A double-layer ball insulation ball valve

Abstract

This invention discloses a double-layer spherical insulated ball valve, comprising: an active pressure relief intelligent control algorithm module, a double-layer spherical insulation optimization algorithm module, an exhaust mechanism control algorithm module, a temperature field prediction algorithm module; a multi-objective collaborative optimization algorithm module; and a system performance evaluation algorithm module, used to comprehensively evaluate the system's operating status through multi-dimensional performance functions, including pressure control performance evaluation, insulation performance evaluation, and operational performance evaluation, as well as a fault detection function based on residual analysis, and feeding back system performance indicators to the multi-objective collaborative optimization algorithm module. This invention has the following beneficial effects: through the collaborative work of multiple algorithm modules, it solves problems such as pressure accumulation, heat loss, and operational safety during high-temperature medium transportation, while simultaneously considering the system's real-time performance, accuracy, safety, and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a double-layer insulated ball valve, suitable for intelligent control and optimization of ball valves in high-temperature media conveying systems in industries such as petrochemicals, energy, and pharmaceuticals. Background Technology

[0002] With the rapid development of industrial automation technology, high-temperature media conveying systems are increasingly widely used in petrochemical, energy, and pharmaceutical industries. As a control element, the performance of ball valves in high-temperature and high-pressure environments affects the safety and economy of the entire system. However, currently available ball valve products have significant technical limitations in high-temperature applications, mainly in the following aspects:

[0003] First, traditional ball valves lack intelligent pressure regulation mechanisms and cannot achieve dynamic balance control. During the transportation of high-temperature media, complex thermodynamic changes occur inside the valve body, leading to pressure buildup. When the internal pressure exceeds the design pressure, it may cause valve deformation or rupture, not only compromising the valve's structural integrity but also potentially causing leaks or more serious safety accidents. Prolonged operation under high pressure will cause the valve body material to gradually deteriorate due to fatigue, reducing its strength and reliability, shortening its service life, and increasing maintenance costs.

[0004] Secondly, existing ball valve products lack effective insulation measures, resulting in significant heat loss. The traditional single-layer ball structure suffers substantial heat conduction loss under high-temperature conditions, not only wasting energy but also posing a risk of external burns. Heat loss also affects the process performance of the medium, reducing production efficiency. Simultaneously, the sealing material is prone to aging and softening at high temperatures, affecting sealing performance and leading to media leakage.

[0005] Third, operational performance deteriorates significantly under high-temperature conditions. High temperatures increase friction between the valve stem and packing, leading to a significant increase in operating torque. This not only increases operational difficulty but may also cause actuator overload. Traditional control strategies cannot dynamically adjust according to actual operating conditions, lacking adaptive capabilities and struggling to cope with complex and variable high-temperature conditions.

[0006] Fourth, existing technologies lack fault prediction and proactive protection functions. Traditional ball valves employ a passive protection method, only detecting and addressing faults after they occur, failing to provide early warnings and posing safety hazards. The lack of comprehensive monitoring and analysis of system operating status prevents preventative maintenance, impacting system reliability.

[0007] Fifth, there is a lack of multi-physics coupled optimization control technology. Under high-temperature conditions, there are strong coupling relationships between the temperature field, pressure field, and flow field, and traditional methods cannot handle this kind of multi-physics coordinated optimization problem. There is a complex mathematical relationship between heat loss and operating performance, which requires multi-objective optimization algorithms for coordinated control, but existing technologies are significantly insufficient in this regard.

[0008] Therefore, how to develop a double-layer spherical insulated ball valve with intelligent control functions to achieve dynamic pressure regulation, minimize heat loss, multi-objective collaborative optimization, and fault prediction has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0009] The technical problem to be solved by this invention is: how to realize intelligent control function in double-layer spherical insulated ball valve, and solve the problems of pressure accumulation, heat loss and operation safety in the process of high temperature medium transportation through the collaborative work of multiple algorithm modules, while taking into account the real-time performance, accuracy, safety and energy efficiency of the system.

[0010] To address the aforementioned technical problems, this invention provides a double-layer spherical insulated ball valve. This method employs a modular design approach, constructing six core algorithm modules to achieve intelligent system control through multi-module collaborative operation. The method includes an active pressure relief intelligent control algorithm module, a double-layer spherical insulation optimization algorithm module, an exhaust mechanism control algorithm module, a temperature field prediction algorithm module, a multi-objective collaborative optimization algorithm module, and a system performance evaluation algorithm module. The active pressure relief intelligent control algorithm module, as the core control unit of the system, is responsible for adjusting the working parameters of the pressure relief mechanism in real time according to the internal pressure state of the valve body, realizing dynamic discharge control of liquids and steam. It includes two functional units: liquid pressure relief flow calculation and pressure relief pipeline resistance loss calculation. It also outputs pressure control performance parameters to the multi-objective collaborative optimization algorithm module, enabling information exchange between modules. The double-layer spherical insulation optimization algorithm module is responsible for minimizing heat loss through multi-mode heat transfer control. It includes two core units: heat conduction loss calculation and vacuum insulation layer radiative heat transfer calculation. Through precise mathematical modeling, it achieves optimized control of insulation performance and transmits insulation performance parameters to the system performance evaluation algorithm module, providing data support for overall system performance assessment. The exhaust mechanism control algorithm module achieves precise steam pressure control through adaptive exhaust port area adjustment. It includes two functional units: steam discharge flow control and exhaust port opening adjustment. This module dynamically adjusts the exhaust strategy based on pressure status information provided by the active pressure relief intelligent control algorithm module, achieving coordinated optimization of pressure control. The temperature field prediction algorithm module achieves accurate prediction of the system temperature state based on a transient temperature distribution prediction algorithm. This module receives heat transfer parameters from the double-layer spherical insulation optimization algorithm module and predicts temperature change trends through mathematical modeling, providing a basis for dynamically optimizing the temperature control strategy. The multi-objective collaborative optimization algorithm module serves as the system's decision center, responsible for the comprehensive optimization of pressure control, insulation performance, operational performance, and safety performance. This includes the calculation and optimization of the system performance comprehensive evaluation function. This module receives pressure control parameters from the active pressure relief intelligent control algorithm module and achieves global coordinated control through a multi-objective optimization algorithm, ensuring the system achieves optimal performance under various operating conditions. The system performance evaluation algorithm module achieves a comprehensive evaluation of the system's operating status through multi-dimensional performance functions, including three dimensions: pressure control performance evaluation, thermal insulation performance evaluation, and operational performance evaluation. This module feeds back system performance indicators to the multi-objective collaborative optimization algorithm module, forming a closed-loop control system to ensure continuous optimization of the control effect.

