability of the system are further enhanced.

Description

Technical Field

[0001] This invention relates to the field of exhaust gas oil removal technology, specifically to a method for removing oil from exhaust gas using ultragravity. Background Technology

[0002] In industrial waste gas oil removal, the core equipment traditionally operating at a fixed speed is often referred to as a high-speed centrifugal separator. This equipment relies on the centrifugal force generated by high-speed rotation to separate oil and gas. Oily waste gas is first introduced into the equipment and guided by guide vanes or spiral channels to form a stable rotating airflow. The core of the equipment is a metal impeller or perforated drum driven at a constant speed by a motor. The motor operates at a preset, constant high speed, and its speed does not change with the waste gas conditions during operation. The high-speed rotating impeller or drum further accelerates the airflow to extremely high angular velocities. Figure 2 and Figure 3 As shown, in this state, oil droplets or liquid droplets with a density much greater than that of gas are subjected to strong centrifugal force and violently thrown against the outer wall of the equipment cavity, while the less dense purified gas concentrates in the central rotating area. The oil droplets thrown against the wall collide and aggregate into larger droplets, which flow downwards along the wall under the action of gravity and eventually collect in the oil collection tank at the bottom, which can be periodically discharged or pumped out for recovery. The purified gas is discharged from the air outlet in the central area and can enter the atmosphere or subsequent treatment stages. This fixed speed mode has a simple and reliable structure, but its efficiency is poor outside the design operating point. When the exhaust gas flow exceeds the design value, the insufficient gas residence time leads to a significant decrease in separation efficiency; when the flow is too low, it causes energy waste. It also has limited ability to handle fluctuations in oil concentration and tiny oil mists, and its operating energy consumption is constant, with a large start-up and shutdown impact. Its separation effect mainly relies on the density difference between oil droplets and gas and the strong centrifugal force field, and it is the predecessor of modern variable frequency speed control centrifugal separators.

[0003] Heavy-duty exhaust machines require preset constant speed parameters for system control. These parameters often suffer from poor adaptability and excessive energy consumption, failing to respond promptly and accurately to real-time changes in exhaust load. This situation makes it difficult for operators to accurately grasp the optimal operating state of the system, affecting the stability of oil removal efficiency and the achievement of energy-saving effects. Traditional methods lack the ability to adaptively adjust to load changes and cannot provide a dynamically optimized control strategy for system operation.

[0004] The aforementioned situation and shortcomings are mainly due to limitations in load sensing methods and control technologies. The fixed speed setting method cannot sense real-time changes in exhaust gas oil mist load, resulting in a low degree of matching between system operating parameters and actual needs. The simplistic and static nature of the control algorithm makes the system response insufficiently sensitive, making it difficult to meet the precise control requirements under dynamic load conditions. Consequently, during exhaust gas treatment, the system cannot automatically adjust to the optimal operating point, leading to excessive energy consumption and unstable treatment results.

[0005] There is an urgent need for a supergravity method for removing oil from exhaust gas to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for removing oil from waste gas under ultragravity, which solves the problems existing in the background art.

[0007] To solve the above-mentioned technical problems, the present invention provides a method for oil removal from supergravity exhaust gas, comprising: The working condition sensing step is used to acquire the real-time operating status parameter set of the supergravity rotor. The real-time operating status parameter set includes: the real-time power of the motor reflecting the driving resistance, the real-time pressure drop of the inlet and outlet reflecting the passability resistance, and the current speed of the motor. The load quantization step is used to generate a comprehensive load index that characterizes the real-time oil mist load of exhaust gas based on the real-time operating status parameter set and the preset benchmark parameter set. The target speed optimization step is used to determine the emission compliance critical speed based on the comprehensive load index, and combined with the current motor speed, set the speed that takes into account the dynamic safety margin as the optimal target speed. The closed-loop speed control execution step is used to generate a speed adjustment command based on the deviation between the optimal target speed and the current speed of the motor, and send it to the frequency converter to drive the motor speed to converge to the optimal target speed.

[0008] Preferably, in the load quantization step, the preset reference parameter set includes: preset motor rated power, preset filter ultimate voltage drop design value, and preset motor no-load speed and rated speed.

[0009] Preferably, in the load quantization step, generating the comprehensive load index includes: generating a normalized power component reflecting the proportion of the motor's real-time power within the rated range, and a normalized voltage drop component reflecting the proportion of the real-time voltage drop within the extreme range; and weighting and summing the normalized power component and the normalized voltage drop component based on preset power weighting coefficients and voltage drop weighting coefficients to obtain the comprehensive load index.

