Converter station waste heat recovery management optimization model

Abstract

The invention relates to a converter station waste heat recovery management optimization model, and belongs to the field of converter station waste heat recovery. The method mainly comprises the steps of thermal resistance analysis, scaling process control, turbulence enhanced heat transfer, online monitoring and dynamic adjustment, cleaning period optimization and the like. The waste heat recovery performance is optimized through the measures of analyzing the total heat transfer resistance of the condenser, restraining dirt deposition, arranging a turbulence promoting device to enhance the heat transfer efficiency and the like. And meanwhile, real-time monitoring and dynamic optimization of operation conditions are carried out by virtue of a computational fluid mechanics simulation and dirt growth coupling model. And finally, the scaling period is prolonged through a turbulence strengthening device and a surface modification technology, and the waste heat recovery efficiency of the converter station is improved. According to the method, the dirt deposition rate can be effectively reduced, the heat energy recovery efficiency and the equipment operation safety are improved, and efficient use of energy is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of waste heat recovery in converter stations, and more particularly relates to a waste heat recovery management optimization model for converter stations. Background Art

[0002] The demand for clean energy connected to the power system continues to grow. As the core hub for power conversion, the operating efficiency and comprehensive energy utilization of high-voltage direct current (HVDC) converter stations have attracted much attention. Thyristor valves, transformers and other equipment in the converter stations generate a large amount of waste heat during operation, which is usually discharged to the environment through cooling systems (such as circulating water cooling and air cooling), resulting in energy waste and thermal pollution. It is estimated that the waste heat loss of a typical converter station accounts for 3%-5% of the input electrical energy. If efficient recovery is achieved, energy utilization efficiency can be significantly improved and carbon emissions can be reduced. However, the existing waste heat recovery system has technical bottlenecks in terms of condenser heat transfer efficiency, fouling control and operation and maintenance costs, which restrict its large-scale application.

[0003] In waste heat recovery technology, the condenser is the core heat transfer equipment, and its heat transfer efficiency directly determines the system performance. Traditional condenser designs are mostly based on steady-state thermal resistance models, which regard the heat transfer coefficient as a fixed value and do not consider the impact of dynamic factors such as scale deposition and fluid morphology changes on thermal resistance. In actual operation, scale (such as calcium and magnesium salts, microbial films, and particulate matter) on the cooling water side continues to deposit on the pipe wall, resulting in scale thermal resistance accounting for as much as 30%-50% of the total thermal resistance, significantly reducing heat transfer efficiency. Existing solutions mainly rely on regular shutdown cleaning or the addition of chemical antiscalants, but the former reduces the system's continuous operation capability, and the latter has high chemical costs, environmental pollution risks, and cannot fundamentally cure the physical mechanism of scale formation. In addition, traditional monitoring methods are limited to macroscopic parameters such as temperature and pressure, and lack the ability to perceive scale thickness and boundary layer status in real time, making it difficult to accurately guide maintenance decisions.

[0004] In terms of heat transfer enhancement, early technologies improved efficiency by increasing flow rate or increasing heat transfer area, but high flow rate leads to a surge in pump work, and expanding the heat transfer area is limited by equipment size and cost. In recent years, turbulence enhancement technologies (such as built-in vortex generators and surface roughening) have been widely studied, which enhance convective heat transfer by destroying the laminar boundary layer. However, fixed-structure turbulence devices (such as ordinary spiral twisted ribbons) are prone to cause excessive local pressure drop, and after long-term operation, they themselves may become "hot spots" for fouling deposition. At the same time, traditional turbulence design lacks quantitative analysis of the synergistic effect of the concentration boundary layer and the temperature field, making it difficult to balance the heat transfer enhancement effect and energy consumption.