[0011] In summary, the present invention has the following beneficial effects:

[0012] This double-layer insulated ball valve effectively solves the technical problems of pressure buildup, heat loss, and operational safety during high-temperature media transportation. Through the collaborative work of multiple algorithm modules, it achieves comprehensive optimization of system performance. Compared with traditional valves, it improves control accuracy, response speed, and stability, significantly enhancing the system's dynamic performance. Increased insulation efficiency and reduced operating energy consumption result in significant energy savings, with annual energy savings reaching tens of thousands of yuan. Improved fault prediction accuracy reduces the accident rate, extends the mean time between failures (MTBF), and lowers maintenance costs, greatly enhancing the system's safety and reliability.

[0013] This invention possesses the ability to automatically identify operating conditions and adjust parameters, exhibiting strong adaptability and the capacity to handle more than 20 different operating conditions. It significantly improves control stability, providing technical support and broad application prospects for the development of intelligent control technology in the field of industrial automation. Attached Figure Description

[0014] Figure 1 This is a system architecture diagram of the double-layer spherical insulated ball valve of the present invention;

[0015] Figure 2 This is a flowchart of the active pressure relief intelligent control algorithm module of the present invention;

[0016] Figure 3 This is a flowchart illustrating the implementation of the double-layer spherical insulation optimization algorithm module of the present invention.

[0017] Figure 4 This is the control logic diagram of the exhaust mechanism control algorithm module of the present invention;

[0018] Figure 5 This is a flowchart of the calculation process of the temperature field prediction algorithm module of the present invention;

[0019] Figure 6 This is an optimization strategy diagram of the multi-objective collaborative optimization algorithm module of the present invention;

[0020] Figure 7 This is a diagram illustrating the evaluation system architecture of the system performance evaluation algorithm module of this invention.

[0021] Figure 8 This is a diagram showing the parameter interaction relationships between the various algorithm modules of this invention;

[0022] Figure 9 This is a schematic diagram of the working principle of the overall control system of the present invention. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1As shown, the double-layer spherical insulated ball valve of the present invention adopts a modular design architecture, including six core algorithm modules, which realize intelligent control of the system through parameter interaction and collaborative work between modules.

[0025] like Figure 2 As shown, the active pressure relief intelligent control algorithm module is the core control unit of the system. This module dynamically adjusts the operating parameters of the pressure relief mechanism by monitoring the internal pressure of the valve body in real time, thereby achieving intelligent discharge control of liquids and steam. The liquid pressure relief flow rate calculation of this module adopts the modified Bernoulli equation, and the formula is Q. liquid =C d ×A eff ×√(2×ΔP / ρ liquid )×η correction Q liquid This indicates the liquid pressure relief flow rate, measured in cubic meters per second. This parameter reflects the system's liquid discharge capacity, and its value typically ranges from 0.1 to 2.0 cubic meters per hour, depending on the valve body size and operating conditions. C d The dynamic flow coefficient is a dimensionless parameter with a value ranging from 0.6 to 0.95. This coefficient varies with the geometry, surface roughness, and fluid properties of the pressure relief mechanism, and is typically around 0.8 under turbulent conditions. eff The effective pressure relief cross-sectional area, measured in square meters, is determined by the geometric parameters of the pressure relief pipe and the valve opening state. This parameter is the variable controlling the pressure relief flow rate. ΔP is the instantaneous pressure difference, measured in Pascals, representing the difference between the internal pressure of the valve body and the external ambient pressure. This parameter changes in real time and is the main input signal for the control algorithm. ρ liquid η represents the density of a high-temperature liquid, expressed in kilograms per cubic meter. This parameter varies with temperature and is a temperature-dependent material property; the density at high temperatures is typically 5% to 15% lower than at room temperature. correction This is a correction factor, a dimensionless parameter used to account for non-ideal flow effects under high-temperature conditions, including viscous and compressible effects, and is typically between 0.9 and 1.1.

[0026] The pressure relief pipeline resistance loss is calculated using a modified form of the Darcy-Weisbach equation, with the formula ΔP. friction =f friction ×(L pipe / D pipe )×(ρ liquid ×v flow 2 / 2)×K bend Where ΔP friction This represents the pressure loss due to pipeline friction, measured in Pascals. This parameter affects the actual pressure relief effect and needs to be compensated for in the control algorithm. frictionThe dynamic friction factor is a dimensionless parameter. For a smooth pipe under turbulent conditions, the calculation formula is f. friction =0.316 / Re^0.25, where Re is the Reynolds number. pipe This represents the total length of the pressure relief pipe, in meters, including straight sections and equivalent bend lengths. (D) pipe This refers to the inner diameter of the pressure relief pipe, in meters. This parameter affects flow resistance; the larger the diameter, the lower the resistance. flow The velocity inside the pipe, expressed in meters per second, is calculated using the continuity equation. flow =Q liquid / A pipe A pipe K represents the cross-sectional area of ​​the pipe. bend Let K be the bend resistance coefficient, a dimensionless parameter, considering the influence of pipeline geometry on flow resistance. For a 90-degree bend... bend It is usually taken as 0.9 to 1.5.

[0027] like Figure 3 As shown, the double-layer spherical insulation optimization algorithm module is responsible for minimizing heat loss through multi-mode heat transfer control. This module adopts a layered heat transfer calculation method, considering the combined effects of multiple heat transfer modes such as conduction, convection, and radiation.

[0028] The formula for calculating heat conduction loss is Q. conduction =Σ(k i ×A i ×ΔT i / δ i )×η contact,i .

[0029] Q conduction This represents heat conduction loss, measured in watts. This parameter reflects the heat loss through a solid structure and is an important indicator for evaluating thermal insulation performance. i Let be the thermal conductivity of the i-th layer material, expressed in watts per meter (Kelvin). The thermal conductivity varies greatly among different materials; the thermal conductivity of insulation materials is typically between 0.02 and 0.1 Kelvin per meter, while that of metallic materials ranges from 10 to 400 Kelvin per meter. A i Let be the heat transfer area of ​​the i-th layer, in square meters. For a spherical structure, the area increases with the radius. ΔT i δ represents the temperature difference of the i-th layer, expressed in Kelvin, indicating the temperature difference between the inner and outer surfaces of that layer. i η represents the thickness of the i-th layer, in meters. Insulation layer thickness is typically between 0.05 and 0.2 meters. Increasing the thickness reduces heat conduction but increases cost and weight. contact,i This is the contact thermal resistance correction factor, a dimensionless parameter that takes into account the effect of incomplete interlayer contact on heat transfer, and is typically taken as 0.8 to 1.0.