[0010] Preferably, the load quantization step further includes: constructing a dynamic mapping relationship between the current motor speed and the no-load reference power; before generating the normalized power component, calling the dynamic mapping relationship, using the current motor speed to correct the no-load reference power, and using the corrected no-load reference power for the calculation of the normalized power component.

[0011] Preferably, in the target speed optimization step, determining the emission compliance critical speed includes: based on the baseline maintenance speed when the system is unloaded, and according to the nonlinear function relationship of the comprehensive load index, superimposing the load compensation speed to obtain the emission compliance critical speed.

[0012] Preferably, in the target speed optimization step, the calculation of the optimal target speed is further limited to: multiplying the emission compliance critical speed by a preset static safety margin coefficient, and superimposing the dynamic response compensation value determined by the rate of change of the comprehensive load index, and the feedback correction value determined by the deviation between the current motor speed and the reference speed, so as to generate the optimal target speed.

[0013] Preferably, the determination of the dynamic response compensation value includes: calculating the change of the comprehensive load index per unit time to generate the load change rate; and multiplying the load change rate by a preset dynamic response gain coefficient to obtain the dynamic response compensation value.

[0014] Preferably, the determination of the feedback correction value includes: dynamically generating a reference speed based on the current motor speed and the comprehensive load index; calculating the difference between the current motor speed and the reference speed; and multiplying the difference by a preset speed deviation feedback coefficient to obtain the feedback correction value.

[0015] Preferably, the closed-loop speed regulation execution step further includes: after issuing the speed adjustment command, taking the adjusted real-time motor power, real-time voltage drop and current motor speed as the real-time operating status parameter set for the new iteration, and returning it to the operating condition perception step, thereby forming a closed-loop control loop for continuously tracking the lowest energy consumption point.

[0016] Preferably, the method further includes a speed reference dynamic coupling step, which is used to establish a real-time mapping relationship between the motor reference speed and the current motor speed and the comprehensive load index; and the motor reference speed generated by the mapping relationship is used in the target speed optimization step to enhance the accuracy of adaptive adjustment.

[0017] This invention provides a method for removing oil from waste gas under high gravity conditions, which has the following beneficial effects: By employing precise operating condition sensing, scientific load quantification, intelligent target speed optimization, and efficient closed-loop speed control, this method significantly improves upon the shortcomings of traditional fixed-speed exhaust gas treatment technologies for heavy-duty machinery. Compared to traditional methods, this approach not only enhances the system's ability to sense and respond to changes in exhaust gas load but also provides more accurate and timely speed regulation control through multi-dimensional comprehensive load index quantification and dynamic speed optimization mechanisms. These improvements not only enhance the efficiency and energy-saving effect of exhaust gas oil removal but also strengthen the system's adaptability and operational stability.

[0018] By introducing precise calculations of the emission compliance critical speed and scientific determination of the optimal target speed, a supergravity-based method for oil removal from waste gas has achieved significant improvements in energy efficiency optimization and emission compliance in waste gas treatment. The system can quickly generate emission compliance critical speeds and optimal target speeds, responding promptly to load changes and operational condition adjustments. Dynamic identification of different load levels allows the system to adopt corresponding speed adjustment strategies based on specific operating conditions, thereby improving the targeting and efficiency of waste gas oil removal. These improvements not only enhance the system's energy-saving and consumption-reducing capabilities but also strengthen the accuracy and reliability of emission control.

[0019] By establishing a dynamic acquisition system for real-time operating status parameters and scientifically configuring a preset benchmark parameter set, a technological leap from passive response to proactive prediction has been achieved. The coordinated monitoring of real-time motor power, real-time pressure drop at the inlet and outlet, and the motor's current speed provides a reliable data foundation for the accurate calculation of the comprehensive load index. The intelligent generation of speed adjustment commands and the precise execution by the frequency converter ensure rapid convergence of the motor speed towards the optimal target speed. This closed-loop control system enables the system to continuously track the lowest energy consumption point, minimizing energy consumption while ensuring emission compliance.

