Pressure regulating and loss reducing method based on steam simulation and condensate water analysis

Abstract

The invention relates to the technical field of smart city operation, and discloses a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, comprising the following steps: step S1, collecting operation parameters of a steam pipe network system; s2, converting the preprocessed operation parameters into scale space parameters; s3, establishing a condensate water source term equation, a heat source term equation and a condensate water generation model to obtain pressure, temperature and condensate water content; s4, solving the optimal pressure adjustment amount and the condensate water adjustment amount by combining the pressure and condensate water coupling matrix, and optimizing the circulation degree; and S5, correcting model parameters in the condensate water source term equation, the heat source term equation and the condensate water generation model based on a comparison result. By coupling and simulating the condensate water source item, the heat source item and the generation model, dynamic distribution of condensate water can be accurately analyzed, and the problems that in the prior art, calculation efficiency and model precision are difficult to balance, and dynamic coupling is lacked are solved.

Description

Technical Field

[0001] The present invention relates to the field of smart city operation technology, and specifically to a pressure regulation and loss reduction method based on steam simulation and condensate water analysis. Background Art

[0002] Steam pipeline systems, as critical infrastructure for industrial production and energy transportation, are widely used in a variety of fields, including thermal power generation, petrochemicals, centralized heating, and manufacturing. In these systems, steam, acting as an energy carrier, originates from a steam source (such as a boiler or upstream pipeline network) and is transported to various end users through a complex network of pipelines. However, during this transportation process, some of the high-temperature, high-pressure steam undergoes a phase change and condenses to form condensate due to the inevitable heat dissipation in the pipelines and the pressure and temperature drop caused by the resistance along the steam path.

[0003] The presence of condensate has multiple adverse effects on steam system operation. First, it significantly reduces steam dryness, resulting in a decrease in the effective heat energy carried per unit mass of steam, thereby reducing the thermal efficiency and energy utilization of the entire system. Second, the high-velocity steam can cause a "water hammer" phenomenon, generating enormous instantaneous impact forces that can severely damage pipelines, flow control devices (including but not limited to various valves and partitions), and equipment, threatening the safe and stable operation of the system. Furthermore, dissolved gases in condensate can accelerate electrochemical corrosion of pipeline walls, shortening equipment life and increasing maintenance costs. Therefore, accurately analyzing the condensate content in steam pipeline networks and effectively regulating pressure and reducing losses based on this analysis are of vital practical significance for ensuring system safety and improving economic efficiency.

[0004] In the prior art, various steam system pressure regulation, loss reduction and condensate water analysis methods have been developed.

[0005] One type of method relies on empirical formulas or static models for estimation and control. These traditional methods typically rely on simplified thermodynamic equations (such as simplified applications of the Clausius-Clapeyron equation) and fixed design parameters (such as pipeline length, nominal diameter, and insulation material) to roughly estimate condensate flow. Control strategies often employ fixed pressure thresholds to regulate flow rate, or passively adjust flow based on statistical averages of historical operating data. However, these methods have significant inherent flaws. First, the accuracy of condensate analysis is severely insufficient. Static models are unable to reflect the dynamic flow characteristics of steam in complex pipe network topologies and are unable to respond to changes in operating conditions, resulting in significant deviations between condensate predictions and actual conditions. Second, their pressure regulation strategies are overly simplistic and rigid, unable to adapt to real-time fluctuations in user load or changes in ambient temperature, making it difficult to achieve optimal operating conditions.