[0005] In the field of scale inhibition, existing technologies mostly focus on a single link, such as reducing the hardness of cooling water through chemical treatment, or using mechanical scraping devices to remove deposited scale, but fail to systematically intervene in the dynamic process of scale formation (induction, migration, adsorption, hardening, and shedding). Studies have shown that the migration rate and adsorption probability of dirt particles in the boundary layer are closely related to their flow morphology and wall characteristics, while traditional methods lack active control measures for key parameters such as concentration boundary layer thickness and wall wettability, resulting in low scale inhibition efficiency. In addition, existing surface modification technologies (such as hydrophobic coatings) are prone to peeling failure under high temperature and high flow rate conditions due to insufficient durability.

[0006] At the operational maintenance level, converter station waste heat recovery systems generally rely on regular shutdown inspections and empirically set cleaning cycles, lacking optimization strategies based on real-time heat transfer performance. Although some studies have attempted to introduce sensor networks to monitor parameters such as condenser back pressure and end differential, the threshold alarm mechanism of a single parameter cannot accurately reflect the dynamic changes in fouling thermal resistance and does not form a closed-loop feedback loop with control strategies such as online cleaning and flow rate regulation. Furthermore, traditional computational fluid dynamics (CFD) simulations are mostly designed for clean operating conditions and are not coupled with fouling growth models. This results in large deviations between simulation results and actual operation, making it difficult to guide dynamic optimization.

[0007] In summary, existing converter station waste heat recovery technology faces four core challenges: (1) The static thermal resistance model cannot adapt to the heat transfer efficiency degradation caused by dynamic fouling deposition; (2) The fouling suppression methods are fragmented and do not cover the entire life cycle of fouling formation; (3) The turbulence enhancement device has a low energy efficiency ratio, making it difficult to balance pressure drop and heat transfer gain; (4) The operation and maintenance strategy lacks intelligence and adaptability, and the maintenance cost remains high. To address the above problems, a comprehensive solution that integrates dynamic thermal resistance analysis, staged fouling control, adjustable turbulence enhancement, and intelligent operation and maintenance is urgently needed to achieve efficient, stable, and sustainable operation of the converter station waste heat recovery system. Summary of the Invention

[0008] The present invention addresses four major technical problems existing in the existing converter station waste heat recovery system: First, the traditional condenser static thermal resistance model cannot adapt to the attenuation of heat transfer efficiency caused by dynamic deposition of scale, and lacks real-time monitoring means of scale thickness and boundary layer state; second, the scaling inhibition means are single, and fail to synergistically intervene in the entire life cycle of scale formation, migration, adsorption, hardening and shedding, resulting in low scale inhibition efficiency; third, the turbulent enhanced heat transfer device has a fixed structure or unreasonable parameter design, making it difficult to balance the pressure drop and heat transfer efficiency, and it is easy to become a scale deposition point after long-term operation; fourth, the operation and maintenance strategy relies on regular cleaning and empirical threshold adjustment, and lacks an adaptive optimization mechanism based on dynamic changes in the heat transfer coefficient, resulting in high maintenance costs and insufficient stability of waste heat recovery.

[0009] In order to achieve the above object, the present invention is implemented by adopting the following technical solution: comprising the following steps:

[0010] Thermal resistance analysis: Decompose the total heat transfer resistance of the condenser to identify the convection resistance of the medium in the tube and the fouling resistance as the main influencing factors. By real-time monitoring of the changing trends of fouling thickness, back pressure, and outlet end difference, a dynamic relationship model between heat transfer coefficient and thermal resistance is established;

[0011] Fouling process control: Based on the analysis of the induction, migration, adsorption, hardening and shedding stages of fouling formation, fouling deposition is inhibited by changing the fluid morphology, optimizing the concentration boundary layer thickness and surface modification;

[0012] Turbulence-enhanced heat transfer: Turbulence-promoting devices, such as spiral twisted ribbons or surface microstructures, are installed in the pipeline to increase the Reynolds number of the fluid to a turbulent state, reduce the thickness of the boundary layer, and enhance heat transfer efficiency;

[0013] Online monitoring and dynamic adjustment: Combining computational fluid dynamics (CFD) simulation with a fouling growth coupling model, real-time monitoring of heat transfer performance parameters is used to dynamically optimize operating conditions through online cleaning or flow rate adjustment strategies.

