A direct air-cooling unit vacuumizing condensate water recovery control method based on fuzzy PID algorithm

Abstract

The application discloses a direct air-cooling unit vacuum-pumping condensate water recovery control method based on a fuzzy PID algorithm and belongs to the technical field of vacuum system optimization control of thermal power generating units, and comprises the following steps: S1. high back pressure scene judgment; S2. reference pressure difference calculation; S3. total table construction and update of coupling correction coefficients; S4. real-time measurement point data acquisition and calculation; S5. comprehensive coupling correction coefficient calculation; S6. fuzzy PID control model operation; S7. valve opening correction and regulation; and S8. hierarchical continuous regulation. The application improves the vacuum system operation stability and reduces the unit back pressure level, and solves the problems of the existing direct air-cooling unit vacuum-pumping condensate water recovery system, such as insufficient control self-adaptive ability, low cooling water regulation precision, easy over-regulation or lag and difficulty in considering the vacuum safety margin and economy under the conditions of high ambient temperature and high load in summer.

Description

Technical Field

[0001] This invention relates to the field of optimized control technology for vacuum systems of thermal power units, specifically to a control method for condensate recovery during vacuuming in direct air-cooled units based on a fuzzy PID algorithm. Background Technology

[0002] Direct air-cooled units are widely used in coal-fired power plants in water-scarce areas of northern my country due to their water-saving advantages. The vacuum condensate recovery system is one of the core auxiliary systems of direct air-cooled units, and its operational stability directly affects the unit's vacuum level, heat exchange efficiency, and safety and economy. Among these systems, the regulation of the interstage cooler cooling water in the Roots vacuum pump unit is a crucial aspect of condensate recovery. Under high-temperature and high-load conditions in summer, the back pressure of the main unit of the direct air-cooled unit easily rises, entering a high back pressure operating state. The pressure difference between the main unit's exhaust system and the interstage cooler fluctuates drastically. If the cooling water inflow is not regulated in a timely or accurate manner, condensate may not flow back normally, or even backflow may occur, affecting the normal operation of the vacuum pump unit, thereby reducing the unit's vacuum level and increasing energy consumption.

[0003] In existing technologies, the cooling water regulation of vacuum condensate recovery systems in direct air-cooled units mostly adopts traditional PID control algorithms. This algorithm has poor adaptability to operating condition fluctuations. Under high temperature, high load, and high back pressure conditions in summer, it is difficult to respond quickly to dynamic changes in pressure difference, and it is prone to problems such as overshoot and oscillation, resulting in insufficient regulation accuracy. At the same time, the existing control strategy does not consider the changes in operating characteristics of the unit during long-term operation and cannot adaptively correct the regulation parameters, causing the control effect to gradually decline over time, making it difficult to meet the precise regulation requirements of condensate recovery under all operating conditions in summer.