[0006] Another approach employs advanced computational fluid dynamics (CFD) simulation technology. By creating a detailed three-dimensional geometric model of the steam network within specialized numerical simulation software, these methods can more accurately simulate the pressure, temperature, and velocity distributions within the network. While these methods offer a significant improvement in simulation accuracy compared to traditional approaches, they still face significant challenges in practical application. The primary issue lies in the conflict between computational efficiency and accuracy: high-precision three-dimensional simulations require enormous computing resources and lengthy computation times, making them completely unable to meet the demands of industrial sites for real-time analysis and online control. These simulations are typically only used for offline design verification or accident analysis. Furthermore, the core focus of most existing simulation applications remains on the macroscopic balance between pressure and flow. Analyses of the dynamic and in-depth coupling between the condensate generation mechanism and its relationship with fluid flow and heat transfer processes are insufficient, limiting their effectiveness in refined condensate prediction and control. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, which solves the problems in the existing technology that the condensate water analysis model is difficult to strike a balance between calculation accuracy and real-time performance, and the pressure regulation strategy cannot be adaptively optimized and adjusted according to the dynamic condensate water content.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, comprising the following steps: Step S1, collecting operating parameters of the steam pipe network system; Step S2: performing preprocessing based on the operating parameters, wherein the preprocessing includes filtering abnormal data and converting the preprocessed operating parameters into scale space parameters; Step S3: Coupled simulation of steam flow and condensate, establishing a condensate source term equation, a heat source term equation, and a condensate generation model based on scale space parameters, and calculating the pressure, temperature, and condensate content of each node in the pipeline network; Step S4: constructing a pressure and condensate coupling matrix based on the calculated pressure, temperature, and condensate content of each node in the pipeline network, solving the optimal pressure adjustment amount and condensate adjustment amount based on the pressure and condensate coupling matrix in parallel, and optimizing the flow rate based on the pressure adjustment amount and the condensate adjustment amount; Step S5: collecting the real-time operating parameters after the flow rate is optimized, comparing the real-time operating parameters with the calculated pressure, temperature, and condensate content of each node in the pipeline network, and correcting the model parameters in the condensate source term equation, heat source term equation, and condensate generation model based on the comparison results to form a closed-loop correction mechanism.

[0009] Preferably, in step S2, the operating parameters of the steam pipe network system include steam information and pipeline information; The steam information includes: steam source pressure ,temperature, ,flow , user-side pressure , user-side temperature , user-side traffic ; The pipeline information includes: length ,diameter , thermal insulation coefficient and liquidity .

[0010] Preferably, in step 2, the preprocessing uses a scale isomorphism method to convert the physical quantity from the original dimension space to the scale space through mathematical transformation; The formula for converting to scale space includes: ; ; ; Where, is the dimensionless pressure; is the original pressure; is the base pressure; is the dimensionless temperature; is the original temperature; is the reference temperature; is the dimensionless flow rate; is the original traffic; is the base flow rate.

[0011] Preferably, in step S3, the expression formula of the condensate water source term equation includes: ; Where, is the density of steam; For time; is the divergence operator, which represents the spatial variation of steam mass flow rate; is the steam velocity vector; is the condensate source term, which indicates the generation rate of condensate per unit volume; Indicates steam density Over time The rate of change.

[0012] Preferably, in step S3, the heat source term equation may be expressed as follows: ; Where, is the rate of change of steam enthalpy per unit volume with time; is the enthalpy flux, which indicates the transfer of enthalpy with steam flow; is the thermal conductivity term, is the thermal conductivity, is the temperature gradient; is the heat source term, the latent heat released by condensation, is the latent heat of vaporization of water.

[0013] Preferably, in step S3, the condensate generation amount of the condensate generation model is Determined by the steam saturation curve and the local temperature gradient, the The calculation formula includes: ; Where, is the temperature along the pipe axis rate of change; is the derivative of saturation pressure with respect to temperature; and are the starting and ending points of the integration interval.

[0014] Preferably, in step S4, the pressure and condensate coupling matrix includes a pressure sensitivity matrix and condensate water content matrix ; The pressure sensitivity matrix The condensate water content matrix is used to quantify the effect of flow rate changes on node pressure. Used to quantify the impact of mobility changes on node condensate.

[0015] Preferably, the pressure sensitivity matrix The expressions include: ; Where, Indicates the The change of the circulation degree of the first The influence coefficient of node pressure is calculated by steady-state simulation to determine the sensitivity of each node pressure to the flow control device; Indicates in The segment flow rate is When the simulation calculation is Steam pressure at each node; is the circulation disturbance; For the Segment Circulation The instantaneous change of The pressure of each network node The rate of change caused.

[0016] Preferably, the condensate water content matrix The expressions include: ; Where, Indicates the The change of the circulation degree of the first The influence coefficient of node condensate is solved by the condensate model and the condensate source term equation; Indicates in The segment flow rate is When the simulation calculation is Condensate water content at each node; is the circulation disturbance; Represents the The instantaneous change of the flow rate of the segment Condensate water from each pipe network node The rate of change caused.