[0014] Cleaning cycle optimization: Through turbulence enhancement devices and surface modification technology, the scaling cycle is extended, the scale deposition rate is significantly reduced, and the waste heat recovery efficiency of the converter station is improved.

[0015] In one embodiment, the thermal resistance analysis step includes real-time monitoring of the condenser back pressure and outlet end difference, and inverting the variation trend of the fouling thermal resistance through the energy conservation equation.

[0016] In one embodiment, the scaling process control step reduces the concentration and adsorption probability of the scaling particles by optimizing the cooling water chemical treatment.

[0017] In one embodiment, the turbulence enhanced heat transfer step is performed by installing a spiral twisted ribbon to induce a secondary flow, thereby increasing the turbulence intensity and significantly reducing the concentration boundary layer thickness.

[0018] In one embodiment, the online monitoring and dynamic adjustment step uses computational fluid dynamics (CFD) simulation to optimize the flow field distribution and temperature gradient to avoid local overheating or scaling risks.

[0019] In one embodiment, the cleaning cycle optimization step increases the wall contact angle through surface hydrophobic modification technology, reduces the probability of adhesion of dirt particles, and significantly extends the cleaning cycle.

[0020] In one embodiment, the turbulence enhanced heat transfer step optimizes the ratio of the curvature radius of the spiral twisted ribbon to the tube diameter to enhance the Dean vortex intensity to balance the pressure drop and heat transfer efficiency.

[0021] In one embodiment, the dynamic adjustment strategy includes triggering an online cleaning device or adjusting the flow rate based on real-time monitoring data to maintain the heat transfer coefficient within the design range.

[0022] Beneficial effects of the present invention:

[0023] Through the dynamic thermal resistance model, fouling deposition and boundary layer status are perceived in real time. Combined with the staged fouling control technology (covering fouling induction inhibition, migration intervention and surface hardening blocking), the heat transfer efficiency of the condenser is improved and the fouling prevention period is extended by more than 3 times; an adjustable turbulence enhancement device is used to dynamically adjust the vortex intensity and flow velocity distribution based on the pressure drop-heat transfer collaborative optimization algorithm, thereby improving the heat transfer coefficient under the same pump power and avoiding fouling of the device itself; an integrated intelligent operation and maintenance system is used to dynamically predict the heat transfer coefficient and adaptively implement cleaning strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the model of the present invention;

[0025] Figure 2 Schematic diagram of the laminar boundary layer near the pipe wall of the present invention. DETAILED DESCRIPTION

[0026] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate exemplary embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those understood by those skilled in the art to which the present invention pertains. The terms used in the present specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. To facilitate understanding of the present invention, a more comprehensive description of the present invention will be provided below with reference to the accompanying drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0028] like Figure 1 As shown, a converter station waste heat recovery management optimization model includes:

[0029] S1. Analysis of condenser thermal resistance factors

[0030] Increasing fouling thickness on heat exchange tubes reduces the condenser's heat transfer coefficient. For every 0.05mm increase in fouling thickness, the condenser's optimal operating range shifts, the condenser's back pressure increases by an average of approximately 200 Pa, and the outlet temperature difference increases by an average of approximately 0.8°C. The condenser's total heat transfer resistance is primarily composed of four components: convection resistance of the medium within the tubes, fouling resistance, conduction resistance of the tube walls, and thermal resistance of the condensate film outside the tubes. Convection resistance and fouling resistance account for over 85% of the total thermal resistance and are key factors affecting heat transfer efficiency. Condenser thermal resistance analysis indicates that reducing convection resistance and preventing tube scaling are crucial for improving condenser heat transfer efficiency.