[0030] The formula for calculating radiative heat transfer in a vacuum insulation layer is:

[0031] Q radiation =ε eff ×σ×A radiation ×(T inner 4 -T outer 4 )×F view ×F vacuum Q radiation ε represents radiative heat loss, measured in watts. This parameter dominates at high temperatures and increases with the fourth power of temperature. eff The effective emissivity is a dimensionless parameter, calculated using the formula ε. eff =1 / (1 / ε inner +1 / ε outer -1), where ε inner and ε outer These represent the emissivity of the inner and outer surfaces, respectively. σ is the Stefan-Boltzmann constant, with a value of 5.67 x 10^-8 watts per square meter Kelvin 4, a physical constant. A radiation T represents the radiative heat transfer area, expressed in square meters. For concentric spherical structures, the outer surface area of ​​the inner sphere is used. inner T represents the temperature of the inner sphere, measured in Kelvin, and is the temperature of the high-temperature medium, typically between 500 and 800 Kelvin. outer This refers to the temperature of the outer sphere, measured in Kelvin, close to ambient temperature, typically between 300 and 350 Kelvin. F view Let F be the perspective factor, for concentric spheres F view A value of 1 indicates that all radiation emitted by the inner sphere is received by the outer sphere. F vacuum The vacuum degree influence factor is a dimensionless parameter, calculated using the formula F. vacuum =P vacuum / P atmosphere , where P vacuum For vacuum layer pressure, P atmosphere The pressure is atmospheric pressure. The higher the vacuum level, the smaller this factor becomes, and the more dominant radiative heat transfer becomes.

[0032] like Figure 4 As shown, the exhaust mechanism control algorithm module achieves precise control of steam pressure through adaptive exhaust port area adjustment.

[0033] The formula for controlling steam discharge flow rate is:

[0034] Q steam =C dsteam ×A vent ×√(2×ΔP×γ / (γ-1)×P upstream / ρsteam ×[(P downstream / P upstream )^(2 / γ)-(P downstream / P upstream )^((γ+1) / γ)]), is the critical flow formula based on compressible fluid mechanics, considering the compressibility of steam. Where Q steam This indicates the steam discharge flow rate, measured in cubic meters per second. This parameter determines the steam discharge rate and affects the pressure control effect. dsteam is the steam flow coefficient, a dimensionless parameter ranging from 0.65 to 0.85, taking into account the influence of the geometry and flow characteristics of the vent hole on the flow rate. A vent The variable vent area, measured in square meters, is the control variable; the vent flow rate is controlled by adjusting the vent opening. ΔP is the instantaneous pressure difference, and γ is the steam specific heat ratio, a dimensionless parameter. For high-temperature steam, γ is approximately 1.3. This parameter reflects the thermodynamic properties of the steam. upstream P represents the upstream pressure, i.e., the internal pressure of the valve body, measured in Pascals, and is the primary controlled variable. downstream This represents downstream pressure, i.e., ambient pressure, measured in Pascals, and is usually equal to atmospheric pressure. ρ steam The vapor density is expressed in kilograms per cubic meter. This parameter varies with temperature and pressure and can be calculated using the equation of state.

[0035] The adaptive exhaust port area control formula is:

[0036] A vent (t)=A max ×sin 2 (π×θ(t) / (2×θ max ))×K adaptive (P internal (t)). Where A is... vent (t) represents the exhaust port area at time t, in square meters. This parameter changes dynamically over time to adapt to different control requirements. A max θ represents the maximum exhaust port area, measured in square meters, determined by the mechanical design of the exhaust port, and is a design parameter of the system. θ(t) represents the rotation angle of the T-cone, measured in degrees, and is the position signal of the actuator, typically ranging from 0 to 90 degrees. max This is the maximum rotation angle, measured in degrees, determined by mechanical limits, and is typically 90 degrees. K adaptive The adaptive adjustment coefficient is a dimensionless parameter, and its calculation formula is K. adaptive =1+α×(P internal -P setpoint ) / P setpoint Where α is the adaptive gain, typically ranging from 0.1 to 0.3, and P... internal For internal pressure, Psetpoint To set the pressure.

[0037] like Figure 5 As shown, the temperature field prediction algorithm module achieves accurate prediction of transient temperature distribution based on solving partial differential equations. The transient temperature distribution prediction formula is as follows: T / t=α× 2 T+S heat / (ρ×c p This is a three-dimensional unsteady-state heat conduction equation. Here, T represents temperature in Kelvin, and is a function of space and time, T(x,y,z,t). t represents time in seconds, with a time step typically between 0.1 and 1 second. α is the thermal diffusivity in square meters per second, calculated using the formula α = k / (ρ×c). p ), where k is thermal conductivity, ρ is density, and c is density. p Specific heat capacity is a parameter that reflects the material's ability to conduct heat. 2 T is the Laplace operator for temperature, representing the second spatial derivative of temperature in a Cartesian coordinate system. 2 T= 2 T / x 2 + 2 T / y 2 + 2 T / z 2 S heat This is the internal heat source term, measured in watts per cubic meter, representing the heat generation power per unit volume. For ball valves, this mainly originates from frictional heat generation. ρ is the material density, measured in kilograms per cubic meter; different materials have significantly different densities. c p Specific heat capacity, expressed in joules per kilogram of Kelvin, represents the amount of heat required to raise the temperature of a unit mass of material by 1 Kelvin.

[0038] like Figure 6 As shown, the multi-objective collaborative optimization algorithm module is the system's decision-making module, responsible for the comprehensive optimization of multiple performance indicators.

[0039] The system performance comprehensive evaluation function is:

[0040] F objective =w1×f pressure (ΔP)+w2×f thermal (Q loss )+w3×f operation (T torque )+w4×fsafety (S factor ). Among them, F objective The objective function is a dimensionless parameter, ranging from 0 to 1, with larger values ​​indicating better system performance. w1, w2, w3, and w4 are weighting coefficients, also dimensionless parameters, satisfying the condition that the sum of all weighting coefficients equals 1, i.e., w1 + w2 + w3 + w4 = 1. These weights reflect the importance of different performance indicators and can be adjusted according to actual application requirements. pressure This is the pressure control performance function, reflecting the effectiveness of pressure control. thermal This is a thermal insulation performance function, reflecting the effectiveness of heat loss control. operation This is an operational performance function that reflects ease of operation. safety This is a safety performance function that reflects the system's safety level.

[0041] The pressure control performance function is f pressure (ΔP)=exp(-k1×|ΔP-ΔP target | / ΔP target ), which is a performance evaluation function based on an exponential function. Here, k1 is a performance adjustment parameter, dimensionless, typically ranging from 1 to 5. This parameter controls the sensitivity of the performance function; the larger k1 is, the more sensitive it is to deviations. ΔP target The target pressure difference, in Pascals, is the setpoint of the control system. The function value is 1 when the actual pressure difference equals the target value; the larger the deviation, the smaller the function value.

[0042] The thermal insulation performance function is f thermal (Q loss )=exp(-k2×Q loss / Q reference ), where k2 is a performance tuning parameter, typically ranging from 0.5 to 2. Q reference This is a reference heat loss value, expressed in watts, and serves as the baseline for system design. This function evaluates insulation performance; lower heat loss indicates better performance.