[0017] Preferably, the calculation formula of the simultaneous pressure and condensate water coupling matrix includes: ; Where, For the The new set circulation value calculated after the optimization iteration; For the The set flow value of the segment before the optimization iteration; is the learning rate; is the sum of all nodes; is the pressure weight coefficient; is the condensate water weight coefficient; Indicates the The change of the circulation degree of the first The influence coefficient of the node pressure is provided by the pressure sensitivity matrix; Indicates the The change of the circulation degree of the first The influence coefficient of node condensate water is provided by the condensate water content matrix.

[0018] The present invention provides a method for pressure regulation and loss reduction based on steam simulation and condensate analysis. It has the following beneficial effects: 1. The present invention achieves the technical effect of accurately revealing the dynamic distribution law of condensate in the pipeline network by coupling the condensate source term equation, the heat source term equation and the condensate generation model for simulation. Compared with the existing technology that relies on empirical formulas for rough estimation or adopts high-resource consumption offline analysis technology solutions, the present invention solves the inherent defects of the existing technology that are difficult to balance between computational efficiency and model accuracy, and cannot fully reflect the coupling relationship between condensate and flow.

[0019] 2. The present invention realizes refined and adaptive closed-loop control of the pipeline network by constructing a pressure and condensate water coupling matrix and solving the optimal flow adjustment technical means in parallel. Compared with the control method of passive adjustment using fixed thresholds or static parameters in the existing technology, it effectively overcomes the shortcomings of its rigid pressure regulation strategy, inability to adapt to dynamic changes in working conditions, and difficulty in achieving optimal system operation.

[0020] 3. The present invention obtains the appropriate flow rate and number by constructing a pressure and condensate coupling matrix when the existing condensate water content is relatively large. This result is reassigned to the condensate water source term equation, heat source term equation and condensate water generation model established based on the scale space parameters, and the pressure, temperature and condensate water content of each node in the pipeline network are calculated. If the condensate water content does not drop to the expected value, the pressure and condensate water coupling matrix is constructed again to obtain a new appropriate flow rate and number. By looping the above steps, continuous adjustment and calculation, the condensate water content is eventually reduced to within the range, forming a complete closed-loop correction mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0024] Please see the attached Figure 1 The embodiment of the present invention provides a method for pressure regulation and loss reduction based on steam simulation and condensate water analysis, comprising the following steps: Step S1, collecting operating parameters of the steam pipe network system; In this embodiment, to implement the proposed steam simulation-based pressure regulation and loss reduction method, accurate and reliable input data must first be prepared for the subsequent coupled simulation and optimization control modules. Therefore, comprehensive collection and detailed preprocessing of various steam network system operating parameters are necessary to ensure the accuracy of the subsequent simulation model and the effectiveness of the optimization strategy.

[0025] Specifically, the method first performs step S1, i.e., collecting the operating parameters of the steam pipe network system. These operating parameters comprehensively describe the physical state, energy state, and boundary conditions of the pipe network at a certain moment.

[0026] The operating parameters can be categorized into two main categories: steam information and pipeline information. These two types of information together form the basis for accurate mathematical modeling and simulation of the pipeline network system.

[0027] Specifically, steam information is mainly used to define the thermodynamic state and flow boundary of the working fluid in the system.

[0028] In a possible implementation, the steam information includes: Steam source pressure, steam source temperature and steam source flow rate, these parameters define the initial energy and mass inlet conditions of steam entering the pipeline network.

[0029] User-end pressure, user-end temperature and user-end flow, these parameters define the load demand and outlet boundary conditions of the pipeline network, and are one of the target constraints for system operation.

[0030] At the same time, pipeline information is mainly used to describe the physical constraints and geometric environment of steam flow.

[0031] In some embodiments, pipeline information includes: The geometric properties of the pipeline, such as the length and diameter of each pipe section, are the basic basis for calculating flow resistance and pressure drop.

[0032] The physical properties of the pipeline, such as the insulation coefficient corresponding to the pipeline material, which determines the heat loss of steam during transportation and is a key parameter for accurately calculating the amount of condensate generated.

[0033] The status of the control elements of the pipeline network, such as the real-time flow rate of each section, is the active control variable of the system and the execution object of the subsequent optimization and control strategy.

[0034] As an extension, the flow velocity in a pipe cross section can be described by: ; Where, is the volume flow rate; is the cross-sectional area of the pipe; is the average velocity of fluid particles passing through a certain cross section of the pipeline; After obtaining the raw operating parameters, they must be preprocessed to ensure data quality before being input into the simulation model. Raw data collected directly from sensors or industrial control systems inevitably contains noise, outliers, or significant differences in magnitude due to inconsistent units.