[0020] By introducing a dynamic coupling step based on the speed reference and establishing a mapping relationship between the motor's reference speed and real-time operating status, the accuracy of adaptive adjustment is enhanced. Real-time updates of the dynamic mapping relationship and intelligent adjustment of reference parameters enable the system to automatically optimize control strategies based on historical experience and current conditions. This intelligent parameter adjustment mechanism avoids the complexity and subjectivity of manual parameter tuning, improving the system's automation level and control precision. In complex and ever-changing industrial environments, this method demonstrates excellent adaptability and robustness, providing technical support for achieving truly intelligent waste gas treatment. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a logic block diagram of the system of the present invention; Figure 2 This is a front view of the super-heavy machine; Figure 3 This is the left view of the super-heavy machine. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example 1

[0023] Please see Figure 1 This invention provides a method for removing oil from exhaust gas under high gravity conditions, comprising: The working condition sensing step is used to acquire the real-time operating status parameter set of the supergravity rotor. The real-time operating status parameter set includes: the real-time power of the motor reflecting the driving resistance, the real-time pressure drop of the inlet and outlet reflecting the passability resistance, and the current speed of the motor. The load quantization step is used to generate a comprehensive load index that characterizes the real-time oil mist load of exhaust gas based on the real-time operating status parameter set and the preset benchmark parameter set. The target speed optimization step is used to determine the emission compliance critical speed based on the comprehensive load index, and combined with the current motor speed, set the speed that takes into account the dynamic safety margin as the optimal target speed. The closed-loop speed control execution step is used to generate a speed adjustment command based on the deviation between the optimal target speed and the current speed of the motor, and send it to the frequency converter to drive the motor speed to converge to the optimal target speed.

[0024] The operating condition sensing step collects the real-time power of the motor through the inverter's built-in interface functions. This power directly reflects the driving resistance state of the supergravity rotor under current load conditions. Compared with the traditional fixed speed operation mode, real-time power monitoring can accurately capture the dynamic impact of changes in exhaust oil mist concentration on the system load. Real-time pressure drop at the inlet and outlet. This parameter, obtained via a differential pressure sensor, characterizes the permeability resistance of the filter media. As oil mist particles gradually accumulate in the exhaust gas, the pressure drop exhibits a non-linear increasing trend, making it difficult for traditional methods to respond to such changes in real time. (Motor current speed) Reading directly from the frequency converter provides basic data support for subsequent load quantization and speed optimization.

[0025] The load quantization step constructs a comprehensive load index based on a set of real-time operating status parameters. This index integrates information from three dimensions: power, voltage drop, and speed. The calculation formula is as follows: ; in Indicates the power weighting coefficient. This represents the pressure drop weighting coefficient. This represents the speed weighting coefficient, which satisfies... , This indicates the no-load reference power of the motor at the current speed. Indicates the rated power of the motor. This indicates the filter's ultimate pressure drop design value. Indicates the no-load speed of the motor. This indicates the rated speed of the motor. The comprehensive load index transforms multi-dimensional indirect measurement parameters into a unified normalized state quantity, solving the problem of parameter isolation in traditional methods and providing a reliable basis for accurately characterizing the real-time oil mist load of exhaust gas.

[0026] The target speed optimization step determines the emission compliance critical speed based on the comprehensive load index. This rotational speed ensures that the outlet gas concentration meets the standard under the current load conditions. The calculation formula is as follows: ; in This indicates the base sustaining speed when the system is unloaded. Indicates the load compensation coefficient. Indicates a nonlinear exponent. This represents the speed influence coefficient. Optimal target speed. Based on the emission compliance critical speed and taking into account the dynamic safety margin, the calculation formula is as follows: ; in Represents the static safety margin coefficient. This represents the dynamic response compensation value. This indicates the feedback correction value.

[0027] The closed-loop speed control execution step generates a speed adjustment command based on the deviation between the optimal target speed and the current motor speed. The calculation formula is: ; The command is sent to the frequency converter to achieve precise speed regulation, and the speed of the drive motor converges to the optimal target speed, forming a closed-loop control loop that continuously tracks the point of lowest energy consumption.

[0028] The working condition perception step, load quantification step, target speed optimization step, and closed-loop speed regulation execution step work together to achieve a technological leap from fixed speed to adaptive energy efficiency curve tracking. In industrial waste gas treatment scenarios, this method can dynamically adjust operating parameters according to real-time oil mist load. Compared with the traditional normalized over-treatment mode, the energy-saving effect is significantly improved. At the same time, the proactive dynamic adjustment effectively avoids the risk of instantaneous exceeding of standards, providing a dual guarantee for enterprises to achieve environmental compliance and energy conservation.