[0031] The implementation process of condenser thermal resistance factor analysis requires the combination of thermodynamics, fluid mechanics and heat transfer theory, and by establishing a mathematical relationship model between the heat transfer coefficient and each thermal resistance component, the influence of dirt thickness on heat transfer performance can be quantified. First, the total thermal resistance R total It consists of four parts in series: convection thermal resistance R conv,in , dirt thermal resistance R foul , pipe wall conduction thermal resistance R cond And the thermal resistance of the condensed water film outside the tube R conv,out , whose expression is R total =R conv,in +R foul +R cond +R conv,out Among them, the convection thermal resistance and dirt thermal resistance in the tube are the main contributors, accounting for more than 85% of the total thermal resistance. The calculation of the convection thermal resistance in the tube is based on Newton's law of cooling, and the expression is where h in is the convection heat transfer coefficient in the tube, and A is the heat transfer area. According to the Dittus-Boelter formula, the convection heat transfer coefficient in the tube under turbulent state can be expressed as in is the Reynolds number, ρ, v, μ, and d are the fluid density, flow velocity, dynamic viscosity, and pipe diameter, respectively. is the Prandtl number, c p is the constant pressure specific heat capacity, k is the fluid thermal conductivity. The expression of fouling thermal resistance is Where δ is the thickness of the dirt layer, k foul is the equivalent thermal conductivity of the dirt. When the dirt thickness increases, the dirt thermal resistance increases linearly, which in turn leads to an increase in the total thermal resistance. According to experimental data, for every increase in dirt thickness Δδ = 0.05 mm, the increment of the total thermal resistance can be approximately expressed as Corresponding heat transfer coefficient The decrease in The reduction of heat transfer coefficient will directly lead to the condenser heat load Q = KAΔT lm The decrease (ΔT lmis the logarithmic mean temperature difference), which in turn causes the condenser back pressure p back Through the energy conservation equation and steam physical property correlation, the back pressure change Δp back Can be modeled as Where Q0 is the design operating heat load, m steam is the steam mass flow rate, R v is the steam gas constant, T sat is the saturation temperature. The experimental observation shows that the back pressure increases by about 200Pa for every 0.05mm increase in dirt thickness, indicating that Δp back ∝ΔR total , the proportional coefficient can be determined by parameter calibration. In addition, the outlet end difference ΔT out =T steam,out -T coolant,out The increase in is due to insufficient steam cooling caused by the decrease in heat transfer efficiency, and the change in It is nonlinearly positively correlated with the dirt thickness.

[0032] In actual projects, it is necessary to monitor the fouling thickness, back pressure and end difference data online, and modify the operating parameters in real time based on the above model. In addition, the convective thermal resistance can be reduced and fouling growth can be inhibited by optimizing the flow rate, adding turbulence promoting devices or improving water quality treatment, so as to maintain the efficient operation of the condenser.

[0033] S2. Analysis of the convective thermal resistance of the condenser: When the fluid flows in the pipe, there is a laminar boundary layer near the pipe wall. The turbulence of the main flow of the fluid will only change the thickness of this boundary layer, but this boundary layer will always exist.

[0034] The greater the turbulence, the thinner the laminar boundary layer. According to the principle of convective heat transfer, thermal resistance is primarily concentrated in the laminar boundary layer. The thinner the boundary layer, the smaller the thermal resistance and the greater the heat transfer coefficient. In the case of convective heat transfer, the heat transfer coefficient in turbulent conditions can even be several times greater than that in laminar conditions. Therefore, turbulent enhanced heat transfer is widely used in industry.

[0035] The implementation process of condenser convective thermal resistance analysis is based on the coupling of fluid dynamics and heat transfer. Its core lies in quantifying the influence of laminar boundary layer on heat transfer through theoretical modeling and experimental correlation, and establishing a quantitative relationship between Reynolds number (Re) and convective heat transfer coefficient (h). When the fluid flows through the pipe, the laminar boundary layer near the wall is the main resistance area for heat transfer. According to Prandtl boundary layer theory, the velocity gradient of the fluid at the wall causes the momentum transfer to be blocked, thereby forming a laminar bottom layer where the thermal resistance is concentrated. The thickness of this boundary layer (δ th ) is closely related to the flow state and can be approximately expressed by the Blasius solution as Where d is the pipe diameter and Pr is the Prandtl number. When it increases (by increasing the flow velocity v or increasing the pipe diameter d), the boundary layer thickness is significantly reduced, which reduces the resistance of heat penetrating the boundary layer, thereby improving the heat transfer efficiency.