[0035] Furthermore, to accurately solve the fluid dynamics equations in subsequent simulation steps, it is necessary to establish a relationship between the steam thermodynamic state parameters. Within the operating pressure and temperature range of the steam network, the steam state is described by the ideal gas state equation. Therefore, using the collected and preprocessed pressure and temperature data, the steam density, a key fluid property, can be calculated using the density form of the ideal gas state equation.

[0036] As an extension, there is a clear physical relationship between the collected mass flow rate and the geometric dimensions of the pipeline and the flow state of the steam. The mass flow rate of the pipeline cross section can be described by the following formula: ; Where, is the steam density; is the absolute pressure of the steam; is the gas constant of water vapor; The absolute temperature at which the steam is exposed; Therefore, data acquisition can provide a complete, standardized and physically clear data foundation for the steam flow and condensate water coupling simulation in subsequent steps, thereby ensuring the robustness and efficiency of the entire technical solution at the mathematical level.

[0037] Step S2: preprocessing based on the operating parameters, the preprocessing includes filtering abnormal data and converting the preprocessed operating parameters into scale space parameters; In this embodiment, after completing the initial collection of the pipe network system operating parameters in step S1, the resulting raw data stream cannot be directly used for subsequent high-precision coupled simulation calculations. This data often originates from different sensors or systems, is interspersed with noise and transient outliers, and due to differences in physical units and dimensions, the values of the various parameters vary significantly. Therefore, the present invention performs necessary preprocessing on these raw parameters in step S2 to provide a standardized and numerically stable data foundation for subsequent simulation and optimization modules.

[0038] Specifically, the core of step S2 is to perform two key processes on the operating parameters collected from step S1: first, filtering abnormal data, and then converting the physical quantity from the original dimensional space to a unified scale space.

[0039] Specifically, the primary task of preprocessing is to ensure the validity and reliability of the data.

[0040] Generally speaking, sensors may generate invalid abnormal data during the collection process due to electromagnetic interference, equipment aging or sudden working conditions.

[0041] Alternatively, a threshold method can be used to filter out abnormal data. For example, a threshold value can be used to set physically reasonable upper and lower limits for physical quantities such as pressure and temperature. Any collected values outside this range are identified as abnormal data and can be eliminated or replaced by interpolation using valid data from nearby times.

[0042] In one possible implementation, more sophisticated statistical methods can be used for filtering. For example, criteria can be applied to dynamically identify and address outliers that significantly deviate from the normal fluctuation range by calculating the moving average and standard deviation of the data stream, thereby enhancing the stationarity of the data series.

[0043] After data filtering, the present invention employs a scaling isomorphism method to address the significant differences in magnitude between different physical quantities. This method uses a specific mathematical transformation to uniformly map all filtered physical quantities from their original dimensionless spaces to a dimensionless scale space with similar magnitudes. This step is crucial for improving convergence speed and computational accuracy when solving complex numerical models.

[0044] The formulas for converting to scale space include: ; ; ; Where, is the dimensionless pressure; is the original pressure; is the base pressure; is the dimensionless temperature; is the original temperature; is the reference temperature; is the dimensionless flow rate; is the original traffic; is the base flow rate.

[0045] In summary, by executing the above process in step S2, the present invention successfully converts the original, noisy, and multidimensional operating parameters into a set of clean, standardized, and dimensionless scale-space parameters. This set of parameters is then input into the simulation module in step S3, providing high-quality input for establishing and solving the condensate source term equation and the heat source term equation.

[0046] Step S3: Coupled simulation of steam flow and condensate, establishing a condensate source term equation, a heat source term equation, and a condensate generation model based on scale space parameters, and calculating the pressure, temperature, and condensate content of each node in the pipeline network; In this embodiment, after preprocessing the operating parameters in step S2, the present invention proceeds to its core technical step S3: coupled steam flow and condensate simulation. This step uses the normalized scale-space parameters obtained from the previous processing as boundary and initial conditions to accurately simulate the dynamic behavior of steam in a complex pipe network by solving a set of coupled physical governing equations. The purpose of this step is to obtain the pressure, temperature, and crucial condensate content at any location in the pipe network under the current operating conditions, providing a decision-making basis for subsequent quantitative analysis and optimized control.