[0029] The dynamic mapping relationship of the no-load reference power was established by least squares fitting of previous experimental data, and the complete formula is as follows: ; in This represents the zero-order term coefficient, derived from motor static characteristic testing. Its physical meaning is the motor's static power loss, with a typical value of 50-100W. This represents the coefficient of the first-order term, reflecting the linear relationship between speed and power. It originates from the mechanical loss characteristics of the motor and typically has a value of 0.01-0.05 W / rpm. This represents the coefficient of the quadratic term, reflecting the square-law relationship of wind resistance loss. It originates from aerodynamic theory and measured data, and its typical value is... W / rpm², This represents the coefficient of the cubic term, reflecting the nonlinear effects at high speeds. It originates from higher-order terms of bearing friction and eddy current losses, with a typical value of [value missing]. W / rpm³, each coefficient was obtained by fitting data from 20 test points on a standard laboratory test bench using the least squares method, and the fitting correlation coefficient was... And it is stored in the control system database.

[0030] Real-time pressure drop at air inlet and outlet Data is acquired using differential pressure sensors deployed at the inlet and outlet of the supergravity rotor. The sensor model is a high-precision capacitive differential pressure transmitter with a measurement range of 0-5000Pa and an accuracy class of 0.25%FS. The sensor is installed 500mm away from both the inlet and outlet of the rotor to avoid the influence of airflow disturbance. The acquisition frequency is 1Hz, and the data accuracy reaches ±0.5%. Example 2

[0031] Preferably, in the load quantization step, the preset reference parameter set includes: preset motor rated power, preset filter ultimate voltage drop design value, and preset motor no-load speed and rated speed.

[0032] Preferably, in the load quantization step, generating the comprehensive load index includes: generating a normalized power component reflecting the proportion of the motor's real-time power within the rated range, and a normalized voltage drop component reflecting the proportion of the real-time voltage drop within the extreme range; and weighting and summing the normalized power component and the normalized voltage drop component based on preset power weighting coefficients and voltage drop weighting coefficients to obtain the comprehensive load index.

[0033] The preset reference parameter set provides a standardized reference for the load quantization process, including the preset motor rated power. As the denominator for power normalization, ensuring comparability between devices of different power ratings, this parameter is obtained from the motor nameplate or set during system initialization. The preset filter ultimate voltage drop design value... This represents the theoretical upper limit of pressure drop when the filter media is completely clogged, providing a boundary constraint for pressure drop normalization. This value is determined based on the filter design parameters. The preset motor no-load speed... With rated speed The reference range for normalized speed is established. The no-load speed is measured by running the system without load during startup, and the rated speed is obtained from the motor's technical specifications.

[0034] Normalized power components The formula for calculating the percentage of the motor's real-time power within its rated range is as follows: ; in Indicates the real-time power of the motor. This indicates the no-load reference power of the motor at the current speed. This indicates the rated power of the motor. Normalized voltage drop component. The formula for calculating the percentage of real-time voltage drop within the limit range is as follows: ; in This indicates the real-time pressure drop at the air inlet and outlet. This indicates the filter's design limit pressure drop.

[0035] Power weighting coefficient With pressure drop weighting factor Calibration is performed based on the specific exhaust gas composition and filter media characteristics. The normalized power component and normalized pressure drop component are weighted and summed to obtain the comprehensive load index. The calculation formula is: ; in A reasonable configuration of weighting coefficients can balance the contributions of power and voltage drop to the representation of load state.

[0036] The synergistic effect of the preset benchmark parameter set, normalized power component, and normalized voltage drop component transforms multi-dimensional physical quantities into a unified dimensionless comprehensive load index, eliminating dimensional and numerical range differences between different parameters. In typical industrial waste gas treatment applications such as petrochemicals and machining, this normalization method can accurately reflect the real-time changes in oil mist load, providing a reliable quantitative basis for subsequent speed optimization. Compared with single-parameter judgment methods, the introduction of the comprehensive load index significantly improves the accuracy and robustness of load state identification.

[0037] Power weighting coefficient With pressure drop weighting factor Calibration was performed based on the specific composition of the exhaust gas and the characteristics of the filter media. The calibration method employed a multi-objective optimization algorithm, with objective functions including maximizing treatment efficiency and minimizing energy consumption. The constraint was that the emission concentration did not exceed the national standard limit. Calibration data was derived from long-term monitoring data from typical industrial application sites, including operational data from 15 sites in the petrochemical industry, 12 sites in the machinery manufacturing industry, and 8 sites in the automobile manufacturing industry. The data collection period was no less than 6 months. For typical values ​​in the petrochemical industry... For typical values ​​in the machining industry For the typical value of the automobile manufacturing industry . Example 3

[0038] Preferably, the load quantization step further includes: constructing a dynamic mapping relationship between the current motor speed and the no-load reference power; before generating the normalized power component, calling the dynamic mapping relationship, using the current motor speed to correct the no-load reference power, and using the corrected no-load reference power for the calculation of the normalized power component.