[0036] The mathematical expression of convection thermal resistance depends on the calculation of the convection heat transfer coefficient h. For turbulent forced convection in the tube, the Dittus-Boelter empirical formula is used:

[0037]

[0038] Where k is the thermal conductivity of the fluid, and n is 0.4 (the fluid is heated) or 0.3 (the fluid is cooled). This shows that increasing the flow rate or pipe diameter can reduce thermal resistance. For example, when the flow rate increases from laminar flow (Re < 2000) to turbulent flow (Re > 4000), if Re increases from 2000 to 10000 (flow rate increases 5 times), then h will increase by approximately (10000 / 2000). 0.8 ≈3.98 times, and the corresponding convective thermal resistance is reduced to 25% of the original value, verifying the significant effect of turbulence-enhanced heat transfer.

[0039] In actual engineering, the flow state can be actively controlled by adjusting operating parameters or structural design. For example, installing turbulence promoting devices such as spiral twisted ribbons or static mixers in the condenser pipe can destroy the stability of the laminar boundary layer, causing the fluid to generate secondary flow and further thinning the boundary layer thickness. The heat transfer enhancement effect of such devices can be measured by the modified Reynolds number. Description: where e is the height of the turbulent element and C is the geometric shape factor. Experiments have shown that when e / d = 0.05, h can be improved by an additional 15% to 20%. In addition, numerical simulations (such as CFD) can quantify the flow field distribution and temperature gradient under different flow rates, pipe diameters, and turbulent element combinations, thereby optimizing the spatial distribution of h and avoiding the risk of local overheating or scaling.

[0040] It is worth noting that although increasing the flow rate can reduce the thermal resistance, it will significantly increase the pumping power consumption P∝v 3 Therefore, a trade-off between heat transfer efficiency and energy consumption is required. By introducing the efficiency-resistance synergy factor (Δp is the pressure drop), the comprehensive performance of different working conditions can be evaluated. For example, when Re increases from 4000 to 8000, if h increases by 70% and Δp increases by 120%, then η decreases by about 23%. At this time, it is necessary to combine economic analysis to determine the optimal Re range. Ultimately, the optimization of the condenser convection thermal resistance needs to be achieved through a multi-physics field coupling model, integrating the fluid mechanics equations, energy equations and cost functions to provide a quantitative basis for engineering decisions. Schematic diagram of the laminar boundary layer near the pipe wall, as shown Figure 2 shown.

[0041] The Reynolds number is a dimensionless parameter that describes the ratio of the inertial and viscous forces of a fluid in motion. In engineering, a flow with a Reynolds number less than 2000 is considered laminar, while a flow with a Reynolds number greater than 4000 is considered turbulent. Flows in between are considered to be in the transitional stage from laminar to turbulent. According to the Reynolds number formula: where are the flow velocity, density, and viscosity of the fluid, respectively, and d represents the pipe diameter in a circular pipe, the condenser's Reynolds number can be increased by increasing the flow velocity or pipe diameter, thereby reducing the condenser's convective thermal resistance.