[0047] Specifically, this coupled simulation is achieved by establishing and solving a system of partial differential equations involving mass conservation and energy conservation. This system primarily consists of the condensate source term equation and the heat source term equation, which, together with the condensate generation model, closes the entire system of equations.

[0048] Specifically, the expression formula of the condensate source term equation includes: ; Where, is the density of steam; For time; is the divergence operator, which represents the spatial variation of steam mass flow rate; is the steam velocity vector; is the condensate source term, which indicates the generation rate of condensate per unit volume; Indicates steam density Over time The rate of change.

[0049] The heat source term equation is expressed as follows: ; Where, is the rate of change of steam enthalpy per unit volume with time; is the enthalpy flux, which indicates the transfer of enthalpy with steam flow; is the thermal conductivity term, is the thermal conductivity, is the temperature gradient; is the heat source term, the latent heat released by condensation, is the latent heat of vaporization of water.

[0050] In step S3, the condensate generation amount of the condensate generation model Determined by the steam saturation curve and local temperature gradient, The calculation formula includes: ; Where, is the temperature along the pipe axis rate of change; is the derivative of saturation pressure with respect to temperature; and are the starting and ending points of the integration interval, and Empirical coefficients applicable to steam at 1-100°C, derived from ASME steam tables.

[0051] In one embodiment, the condensate source term equation is coupled with the heat source term equation: The condensate source term equation provides the density and flow rate , substitute into the heat source equation to calculate the temperature ;temperature Feedback to ideal gas equation update , forming a closed iteration.

[0052] Input original pressure , original temperature ,Original traffic ,calculate and .

[0053] Updated by heat source term equation .

[0054] according to Calculation of condensate ,renew .

[0055] Repeat to residual , .

[0056] In summary, the aforementioned condensate source term equation, heat source term equation, and related physical models (such as the ideal gas equation and saturated water vapor properties) are combined, and combined with the network boundary conditions (such as steam source pressure and user-end flow) defined by the scale space parameters obtained in step S2, a numerical calculation method (for example, the finite volume method) is used to discretize and solve the entire network. Ultimately, the pressure and temperature at all discrete nodes in the network are calculated, as well as the condensate content obtained by integrating over time and space. These distribution results, in the form of data fields, provide a comprehensive and quantitative basis for the next step of sensitivity analysis and optimization control. Step S4: constructing a pressure and condensate coupling matrix based on the calculated pressure, temperature, and condensate content of each node in the pipeline network, solving the optimal pressure adjustment amount and condensate adjustment amount in parallel with the pressure and condensate coupling matrix, and optimizing the flow rate based on the pressure adjustment amount and the condensate adjustment amount; In this embodiment, after obtaining the pressure, temperature, and condensate content of the entire steam network through high-precision simulation in step S3, step S4 proceeds. Its core goal is to transform the simulated state "snapshot" into a practical, executable optimization control action. This step connects simulation analysis with physical control. By constructing a quantitative sensitivity model, it calculates the optimal flow adjustment strategy, thereby proactively and targetedly improving the network's operating status and ultimately achieving the goal of pressure regulation and loss reduction.

[0057] Specifically, step S4 constructs a pressure and condensate coupling matrix to quantitatively analyze the influence of each flow control device on the overall state of the pipeline network, and uses an iterative optimization algorithm based on the matrix to calculate the optimal flow adjustment amount for each section.

[0058] Specifically, in order to decouple and quantify the impact of the flow control device on the two core optimization objectives of pressure and condensate, the pressure and condensate coupling matrix is composed of two independent sub-matrices: the pressure sensitivity matrix and the condensate content matrix.

[0059] The pressure sensitivity matrix is used to accurately describe the response of the pressure at any node in the pipe network to any change in the regulated flow rate.

[0060] As an option, each element in the pressure sensitivity matrix, i.e., the pressure influence coefficient, can be calculated by combining the numerical perturbation method with the simulation model of step S3. Its expression is as follows: ; Where, Indicates the The change of the circulation degree of the first The influence coefficient of node pressure is calculated by steady-state simulation to determine the sensitivity of each node pressure to the flow control device; Indicates in The segment flow rate is When the simulation calculation is Steam pressure at each node; is the circulation disturbance; For the Segment Circulation The instantaneous change of The pressure of each network node The rate of change caused.