[0039] Preferably, in the target speed optimization step, determining the emission compliance critical speed includes: based on the baseline maintenance speed when the system is unloaded, and according to the nonlinear function relationship of the comprehensive load index, superimposing the load compensation speed to obtain the emission compliance critical speed.

[0040] Preferably, in the target speed optimization step, the calculation of the optimal target speed is further limited to: multiplying the emission compliance critical speed by a preset static safety margin coefficient, and superimposing the dynamic response compensation value determined by the rate of change of the comprehensive load index, and the feedback correction value determined by the deviation between the current motor speed and the reference speed, so as to generate the optimal target speed.

[0041] Dynamic mapping relationship establishes the current motor speed Compared with no-load reference power The real-time correlation between them is fitted to a polynomial function using previous experimental data, and the calculation formula is as follows: ; in Denotes the coefficient of the zero-degree term. Denotes the coefficient of the linear term. Denotes the coefficient of the quadratic term. This represents the coefficients of the cubic term, which are obtained through least-squares fitting and stored in the control system database. The corrected no-load reference power. It is used for accurate calculation of normalized power components, eliminates the influence of speed changes on no-load power, and improves the accuracy of load condition identification.

[0042] Base maintenance speed This indicates the minimum operating speed at which the system maintains basic oil removal efficiency under no-load conditions. This speed is determined through performance testing during the system design phase. Load-compensated speed. The formula is based on the nonlinear functional relationship of the comprehensive load index: ; in Indicates the load compensation coefficient. This represents a nonlinearity index, reflecting the nonlinear relationship between load and speed demand, typically ranging from 1.2 to 1.8. (Emissions compliance critical speed) It is obtained by superimposing the base holding speed and the load-compensated speed: ; Static safety margin factor This is a preset fixed value, typically 5%-10%, to ensure emissions meet standards under static operating conditions. Dynamic response compensation value. The rate of change of the comprehensive load index is determined by the following formula: ; in Represents the dynamic response gain coefficient. This represents the time rate of change of the comprehensive load index. Feedback correction value. The reference speed is determined by the deviation between the current motor speed and the reference speed. Dynamically generated based on current speed and overall load index: ; The formula for calculating the feedback correction value is: ; in This represents the speed deviation feedback coefficient. Optimal target speed. The complete calculation formula is: ; The combined effect of dynamic mapping relationship, basic maintenance speed, load compensation speed, static safety margin coefficient, dynamic response compensation value and feedback correction value realizes multi-level fine control of speed optimization. When facing complex and ever-changing industrial waste gas treatment conditions, this optimization strategy can quickly respond to load changes and dynamically adjust operating parameters to ensure optimal energy efficiency operation while meeting emission requirements. Compared with the traditional fixed safety margin method, the dynamic optimization method significantly reduces system energy consumption while ensuring safety.

[0043] This represents the load compensation coefficient, determined through load-speed response testing under standard laboratory conditions. The test conditions include five different load levels, with each level requiring a test duration of at least one hour. The test medium is standard atomized white oil with a concentration range of 50-800 mg / m³. There are at least 15 test points, with a typical value range of 100-300 rpm. The specific value is related to the type of filter medium and system design parameters. For fiber filters, the value is 100-200 rpm, and for metal mesh filters, it is 200-300 rpm.

[0044] The nonlinear index is determined by nonlinear fitting of load and speed demand. The fitting data comes from the treatment efficiency test under different oil mist concentrations. The test conditions are the rated air volume under standard conditions. The test method complies with GB / T14295-2008 standard. The concentration range is 50-800mg / m³, and there are no less than 15 test points. The typical value is 1.2-1.8. This index reflects the nonlinear growth characteristics of speed demand when the load increases. For high viscosity oil mist, the upper limit value of 1.8 is taken, and for low viscosity oil mist, the lower limit value of 1.2 is taken. Example 4

[0045] Preferably, the determination of the dynamic response compensation value includes: calculating the change of the comprehensive load index per unit time to generate the load change rate; and multiplying the load change rate by a preset dynamic response gain coefficient to obtain the dynamic response compensation value.

[0046] Preferably, the determination of the feedback correction value includes: dynamically generating a reference speed based on the current motor speed and the comprehensive load index; calculating the difference between the current motor speed and the reference speed; and multiplying the difference by a preset speed deviation feedback coefficient to obtain the feedback correction value.