[0042] S3. Condenser fouling analysis

[0043] As the condenser operates for a longer time, scale (scale, microorganisms, silt, and other sediments) gradually forms on the water side of the cooling tubes due to the presence of a water boundary layer on the inner wall of the cooling tubes. Scaling on the cooling tube water side has a significant impact on the condenser heat transfer coefficient. Condenser tube bundle scaling is a very complex issue, and the scaling formation process is roughly as follows: induction process → migration of scale particles to the heat transfer surface → adsorption of scale particles on the heat transfer surface → hardening of scale attachments → scaling shedding process. Domestic and foreign research results show that in the scale formation process, the supersaturation state of the solution, the precipitation and dissolution of crystals (crystal surface free energy), and the contact time between the solution and the surface are key factors. Through research on the condenser scaling mechanism, in order to delay the scaling of the condenser pipes, this can be achieved by destroying the scaling boundary conditions. Our company improves the heat exchange efficiency of the condenser and extends the scaling cycle of the tube bundle by changing the fluid morphology, thereby improving the energy efficiency of the waste heat power generation unit.

[0044] The implementation process of condenser fouling analysis requires the combination of multiphase flow dynamics, surface chemistry and unsteady heat transfer theory. By establishing a dynamic model of fouling formation and shedding, the impact of fouling on the heat transfer coefficient can be quantified and a suppression strategy can be formulated. The fouling process can be decomposed into five stages: induction, migration, adsorption, hardening and shedding. Its kinetic model is based on the principles of mass conservation and surface reaction. For the induction stage, the solution supersaturation It is the core parameter driving crystallization, where C is the actual concentration of solute, C sat is the solubility. According to the crystal growth theory, the precipitation rate Following the Burton-Cabrera-Frank (BCF) equation:

[0045]

[0046] where k cryst is the crystallization rate constant, ΔG * is the critical nucleation free energy, n is the reaction order, k Bis the Boltzmann constant, and T is the temperature. When S > 1, the supersaturated solution forms crystal nuclei on the heat transfer surface and grows, causing the fouling layer to thicken. The migration phase is dominated by fluid shear force and diffusion. The transport flux J of dirt particles to the wall can be expressed as:

[0047]

[0048] Where D is the diffusion coefficient, is the concentration boundary layer thickness (Sc=ν / D is the Schmidt number), ρ p is the particle density, d p is the particle diameter, is the shear rate. The adsorption stage involves surface energy balance, and the probability of dirt particles adhering to the wall is P ads The difference in free energy with the surface is Δγ=γ wall -γ particle Related to, meet:

[0049]

[0050] In the hardening stage, the Debye-Hückel theory is used to describe the migration resistance of ions in the dirt layer, resulting in the equivalent thermal conductivity k of the dirt layer. foul As the porosity ε decreases, it decreases, satisfying k foul =k solid (1-ε)+k fluid ε. The dynamics of the shedding phase is determined by the wall shear stress τ w =0.5fρv 2 (f is the friction factor) and the adhesion strength of the dirt layer σ adh The competition relationship determines that when τ w >σ adh When erosion occurs, the erosion rate satisfy where k ero is the erosion coefficient. To inhibit scaling, the boundary conditions of the above process need to be destroyed. By changing the fluid morphology (such as turbulent disturbance), the boundary layer stability can be weakened and the fouling deposition time can be reduced. For example, setting spiral twisted ribbons or surface microstructures in the pipeline can induce secondary flow and enhance the turbulence intensity, increasing the Reynolds number Re to 10 4 ~10 5 range, thus the concentration boundary layer thickness δ BL The compression is reduced to 30% to 50% of the original value, significantly reducing the migration flux J. According to the Dean vortex theory, the ratio of the curvature radius R of the spiral flow channel to the pipe diameter d κ = R / d determines the secondary flow intensity, and its dimensionless Dean number It needs to be optimized to 50-200 to balance the pressure drop and heat transfer enhancement effect. In addition, surface hydrophobic modification (such as coating contact angle θ>120°) can increase Δγ and make Pads It decreases by 40% to 60%, delaying the adsorption process.