[0061] The role of the condensate content matrix is similar to that of the pressure sensitivity matrix, but its goal is to accurately describe the response of the condensate content at any node in the pipeline network to any change in the regulated flow rate.

[0062] In one possible implementation, the elements of the condensate water content matrix, namely the condensate water influence coefficient, are also calculated using the numerical perturbation method, and the expression is: ; Where, Indicates the The change of the circulation degree of the first The influence coefficient of node condensate is solved by the condensate model and the condensate source term equation; Indicates in The segment flow rate is When the simulation calculation is Condensate water content at each node; is the circulation disturbance; Represents the Segment flow The instantaneous change of Condensate water from each pipe network node The rate of change caused.

[0063] After the pressure sensitivity matrix and the condensate water content matrix are constructed, the present invention combines these two matrices and solves the optimal flow rate adjustment amount through iterative calculation.

[0064] The calculation formula of the combined pressure and condensate coupling matrix is as follows: ; Where, For the The new set circulation value calculated after the optimization iteration; For the The set flow value of the segment before the optimization iteration; is the learning rate; is the sum of all nodes; is the pressure weight coefficient; is the condensate water weight coefficient; Indicates the The change of the circulation degree of the first The influence coefficient of the node pressure is provided by the pressure sensitivity matrix; Indicates the The change of the circulation degree of the first The influence coefficient of node condensate water is provided by the condensate water content matrix.

[0065] In summary, by performing the above calculations, the new circulation setting values of all circulation control devices are obtained. ,These set values are then sent to the control system of the pipe network to complete the actual adjustment of the flow control device, thus completing a closed-loop operation from simulation, analysis to control.

[0066] Step S5: collecting the real-time operating parameters after the flow rate is optimized, comparing the real-time operating parameters with the calculated pressure, temperature, and condensate content of each node in the pipeline network, and correcting the model parameters in the condensate source term equation, heat source term equation, and condensate generation model based on the comparison results to form a closed-loop correction mechanism.

[0067] In this embodiment, after the simulation analysis and optimization control of the pipeline network are completed in the previous steps, there will inevitably be deviations between any theoretical model and physical reality. In order to ensure that the voltage regulation and loss reduction method proposed in the present invention always maintains high precision and strong adaptability in long-term operation, it is crucial to set up a closed-loop feedback correction link. Therefore, an adaptive model correction mechanism is introduced in step S5. Its core is to continuously and dynamically optimize the simulation model itself by comparing the simulation prediction with the real-world operating data, thereby forming a complete intelligent closed loop from perception to analysis to decision-making to execution to learning.

[0068] Specifically, step S5 is an execution step, and its specific implementation process is as follows: After optimizing and adjusting the flow rate of one or more sections in the pipeline network in step S4, the system will not immediately enter the next round of optimization, but will wait for a period of time to allow the flow and thermal state of the entire steam pipeline network to reach a new stable or quasi-stable operating condition under the new control parameters.

[0069] After the system stabilizes, the present invention restarts the data collection process, collecting real-time operating parameters after flow optimization. These parameters are identical to those collected in step S1, including real-time pressure, temperature, and flow rate information at the steam source, user end, and key nodes in the pipeline network. This newly collected data truly reflects the physical effects of the optimization and control measures.

[0070] Subsequently, these newly collected operating parameters reflecting the actual operating conditions are quantitatively compared point by point or section by section with the simulation results of the pressure, temperature and condensate content of each node of the pipeline network calculated in step S3 and used as the basis for the optimization and control in step S4.

[0071] This comparison yields an error signal or set of signals that intuitively reflect the degree of deviation between the current simulation model and physical reality. For example, if the actual receiving temperature at a particular user is significantly lower than the model's prediction, this may indicate that the actual heat loss or condensate generation in the pipeline from the steam source to that user far exceeds the model's calculations.

[0072] Based on the comparison result, the present invention modifies the parameters of the simulation model established in step S3 to implement an adaptive correction process, aiming to make the behavior of the model infinitely close to the real behavior of the physical entity.

[0073] Specifically, after obtaining the real-time operating parameters in step S5, the system first compares them point by point with the pressure, temperature, and condensate water content data obtained by simulation in step S3, and calculates the error signal. The error can be expressed as: ; ; ; Where, represents the error in node pressure; represents the error in node temperature, Indicates the error of the condensate water content at the node; Indicates the node pressure measured in real time; represents the node pressure obtained by simulation calculation; Indicates the node temperature measured in real time; represents the node temperature obtained by simulation calculation; Indicates the node condensate water content measured in real time; Indicates the node condensate water content obtained by simulation calculation.