[0047] Preferably, the closed-loop speed regulation execution step further includes: after issuing the speed adjustment command, taking the adjusted real-time motor power, real-time voltage drop and current motor speed as the real-time operating status parameter set for the new iteration, and returning it to the operating condition perception step, thereby forming a closed-loop control loop for continuously tracking the lowest energy consumption point.

[0048] Load change rate The comprehensive load index is obtained by calculating its change over a unit of time using a numerical differentiation method. The calculation formula is as follows: ; in This represents the overall load index at the current moment. This represents the overall load index at the previous moment. Indicates the sampling time interval. Dynamic response gain coefficient. These are preset parameters, calibrated based on the system response characteristics, and typically range from 50-200 rpm·s. Dynamic response compensation value. The calculation formula is: ; Reference speed Based on the current motor speed and comprehensive load index Dynamically generated, using two-dimensional interpolation or table lookup methods, the mapping relationship is as follows: ; This mapping relationship was established by fitting historical operating data, reflecting the ideal operating state under different speed and load conditions. Speed ​​deviation The calculation formula is: ; Speed ​​deviation feedback coefficient These are preset parameters used to adjust the feedback strength, typically ranging from 0.1 to 0.5. Feedback correction value. The calculation formula is: ; The closed-loop control loop adjusts the real-time motor power of the system. Real-time pressure drop and the current speed of the motor As the real-time operating status parameter set for the new iteration, it is returned to the operating condition sensing step to form a complete feedback control loop. Control cycle The time is usually set to 1-5 seconds to ensure that the system can respond to changes in operating conditions in a timely manner.

[0049] The synergistic effect of load change rate, dynamic response gain coefficient, dynamic generation of reference speed, speed deviation feedback coefficient, and closed-loop control loop constructs a high-precision adaptive control system. When faced with sudden load changes or gradual load drift, this control strategy can quickly identify and respond. Through the dual mechanisms of forward compensation and feedback correction, it ensures that the system always operates near the optimal energy efficiency point. In application scenarios such as chemical and pharmaceutical industries where the stability of waste gas treatment is extremely important, this closed-loop control method significantly improves the adaptability and reliability of the system.

[0050] This represents the dynamic response gain coefficient, calibrated based on system response time and stability requirements. The calibration method is a step response test. The input is a step change in the load index, and the output is the time it takes for the system to reach a new steady state. The required response time is less than 30 seconds and the overshoot is less than 10%. The optimal gain value is determined through an optimization algorithm to minimize the response time and overshoot. The typical value is 50-200 rpm·s. For systems with large inertia, a smaller value of 50-100 rpm·s is used, and for systems with small inertia, a larger value of 150-200 rpm·s is used.

[0051] The speed deviation feedback coefficient is determined through closed-loop control stability analysis using root locus and frequency domain analysis. This ensures a system stability margin greater than 6dB, a phase margin greater than 45°, an overshoot of less than 10% in the step response, and a settling time of less than 30 seconds. The typical value range is 0.1-0.5. For systems requiring fast response, a larger value of 0.3-0.5 is used, while for systems with high stability requirements, a smaller value of 0.1-0.3 is used.

[0052] Reference speed Based on the current motor speed and comprehensive load index Dynamically generated using a two-dimensional linear interpolation method, the complete mapping relationship is as follows: ; in This represents a constant term, which physically represents the system's reference operating speed setting. It is derived from the system design parameters, and its typical value is 1000-1500 rpm. This represents the speed term coefficient, reflecting the weight of the current speed's influence on the reference speed. It originates from speed tracking characteristic analysis, and its typical value is 0.8-1.2. This represents the load term coefficient, reflecting the weight of the load index on the reference speed. It is derived from the linearization analysis of the load-speed relationship, and its typical value is 200-500 rpm. The coefficients represent the interaction term, reflecting the coupling effect between speed and load. They are derived from the interaction analysis of multivariate systems, with typical values ​​of 50-150 rpm. Each coefficient is obtained through multiple regression analysis of historical operating data. The regression data comes from at least 100 hours of continuous operating records, with a data acquisition interval of 10 seconds. The coefficient of determination in the regression model is also mentioned. .

[0053] Load change rate The first-order backward difference numerical differentiation method is used for calculation. To improve the accuracy and noise resistance of the numerical differentiation, a moving average filter is used to smooth the load change rate. The filter length is 3-5 sampling points. The formula for calculating the filtered load change rate is as follows: ; in This indicates the filter length, with a typical value of 3. This indicates the sampling time interval, which is set according to the system response requirements, with a typical value of 1-5 seconds.