[0043] The performance function is f operation (T torque )=exp(-k3×T torque / T reference ), where k3 is a performance tuning parameter, typically ranging from 1 to 3. T reference This is a reference operating torque, measured in Newton-meters, representing the typical torque value during normal operation. This function evaluates ease of operation; the smaller the torque, the easier the operation.

[0044] like Figure 7 As shown, the system performance evaluation algorithm module achieves a comprehensive assessment of the system's operating status through multi-dimensional analysis. This module includes a status monitoring and prediction algorithm, and the stress prediction formula is P. predict(t+Δt)=P(t)+(dP / dt)×Δt+(d 2 P / dt 2 )×Δt 2 / 2 is a second-order prediction formula based on Taylor series expansion. Where P predict To predict pressure, the unit is Pascal, representing the pressure value at a future time. t is the current time, and P(t) is the pressure at the current time, also in Pascals, measured in real-time by a pressure sensor. Δt is the prediction time step, in seconds, typically between 0.1 and 1 second; a smaller step size increases prediction accuracy but also computational complexity. dP / dt and d... 2 P / dt 2 These are the first and second time derivatives of the pressure, respectively, calculated using numerical differentiation methods.

[0045] The formula for the control decision algorithm is:

[0046] u control (t)=K p ×e(t)+K i ×∫e(τ)dτ+K d ×de(t) / dt+u feedforward (t) represents a combination of classic PID control algorithm and feedforward control. Where u control For control output, signals such as valve opening degree and heating power can be used. e(t) is the error signal, equal to the set value minus the measured value. K p The proportional gain determines the strength of the controller's response to the current error, and its value typically ranges from 0.1 to 10. i The integral gain is used to eliminate steady-state errors; an excessively large value can lead to system instability. e(τ) represents the error signal at time τ; K d Differential gain provides predictive control, improving the dynamic performance of the system. feedforward It is a feedforward control term that predicts disturbances and compensates for them in advance based on the system model.

[0047] like Figure 8 As shown, a comprehensive parameter interaction mechanism has been established between the various algorithm modules. The active pressure relief intelligent control algorithm module also includes several relational algorithms.

[0048] The pressure-flow relationship algorithm is: dP / dt=(Q in -Q out -Q steam ) / V chamber ×β compressibility , is a formula for calculating the rate of change of pressure based on mass conservation and the equation of state. Where Q in The inflow rate is expressed in cubic meters per second, representing the flow rate of the medium entering the valve body. Q outThe discharge flow rate, expressed in cubic meters per second, is the flow rate discharged through the main outlet. Q steam V represents the steam discharge flow rate, measured in cubic meters per second, which is the flow rate of steam discharged through the exhaust system. chamber β represents the cavity volume, expressed in cubic meters, and is the effective internal volume of the valve body. compressibility The compressibility coefficient, measured in Pascals, reflects the compressibility of a medium. The compressibility coefficient of liquids is typically in the range of 10^(-9) to 10^(-10) Pascals.

[0049] The temperature-pressure relationship is calculated as P / P0 = (T / T0)^(γ / (γ-1)) × (ρ / ρ0), a modified form based on the ideal gas law. Here, P0 is the reference pressure in Pascals, typically taken as standard atmospheric pressure (101325 Pascals). T0 is the reference temperature in Kelvin, typically taken as standard temperature (273.15 Kelvin). ρ0 is the reference density in kilograms per cubic meter, the density of the medium under the reference conditions. γ is the specific heat ratio, a constant for ideal gases, but varying with temperature and pressure for real gases.

[0050] The operating torque-pressure relationship algorithm is T torque =T base +k friction ×P internal ×A seal ×μ friction ×R effective Among them, T torque T represents the operating torque, measured in Newton-meters (Nm), which is the torque required to operate the valve. base The basic torque, measured in Newton-meters, includes the basic resistance torque generated by factors such as mechanical friction and seal deformation. friction P is the coefficient of friction, a dimensionless parameter that typically ranges from 0.1 to 0.3, depending on the material and lubrication conditions. internal This refers to the internal pressure, measured in Pascals, representing the pressure of the medium inside the valve body. A seal The sealing area, measured in square meters, is the effective contact area between the sealing ring and the valve body. (μ) friction R is the friction coefficient, a dimensionless parameter that depends on the sealing material and surface condition. effective The effective radius is expressed in meters, and the effective length of the torque arm is also expressed.

[0051] like Figure 9 As shown, the multi-objective collaborative optimization algorithm module also includes several optimization algorithms. The adaptive weight adjustment algorithm is w i (t+1)=w i (t)+η× F objective / w i×sigmoid(performance error ), which is a weight optimization algorithm based on gradient descent and adaptive learning. Where w i Let be the i-th weight coefficient, where i = 1, 2, 3, 4 correspond to the four performance indicators: pressure, insulation, operation, and safety, respectively. η is the learning rate, a dimensionless parameter that typically ranges from 0.001 to 0.1, controlling the speed of weight adjustment. F objective / w i The gradient of the objective function with respect to the weights represents the sensitivity of the objective function to changes in the weights. (performance) error This represents performance error, reflecting the gap between actual and expected performance. The sigmoid function is used to limit the adjustment range, preventing excessive weight changes from causing system instability.

[0052] The dynamic compensation algorithm is u compensation =K nonlinear ×f nonlinear (x, , )+K coupling ×g coupling (x1,x2,x3) represents an advanced control algorithm considering nonlinearity and coupling effects. Where u compensation To compensate for the control input, used to correct the basic control output. K nonlinear This is a nonlinear gain, used to amplify the nonlinear compensation term. K coupling This is the coupling gain, used to handle the interactions between multiple variables. nonlinear For nonlinear functions, the nonlinear characteristics of a system are typically handled using polynomials or neural networks. coupling This is a coupling function that handles the coupling relationships between multiple control variables. x is a state variable. The first derivative of the state variable. The second derivative of the state variable provides information about the dynamic characteristics of the system.

[0053] The fault detection algorithm is S fault =Σ|X measured -X predicted | / σ X This is a fault detection method based on residual analysis. Where S... fault X is a fault indicator, a dimensionless parameter; the larger the value, the higher the probability of a fault. measured These are measured values, including multiple monitored variables such as temperature, pressure, and flow rate. X predicted This is a predicted value, a theoretical value calculated based on the system model. σ XS is the standard deviation, representing the range of fluctuation of measured values ​​under normal operating conditions, used to normalize residuals. fault When the set threshold is exceeded, the system determines that a fault may exist and activates the corresponding protection measures.

[0054] To verify the above technical solution, the present invention designed the following calculations to prove the effectiveness of the double-layer spherical insulated ball valve.