[0004] Therefore, for the high temperature, high load and high back pressure conditions in summer, designing an optimized control strategy for the vacuum condensate recovery system of a direct air-cooled unit based on fuzzy PID algorithm, which can adapt to changes in operating conditions and provide precise and stable regulation, has become the key to solving the above technical problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a control method for vacuum condensate recovery of direct air-cooled units based on fuzzy PID algorithm, so as to solve the problems of insufficient control adaptability, low precision of cooling water regulation, easy over-adjustment or lag, and difficulty in balancing vacuum safety margin and economy in the existing vacuum condensate recovery system of direct air-cooled units under high ambient temperature and high load conditions in summer.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0007] A control method for vacuum condensate recovery in direct air-cooled units based on fuzzy PID algorithm, implemented using a vacuum condensate recovery system for direct air-cooled units, includes the following steps: S1. Analyze the power plant's operating parameters to determine the highest back pressure values ​​of the unit during the highest temperature and full-load phases in summer. ;like If the back pressure is in the high range, proceed with steps S2 to S8. S2. Average back pressure of the main exhaust system of the unit during the high-temperature, full-load phases of spring and autumn. Average steam-side pressure of vacuum pump interstage cooler Calculate the reference pressure difference ; S3. Collect historical operating data of the unit under all summer conditions. After preprocessing, use multiple linear regression to fit the data and construct the mapping relationship between ambient temperature, unit load, cooling water inlet and outlet temperature difference and single parameter correction coefficient under summer conditions. This will yield a summary table of multi-parameter coupling correction coefficients for summer. S4. During summer operation, the unit acquires real-time measurement data from the DCS, including the steam-side pressure of the main exhaust system. Interstage cooler side pressure Ambient temperature Real-time load of the unit Cooling water inlet temperature Cooling water return temperature Calculate real-time pressure difference Cooling water inlet and outlet temperature difference ; S5. Based on ambient temperature under summer operating conditions Real-time load of the unit and the temperature difference between the cooling water inlet and outlet The corresponding single-parameter correction coefficients are obtained by matching from the total table of coupling correction coefficients, and the comprehensive coupling correction coefficient is calculated by multiplicative weighting. ; S6. Set the critical value ,and ,Compare and :when < When the cooling water inlet electric regulating valve is fully open; when Calculate the real-time differential pressure deviation value. and the rate of change of deviation value , construct The initial opening of the cooling water inlet electric regulating valve is obtained from the input fuzzy PID control model through fuzzification, fuzzy inference, and defuzzification processes. ; S7. When step S6 enters the fuzzy PID control branch, the comprehensive coupling correction coefficient is used. right After making corrections, the final opening command for the cooling water inlet electric regulating valve is obtained. And send it to the cooling water inlet electric regulating valve; S8. Match the corresponding DCS measuring point data acquisition cycle and fuzzy PID control intensity to different differential pressure ranges to achieve graded, continuous, and precise differential pressure control under all operating conditions in summer, ensuring... .

[0008] Preferably, in step S1, the high back pressure range refers to the highest back pressure value. The back pressure is higher than the design back pressure value of the unit under the design cooling water temperature and full load conditions in summer; when the unit does not have a clear design back pressure value, the rated back pressure value is used as the judgment benchmark. In step S2, the high-temperature full-load stage in spring and autumn refers to the period when the ambient temperature reaches 25℃ or above and the unit is operating at full load.

[0009] Preferably, in step S3, the summer full-condition operation refers to all operating periods of the unit from June to August each year; the historical operating data preprocessing includes removing invalid data generated by sensor failures, unit start-ups and shutdowns, and equipment maintenance, and using linear interpolation to complete missing data; the single-parameter correction coefficient includes a summer ambient temperature correction coefficient. Summer unit load correction factor Cooling water inlet and outlet temperature difference correction factor The range of values ​​for the three The multiple linear regression fitting is updated on a rolling basis according to a set period to adaptively correct the single-parameter correction coefficients.

[0010] Preferably, in step S5, the comprehensive coupling correction coefficient is... , ,in, It decreases linearly with the increase of ambient temperature in summer. The load on generating units decreases linearly as the summer load increases. The temperature increases linearly with the increase of the temperature difference between the inlet and outlet cooling water.

[0011] Preferably, in step S6, the critical value .

[0012] Preferably, in step S6, in the fuzzy control unit of the fuzzy PID control model, the deviation The basic domain of discourse is Fuzzy domain is Deviation change rate The basic domain of discourse is Fuzzy domain is PID parameter correction amount The fuzzy domain is The fuzzy subsets are all set to These correspond to negative large, negative medium, negative small, negative zero, positive zero, positive small, positive medium, and positive large, respectively.

[0013] Preferably, the fuzzy inference employs the Mamdani inference method with weighted factors, and the deviation... The weighting factor is set to 0.6~0.7, and the deviation change rate is... The weighting factor is set to 0.3~0.4; the defuzzification adopts the improved centroid method, which improves the resolution of the accurate value after defuzzification by performing weighted interpolation on the fuzzy universe.

[0014] Preferably, in step S8, the three-level differential pressure control interval is designed to match the corresponding DCS measurement point data acquisition cycle and fuzzy PID control intensity, and the first-level interval adopts bypass control, which takes priority over fuzzy PID adjustment output; the cooling water inlet electric adjustment valve is continuously stepless from 0 to 100%, with an adjustment accuracy of ≤1%.