[0054] The accuracy of adaptive adjustment is enhanced by coupling the dynamic reference speed with the real-time operating status, and the coupling strength is improved. Defined as the sensitivity of the reference speed to changes in operating conditions, the calculation formula is: ; in, , The greater the coupling strength, the more sensitive the system is to changes in operating state and the higher the adjustment accuracy. The typical value range is 0.5-2.0. When the coupling strength is too large, damping needs to be increased to ensure system stability. Example 5

[0055] Preferably, the method further includes a speed reference dynamic coupling step, which is used to establish a real-time mapping relationship between the motor reference speed and the current motor speed and the comprehensive load index; and the motor reference speed generated by the mapping relationship is used in the target speed optimization step to enhance the accuracy of adaptive adjustment.

[0056] Establishing the motor reference speed through dynamic coupling steps With the current speed of the motor and comprehensive load index The real-time mapping relationship is established using a three-dimensional surface fitting method, and its mathematical expression is: ; in Represents the fitting coefficient matrix. Indicates the highest number of times the rotational speed term is given. This represents the highest degree of the load index term, and the coefficient matrix is ​​obtained through least-squares fitting of a large amount of historical operating data. The real-time mapping relationship can dynamically adjust the reference speed according to the current operating state, eliminating the insufficient adaptability caused by a fixed reference value.

[0057] motor reference speed In the target speed optimization step, the original fixed foundation is replaced to maintain the speed. Revised emission compliance critical speed The calculation formula is: ; in Indicates the load compensation coefficient. Indicates a nonlinear exponent. Indicates the influence coefficient of rotational speed. Indicates the no-load speed of the motor. This indicates the rated speed of the motor. The introduction of a dynamic reference speed makes the calculation of critical speed more accurate and better adaptable to different operating conditions.

[0058] The accuracy of adaptive adjustment is enhanced by coupling the dynamic reference speed with the real-time operating status, and the coupling strength is improved. Defined as: ; in This indicates the sensitivity of the reference speed to the current speed. This indicates the sensitivity of the reference speed to the load index. The stronger the coupling, the more sensitive the system is to changes in operating conditions, and the higher the adjustment accuracy.

[0059] The synergistic effect of dynamic coupling steps for speed reference, real-time mapping relationship, and enhanced accuracy of adaptive adjustment enables intelligent dynamic adjustment of reference parameters. When facing variable industrial waste gas treatment conditions, this coupling mechanism can automatically optimize the reference setting based on historical experience and current status, avoiding the complexity and subjectivity of manual parameter adjustment. In complex industrial environments such as large-scale petrochemical plants and automobile manufacturing, the dynamic coupling method significantly improves the system's adaptability and control accuracy, providing technical support for achieving truly intelligent waste gas treatment.

[0060] Establishing the motor reference speed through dynamic coupling steps With the current speed of the motor and comprehensive load index The real-time mapping relationship is established using a three-dimensional surface fitting method, and its simplified form is a quadratic polynomial: ; in This represents a constant term, typically with a value of 800-1200 rpm. This represents the coefficient for the first term of rotational speed, with a typical value of 0.1-0.3. This represents the coefficient for the first term of the load, with a typical value of 200-400 rpm. This represents the coefficient of the quadratic term of the rotational speed, with a typical value of [value missing]. rpm -1 , This represents the interaction term coefficient, with a typical value of 50-150 rpm. The coefficients represent the quadratic load coefficients, typically ranging from 100-300 rpm. These coefficients are obtained by fitting a large amount of historical operating data, including laboratory test data and field operating data. Laboratory data is obtained from standard test benches, with test conditions including combinations of 5 speed levels and 7 load levels. Field data is obtained from no fewer than 20 typical industrial application sites, with a data acquisition period of no less than 6 months. The coefficient of determination of the fitted model is also mentioned. .

[0061] In the application of waste gas treatment for ethylene plants in the petrochemical industry, the plant has an annual ethylene production capacity of 800,000 tons and a waste gas treatment capacity of 15,000 m³ / h. Compared with the traditional normalized over-treatment mode, the energy saving effect reaches 25%-30%, the outlet oil mist concentration is stably controlled below 15 mg / m³, and the load change response time is 15 seconds. In the application of CNC machining centers, the oil mist removal rate of the system with a treatment capacity of 100-500 m³ / h is maintained above 97%, the load identification accuracy reaches ±3%, the false judgment rate is less than 1%, and the working condition switching time is 8 seconds. In the application of engine bench testing in automobile manufacturing, the stability test of the system with a treatment capacity of 500-2000 m³ / h during continuous operation for 72 hours shows that the speed fluctuation range is controlled within ±2%, the failure rate is reduced by more than 60%, the maintenance cycle is extended to 3 times the original, and the investment payback period is shortened to 15-18 months.

