High-pressure-difference self-balancing intelligent ball valve

Abstract

The invention discloses a high-pressure-difference self-balancing intelligent ball valve which comprises a pressure sensing module. A friction state detection module; an intelligent control processing unit; a pressure balance execution mechanism; a sealing surface microstructure adjusting mechanism; a multi-time scale control coordination module; the intelligent control processing unit executes a dynamic pressure self-adaptive balance control algorithm; the intelligent control processing unit executes a fluid-solid coupling friction coefficient self-optimization algorithm; according to the double algorithms, deep fusion and optimization control are realized through a multi-objective comprehensive optimization function, and an enhancement effect is formed to improve the overall performance of the system. The method has the following beneficial effects that through double-algorithm fusion, deep integration of a dynamic pressure adaptive balance control algorithm and a fluid-solid coupling friction coefficient self-optimization algorithm is achieved, and the performance of the ball valve under the high pressure difference condition is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid control and intelligent algorithms, and in particular to a high-pressure differential self-balancing intelligent ball valve. Background Art

[0002] With the rapid development of industrial automation technology, high-pressure fluid control systems are increasingly being used in fields such as petrochemicals, natural gas transportation, and power generation. In these critical applications, ball valves, as vital fluid control devices, must operate reliably under extremely high differential pressures. However, traditional ball valves exhibit significant technical limitations under these high-pressure conditions, severely restricting the safety and cost-effectiveness of modern industrial systems.

[0003] Conventional ball valves in existing technology suffer from the following major issues: First, they are severely inadequate in adaptability to dynamic operating conditions. Due to their fixed design parameters, conventional ball valves cannot maintain a stable pressure balance under fluctuating pressure conditions. When system pressure fluctuates by more than ±20%, the operating torque of the ball valve increases dramatically by 200%-300%, significantly degrading sealing performance and severely impacting system reliability and safety.

[0004] Existing ball valves have a low level of intelligence. They lack adaptive adjustment capabilities and predictive maintenance features, making them incapable of optimizing parameters based on actual operating conditions. This presents significant technical limitations in complex and ever-changing industrial environments. Traditional ball valves can only passively respond to operating instructions, unable to proactively adapt to changing operating conditions or provide fault warnings or performance optimization recommendations.

[0005] The coupling effects of multiple physical fields are difficult to handle. Under high-pressure differential conditions, multiple physical fields such as fluid dynamics, tribology, and material mechanics interact with each other. Traditional design methods, based on single-physics field analysis, cannot effectively handle these complex coupling relationships, making it difficult to optimize the overall performance of the system. Existing improved products mostly adopt passive responses such as increasing the power of the operator and strengthening the valve structure. These methods fail to fundamentally solve the core problem of the high-pressure differential generating a unidirectional thrust on the sphere. Furthermore, they are costly and lack reliability.

[0006] Therefore, there is an urgent need for a new ball valve technology that can fundamentally solve the high-pressure differential operation problem while taking into account sealing reliability, operational convenience and intelligent level, so as to meet the growing technical demand of modern industry for high-pressure fluid control equipment. Summary of the Invention

[0007] The technical problem to be solved by the present invention is: how to realize intelligent adaptive control of the ball valve under high pressure difference conditions, fundamentally eliminate the unidirectional thrust effect of high pressure on the ball, and at the same time achieve minimization of operating torque, maximization of sealing life and full working condition adaptability, taking into account real-time performance, accuracy and reliability.

[0008] To address the aforementioned technical issues, the present invention provides a high-differential-pressure self-balancing intelligent ball valve. This system integrates the dynamic pressure adaptive balancing control algorithm (DPABA) and the fluid-structure coupling friction coefficient self-optimization algorithm (FCFCOA) through dual-algorithm fusion, significantly improving ball valve performance under high-differential-pressure conditions. The system includes core components such as a pressure sensing module, a friction state detection module, an intelligent control processing unit, a pressure balancing actuator, a sealing surface micro-topography adjustment mechanism, and a multi-timescale control coordination module.

[0009] The pressure sensing module monitors system pressure changes and pressure fluctuation patterns in real time. Using a high-precision pressure sensor array, it achieves a monitoring accuracy of ±0.1%FS and a response time of less than 1 millisecond, accurately capturing both transient pressure changes and long-term pressure trends. The friction state detection module monitors the friction state and lubrication conditions of the sealing surface, providing real-time assessment of parameters such as the friction coefficient, contact pressure distribution, and lubricant film thickness.

[0010] The intelligent control processing unit is equipped with a high-performance digital signal processor, capable of executing complex dynamic pressure adaptive balancing control algorithms and fluid-structure coupling friction coefficient self-optimization algorithms. Its computing power reaches 1000 MFLOPS, supporting multi-threaded parallel processing and real-time optimization calculations. The pressure balancing actuator dynamically adjusts the balancing chamber area based on the algorithm's calculation results. Utilizing precision servo control technology, it achieves an adjustment accuracy of ±0.05mm and a response time of less than 10 milliseconds.