[0074] If any error exceeds the preset threshold , then the correction logic is triggered; Then, according to the source of the error signal, the model parameters are corrected using the following strategies: like : Automatically add thermal conductivity to the heat source term equation , to correct the temperature prediction deviation; like : Adjust the condensate source term in the condensate generation model , improve the response capability to the condensation process; like : Modify the density and pressure conversion relationship in the condensate water source term equation to improve the accuracy of flow field simulation.

[0075] Where, is the temperature error allowable threshold; is the condensate water error allowable threshold; Pressure error tolerance threshold.

[0076] Specifically, each model parameter is corrected using an incremental update formula based on the error direction, including: ; Where, Indicates the A model parameter to be modified in the iteration (such as heat source intensity, condensation factor, resistance coefficient); Indicates the updated Secondary parameter value; To update the step size coefficient (also known as learning rate), control the amplitude of each parameter correction, usually with a value between 0.01 and 0.1; is the error signal corresponding to the current parameter, obtained from the error signal (including 、 、 ).

[0077] After the correction is completed, re-execute steps S3 and S4 to obtain new simulation results, and continue to compare with real-time parameters. If the errors in two consecutive rounds are within the threshold range, the closed-loop correction is considered to have converged, otherwise the iterative update continues until the steady-state conditions are met. In addition, all parameter changes in the correction process will be written into the system's internal database to form a long-term accumulated dynamic parameter model, providing data-driven support for subsequent predictions and voltage regulation under different working conditions. The closed-loop correction mechanism constructs a closed-loop correction mechanism from data acquisition, error judgment, model correction, simulation restart, optimization control, and re-acquisition, which can significantly enhance the robustness and adaptability of the system, and is particularly suitable for steady-state maintenance and energy-saving and loss-reduction control under multi-time periods, seasonal changes or old pipeline conditions.

[0078] Specifically, the correction process is targeted at: For the condensate generation model, if the actual condensate flow (inferred indirectly through terminal steam trap discharge, abnormal temperature drops along the process, or directly measured) is greater than the model's predicted value, it indicates that the model underestimates the current condensate rate. In this case, the system automatically increases model parameters related to the condensate rate (for example, the comprehensive condensate coefficient) to enable the model to predict more condensate in the next simulation under the same operating conditions.

[0079] Corrections to the heat source term are primarily related to heat loss along the pipeline. If the measured temperature drop along the pipeline is significantly higher than the simulation result, this indicates that the actual insulation effect is worse than the insulation coefficient specified in the model. Alternatively, the correction process increases the equivalent heat loss coefficient for that section of the pipeline accordingly, ensuring that the energy conservation equation more accurately reflects the actual heat dissipation along the pipeline.

[0080] For the condensate source term equation, the physical law form (mass conservation) is fixed, but its calculation accuracy depends on the input physical property parameters. In one possible implementation, the correction process can compensate for the density calculation deviation caused by the real gas effect under high pressure by fine-tuning the correction coefficient in the state equation (such as the ideal gas equation) or introducing a more complex physical property model, thereby indirectly improving the overall accuracy of the mass conservation calculation.

[0081] This correction process can be set to execute automatically and periodically, for example, by performing a full model calibration every 24 hours. Alternatively, it can be triggered by an event, that is, when the monitored simulation error exceeds a preset allowable threshold multiple times in a row, the correction procedure is automatically started. Therefore, through a closed-loop, continuous correction mechanism, it is ensured that the simulation model of the present invention can dynamically track and adapt to slow changes in the steam pipeline network due to factors such as seasonal changes, pipeline aging, and insulation layer performance degradation, thereby maintaining the high accuracy and robustness of the entire pressure regulation and loss reduction method in long-term operation.