[0062] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for removing oil from waste gas under high gravity conditions, characterized in that, include: The working condition sensing step is used to acquire the real-time operating status parameter set of the supergravity rotor. The real-time operating status parameter set includes: the real-time power of the motor reflecting the driving resistance, the real-time pressure drop of the inlet and outlet reflecting the passability resistance, and the current speed of the motor. The load quantization step is used to generate a comprehensive load index that characterizes the real-time oil mist load of exhaust gas based on the real-time operating status parameter set and the preset benchmark parameter set. The target speed optimization step is used to determine the emission compliance critical speed based on the comprehensive load index, and combined with the current motor speed, set the speed that takes into account the dynamic safety margin as the optimal target speed. The closed-loop speed control execution step is used to generate a speed adjustment command based on the deviation between the optimal target speed and the current speed of the motor, and send it to the frequency converter to drive the motor speed to converge to the optimal target speed.

2. The method for removing oil from waste gas under ultragravity conditions according to claim 1, characterized in that, In the load quantization step, the preset reference parameter set includes: preset motor rated power, preset filter limit voltage drop design value, and preset motor no-load speed and rated speed.

3. The method for removing oil from waste gas under ultragravity conditions according to claim 1, characterized in that, In the load quantization step, generating the comprehensive load index includes: generating a normalized power component that reflects the proportion of the motor's real-time power within the rated range, and a normalized voltage drop component that reflects the proportion of the real-time voltage drop within the extreme range; and weighting and summing the normalized power component and the normalized voltage drop component based on preset power weighting coefficients and voltage drop weighting coefficients to obtain the comprehensive load index.

4. The method for removing oil from waste gas under ultragravity conditions according to claim 3, characterized in that, The load quantization step also includes: constructing a dynamic mapping relationship between the current motor speed and the no-load reference power; before generating the normalized power component, the dynamic mapping relationship is called first, the no-load reference power is corrected using the current motor speed, and the corrected no-load reference power is used for the calculation of the normalized power component.

5. The method for removing oil from waste gas under ultragravity conditions according to claim 1, characterized in that, In the target speed optimization step, determining the emission compliance critical speed includes: based on the baseline maintenance speed when the system is unloaded, and according to the nonlinear function relationship of the comprehensive load index, superimposing the load compensation speed to obtain the emission compliance critical speed.

6. The method for removing oil from waste gas under ultragravity conditions according to claim 5, characterized in that, In the target speed optimization step, the calculation of the optimal target speed is limited to: multiplying the emission compliance critical speed by a preset static safety margin coefficient, and adding the dynamic response compensation value determined by the rate of change of the comprehensive load index, as well as the feedback correction value determined by the deviation between the current motor speed and the reference speed, to generate the optimal target speed.

7. The method for removing oil from waste gas under ultragravity conditions according to claim 6, characterized in that, The determination of the dynamic response compensation value includes: calculating the change of the comprehensive load index per unit time to generate the load change rate; and multiplying the load change rate by the preset dynamic response gain coefficient to obtain the dynamic response compensation value.

8. The method for removing oil from waste gas under ultragravity conditions according to claim 6, characterized in that, The determination of the feedback correction value includes: dynamically generating a reference speed based on the current motor speed and the comprehensive load index; calculating the difference between the current motor speed and the reference speed; and multiplying the difference by a preset speed deviation feedback coefficient to obtain the feedback correction value.

9. The method for removing oil from waste gas under ultragravity conditions according to claim 1, characterized in that, The closed-loop speed regulation execution steps also include: after issuing the speed adjustment command, taking the adjusted real-time motor power, real-time voltage drop and current motor speed as the real-time operating status parameter set for the new iteration, and returning it to the operating condition sensing step, thereby forming a closed-loop control loop for continuously tracking the lowest energy consumption point.

10. The method for removing oil from waste gas under ultragravity conditions according to claim 1, characterized in that, The method also includes a dynamic coupling step for the speed reference, which is used to establish a real-time mapping relationship between the motor reference speed and the current motor speed and the comprehensive load index. The motor reference speed generated by this mapping relationship is then used in the target speed optimization step to enhance the accuracy of adaptive adjustment.

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