[0055] I. Test Scenario and System Parameter Settings

[0056] To verify the effectiveness of this invention, a high-temperature heat transfer oil transportation system in a large-scale petrochemical plant was used as the main component. This system incorporates intelligent control of high-temperature media under multiple operating conditions, optimized double-layer spherical insulation, multi-objective collaborative control functions, and a fault prediction and protection mechanism for collaborative optimization control. The system configuration is as follows:

[0057] 1.1 Basic System Configuration

[0058] Main controller: Industrial-grade ARM Cortex-A9 processor, 800MHz clock speed, with floating-point arithmetic unit, supporting multi-task real-time processing;

[0059] Sampling frequencies: Temperature sampling 1kHz, pressure sampling 2kHz, flow sampling 1kHz, actuator status monitoring 5kHz;

[0060] Memory: 1GB DDR3 memory, 8GB eMMC storage, 128KB cache for algorithm calculation;

[0061] Communication interfaces: Ethernet interface, supporting Modbus TCP / IP protocol, RS485 serial communication, CAN bus interface;

[0062] Temperature detection accuracy: ±0.1℃, resolution: 0.01℃, measuring range: 0-1000℃, response time: less than 2 seconds;

[0063] Pressure detection accuracy: ±0.05%FS, resolution 0.001MPa, measuring range 0-5MPa, response time less than 0.5 seconds;

[0064] Flow detection accuracy: ±0.2%, resolution 0.01m 3 / h, measuring range 0-50m 3 / h, response time is less than 1 second;

[0065] Actuator: Electric actuator, torque range 0-500 N·m, position accuracy ±0.1°, response time less than 3 seconds.

[0066] 1.2 Test Operating Parameters

[0067] Ball valve specifications: nominal diameter DN200, design pressure 4.0MPa, design temperature 750℃, material 316L stainless steel;

[0068] Medium characteristics: High-temperature heat transfer oil, operating temperature 650℃, operating pressure 2.8MPa, density 750kg / m³ 3 Viscosity 0.8 Pa·s;

[0069] Double-layer sphere parameters: Inner sphere diameter 180mm, outer sphere diameter 220mm, vacuum interlayer thickness 20mm, vacuum degree 10. -3 Pa;

[0070] Insulation layer parameters: Aerogel insulation material, thickness 60mm, thermal conductivity 0.025W / (m·K), density 150kg / m³ 3 ;

[0071] Environmental conditions: ambient temperature 25℃, relative humidity 65%, atmospheric pressure 101.3kPa;

[0072] Test conditions: five common operating conditions, including normal operation, startup, shutdown, emergency operation, and variable load operation.

[0073] Test duration: 720 hours of continuous operation, recording 8640 data points, with sampling every 5 minutes.

[0074] II. Calculation Process of Active Pressure Relief Intelligent Control Algorithm

[0075] 2.1 Algorithm Parameter Settings

[0076] Based on the actual working conditions, the algorithm parameters are set as follows:

[0077] Density of high-temperature liquids: ρ liquid =750kg / m 3 (Density of heat transfer oil at 650℃);

[0078] Dynamic flow coefficient: C d =0.82 (empirical value under turbulent conditions);

[0079] Effective pressure relief cross-sectional area: A eff =0.002m 2 (Area when the pressure relief valve is fully open);

[0080] Pressure relief pipe inner diameter: D pipe =0.05m (50mm inner diameter pipe);

[0081] Pressure relief pipe length: L pipe =1.5m (including equivalent bend length);

[0082] Bending resistance coefficient: Kbend =1.2 (including two 90° bends);

[0083] Correction factor: η correction =0.95 (correction value under high temperature conditions);

[0084] Compressibility coefficient: β compressibility =4.5×10^(-10) / Pa (liquid compressibility).

[0085] 2.2 Data Acquisition Example

[0086] At test time t=120s, the system collected the following real-time data:

[0087] Valve body internal pressure: P internal =2.85MPa (collected by a high-precision pressure sensor);

[0088] Environmental stress: P external =0.1013MPa (standard atmospheric pressure);

[0089] Medium temperature: T medium =648℃ (actual temperature of the medium);

[0090] Pressure relief valve opening: θ valve =25% (current opening degree of pressure relief valve);

[0091] Medium flow rate: v medium =2.3 m / s (flow velocity inside the pipe);

[0092] Operating torque: T current =185 N·m (current operating torque).

[0093] 2.3 Calculation of Liquid Relief Flow Rate and Analysis of Pressure Loss

[0094] Step 1: Calculate the instantaneous pressure difference

[0095] ΔP=P internal -P external =2.85MPa-0.1013MPa=2.7487MPa=2748700Pa.

[0096] Step 2: Calculate the effective pressure relief cross-sectional area

[0097] A eff,actual =A eff ×(θ valve / 100%)=0.002m 2 ×0.25=0.0005m 2 .

[0098] Step 3: Calculate the liquid pressure relief flow rate

[0099] Q liquid =C d ×A eff,actual ×√(2×ΔP / ρ liquid )×η correction =0.82×0.0005×√(2×2748700 / 750)×0.95=0.82×0.0005×√7329.87×0.95=0.82×0.0005×85.61×0.95=0.0333m 3 / s=119.9m 3 / h.

[0100] Step 4: Calculate the flow velocity inside the pipe

[0101] v flow =Q liquid / A pipe =0.0333 / (π×(0.025) 2 =0.0333 / 0.00196=16.98m / s.

[0102] Step 5: Calculate the Reynolds number and friction factor

[0103] Re=ρ liquid ×v flow ×D pipe / μ=750×16.98×0.05 / 0.8=796.1, since Re<2300, the flow is laminar. f friction =64 / Re=64 / 796.1=0.0804.

[0104] Step 6: Calculate the pressure relief pipeline resistance loss

[0105] ΔP friction =f friction ×(L pipe / D pipe )×(ρ liquid ×v flow 2 / 2)×K bend =0.0804×(1.5 / 0.05)×(750×16.98 2 / 2)×1.2=0.0804×30×108140.25×1.2=314196Pa=0.314MPa.

[0106] Step 7: Calculate the actual available pressure difference

[0107] ΔP effective =ΔP-ΔP friction=2.749MPa-0.314MPa=2.435MPa.

[0108] 2.4 Calculation of Pressure Change Rate Prediction

[0109] Step 1: Calculate the difference between inflow and outflow Q in =0.05m 3 / s (system feed rate) Q out =Q liquid =0.0333m 3 / s (pressure relief flow rate) Q steam =0.002m 3 / s (steam discharge flow rate).

[0110] Step 2: Calculate the rate of pressure change

[0111] dP / dt=(Q in -Q out -Q steam ) / V chamber ×β compressibility ;

[0112] V chamber =0.15m 3 (Internal volume of the valve body);

[0113] dP / dt=(0.05-0.0333-0.002) / 0.15×(1 / 4.5×10 -10 )

[0114] =0.0147 / 0.15×2.22×10 9 =217560Pa / s=0.218MPa / s.

[0115] Step 3: Predict future pressure value Δt = 10s (prediction time step) P predict =P internal +(dP / dt)×Δt=2.85+0.218×10=5.03MPa.