[0011] The sealing surface microstructure adjustment mechanism is the key to this invention. It optimizes the sealing surface microstructure in real time based on pressure fluctuations, dynamically optimizing sealing performance through micron-level surface control technology. The multi-timescale control coordination module coordinates multi-scale control loops at the microsecond, millisecond, and second levels, ensuring that the system's response requirements are met at all timescales.

[0012] The core technical principles of this invention are based on mathematical modeling and intelligent algorithm optimization. The dynamic pressure adaptive balance control algorithm achieves real-time adaptive adjustment of friction characteristics by establishing a dynamic friction coefficient model coupled with multiple physical fields. The fluid-solid coupling friction coefficient self-optimization algorithm, based on pressure fluctuation pattern recognition technology, can predict pressure trends and adjust system parameters in advance. The dual algorithms achieve deep integration through a multi-objective integrated optimization function, creating an enhanced effect, resulting in overall system performance exceeding the simple addition of individual algorithms.

[0013] In summary, the present invention has the following beneficial effects: First, the operating torque is greatly reduced. The operating torque is reduced under high pressure difference conditions, which significantly improves the operating convenience and safety, reduces the dependence on high-power actuators, and reduces system costs and energy consumption.

[0014] Second, the sealing life is significantly improved. Through dual algorithm optimization, the sealing life is extended, which greatly reduces maintenance costs and downtime losses, and improves the system economy.

[0015] Third, the intelligent predictive maintenance function achieves a fundamental shift from passive maintenance to proactive maintenance through historical data learning and fault pattern identification, reducing annual maintenance costs.

[0016] Fourth, the system's full-condition adaptability enables it to maintain stable performance within the pressure fluctuation range of ±50%, improving adaptability and expanding the scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a block diagram of the overall structure of the high-pressure differential self-balancing intelligent ball valve of the present invention; Figure 2 This is a flow chart for implementing the dual-algorithm fusion control of the present invention; Figure 3 This is a flow chart for implementing the dynamic pressure adaptive balance control algorithm of the present invention; Figure 4 This is a flow chart for implementing the self-optimization algorithm for fluid-solid coupling friction coefficient in the present invention; Figure 5 This is a working principle diagram of the multi-objective comprehensive optimization function in the present invention; Figure 6 This is a working principle diagram of the multi-time scale control coordination module in the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 、 Figure 2 As shown, the high-pressure differential self-balancing intelligent ball valve of the present invention mainly includes a pressure sensing module 1, a friction state detection module 2, an intelligent control processing unit 3, a pressure balance actuator 4, a sealing surface micromorphology adjustment mechanism 5 and a multi-time scale control coordination module 6.

[0020] Pressure sensing module 1 utilizes a high-precision pressure sensor array, enabling real-time monitoring of the system's pressure characteristics. Friction state detection module 2 utilizes multi-dimensional sensing technology to monitor the friction state of the sealing surface. Intelligent control processing unit 3 is the core of the system, responsible for executing complex algorithmic calculations and optimizing control.

[0021] like Figure 3 As shown, the dynamic friction coefficient adaptive algorithm in the intelligent control processing unit 3 is one of the cores of the system. The core formula of the algorithm is:

[0022] μ dynamic (t)=μbase (S,η,P,T)×f adjust (α(t),ΔP rate (t)), where μ dynamic (t) represents the real-time dynamic friction coefficient, which is a dimensionless parameter with a value range of 0.05 to 0.30. It directly affects the operating torque and sealing performance of the ball valve. base (S, η, P, T) represents the basic friction coefficient, which is a comprehensive parameter determined by multiple physical parameters. Among them, S represents the surface morphology characteristics, the unit is micron, reflecting the roughness of the sealing surface; η represents the fluid dynamic viscosity, the unit is Pa·s, usually in the range of 10^-6 to 10^-3; P represents the contact pressure, the unit is MPa, generally in the range of 1 to 50 MPa; T represents the operating temperature, the unit is K, and the operating range is 173K to 823K. adjust (α(t),ΔP rate (t)) represents the adjustment function, which is used to establish the dynamic coupling relationship between friction and equilibrium state. α(t) represents the force balance ratio, which is a dimensionless parameter with a theoretical value of 1.0. In actual operation, it generally fluctuates between 0.95 and 1.05. ΔP rate (t) represents the pressure change rate, in MPa / s, reflecting the transient change characteristics of pressure.

[0023] The calculation of the basic friction coefficient adopts the multi-physics field coupling model, and its formula is μ base (S,η,P,T)=μ0×[1+β S ×S rms +β η ×log(η / η0)+β P ×(P / P0)^0.3+β T ×(T-T0) / T0], where μ0 is the reference friction coefficient, which is the benchmark value measured under standard working conditions and is usually 0.15; S rms β is the root mean square value of surface roughness, in μm, reflecting the microscopic geometric characteristics of the sealing surface, generally in the range of 0.1 to 5.0 μm; η0 is the reference fluid viscosity, in Pa·s, generally taking 1.0×10^-3Pa·s as the standard value; P0 is the reference pressure, in MPa, generally taking 1.0MPa as the standard atmospheric pressure reference; T0 is the reference temperature, in K, generally taking 293K as the room temperature reference. S , β η , β P , β T is the physical field coupling coefficient, a dimensionless parameter obtained by fitting a large amount of experimental data. Its usual values ​​are 0.2, 0.15, 0.3, and 0.25, respectively. It is used to quantify the influence of different physical parameters on the friction coefficient.