[0015] Preferably, the three-stage differential pressure control range is: Level 1 interval: The data acquisition cycle is 2 seconds, with weak control and the valve fully open. Secondary interval: The data acquisition cycle is 1 second, using conventional control, with valve opening at 50%~100%. Level 3 interval: The data acquisition cycle is 0.5s, and the control is strong. The valve opening is 0%~50%.

[0016] Preferably, the direct air-cooled unit vacuum condensate recovery system includes an air-cooled island condenser, a main unit exhaust device, a controller, a Roots vacuum pump set, an interstage cooler, a cooling water inlet electric regulating valve and matching valve group; the controller adopts a DCS distributed control system for regulation.

[0017] Due to the adoption of the above technical solutions, the technical progress achieved by this invention is as follows.

[0018] This invention features high control precision and fast response: It employs a fuzzy PID control algorithm, combined with differential pressure deviation... and rate of change of deviation Dual-input control, compared to traditional PID algorithms, is more adaptable to pressure fluctuations under high temperature, high load and high back pressure conditions in summer, effectively solving the problems of overshoot and oscillation in traditional PID control, and improving the control accuracy and stability of cooling water inlet flow.

[0019] This invention can prevent condensate backflow: by precisely matching the differential pressure with the valve opening, it can ensure normal condensate return, avoid backflow, and ensure the safe operation of the vacuum pump unit.

[0020] This invention achieves multi-parameter adaptive correction: by fitting multiple linear regression, a mapping relationship between ambient temperature, unit load, cooling water temperature difference and correction coefficients is constructed. The "offline rolling, online updating" method is adopted, with parameters refitted offline every month and the correction coefficient summary table automatically updated online. No downtime is required, which realizes the adaptive correction of the control strategy to changes in the long-term operating characteristics of the unit, and ensures the stability of the control effect during long-term operation.

[0021] This invention achieves graded continuous control: by designing a three-level differential pressure control range, matching differentiated acquisition cycles (2s / 1s / 0.5s) and control intensity, and combining a comprehensive coupling correction coefficient to accurately correct the valve opening, graded continuous control of differential pressure is achieved under all operating conditions in summer, taking into account condensate recovery efficiency, heat exchange efficiency and auxiliary energy consumption optimization.

[0022] The present invention has significant energy-saving effects: compared with the traditional PID control strategy, after adopting the present invention, the vacuum degree of the unit is increased by 0.3~0.5kPa, and the energy consumption of the auxiliary equipment of the Roots vacuum pump unit is reduced by 5%~8%. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention.

[0024] Figure 2 This is a diagram of the vacuum condensate recovery system for the direct air-cooled unit of the present invention. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0026] A control method for vacuum condensate recovery in direct air-cooled units based on fuzzy PID algorithm is proposed. This method is implemented using a vacuum condensate recovery system for the direct air-cooled unit, which includes an air-cooled condenser, a main unit exhaust system, a controller, a Roots vacuum pump unit, an interstage cooler, an electrically operated cooling water inlet regulating valve, and associated valve groups. The controller employs a DCS distributed control system for regulation. Figure 1 As shown, this method includes the following steps: S1. Analyze the power plant's operating parameters to determine the highest back pressure values ​​of the unit during the highest temperature and full-load phases in summer. ;like If the back pressure is in the high range, proceed with subsequent steps S2 to S8.

[0027] In this step, the determination of the high back pressure range is based on the well-known economic and restricted operating ranges of back pressure for direct air-cooled units. Specifically, the "high back pressure range" refers to the highest back pressure value. The back pressure is higher than the design back pressure or rated back pressure of the unit under summer design cooling water temperature and full load conditions. When the unit does not have a clear design back pressure value, the rated back pressure value shall be used as the judgment benchmark.

[0028] S2. Average back pressure of the main exhaust system of the unit during the high-temperature, full-load phases of spring and autumn. Average steam-side pressure of vacuum pump interstage cooler Calculate the reference pressure difference .

[0029] In this step, the high-temperature full-load stage in spring and autumn refers to the period when the ambient temperature reaches 25℃ or above and the unit is operating at full load.

[0030] S3. Collect historical operating data of the unit under all summer operating conditions. After preprocessing, use multiple linear regression to fit and construct the mapping relationship between ambient temperature, unit load, cooling water inlet and outlet water temperature difference and single parameter correction coefficients under summer operating conditions, and obtain a summary table of multi-parameter coupling correction coefficients for summer.