[0116] III. Calculation Process of Double-Layer Spherical Insulation Optimization Algorithm

[0117] 3.1 Algorithm Parameter Settings

[0118] Thermal conductivity of inner sphere material: k inner =45W / (m·K) (heat resistant alloy);

[0119] Thermal conductivity of outer spherical material: k outer =16W / (m·K) (stainless steel);

[0120] Thermal conductivity of insulation layer: k insulation=0.025W / (m·K) (aerogel);

[0121] Emissivity of inner sphere surface: ε inner =0.85 (high-temperature oxidized surface);

[0122] Emissivity of the outer sphere surface: ε outer =0.15 (polished stainless steel surface);

[0123] Stefan-Boltzmann constant: σ = 5.67 × 10 -8 W / (m 2 ·K 4 );

[0124] Contact thermal resistance correction factor: η contact =0.9 (considering incomplete contact);

[0125] Vacuum Degree Influence Factor: F vacuum =0.001 (high vacuum conditions).

[0126] 3.2 Calculation of heat transfer loss in multiple layers

[0127] Step 1: Calculate the radius of the sphere within each layer's geometric parameters: r inner =0.09m outer sphere inner radius: r outerinner =0.11m; Outer radius of the outer sphere: r outerouter =0.13m outer radius of insulation layer: r insulation =0.19m.

[0128] Surface area of ​​the inner sphere: A inner =4π×r inner 2 =4π×0.09 2 =0.1018m 2 Surface area of ​​the outer sphere:

[0129] A outerinner =4π×r outerinner 2 =4π×0.11 2 =0.1521m 2 Surface area of ​​the outer sphere:

[0130] A outerouter =4π×r outerouter 2 =4π×0.13 2 =0.2124m 2 Insulation layer outer surface area:

[0131] A insulation =4π×r insulation 2 =4π×0.192 =0.4536m 2

[0132] Step 2: Set the temperature of the sphere within each layer: T inner =648℃=921K Temperature of the inner surface of the outer sphere: T outerinner =350℃=623K; Outer sphere surface temperature: T outerouter =150℃=423K. Temperature of the outer surface of the insulation layer: T insulation =45℃=318K Ambient temperature: T ambient =25℃=298K.

[0133] Step 3: Calculate the effective emissivity

[0134] ε eff =1 / (1 / ε inner +1 / ε outer -1)=1 / (1 / 0.85+1 / 0.15-1)=1 / (1.176+6.667-1)=0.143.

[0135] Step 4: Calculate the radiative heat transfer loss of the vacuum layer.

[0136] Q radiation =ε eff ×σ×A inner ×(T inner 4 -T outerinner 4 )×F view ×F vacuum =0.143×5.67×10^(-8)×0.1018×(921 4 -623 4 )×1×0.001=0.143×5.67×10^(-8)×0.1018×(7.198×10^11-1.507×10^11)×0.001=0.143×5.67×10^(-8)×0.1018×5.691×10^11×0.001=47.2W.

[0137] Step 5: Calculate the heat conduction loss of the outer sphere wall.

[0138] δ outer =r outerouter -r outerinner =0.13-0.11=0.02m;

[0139] ΔT outer =T outerinner -T outerouter =623-423=200K;

[0140] Q conductionouter =k outer ×A outerinner ×ΔT outer / δ outer ×η contact =16×0.1521×200 / 0.02×0.9=16×0.1521×200×45×0.9=1975W.

[0141] Step 6: Calculate the heat conduction loss of the insulation layer

[0142] δ insulation =r insulation -r outerouter =0.19-0.13=0.06m;

[0143] ΔT insulation =T outerouter -T insulation =423-318=105K;

[0144] Q conductioninsulation =k insulation ×A outerouter ×ΔT insulation / δ insulation ×η contact =0.025×0.2124×105 / 0.06×0.9=0.025×0.2124×105×16.67×0.9=84.4W.

[0145] Step 7: Calculate the total heat transfer loss

[0146] Q total =Q radiation +Q conductionouter +Q conductioninsulation =47.2+1975+84.4=2106.6W.

[0147] 3.3 Calculation of thermal insulation efficiency

[0148] Step 1: Calculate the input heat power Q input =15000W (system design thermal power).

[0149] Step 2: Calculate the insulation efficiency

[0150] η thermal =1-Q total / Q input =1-2106.6 / 15000=1-0.1404=0.8596=85.96%.

[0151] IV. Calculation Process of Exhaust Mechanism Control Algorithm

[0152] 4.1 Algorithm Parameter Settings

[0153] Steam flow coefficient: C dsteam =0.75 (flow coefficient of vent hole);

[0154] Maximum exhaust port area: A max =0.0008m 2 (Area of ​​the exhaust vents when fully open);

[0155] Steam specific heat ratio: γ = 1.3 (high temperature steam);

[0156] Maximum rotation angle: θ max =90° (maximum angle of the T-shaped cone);

[0157] Adaptive gain: α = 0.2 (adaptive adjustment coefficient);

[0158] Pressure setting: P setpoint =2.5MPa (target control pressure);

[0159] Steam density: ρ steam =12.5kg / m 3 (Steam density at 650℃ and 2.8MPa).

[0160] 4.2 Adaptive Exhaust Port Area Control

[0161] Step 1: Calculate the adaptive adjustment coefficient

[0162] K adaptive =1+α×(P internal -P setpoint ) / P setpoint =1+0.2×(2.85-2.5) / 2.5=1+0.2×0.35 / 2.5=1+0.2×0.14=1+0.028=1.028.

[0163] Step 2: Calculate the current T-cone angle. Since the pressure is higher than the set value, it is necessary to increase the venting. Let the current angle θ(t) = 35°.

[0164] Step 3: Calculate the actual exhaust port area

[0165] A vent (t)=A max ×sin 2 (π×θ(t) / (2×θ max ))×K adaptive =0.0008×sin 2 (π×35 / (2×90))×1.028=0.0008×sin 2(0.611)×1.028=0.0008×(0.573) 2 ×1.028=0.0008×0.328×1.028=0.000270m 2 .

[0166] 4.3 Calculation of Steam Discharge Flow Rate

[0167] Step 1: Calculate the pressure ratio P ratio =P downstream / P upstream =0.1013 / 2.85=0.0355.

[0168] Step 2: Check the critical pressure ratio for critical flow conditions:

[0169] P critical =(2 / (γ+1))^(γ / (γ-1))=(2 / 2.3)^(1.3 / 0.3)=0.546.

[0170] Because of P ratio =0.0355 <P critical =0.546, the flow is critical flow.

[0171] Step 3: Calculate the steam flow rate under critical flow conditions. For critical flow: Q steam =C dsteam ×A vent ×√(γ×P upstream / ρ steam ×(2 / (γ+1))^((γ+1) / (γ-1)))=0.75×0.000270×√(1.3×2850000 / 12.5×(2 / 2.3)^(2.3 / 0.3))=0. 75×0.000270×√(1.3×228000×0.472)=0.75×0.000270×√139968=0.75×0.000270×374.1=0.0758m 3 / s=273m 3 / h.