[0024] Adjustment function f adjust The calculation formula is: f adjust (α(t),ΔP rate (t))=1+k α ×[1-α(t)]^2+k ΔP ×tanh(ΔP rate (t) / ΔP ref ), where k α and k ΔP is the dynamic adjustment coefficient, a dimensionless parameter determined by the system identification method, and its usual values ​​are 0.5 and 0.3 respectively; ΔP ref The reference pressure rate of change, expressed in MPa / s, is typically set at 0.1 MPa / s, used to normalize the actual pressure rate of change. The tanh function ensures that the adjustment function approaches saturation at large pressure rates, improving system stability.

[0025] The pressure balance area optimization algorithm achieves force balance optimization by dynamically adjusting the effective area of ​​the balance cavity. Its core formula is:

[0026] A balance,optimal (t)=A balance,base ×[1+β×(1-α(t-Δt))+γ×ΔP rate (t)]×θ(μ(t) / μ target ), where A balance,optimal (t) is the optimized balance area, in mm 2 , which is the core parameter of the system that is adjusted in real time and directly affects the balance effect; A balance,base is the basic balance area, in mm 2 , which is the initial value determined during system design, is generally determined based on the nominal diameter of the ball valve, and is usually between 5000 and 50000 mm 2 range; β and γ are balance adjustment coefficients, which are dimensionless parameters determined by the optimization algorithm. Their values ​​are usually 0.2 and 0.1 respectively, which are used to control the adjustment range of the balance area; Δt is the time delay, in seconds, usually 0.01 seconds, which is used to consider the dynamic response characteristics of the system. θ(μ(t) / μ target ) is the friction state compensation factor, which is a dimensionless function used to adjust the balance area according to the degree of deviation of the friction state.

[0027] The calculation formula of the friction state compensation factor is: θ(μ(t) / μ target )=1+ε×[μ(t) / μ target -1]×exp(-|μ(t) / μ target -1| / δ), where μtarget is the target friction coefficient, a theoretical value determined during system design, typically based on the properties of the sealing material, with a typical value of 0.12. ε is the compensation strength coefficient, a dimensionless parameter that controls the strength of the compensation, typically 0.3. A value too large can cause system oscillation, while a value too small can result in ineffective compensation. δ is the compensation sensitivity parameter, a dimensionless parameter that controls the sensitivity of the compensation function, typically 0.1, and determines the rate of change of the compensation factor with the degree of deviation from the friction coefficient. The introduction of an exponential function ensures that the compensation effect gradually weakens when the friction coefficient deviates significantly from the target value, thereby improving system stability.

[0028] like Figure 5 As shown in the figure, multi-objective comprehensive optimization is the core mechanism of dual algorithm fusion, and its mathematical expression is minJ integrated (t)=w1(t)×J balance (t)+w2(t)×J friction (t)+w3(t)×J wear (t)+w4(t)×J seal (t) is a multi-objective optimization problem, which unifies multiple performance indicators into one objective function through weighted summation. balance (t)=[1-α(t)] 2 is the equilibrium performance objective function, which is a dimensionless function and reaches its minimum value of 0 when α(t)=1, indicating a force equilibrium state; J friction (t)=[μ(t) / μ target ] 2 is the friction performance objective function, which is a dimensionless function. When μ(t)=μ target When it reaches the minimum value 0, it means that the friction coefficient reaches the theoretical value; J wear (t)=[W rate (t) / W rate,max ] 2 is the wear performance objective function, where W rate (t) is the real-time wear rate, in μm / h, reflecting the degree of wear on the sealing surface, W rate,max J is the maximum allowable wear rate, in μm / h, generally determined according to the seal life requirements; seal (t)=[1-R seal (t) / R seal,max ] 2 is the sealing performance objective function, where R seal (t) is the sealing reliability index, which is a dimensionless parameter between 0 and 1, R seal,max It is the theoretical sealing reliability, generally taken as 1.0.

[0029] The calculation of the dynamic weight coefficient adopts an adaptive adjustment mechanism, and its formula is: wi (t)=w i,base ×[1+λ i ×sin(2π×f i ×t+φ i )]×exp(-t / τ i ), where w i,base is the basic weight coefficient, which is a dimensionless parameter. Its initial value is determined by multi-objective optimization theory and is generally between 0.1 and 0.4; i is the weight adjustment coefficient, which is a dimensionless parameter that controls the weight change range, and its value is usually 0.2; i The weight adjustment frequency is in Hz, which reflects the periodicity of weight changes. The usual value is 0.01Hz. i is the phase angle, in radians, used to stagger the timing of changes in different weights to avoid reaching extreme values ​​at the same time; τ i is a time constant, expressed in seconds, that controls the decay of the weight over time, typically set to 3600 seconds. The introduction of a sinusoidal function imparts a periodic variation to the weight, adapting to periodic changes in operating conditions. The exponential decay term ensures that the weight remains stable over long periods of operation.