[0031] In this step, the full summer operating condition refers to all operating periods of the unit from June to August each year; historical operating data preprocessing includes removing invalid data generated by sensor failures, unit start-ups and shutdowns, and equipment maintenance, and using linear interpolation to complete missing data; single parameter correction coefficients include summer ambient temperature correction coefficients. Summer unit load correction factor Cooling water inlet and outlet temperature difference correction factor The range of values ​​for the three The multiple linear regression fitting is updated on a rolling basis according to a set period to adaptively correct the single-parameter correction coefficients.

[0032] S4. During summer operation, the unit acquires real-time measurement data from the DCS, including the steam-side pressure of the main exhaust system. Interstage cooler side pressure Ambient temperature Real-time load of the unit Cooling water inlet temperature Cooling water return temperature Calculate real-time pressure difference Cooling water inlet and outlet temperature difference .

[0033] S5. Based on ambient temperature under summer operating conditions Real-time load of the unit and the temperature difference between the cooling water inlet and outlet The corresponding single-parameter correction coefficients are obtained by matching from the total table of coupling correction coefficients, and the comprehensive coupling correction coefficient is calculated by multiplicative weighting. .

[0034] In this step, the overall coupling correction coefficient is... , ,in, It decreases linearly with the increase of ambient temperature in summer. The load on generating units decreases linearly as the summer load increases. The temperature increases linearly with the increase of the temperature difference between the inlet and outlet cooling water.

[0035] S6. Set the critical value ,and ,Compare and :when < When the cooling water inlet electric regulating valve is fully open; when Calculate the real-time differential pressure deviation value. and the rate of change of deviation value , construct The initial opening of the cooling water inlet electric regulating valve is obtained from the input fuzzy PID control model through fuzzification, fuzzy inference, and defuzzification processes. .

[0036] In this step, the critical value .

[0037] In the fuzzy control unit of the fuzzy PID control model, the deviation The basic domain of discourse is Fuzzy domain is Deviation change rate The basic domain of discourse is Fuzzy domain is PID parameter correction amount The fuzzy domain is The fuzzy subsets are all set to These correspond to negative large, negative medium, negative small, negative zero, positive zero, positive small, positive medium, and positive large, respectively.

[0038] Fuzzy inference employs the Mamdani inference method with weighted factors, and has biases. The weighting factor is set to 0.6~0.7, and the deviation change rate is... The weighting factor is set to 0.3~0.4; the defuzzification adopts the improved centroid method, which improves the resolution of the accurate value after defuzzification by performing weighted interpolation on the fuzzy universe.

[0039] S7. When step S6 enters the fuzzy PID control branch, the comprehensive coupling correction coefficient is used. right After making corrections, the final opening command for the cooling water inlet electric regulating valve is obtained. And send it to the cooling water inlet electric regulating valve.

[0040] S8. Match the corresponding DCS measuring point data acquisition cycle and fuzzy PID control intensity to different differential pressure ranges to achieve graded, continuous, and precise differential pressure control under all operating conditions in summer, ensuring... .

[0041] Specifically, the design of the three-stage differential pressure control range is matched with the corresponding DCS measurement point data acquisition cycle and fuzzy PID control intensity. The first-stage range adopts bypass control, which takes priority over the fuzzy PID control output. The cooling water inlet electric regulating valve is continuously stepless from 0 to 100%, with an adjustment accuracy of ≤1%.

[0042] The three-level differential pressure control range is: Level 1 interval: The data acquisition cycle is 2 seconds, with weak control and the valve fully open. Secondary interval: The data acquisition cycle is 1 second, using conventional control, with valve opening at 50%~100%. Level 3 interval: The data acquisition cycle is 0.5s, and the control is strong. The valve opening is 0%~50%. Example 1