[0082] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for pressure regulation and loss reduction based on steam simulation and condensate water analysis, characterized in that: The following steps are involved: Step S1, collecting operating parameters of the steam pipe network system; Step S2: performing preprocessing based on the operating parameters, wherein the preprocessing includes filtering abnormal data and converting the preprocessed operating parameters into scale space parameters; Step S3: Coupled simulation of steam flow and condensate, establishing a condensate source term equation, a heat source term equation, and a condensate generation model based on scale space parameters, and calculating the pressure, temperature, and condensate content of each node in the pipeline network; Step S4: constructing a pressure and condensate coupling matrix based on the calculated pressure, temperature, and condensate content of each node in the pipeline network, solving the optimal pressure adjustment amount and condensate adjustment amount based on the pressure and condensate coupling matrix in parallel, and optimizing the flow rate based on the pressure adjustment amount and the condensate adjustment amount; Step S5: collecting the real-time operating parameters after the flow rate is optimized, comparing the real-time operating parameters with the calculated pressure, temperature, and condensate content of each node in the pipeline network, and correcting the model parameters in the condensate source term equation, heat source term equation, and condensate generation model based on the comparison results to form a closed-loop correction mechanism.

2. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 1 is characterized in that: In step S1, the operating parameters of the steam pipe network system include steam information and pipe information; The steam information includes: steam source pressure ,temperature, ,flow , user-side pressure , user-side temperature , user-side traffic ; The pipeline information includes: length ,diameter , thermal insulation coefficient and liquidity .

3. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 2 is characterized in that: In the step 2, the preprocessing uses a scale isomorphism method to convert the physical quantity from the original dimension space to the scale space through mathematical transformation; The formula for converting to scale space includes: ； ； ； Where, is the dimensionless pressure; is the original pressure; is the base pressure; is the dimensionless temperature; is the original temperature; is the reference temperature; is the dimensionless flow rate; is the original traffic; is the base flow rate.

4. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 1 is characterized in that: In step S3, the expression formula of the condensate water source term equation includes: ； Where, is the density of steam; For time; is the divergence operator, which represents the spatial variation of steam mass flow rate; is the steam velocity vector; is the condensate source term, which indicates the generation rate of condensate per unit volume; Indicates steam density Over time The rate of change.

5. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 4 is characterized in that: In step S3, the heat source term equation is expressed as follows: ； Where, is the rate of change of steam enthalpy per unit volume with time; is the enthalpy flux, which indicates the transfer of enthalpy with steam flow; is the thermal conductivity term, is the thermal conductivity, is the temperature gradient; is the heat source term, the latent heat released by condensation, is the latent heat of vaporization of water.

6. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 5 is characterized in that: In step S3, the condensate generation amount of the condensate generation model Determined by the steam saturation curve and the local temperature gradient, the The calculation formula include: ； Where, is the temperature along the pipe axis rate of change; is the derivative of saturation pressure with respect to temperature; and are the starting and ending points of the integration interval.

7. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 1 is characterized in that: In step S4, the pressure and condensate coupling matrix includes a pressure sensitivity matrix and condensate water content matrix ; The pressure sensitivity matrix The condensate water content matrix is used to quantify the effect of flow rate changes on node pressure. Used to quantify the impact of mobility changes on node condensate.

8. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 7 is characterized in that: The pressure sensitivity matrix The expressions include: ； Where, Indicates the The change of the circulation degree of the first The influence coefficient of node pressure is calculated by steady-state simulation to determine the sensitivity of each node pressure to the flow control device; Indicates in The segment flow rate is When the simulation calculation is Steam pressure at each node; is the circulation disturbance; For the Segment Circulation The instantaneous change of The pressure of each network node The rate of change caused.

9. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 8, characterized in that: The condensate water content matrix The expressions include: ； Where, Indicates the The change of the circulation degree of the first The influence coefficient of node condensate is solved by the condensate model and the condensate source term equation; Indicates in The segment flow rate is When the simulation calculation is Condensate water content at each node; is the circulation disturbance; Represents the Segment flow The instantaneous change of Condensate water from each pipe network node The rate of change caused.

10. The method for pressure regulation and loss reduction based on steam simulation and condensate water analysis according to claim 9, characterized in that: The calculation formula of the simultaneous pressure and condensate coupling matrix includes: ； Where, For the The new set circulation value calculated after the optimization iteration; For the The set flow value of the segment before the optimization iteration; is the learning rate; is the sum of all nodes; is the pressure weight coefficient; is the condensate water weight coefficient; Indicates the The change of the circulation degree of the first The influence coefficient of the node pressure is provided by the pressure sensitivity matrix; Indicates the The change of the circulation degree of the first The influence coefficient of node condensate water is provided by the condensate water content matrix.

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