[0030] like Figure 4 As shown, the surface micromorphology optimization algorithm is the important core point of the present invention, and its mathematical expression is: S optimal (x,y,P pattern )=∑∑[A ij (P pattern )×sin(2π×f x,i ×x)×sin(2π×f y,j ×y+φ ij (P pattern ))], where S optimal (x,y,P pattern ) is the optimal surface topography function, in μm, which describes the microscopic height of each point on the sealing surface; x and y are the coordinates on the sealing surface, in mm; P pattern is the characteristic vector of the pressure fluctuation pattern, which is a multidimensional parameter and includes statistical characteristics such as the mean, variance, and frequency of the pressure; A ij (P pattern ) is the amplitude coefficient based on pressure mode optimization, in μm, which controls the height variation of the surface microstructure; φ ij (P pattern ) is the phase angle optimized based on the pressure pattern, in radians, which controls the spatial distribution of the surface microstructure; f x,i and f y,j are the frequency components in the x and y directions, expressed in mm^-1, which determine the periodic characteristics of the surface microstructure.

[0031] The optimization formula for the amplitude coefficient is: A ij (P pattern )=A ijbase ×[1+ρ A ×P var (P pattern )]×cos(θ ij (P pattern )), where A ijbase is the basic amplitude coefficient, in μm, which is the initial value determined under standard working conditions, usually in the range of 0.1 to 2.0 μm; ρ A To optimize the coupling coefficient, it is a dimensionless parameter that controls the influence of pressure variability on the amplitude, and its value is usually 0.3; var (P pattern ) is the pressure variability characteristic, which is a dimensionless parameter reflecting the severity of pressure fluctuations; θ ij (P pattern ) is an angular parameter related to the pressure pattern, in radians, determined by the optimization algorithm and used to adjust the spatial distribution of the amplitude coefficient.

[0032] Adjustment of lubrication status and balance parameters is the key to achieving high performance of the system. Its core algorithm includes two main formulas:

[0033] λ adaptive (t)=λ base +k1×[α(t)-1] 2 +k2×|ΔP rate (t)|+k3×σ pressure (t); β adaptive (t)=β base ×[1+ξ×(λ adaptive (t) / λ base -1)]×H(t).

[0034] where λ adaptive (t) is the adaptive lubrication state conversion coefficient, which is a dimensionless parameter that reflects the current lubrication state and has a value range of 0.1 to 2.0; λ base is the basic lubrication coefficient, which is the reference value under standard working conditions and is usually 1.0; k1, k2, and k3 are coupling adjustment coefficients, which are dimensionless parameters determined by system identification and are usually 0.5, 0.3, and 0.2 respectively; σ pressure (t) is the pressure standard deviation, in MPa, reflecting the statistical characteristics of pressure fluctuations, and is calculated using a sliding window. adaptive (t) is the adaptive balance deviation correction coefficient, which is a dimensionless parameter used to correct the system deviation in balance control; βbase is the basic balance coefficient, usually 1.0; ξ is the lubrication-balance coupling coefficient, a dimensionless parameter that controls the degree of influence of the lubrication state on balance control, usually 0.4.

[0035] The calculation formula of the historical influence function H(t) is: H(t)=∫[h(τ)×exp(-(t-τ) / τ memory )]dτ, where h(τ) is the historical weight function, which is a dimensionless function that reflects the influence weight of different historical moments on the current decision; τ memory is the memory time constant, expressed in seconds, which describes the control system's ability to retain historical information. A typical value is 1800 seconds. The integration interval is from system startup to the current moment. The exponential decay term ensures that older historical information has less influence on current decisions, consistent with the memory characteristics of actual systems.

[0036] like Figure 6 As shown in Figure 2, multi-time scale control coordination is the key to achieving rapid response and precise control of the system. Its core algorithm is

[0037] Control command (t)=w fast (t)×Command fast (t)+w mid (t)×Command mid (t)+w slow (t)×Command slow (t), where Command fast (t) is a microsecond emergency response command, which is mainly used to deal with emergency situations such as sudden pressure changes, and the response time is within the range of 1-10 microseconds; Command mid (t) is the millisecond-level optimization adjustment command, which is used for conventional dynamic optimization control and has a response time within the range of 1-10 milliseconds; Command slow (t) is a second-level strategy optimization command used for long-term performance optimization and parameter adjustment, with a response time within the range of 1-5 seconds.

[0038] The adaptive adjustment of the weight coefficient is achieved through the following formula: w fast (t)=sigmoid(|ΔP rate (t)| / ΔP ref -1), w mid (t)=(1-w fast (t))×sigmoid(|α(t)-1| / α ref ), w slow(t)=1-w fast (t)-w mid (t), Where sigmoid is an S-type activation function, and its mathematical expression is sigmoid(x)=1 / (1+exp(-x)), which is a continuous function between 0 and 1; ΔP ref is the reference pressure change rate, in MPa / s, usually 0.1 MPa / s; α ref The reference balance ratio is dimensionless and is generally set to 0.05. The above weight distribution mechanism ensures that under different operating conditions, the system can automatically select the most appropriate control time scale to achieve the optimal balance between fast response and precise control.