[0043] Taking the vacuum condensate recovery system of a 350MW direct air-cooled unit as an example, it includes the air-cooled island condenser, main unit exhaust device, controller, Roots vacuum pump set, interstage cooler, cooling water inlet electric regulating valve and matching valve group, such as Figure 2 As shown, the air-cooled island condenser is connected to the main unit's exhaust system via a vacuum header, which is then connected to a Roots vacuum pump unit via a pipeline. The Roots vacuum pump is connected in series with an interstage cooler, whose condensate drain pipe is connected to the main unit's exhaust system. The condensate drain side is connected to the main unit's exhaust system via the extraction steam condensate return pipe. The interstage cooler is also connected to both the cooling water inlet pipe and the cooling water return pipe to provide the circulating cooling capacity required for extraction steam condensation. Pressure signals collected by the pressure gauges of the main unit's exhaust system and the interstage cooler's steam side are sent to the DCS controller. The DCS outputs a control signal to the cooling water inlet electric regulating valve to automatically adjust the cooling water flow. This embodiment provides a detailed description of the specific implementation of the invention, and this embodiment is only for illustrating the technical solution of the invention and does not constitute a limitation on the scope of protection of the invention.

[0044] S1. High Back Pressure Scenarios Determination: Back pressure operating data of this 350MW direct air-cooled unit under the highest temperature and full load conditions in summer were statistically analyzed to determine the highest back pressure value P. 11 =45kPa, due to P 11 If the pressure is >25 kPa, the unit is determined to be in a high back pressure operating state, and subsequent steps S2-S8 are executed.

[0045] S2. Baseline Pressure Difference Calculation: Based on statistical analysis of power plant parameters, determine the unit's baseline pressure difference when the ambient temperature is 25℃ or higher in spring and autumn. Average back pressure during the 350MW full-load stage and the average steam-side pressure of the vacuum pump interstage cooler ,in =22kPa, =6kPa, the pressure difference is calculated. = - =22kPa-6kPa=16kPa.

[0046] S3. Construction and Update of the Coupled Correction Coefficient Table: Historical operating data of the unit under all summer operating conditions was collected. After removing invalid data generated by sensor malfunctions, unit start-ups / shutdowns, and equipment maintenance, missing data was supplemented using linear interpolation. Then, through multiple linear regression fitting, the mapping relationship between ambient temperature, unit load, cooling water inlet and outlet temperature difference, and single-parameter correction coefficients under summer operating conditions was constructed, resulting in a comprehensive table of multi-parameter coupled correction coefficients for summer. The single-parameter correction coefficients include the summer ambient temperature correction coefficient. Summer unit load correction factor Cooling water inlet and outlet temperature difference correction factor The range of values ​​for the three .

[0047] The multiple linear regression fitting is performed using an "offline rolling, online update" method: with a fixed update node at 00:00 on the 1st of each month, valid operating data from the previous month's summer operating conditions (June to August) are retrieved offline, the multiple linear regression fitting is re-completed, and the data is updated. The mapping relationship and the total table of coupling correction coefficients are obtained; after the fitting is completed, the updated total table of coupling correction coefficients is automatically synchronized to the controller through the DCS distributed control system. The entire update process does not require shutdown, realizing adaptive correction of control parameters and ensuring that the control strategy adapts to changes in the unit's operating characteristics.

[0048] S4. Real-time Measurement Data Acquisition and Calculation: During summer operation, the unit acquires real-time measurement data from the DCS, including the steam-side pressure of the main exhaust system. Interstage cooler side pressure Ambient temperature Real-time load of the unit Cooling water inlet temperature Cooling water return temperature Calculate real-time pressure difference Cooling water inlet and outlet temperature difference ; S5. Calculation of Comprehensive Coupling Correction Coefficient: Based on the real-time ambient temperature T=35℃, real-time unit load N=280MW (approximately 93.3% of rated load) and cooling water inlet and outlet temperature difference collected by the DCS system. =8℃, the corresponding single-parameter correction coefficient is obtained by precise matching from the total table of coupling correction coefficients: =0.7、 =0.75、 =1.0; according to the formula The comprehensive coupling correction coefficient was calculated. =0.7×0.75×1.0=0.525, which is within the range of [0.5,1.2].

[0049] S6. Fuzzy PID control model calculation: Set the critical value according to design requirements. =0.9 =0.9 × 16 kPa = 14.4 kPa; Assume the pressure difference calculated in real time by the DCS system at a certain moment. =15kPa, the real-time differential pressure deviation value is calculated. = =15kPa - 14.4kPa = 0.6kPa, deviation change rate =0.2kPa / s.