[0172] V. Calculation Process of Multi-Objective Cooperative Optimization Algorithm

[0173] 5.1 Calculation of System Performance Comprehensive Evaluation Function

[0174] Step 1: Set the weighting coefficients w1=0.4 (pressure control weight), w2=0.3 (thermal insulation performance weight), w3=0.2 (operational performance weight), and w4=0.1 (safety performance weight).

[0175] Step 2: Calculate the pressure control performance of each performance function:

[0176] ΔP target =2.5MPa;

[0177] ΔP actual =2.85MPa;

[0178] k1=3;

[0179] f pressure =exp(-k1×|ΔP actual -ΔP target | / ΔP target )=exp(-3×|2.85-2.5| / 2.5)=exp(-3×0.35 / 2.5)=exp(-0.42)=0.657.

[0180] Thermal insulation performance:

[0181] Q reference =3000W;

[0182] k2=1.5;

[0183] f thermal =exp(-k2×Q total / Q reference )=exp(-1.5×2106.6 / 3000)=exp(-1.053)=0.349.

[0184] Operational performance:

[0185] T reference =200 N·m;

[0186] k3=2;

[0187] f operation =exp(-k3×T current / T reference )=exp(-2×185 / 200)=exp(-1.85)=0.157.

[0188] Safety features:

[0189] S factor =0.95 (based on fault detection algorithm results);

[0190] f safety =S factor =0.95.

[0191] Step 3: Calculate the comprehensive objective function

[0192] F objective =w1×f pressure +w2×f thermal +w3×f operation+w4×f safety =0.4×0.657+0.3×0.349+0.2×0.157+0.1×0.95=0.263+0.105+0.031+0.095=0.494.

[0193] 5.2 Adaptive Weight Adjustment Algorithm

[0194] Step 1: Calculate performance error error =1-F objective =1-0.494=0.506.

[0195] Step 2: Calculate the sigmoid function value

[0196] sigmoid (performance) error )=1 / (1+exp(-performance error ))=1 / (1+exp(-0.506))=0.624.

[0197] Step 3: Set the learning rate and gradient

[0198] η=0.01 F objective / w1=f pressure =0.657 F objective / w2=f thermal =0.349 F objective / w3=f operation =0.157 F objective / w4=f safety =0.95.

[0199] Step 4: Update the weight coefficients

[0200] w1(t+1)=w1(t)+η× F objective / w1×sigmoid(performance error )

[0201] =0.4 + 0.01 × 0.657 × 0.624 = 0.4 + 0.0041 = 0.4041;

[0202] w2(t+1)=0.3+0.01×0.349×0.624=0.3+0.0022=0.3022;

[0203] w3(t+1)=0.2+0.01×0.157×0.624=0.2+0.0010=0.2010;

[0204] w4(t+1)=0.1+0.01×0.95×0.624=0.1+0.0059=0.1059.

[0205] After normalization:

[0206] The sum = 1.0132;

[0207] w 1,norm =0.4041 / 1.0132=0.399;

[0208] w 2,norm =0.3022 / 1.0132=0.298;

[0209] w 3,norm =0.2010 / 1.0132=0.198;

[0210] w 4,norm =0.1059 / 1.0132=0.105.

[0211] VI. Optimization and Control Effect Verification

[0212] Based on the above calculation results, optimized control was implemented, and a comparative analysis of the system state before and after control was conducted:

[0213] 6.1 Changes in pressure control performance

[0214]

[0215] 6.2 Optimization effect of thermal insulation performance

[0216]

[0217] 6.3 Improved Operational Performance

[0218]

[0219] 6.4 Synergistic Improvement Effect of Safety Performance

[0220]

[0221] 6.5 Comprehensive Energy Saving Benefit Analysis

[0222] After 720 hours of continuous operation testing, the overall energy-saving benefits are calculated as follows:

[0223] Reduced heat loss: (3225-2106.6)×720=805248Wh=805.25kWh;

[0224] Reduced operating energy consumption: (1.8-1.2)×720=432kWh;

[0225] Total energy saved: 805.25 + 432 = 1237.25 kWh.

[0226] Based on an industrial electricity price of 0.8 yuan / kWh:

[0227] Monthly energy saving benefit: 1237.25 × 0.8 = 989.8 yuan; Annual energy saving benefit: 989.8 × 12 = 11877.6 yuan.

[0228] Considering the indirect benefits of reduced maintenance costs and lower failure rates, the annual comprehensive economic benefit is approximately 25,000 yuan.

[0229] VII. Conclusion

[0230] Based on the above calculations and results verification, the technical benefits of the double-layer spherical insulated ball valve of the present invention in industrial application prospects are as follows:

[0231] Significantly improved intelligent control accuracy: Through active pressure relief intelligent control algorithm and multi-objective collaborative optimization, pressure control accuracy was improved from ±0.15MPa to ±0.05MPa, an improvement of 66.7%, response time was shortened from 25 seconds to 12 seconds, and dynamic performance was improved by 52%, which fully verified the effectiveness of intelligent control algorithm.

[0232] Significantly improved thermal insulation performance: The double-layer spherical insulation optimization algorithm, through multi-mode heat transfer control, increases the insulation efficiency from 78.5% to 85.96%, and reduces the total heat loss from 3225W to 2106.6W, a reduction of 34.7%, especially the radiative heat loss, which is reduced by 62.2%, achieving a significant energy-saving effect.

[0233] Comprehensive optimization of operational performance: Through algorithmic collaborative optimization, the operating torque was reduced from 285 N·m to 185 N·m, a reduction of 35.1%, the operation response speed was improved by 37.5%, and the operation accuracy was improved by 75%, which greatly improved the system's operational performance and user experience.

[0234] Significantly enhanced safety and reliability: Fault prediction accuracy increased from 85% to 98%, fault response time was reduced from 30 seconds to 8 seconds, and safety protection success rate reached 99.5%, providing more reliable safety assurance for industrial applications and effectively reducing the risk of safety accidents.

[0235] Outstanding economic benefits: Annual energy savings reach 1237.25 kWh, energy-saving benefits of 11877.6 yuan. Considering indirect benefits such as reduced maintenance costs, the annual comprehensive economic benefits are about 25,000 yuan. The investment payback period is short and the economic benefits are significant.

[0236] In summary, the multi-algorithm module collaborative working mechanism of this invention operates stably, and the adaptive weight adjustment algorithm effectively improves the system's adaptability, providing technical support and broad application prospects for the development of intelligent control technology in the field of industrial automation.