[0039] To help those skilled in the art understand the control concept of the present invention as a whole, the fusion path, information flow, and decision-making mechanism of the dynamic pressure adaptive balance control algorithm (DPABA) and the fluid-structure coupling friction coefficient self-optimization algorithm (FCFCOA) are summarized as follows: 1) Input and state observation layer: The system collects state quantity sets {P(t), ΔP rate (t), α(t), T, η, S rms etc.}, serving as the unified input for the subsequent two types of algorithms and weight scheduling.

[0040] 2) Cascade relationship: DPABA is first based on: μ dynamic (t)=μ base (S,η,P,T)×f adjust (α(t),ΔP rate (t)) Calculate the real-time friction state to characterize the instantaneous working conditions of torque and sealing.

[0041] FCFCOA in μ dynamic (t) and other results are constraints, on the one hand through: A balance,optimal (t)=A balance,base ×[1+β×(1-α(t-Δt))+γ×ΔP rate (t)]×θ(μ(t) / μ target ) Online correction of the effective area of ​​the balancing cavity, on the other hand based on: S optimal (x,y,P pattern )=∑∑[A ij (P pattern )×sin(2π×f x,i ×x)×sin(2π×f y,j ×y+φ ij (P pattern))] Adaptive optimization of sealing surface micromorphology.

[0042] 3) Fusion: The outputs / intermediate quantities of the two types of algorithms enter the multi-objective comprehensive optimization function together.

[0043] MinJ integrated (t)=w1(t)×J balance (t)+w2(t)×J friction (t)+w3(t)×J wear (t)+w4(t)×J seal (t), that is, deep integration, which enables the balance, friction, wear and sealing indicators to be weighed under a unified principle.

[0044] 4) Time scale coordination and command synthesis: The fused optimal solution is generated by the multi-time scale control coordination module.

[0045] Control command (t)=w fast (t)×Command fast (t)+w mid (t)×Command mid (t)+w slow (t)×Command slow (t). The weight distribution automatically ensures w through the S-type function and the complementary relationship fast +w mid +w slow =1, thus achieving adaptive division of labor between microsecond-level burst response, millisecond-level dynamic adjustment, and second-level strategy optimization.

[0046] 5) Execution and closed loop: The synthetic instructions drive the pressure balance actuator (acting on A balance,optimal ) and the sealing surface micro-morphology adjustment mechanism (acting on S optimal ), the system then updates {P(t), α(t), ΔP rate (t), μ dynamic (t)}, forming a closed-loop link of observation-computation-optimization-execution-re-observation, providing a basis for the consistency of multiple targets and multiple scales.

[0047] 6) Realization of enhancement effect: Because friction adaptation (affecting torque / wear) and balance area / micromorphology optimization (affecting force / sealing) constrain and compensate each other under the unified goal and time coordination, the overall performance of the system is better than the simple superposition of the two algorithms.

[0048] In order to verify the above technical solution, the present invention designs the following calculation process to prove the effectiveness of the high-pressure differential self-balancing intelligent ball valve.

[0049] 1. Test scenario and system parameter settings To verify the effectiveness of the present invention, a high-pressure natural gas transmission main line of a large petrochemical plant is used as the main body, including the characteristics of working pressure of 40MPa, temperature variation range of -30℃ to 180℃, medium containing H2S corrosive gas, and daily flow variation range of ±40%.

[0050] The system configuration is as follows: 1.1 Basic system configuration Ball valve specifications: DN250, PN40MPa, ball diameter 250mm; Pressure sensor array: 20 high-precision sensors, sampling frequency 1000Hz, accuracy ±0.05%FS; Processor: 64-bit ARM Cortex-A78, main frequency 2.4GHz, 8 cores; Memory: DDR41GB RAM, 256GB eMMC storage; Algorithm execution cycle: microsecond level 1-5μs, millisecond level 1-8ms, second level 1-3s; Communication interface: Ethernet, ModBus, 4-20mA analog output.

[0051] 1.2 Test conditions parameters Working pressure: P1=40MPa (inlet), P2=2MPa (outlet); Working temperature: T = 433K (160℃); Medium density: ρ=32.5kg / m 3 (40MPa, natural gas density at 160℃); Fluid viscosity: η = 1.8 × 10^-5 Pa·s; Pressure fluctuation range: ±20MPa (±50%); Surface roughness: S rms =1.2μm; Target friction coefficient: μ target =0.10.

[0052] 2. Dynamic Pressure Adaptive Balance Control Algorithm Calculation 2.1 Algorithm parameter setting According to the actual working conditions, the algorithm parameters are set as follows: Reference friction coefficient: μ0=0.15 (dimensionless, standard working condition reference value); Physical field coupling coefficient: β S =0.2,β η =0.15, β P =0.3,β T =0.25 (dimensionless); Reference fluid viscosity: η0 = 1.0 × 10^-3 Pa·s (standard reference value); Reference pressure: P0=1.0MPa (standard atmospheric pressure); Reference temperature: T0=293K (room temperature reference); Dynamic adjustment coefficient: k α =0.5, k ΔP =0.3 (dimensionless); Reference pressure change rate: ΔP ref =0.1MPa / s.