[0050] The calculated and Input a preset fuzzy PID control model, and first perform fuzzification processing: by using a quantization factor... (fundamental domain [-8,8] kPa) The basic universe of discourse [-4,4] kPa / s is transformed to a preset fuzzy universe of discourse {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, and after fuzzification, we obtain... The blur amount is PS (positive small). The fuzzy quantity is PS (positive small); fuzzy inference is performed using the Mamdani inference method with weighted factors, where the bias... The weighting factor is set to 0.65, and the rate of change of deviation is... The weighting factor is set to 0.35, and combined with the 169 two-dimensional multi-rule inference matrices built into the fuzzy control unit, the PID parameter correction amount is obtained. =1、 =0.5、 =0.2; By performing defuzzification calculations using the improved centroid method, the initial opening of the cooling water inlet electric regulating valve is finally obtained. =80%.

[0051] S7. Valve Opening Correction and Control: Utilizing the calculated comprehensive coupling correction coefficient. For the initial opening Make precise corrections according to the formula. = × The final opening command of the cooling water inlet electric regulating valve is calculated. =80%×0.525=42%; The DCS system sends the opening command to the cooling water inlet electric regulating valve in real time. The electric regulating valve is precisely adjusted to 42% opening according to the command, realizing precise control of cooling water inlet volume and ensuring stable heat exchange effect of interstage cooler.

[0052] S8. Tiered Continuous Control: Performs differentiated control based on a preset three-tier differential pressure control range: current real-time differential pressure. =15kPa, which is in the secondary range, therefore the DCS measurement point data acquisition cycle is set to 1s, and conventional fuzzy PID control intensity is adopted; the cooling water inlet electric regulating valve adopts a 0~100% continuous stepless adjustment mode with an adjustment accuracy of ≤1%, and real-time control ensures It remained stable within the range of 14.4~15.84 kPa, and was always less than =16kPa, achieving stable and efficient recovery of condensate.

[0053] After adopting the optimized control strategy of this embodiment, the 350MW direct air-cooled unit achieves a higher control accuracy of ±1% for the electric regulating valve of the cooling water inlet under high temperature, high load, and high back pressure conditions in summer. The real-time differential pressure fluctuation is controlled within ±0.5kPa, preventing condensate backflow. Compared with the traditional PID control strategy, the unit's vacuum level is increased by 0.3~0.5kPa, and the energy consumption of the Roots vacuum pump unit's auxiliary equipment is reduced by 5%~8%. At the same time, by using the "offline rolling, online updating" method, the problem of control effect decay during long-term unit operation is effectively avoided, ensuring the stability and reliability of condensate recovery under all operating conditions in summer. Continuous and precise matching of differential pressure and valve opening is achieved, significantly improving condensate recovery efficiency and system operational stability.

Claims

1. A method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm, characterized in that: The method is based on a vacuum condensate recovery system for direct air-cooled units and includes the following steps: S1. Analyze the power plant's operating parameters to determine the highest back pressure values ​​of the unit during the highest temperature and full-load phases in summer. ;like If the back pressure is in the high range, proceed with subsequent steps S2 to S8; S2. Average back pressure of the main exhaust system of the unit during the high-temperature, full-load phases of spring and autumn. Average steam-side pressure of vacuum pump interstage cooler Calculate the reference pressure difference ; S3. Collect historical operating data of the unit under all summer conditions. After preprocessing, use multiple linear regression to fit the data and construct the mapping relationship between ambient temperature, unit load, cooling water inlet and outlet temperature difference and single parameter correction coefficient under summer conditions. This will yield a summary table of multi-parameter coupling correction coefficients for summer. S4. During summer operation, the unit acquires real-time measurement data from the DCS, including the steam-side pressure of the main exhaust system. Interstage cooler side pressure Ambient temperature Real-time load of the unit Cooling water inlet temperature Cooling water return temperature Calculate real-time pressure difference Cooling water inlet and outlet temperature difference ; S5. Based on ambient temperature under summer operating conditions Real-time load of the unit and the temperature difference between the inlet and outlet cooling water The corresponding single-parameter correction coefficients are obtained by matching from the total table of coupling correction coefficients, and the comprehensive coupling correction coefficient is calculated by multiplicative weighting. ; S6. Set the critical value ,and ,Compare and :when < When the cooling water inlet electric regulating valve is fully open; when Calculate the real-time differential pressure deviation value. and the rate of change of deviation value , construct The initial opening of the cooling water inlet electric regulating valve is obtained from the input fuzzy PID control model through fuzzification, fuzzy inference, and defuzzification processes. ; S7. When step S6 enters the fuzzy PID control branch, the comprehensive coupling correction coefficient is used. right After making corrections, the final opening command for the cooling water inlet electric regulating valve is obtained. And send it to the cooling water inlet electric regulating valve; S8. Match the corresponding DCS measuring point data acquisition cycle and fuzzy PID control intensity to different differential pressure ranges to achieve graded, continuous, and precise differential pressure control under all operating conditions in summer, ensuring... .

2. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: In step S1, the high back pressure range refers to the highest back pressure value. The back pressure is higher than the design back pressure value of the unit under the design cooling water temperature and full load conditions in summer; when the unit does not have a clear design back pressure value, the rated back pressure value is used as the judgment benchmark. In step S2, the high-temperature full-load stage in spring and autumn refers to the period when the ambient temperature reaches 25℃ or above and the unit is operating at full load.

3. The method for controlling the condensate recovery of a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: In step S3, the summer full-condition operation refers to all operating periods of the unit from June to August each year; the historical operating data preprocessing includes removing invalid data generated by sensor failures, unit start-ups and shutdowns, and equipment maintenance, and completing missing data using linear interpolation; the single-parameter correction coefficient includes a summer ambient temperature correction coefficient. Summer unit load correction factor Cooling water inlet and outlet temperature difference correction factor The range of values ​​for the three The multiple linear regression fitting is updated on a rolling basis according to a set period to adaptively correct the single-parameter correction coefficients.

4. The method for controlling the condensate recovery of a direct air-cooled unit based on a fuzzy PID algorithm according to claim 3, characterized in that: In step S5, the comprehensive coupling correction coefficient is... , ,in, It decreases linearly with the increase of ambient temperature in summer. The load on generating units decreases linearly as the summer load increases. The temperature increases linearly with the increase of the temperature difference between the inlet and outlet cooling water.

5. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: In step S6, the critical value .

6. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: In step S6, in the fuzzy control unit of the fuzzy PID control model, the deviation... The basic domain of discourse is Fuzzy domain is deviation change rate The basic domain of discourse is Fuzzy domain is PID parameter correction amount The fuzzy domain is The fuzzy subsets are all set to These correspond to negative large, negative medium, negative small, negative zero, positive zero, positive small, positive medium, and positive large, respectively.

7. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 6, characterized in that: The fuzzy inference employs the Mamdani inference method with weighted factors, and the deviation... The weighting factor is set to 0.6~0.7, and the deviation change rate is... The weighting factor is set to 0.3~0.4; the defuzzification adopts the improved centroid method, which improves the resolution of the accurate value after defuzzification by performing weighted interpolation on the fuzzy universe.

8. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: In step S8, the three-level differential pressure control interval is designed to match the corresponding DCS measurement point data acquisition cycle and fuzzy PID control intensity, and the first-level interval adopts bypass control, which takes priority over the fuzzy PID adjustment output; the cooling water inlet electric adjustment valve is continuously stepless from 0 to 100%, and the adjustment accuracy is ≤1%.

9. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 8, characterized in that: The three-stage differential pressure control range is as follows: Level 1 interval: The data acquisition cycle is 2 seconds, weak control is used, and the valve is fully open. Secondary interval: The data acquisition cycle is 1 second, using conventional control, with valve opening at 50%~100%. Level 3 interval: The data acquisition cycle is 0.5s, and the control is strong. The valve opening is 0%~50%.

10. The method for controlling the recovery of condensate from vacuum pumping in a direct air-cooled unit based on a fuzzy PID algorithm according to claim 1, characterized in that: The direct air-cooled unit vacuum condensate recovery system includes an air-cooled island condenser, a main unit exhaust device, a controller, a Roots vacuum pump set, an interstage cooler, an electric regulating valve for cooling water inlet, and a matching valve group; the controller is regulated by a DCS distributed control system.

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