[0051] In actual engineering, the effect of scaling thickness δ(t) on heat transfer coefficient can be dynamically predicted by combining computational fluid dynamics (CFD) with the fouling growth coupling model. By real-time monitoring of ΔT out With back pressure p back , inverse fouling thermal resistance This triggers online cleaning or flow rate adjustment strategies. Experimental data shows that the use of turbulence-enhancing devices can reduce the fouling rate dδ / dt by 50% to 70%, extend the fouling cycle from 6 months to 18 months, and increase the heat transfer coefficient k by 15% to 25%, significantly improving the energy efficiency of waste heat power generation units.

[0052] Example:

[0053] Comparison of unit operating parameters

[0054] The operating parameters of the unit before and after the installation of the RCCS device were recorded and compared, as shown in the table below. Through comparison, it was found that after the installation of the RCCS device, the exhaust temperature, back pressure and end difference of the unit were improved. The exhaust temperature of the unit was reduced by 2.1℃, the back pressure of the turbine was increased by 1.0kPa, and the end difference of the condenser was reduced by 2.0℃.

[0055] Under operating conditions, the exhaust steam pressure is reduced and the power generation of the unit is increased.

[0056] Operation data of CDQ unit before and after installation of RCCS device

[0057]

[0058] Summary of average operating data of CDQ units before and after installation of RCCS devices

[0059]

[0060] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0061] It should be understood that the detailed description of the technical solutions of the present invention using the preferred embodiments above is illustrative and not restrictive. A person skilled in the art, after reading the present specification, may modify the technical solutions described in the embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A converter station waste heat recovery management optimization model, characterized by: The following steps are involved: Thermal resistance analysis: Decompose the total heat transfer resistance of the condenser to identify the convection resistance of the medium in the tube and the fouling resistance as the main influencing factors. By real-time monitoring of the changing trends of fouling thickness, back pressure, and outlet end difference, a dynamic relationship model between heat transfer coefficient and thermal resistance is established; Fouling process control: Based on the analysis of the induction, migration, adsorption, hardening and shedding stages of fouling formation, fouling deposition is inhibited by changing the fluid morphology, optimizing the concentration boundary layer thickness and surface modification; Turbulence-enhanced heat transfer: Turbulence-promoting devices, such as spiral twisted ribbons or surface microstructures, are installed in the pipeline to increase the Reynolds number of the fluid to a turbulent state, reduce the thickness of the boundary layer, and enhance heat transfer efficiency; Online monitoring and dynamic adjustment: Combining computational fluid dynamics (CFD) simulation with a fouling growth coupling model, real-time monitoring of heat transfer performance parameters is performed, and operating conditions are dynamically optimized through online cleaning or flow rate adjustment strategies. Cleaning cycle optimization: Through turbulence enhancement devices and surface modification technology, the scaling cycle is extended, the scale deposition rate is significantly reduced, and the waste heat recovery efficiency of the converter station is improved.

2. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The thermal resistance analysis step includes real-time monitoring of the condenser back pressure and outlet end difference, and inverting the change trend of the fouling thermal resistance through the energy conservation equation.

3. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The scaling process control step reduces the concentration and adsorption probability of scale particles by optimizing the cooling water chemical treatment.

4. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The turbulence enhanced heat transfer step induces secondary flow by installing spiral twisted ribbons, thereby increasing turbulence intensity and significantly reducing the thickness of the concentration boundary layer.

5. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The online monitoring and dynamic adjustment steps use computational fluid dynamics (CFD) simulation to optimize the flow field distribution and temperature gradient to avoid local overheating or scaling risks.

6. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The cleaning cycle optimization step increases the wall contact angle through surface hydrophobic modification technology, reduces the probability of adhesion of dirt particles, and significantly extends the cleaning cycle.

7. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The turbulence enhanced heat transfer step optimizes the ratio of the curvature radius of the spiral twisted ribbon to the tube diameter, thereby increasing the Dean vortex intensity to balance the pressure drop and heat transfer efficiency.

8. The converter station waste heat recovery management optimization model according to claim 1 is characterized in that: The dynamic adjustment strategy includes triggering an online cleaning device or adjusting the flow rate to maintain the heat transfer coefficient within the design range based on real-time monitoring data.

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