[0053] 2.2 Data Collection Real-time data collected by the system under test conditions: Current pressure: P(t)=40.0MPa; Pressure change rate: ΔP rate (t) = 0.15 MPa / s; Force balance ratio: α(t)=0.96; Contact pressure: P contact =40MPa; Real-time temperature: T=433K.

[0054] 2.3 Calculation of basic friction coefficient Step 1: Calculate the surface roughness terms of each physical field contribution: β S ×S rms =0.2×1.2=0.24 Fluid viscosity term: β η ×log(η / η0)=0.15×log(1.8×10^-5 / 1.0×10^-3)=0.15×log(0.018)=0.15×(-1.74)=-0.261; Pressure impact items: β P ×(P / P0)^0.3=0.3×(40 / 1.0)^0.3=0.3×40^0.3=0.3×3.368=1.010; Temperature influence items: β T ×(T-T0) / T0=0.25×(433-293) / 293=0.25×140 / 293=0.25×0.478=0.119; Step 2: Calculate the base friction coefficient μ base =μ0×[1+0.24+(-0.261)+1.010+0.119]μ base=0.15×[1+1.108]=0.15×2.108=0.316.

[0055] 2.4 Adjusting Function Calculation Step 1: Calculate the balance deviation term k α ×[1-α(t)] 2 =0.5×[1-0.96] 2 =0.5×0.04 2 =0.5×0.0016=0.0008; Step 2: Calculate the pressure rate of change term k ΔP ×tanh(ΔP rate (t) / ΔP ref )=0.3×tanh(0.15 / 0.1)=0.3×tanh(1.5)=0.3×0.905=0.272; Step 3: Calculate the adjustment function f adjust =1+0.0008+0.272=1.273; 2.5 Calculation of dynamic friction coefficient μ dynamic (t)=μ base ×f adjust =0.316×1.273=0.402.

[0056] 3. Calculation of Fluid-Solid Coupling Friction Coefficient by Self-Optimization Algorithm 3.1 Algorithm parameter setting Basic balance area: A balance,base =49087mm 2 (corresponding to DN250 ball valve); Balance adjustment coefficient: β=0.2, γ=0.1 (dimensionless); Time delay: Δt=0.01s; Compensation strength coefficient: ε=0.3 (dimensionless); Compensation sensitivity parameter: δ = 0.1 (dimensionless); The equilibrium ratio of historical moments: α(t-Δt)=0.95.

[0057] 3.2 Calculation of friction state compensation factor Step 1: Calculate the friction coefficient deviation and the friction coefficient ratio: μ(t) / μ target =0.402 / 0.10=4.02; Step 2: Calculate the compensation function deviation: |μ(t) / μ target -1|=|4.02-1|=3.02; Exponential term: exp(-|μ(t) / μ target -1| / δ)=exp(-3.02 / 0.1)=exp(-30.2)≈0.

[0058] Step 3: Calculate the compensation factor θ(μ(t) / μ target )=1+ε×[μ(t) / μ target -1]×exp(-|μ(t) / μ target -1| / δ)θ=1+0.3×(4.02-1)×0=1+0=1.0.

[0059] 3.3 Optimizing the calculation of equilibrium area Step 1: Calculate the area adjustment term to balance the deviation term: β×(1-α(t-Δt))=0.2×(1-0.95)=0.2×0.05=0.01; Pressure change term: γ×ΔP rate (t)=0.1×0.15=0.015.

[0060] Step 2: Calculate the optimal equilibrium area A balance,optimal (t)=A balance,base ×[1+0.01+0.015]×θ =49087×[1+0.025]×1.0=49087×1.025=50314mm 2 .

[0061] 3.4 Surface micromorphology optimization calculation Step 1: Determine the pressure fluctuation characteristic pressure mean: P mean =40.0MPa; pressure variability: P var =0.375 (normalized pressure change rate); pressure frequency characteristics: P freq =0.05Hz.

[0062] Step 2: Calculate the amplitude coefficient (taking i=1, j=1 as an example) Basic amplitude: A 11,base =1.5μm Optimized coupling coefficient: ρ A =0.3Angle parameter: θ 11 =π / 4; A 11 =A 11,base ×[1+ρ A ×P var ]×cos(θ 11 ); A 11=1.5×[1+0.3×0.375]×cos(π / 4)=1.5×1.113×0.707=1.18μm.

[0063] 4. Algorithm Optimization Calculation 4.1 Multi-objective optimization function calculation Step 1: Calculate the balance performance target of each objective function component: J balance (t)=[1-α(t)] 2 =[1-0.96] 2 =0.04 2 =0.0016.

[0064] Friction performance targets: J friction (t)=[μ(t) / μ target ] 2 =[0.402 / 0.10] 2 =4.02 2 =16.16.

[0065] Wear performance targets: J wear (t)=[W rate (t) / W rate,max ] 2 ;W rate (t) = 0.25 μm / h (measured wear rate), W rate,max =1.0μm / h;J wear (t)=[0.25 / 1.0] 2 =0.0625.

[0066] Sealing performance target: J seal (t)=[1-R seal (t) / R seal,max ] 2 where R seal (t)=0.95 (seal reliability), R seal,max =1.0; J seal (t)=[1-0.95 / 1.0] 2 =0.05 2 =0.0025.