Claims

1. A double-layer spherical insulated ball valve, characterized in that, include: The active pressure relief intelligent control algorithm module is used to adjust the working parameters of the pressure relief mechanism in real time according to the internal pressure state of the valve body, so as to realize the dynamic discharge control of liquid and steam. It includes a liquid pressure relief flow calculation unit based on fluid mechanics principles and a pressure relief pipeline resistance loss calculation unit based on pipeline resistance theory, and outputs pressure control performance parameters to the multi-objective collaborative optimization algorithm module. The double-layer spherical insulation optimization algorithm module is used to minimize heat loss through multi-mode heat transfer control. It includes a heat conduction loss calculation unit based on multi-layer heat transfer theory and a vacuum insulation layer radiation heat transfer calculation unit based on radiation heat transfer theory, and transmits insulation performance parameters to the system performance evaluation algorithm module. The exhaust mechanism control algorithm module is used to achieve precise steam pressure control through adaptive exhaust port area adjustment. It includes a steam discharge flow control unit based on compressible fluid theory and an exhaust port opening adjustment unit based on adaptive control theory. It also dynamically adjusts the exhaust strategy according to the pressure state information provided by the active pressure relief intelligent control algorithm module. The temperature field prediction algorithm module is used to accurately predict the system temperature state based on the transient temperature distribution prediction method of numerical solution of partial differential equations, and to receive heat transfer parameters from the double-layer sphere insulation optimization algorithm module for dynamic optimization of temperature control strategy. The multi-objective collaborative optimization algorithm module is used to achieve comprehensive optimization of pressure control, thermal insulation performance, operational performance and safety performance. It includes the calculation of the comprehensive evaluation function of system performance based on weighted functions and the adaptive weight adjustment mechanism based on gradient descent. It also receives pressure control parameters from the active pressure relief intelligent control algorithm module to achieve global coordinated control. The system performance evaluation algorithm module is used to comprehensively evaluate the system's operating status through multi-dimensional performance functions, including pressure control performance evaluation, thermal insulation performance evaluation, and operational performance evaluation, as well as fault detection function based on residual analysis, and feeds back system performance indicators to the multi-objective collaborative optimization algorithm module; The system performance comprehensive evaluation function is: F objective =w1×f pressure (ΔP)+w2×f thermal (Q loss )+w3×f operation (T torque )+w4×f safety (S factor ); where F objective The objective function is a dimensionless parameter, ranging from 0 to 1, with larger values ​​indicating better system performance. w1, w2, w3, and w4 are weighting coefficients, also dimensionless, whose sum equals 1, i.e., w1 + w2 + w3 + w4 = 1. These weights reflect the importance of different performance indicators and can be adjusted according to actual application requirements. pressure f is a pressure control performance function that reflects the effectiveness of pressure control. thermal f is a thermal insulation performance function that reflects the effectiveness of heat loss control; operation f is an operational performance function that reflects ease of operation; safety This is a safety performance function that reflects the system's safety level. The pressure control performance function is f pressure (ΔP)=exp(-k1×|ΔP-ΔP target | / ΔP target ), which is a performance evaluation function based on an exponential function; where k1 is a performance adjustment parameter, dimensionless, typically ranging from 1 to 5. This parameter controls the sensitivity of the performance function; the larger k1 is, the more sensitive it is to deviations; ΔP target The target pressure difference is expressed in Pascals and is the setpoint of the control system. When the actual pressure difference equals the target value, the function value is 1, and the larger the deviation, the smaller the function value. The thermal insulation performance function is f thermal (Q loss )=exp(-k2×Q loss / Q reference ), where k2 is a performance tuning parameter, typically ranging from 0.5 to 2; Q reference The heat loss is for reference only, and the unit is watts. This is the baseline value used in system design. This function evaluates the insulation effect; the smaller the heat loss, the better the performance. The performance function is f operation (T torque )=exp(-k3×T torque / T reference ), where k3 is a performance adjustment parameter, typically ranging from 1 to 3; T reference The reference operating torque is expressed in Newton-meters and represents the typical torque value during normal operation. This function evaluates ease of operation; the smaller the torque, the easier the operation. The algorithm for the adaptive weight adjustment mechanism is as follows: w i (t+1)=w i (t)+η× F objective / w i ×sigmoid(performance error ), which is a weight optimization algorithm based on gradient descent and adaptive learning; where w i η is the i-th weight coefficient, where i = 1, 2, 3, 4 correspond to the four performance indicators of pressure, heat preservation, operation, and safety, respectively; η is the learning rate, a dimensionless parameter that typically ranges from 0.001 to 0.1, controlling the speed of weight adjustment. F objective / w i The gradient of the objective function with respect to the weights represents the sensitivity of the objective function to changes in the weights; performance error The sigmoid function represents the performance error, reflecting the gap between actual and expected performance. It limits the adjustment range to prevent excessive weight changes from causing system instability.

2. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The active pressure relief intelligent control algorithm module uses the modified Bernoulli equation to calculate the liquid pressure relief flow rate, and uses the Darcy-Weisbach equation to calculate the pressure relief pipeline resistance loss. By introducing dynamic flow coefficient and correction coefficient, the accuracy of flow rate calculation under high temperature conditions is improved.

3. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The exhaust mechanism control algorithm module uses the critical flow theory of compressible fluids to calculate the steam discharge flow rate, and achieves smooth control of the exhaust port area through sine function transformation and adaptive adjustment coefficient, thus avoiding pressure oscillations during the exhaust process.

4. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The double-layer spherical insulation optimization algorithm module uses a layered heat transfer calculation method to handle the combined effects of multiple heat transfer modes, adopts the concept of effective emissivity to handle radiative heat transfer in the vacuum layer, and introduces a contact thermal resistance correction coefficient and a vacuum degree influence factor to improve the accuracy of insulation performance calculation.

5. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The temperature field prediction algorithm module is based on the three-dimensional unsteady heat conduction equation and uses the finite difference or finite element method for numerical solution. It takes into account the influence of internal heat source terms and achieves accurate prediction of the temperature field.

6. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The multi-objective collaborative optimization algorithm module establishes a comprehensive evaluation function that includes pressure control, thermal insulation performance, operational performance, and safety performance. It adopts a performance evaluation method based on exponential functions and achieves optimized control under different working conditions through adaptive adjustment of weight coefficients.

7. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The system performance evaluation algorithm module adopts a pressure prediction method based on Taylor series expansion, combined with PID control and feedforward control to achieve accurate control decisions, and uses residual analysis to achieve early detection and warning of faults.

8. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The active pressure relief intelligent control algorithm module also establishes mathematical relationship models between pressure-flow rate, temperature-pressure, and operating torque-pressure, and achieves accurate prediction and control of system state through multi-parameter coupling analysis.

9. The double-layer spherical insulated ball valve according to claim 1, characterized in that: The multi-objective collaborative optimization algorithm module integrates an adaptive weight adjustment algorithm based on gradient descent, a dynamic compensation algorithm considering nonlinearity and coupling effects, and a fault detection algorithm based on statistical analysis, thereby realizing intelligent and adaptive control of the system.

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