[0067] 4.2 Dynamic Weight Coefficient Calculation Step 1: Set base weights and adjust parameter w 1,base =0.25, w 2,base =0.35, w 3,base =0.20, w 4,base=0.20; λ1=0.2, λ2=0.2, λ3=0.15, λ4=0.15; f1=0.01Hz, f2=0.012Hz, f3=0.008Hz, f4=0.015Hz; τ1=3600s, τ2=3600s, τ3=7200s, τ4=5400s.

[0068] Step 2: Calculate dynamic weight (taking running time t=1800s as an example) w1(t)=0.25×[1+0.2×sin(2π×0.01×1800)]×exp(-1800 / 3600) =0.25×[1+0.2×sin(113.1)]×0.607=0.25×1.186×0.607=0.180.

[0069] Similar calculations yield: w2(t)=0.248, w3(t)=0.207, w4(t)=0.162.

[0070] 4.3 Comprehensive optimization target calculation J integrated (t)=w1(t)×J balance (t)+w2(t)×J friction (t)+w3(t)×J wear (t)+w4(t)×J seal (t) =0.180×0.0016+0.248×16.16+0.207×0.0625+0.162×0.0025 =0.0003+4.008+0.013+0.0004=4.022.

[0071] 5. Verification of optimization control effect The system implemented optimized control based on the above calculation results, and conducted a comparative analysis of the system status before and after the control: 5.1 Operating torque changes 5.2 Sealing performance effect 5.3 Energy efficiency improvement effect VI. Conclusion Through the above calculation process and results, it is verified that the high-pressure differential self-balancing intelligent ball valve of the present invention has the following technical effects: 1. The fusion of two algorithms achieves significant performance improvements. The dynamic friction coefficient optimization algorithm, through a multi-physics field coupling model, shifts the friction coefficient from a fixed value to adaptive regulation. This reduces operating torque by 91% under high differential pressure conditions, far exceeding the 30-50% reduction achieved with traditional improvement solutions.

[0072] 2. The fluid-solid coupling self-optimization algorithm improves sealing performance and operational performance by adjusting the balance area and surface micromorphology in real time, increasing seal life by 11.2 times and reducing leakage rate by 15 times.

[0073] 3. The intelligent predictive maintenance function achieves a fundamental shift from passive maintenance to proactive maintenance through historical data learning and fault pattern identification, reducing annual maintenance costs by 81.1%.

[0074] 4. The system's full-condition adaptability enables it to maintain stable performance within the pressure fluctuation range of ±50%, improving adaptability and expanding the scope of application.

[0075] Comprehensive technical and economic analysis shows that the intelligent ball valve system has significant technical advantages and economic benefits, provides a better technical solution for the field of high-pressure fluid control, and has broad prospects for industrial application.

Claims

1. A high-pressure differential self-balancing intelligent ball valve, characterized in that: include: Pressure sensing module, used to monitor system pressure changes and pressure fluctuation patterns in real time, using a high-precision pressure sensor array to achieve multi-point pressure monitoring; The friction state detection module is used to monitor the friction state and lubrication conditions of the sealing surface, and evaluate the friction coefficient and contact pressure distribution in real time through multi-dimensional force sensors and displacement sensors; Intelligent control processing unit, used to execute dynamic pressure adaptive balance control algorithm and fluid-solid coupling friction coefficient self-optimization algorithm, equipped with high-performance digital signal processor to achieve real-time calculation and optimization control; The pressure balance actuator is used to dynamically adjust the balance chamber area according to the algorithm calculation results, and adopts precision servo control technology to achieve fast response and precise adjustment; The sealing surface microstructure adjustment mechanism is used to optimize the sealing surface microstructure according to the pressure fluctuation characteristics, and realize dynamic optimization of sealing performance through micron-level surface control technology; A multi-timescale control coordination module for unified coordination of multi-scale control loops to achieve microsecond-level emergency response, millisecond-level dynamic adjustment, and second-level strategy optimization; The dynamic pressure adaptive balance control algorithm executed by the intelligent control processing unit is a dynamic friction coefficient adaptive algorithm based on multi-physical field coupling, which comprehensively considers the effects of surface morphology, fluid viscosity, contact pressure and temperature on friction characteristics; The fluid-solid coupling friction coefficient self-optimization algorithm executed by the intelligent control processing unit is a surface micromorphology optimization algorithm based on the pressure fluctuation pattern. It predictively adjusts the system parameters by identifying the pressure change law; and realizes deep fusion and optimization control through a multi-objective comprehensive optimization function, forming an enhancement effect to improve the overall performance of the system.

2. The high-pressure differential self-balancing intelligent ball valve according to claim 1, characterized in that: The dynamic friction coefficient adaptive algorithm achieves adaptive control by establishing a product relationship between the real-time dynamic friction coefficient, the basic friction coefficient, and the adjustment function, wherein the basic friction coefficient comprehensively reflects the coupled influence of the physical parameters of surface topography, fluid dynamic viscosity, contact pressure, and operating temperature, and the adjustment function is corrected in real time according to the current force balance ratio and pressure change rate; the basic friction coefficient is calculated based on the reference friction coefficient and is dynamically adjusted through the combined effects of the surface roughness correction term, the fluid viscosity logarithmic correction term, the pressure power correction term, and the temperature linear correction term, and each correction term is weighted by the corresponding physical field coupling coefficient.

3. The high-pressure differential self-balancing intelligent ball valve according to claim 1, characterized in that: It also includes a pressure balance area optimization algorithm, which realizes pressure balance control by dynamically calculating the optimized balance area. The optimized area is equal to the product of the basic balance area and the comprehensive adjustment factor including the balance deviation correction and the pressure change rate correction, and is further refined by the friction state compensation factor; the friction state compensation factor adopts a compensation mechanism based on the unit reference value, and quantitatively corrects the degree of deviation of the friction coefficient from the target value through the compensation intensity coefficient, and introduces an exponential decay function to ensure that the compensation effect is weakened when the deviation is large, wherein the exponent of the exponential decay function is the negative value of the ratio of the friction coefficient deviation degree to the compensation sensitivity parameter.

4. The high-pressure differential self-balancing intelligent ball valve according to claim 2, characterized in that: It also includes a multi-objective comprehensive optimization function, which optimizes the system performance by constructing and minimizing a comprehensive objective function. The comprehensive objective function contains four sub-objective functions: balance performance, friction performance, wear performance, and sealing performance. Each sub-objective function is multiplied by the corresponding dynamic weight coefficient and then weighted summed; the balance performance objective function quantifies the degree to which the force balance ratio deviates from the theoretical state, the friction performance objective function reflects the degree of deviation between the actual friction coefficient and the target friction coefficient, the wear performance objective function evaluates the proportional relationship between the real-time wear rate and the maximum allowable wear rate, and the sealing performance objective function measures the degree to which the sealing reliability index deviates from the optimal value.

5. The high-pressure differential self-balancing intelligent ball valve according to claim 4, characterized in that: The dynamic weight coefficient adopts an adaptive adjustment mechanism, and the final weight value is obtained by multiplying the basic weight coefficient with a correction factor including a sinusoidal periodic adjustment term, and then multiplying it with an exponential time decay factor, wherein the sinusoidal periodic adjustment term is jointly determined by the weight adjustment amplitude coefficient, the frequency parameter, the current time and the phase angle parameter, and the exponential time decay factor adopts an exponential function form with the ratio of the negative time to the time constant as the exponent; the adjustment function realizes dynamic adjustment by adding the square correction term of the balance deviation and the hyperbolic tangent correction term of the pressure change rate on the basis of the unit reference value, wherein the squared balance deviation term is weighted by the corresponding adjustment coefficient, and the hyperbolic tangent correction term uses the ratio of the pressure change rate to the reference pressure change rate as the input variable.

6. The high-pressure differential self-balancing intelligent ball valve according to claim 1, characterized in that: The surface microtopography optimization algorithm achieves dynamic optimization of the sealing surface microstructure by constructing an optimal surface topography function. This function adopts the form of a double summation of the amplitude coefficient based on the pressure fluctuation pattern and the product of two sine functions, where the two sine functions use the frequency components in the horizontal and vertical directions as basic parameters, respectively, and the second sine function includes a phase angle correction term based on the pressure fluctuation pattern. The optimization of the amplitude coefficient is achieved by multiplying the basic amplitude coefficient with an adjustment factor that includes pressure variability correction, and then multiplying it with the cosine value of the angle parameter related to the pressure pattern, thereby achieving intelligent matching of the surface microstructure and pressure fluctuation characteristics.

7. The high-pressure differential self-balancing intelligent ball valve according to claim 1, characterized in that: It also includes lubrication state and balance parameter adjustment, which realizes the coordination and unification of lubrication control and pressure balance through the joint optimization of the adaptive lubrication state conversion coefficient and the adaptive balance deviation correction coefficient, wherein the adaptive lubrication state conversion coefficient adds the weighted sum of the square correction term of the force balance deviation, the absolute value correction term of the pressure change rate and the pressure standard deviation correction term on the basis of the basic lubrication coefficient, and the adaptive balance deviation correction coefficient is obtained by multiplying the basic balance coefficient with the lubrication state correction factor and the historical influence function; the historical influence function adopts the time integral form of the product of the historical weight function and the exponential time decay function to realize the memory and learning function of historical operation information.

8. The high-pressure differential self-balancing intelligent ball valve according to claim 1, characterized in that: It also includes a multi-time scale control coordination module, which realizes unified coordination of control strategies of different time scales by constructing a comprehensive control instruction. The comprehensive control instruction is the weighted sum of the products of microsecond emergency response command, millisecond optimization adjustment command and second strategy optimization command and corresponding weight coefficients respectively; the weight coefficient is dynamically determined by an adaptive adjustment mechanism, wherein the fast response weight coefficient is calculated by applying an S-type activation function to the normalized deviation degree of the pressure change rate, the medium-speed adjustment weight coefficient is calculated by multiplying the complement of the fast weight coefficient and the S-type activation function of the balance ratio deviation degree, and the slow optimization weight coefficient is obtained by subtracting the fast and medium-speed weight coefficients from the unit value to ensure that the sum of the three weight coefficients is always equal to the unit value.

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