A hot water boiler and an intelligent control method thereof

Abstract

The present application relates to the technical field of central heating control, and discloses a hot water boiler and an intelligent regulation and control method thereof, wherein the intelligent regulation and control method comprises: acquiring joint initial state data sets of multiple parallel hot water boilers; generating a cross-boiler temperature transfer matrix and a cross-boiler thermal inertia coupling parameter matrix; generating pipe network transient hydraulic propagation parameter sets by using the method of characteristic lines and forming a unified parameter library; generating joint prediction results containing water collector temperature oscillation amplitudes and pipe network node pressure extreme values based on a ramp output adjustment function; performing double-domain constraint inspection and generating a joint sensitivity matrix; performing joint optimization solution by sequential quadratic programming to generate optimal variable load starting time and variable load rate curves of each boiler; issuing a joint regulation and control execution scheme and outputting regulation and control instructions.

Description

Technical Field

[0001] This invention relates to the field of centralized heating control technology, and more specifically, to a hot water boiler and its intelligent control method. Background Technology

[0002] In large-scale centralized heating systems, multiple hot water boilers operate in parallel and share a common water collector and main water supply pipe, delivering heat to various heat exchange stations through the pipeline network. When the heating load changes significantly, it is necessary to quickly coordinate and adjust the output of each boiler to keep the total supply water temperature after mixing in the water collector stable, while ensuring that the pressure and flow rate at each node in the pipeline network are within a safe range.

[0003] Existing intelligent control methods for hot water boilers typically implement independent thermal inertia compensation control for each boiler and calculate the force distribution scheme based on steady-state hydraulic assumptions, optimizing thermal inertia coordination and hydraulic safety independently.

[0004] However, in the control scenario of multiple hot water boilers operating in parallel with rapid load changes, there is a constraint conflict between suppressing thermal inertial coupling and ensuring the transient hydraulic safety of the pipeline network: to eliminate the cross-boiler thermal inertial coupling oscillations generated by each boiler through the water collector, the output adjustment actions of adjacent boilers need to be staggered and dispersed in time. However, this staggered timing may cause some boilers to be scheduled for concentrated load changes during periods when transient pressure waves have accumulated in the pipeline network, leading to the risk of water hammer. Conversely, when the load change rate of all boilers is strictly limited to suppress water hammer in the pipeline network, the adjustment actions of each boiler tend to be synchronized in time, which further exacerbates the thermal inertial coupling oscillations at the water collector. Existing methods cannot simultaneously satisfy both the thermal inertial coupling suppression and the transient hydraulic safety of the pipeline network during the same control process. Summary of the Invention

[0005] This invention provides a hot water boiler and its intelligent control method, which solves the technical problems in related technologies where multiple hot water boilers operating in parallel cause excessive oscillations in the mixing temperature of the water collector and excessive pressure at the pipe network nodes due to the intertwining of thermal inertia coupling across boilers and transient hydraulic disturbances in the pipe network, and the lack of unified and coordinated control methods.

[0006] This invention discloses a hot water boiler and its intelligent control method, including the following steps: acquiring the current output value, outlet water temperature and outlet water flow of each hot water boiler operating in parallel, the total water supply temperature and total flow of the water collector, the real-time pressure data of each key node of the pipeline network, as well as the target load value and required response time, and integrating them to form a joint initial state dataset;

[0007] For each hot water boiler, extract multi-level thermal inertia parameter vectors, calculate the real-time contribution coefficient based on the proportion of water output flow of each boiler, generate a cross-boiler temperature transfer matrix, and calculate the transmission time and attenuation coefficient of the output adjustment value of each boiler fed back to the return water end of other boilers through the water collector and return water pipeline, and generate a cross-boiler thermal inertia coupling parameter matrix.

[0008] The transient hydraulic propagation parameter set of the pipeline network is calculated using the method of characteristics and integrated with the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix into a unified parameter library;

[0009] The variable load of each boiler is parameterized into a ramp output adjustment function with the start time of variable load and the variable load rate as decision variables. The time-domain response of the mixing temperature of the water collector and the transient pressure time history curves of each key node in the pipeline network are predicted using a unified parameter library to generate joint prediction results.

[0010] The thermally coupled violation index and the hydraulic violation index are extracted from the joint prediction results. The partial derivatives of each decision variable with respect to the two types of violation indexes are calculated to generate a joint sensitivity matrix.

[0011] The joint sensitivity matrix is ​​input into the optimization solver. With the goal of minimizing the weighted sum of the two types of violation indicators after normalization, and with the constraints of satisfying the total load and response time, a coupling contribution pre-deduction correction is applied to the output adjustment of each boiler. The optimal load change start time and load change rate of each boiler are solved, and a joint control execution scheme is generated and sent to the control system of each boiler.

[0012] Furthermore, the multi-level thermal inertia parameter vector includes thermal inertia parameters at four levels sequentially passing through the furnace radiative heat transfer surface, convective heat transfer surface, economizer heat transfer surface, and boiler water body along the flue gas flow direction. Specifically, the thermal inertia parameters of the furnace radiative heat transfer surface include the heat storage capacity of the furnace water-cooled wall metal and the radiative heat transfer coefficient; the thermal inertia parameters of the convective heat transfer surface include the heat storage capacity of the convective heat-receiving surface tube bundle metal and the convective heat transfer coefficient; the thermal inertia parameters of the economizer heat transfer surface include the heat storage capacity of the economizer tube bundle metal and the feedwater heating time constant; and the thermal inertia parameters of the boiler water body include the boiler body water volume and the water body heat capacity. The feedwater heating time constant is obtained by the ratio of the economizer tube bundle metal heat storage capacity to the feedwater side heat transfer coefficient.

[0013] Furthermore, the instantaneous contribution coefficient is the ratio of the effluent mass flow rate of the i-th boiler at time t to the sum of the effluent mass flow rates of all boilers in the boiler group at that time; the cross-boiler temperature transfer matrix is ​​an N×N matrix, whose diagonal elements are the instantaneous contribution coefficients of the corresponding boilers, and whose off-diagonal elements are the product of the instantaneous contribution coefficient of the source boiler and the attenuation coefficient on the transmission path from the water collector to the return water end of the affected boiler, where N is the total number of boilers in the boiler group; the cross-boiler thermal inertia coupling parameter matrix is ​​an N×N matrix, whose elements consist of two parameters: transmission time and attenuation coefficient. The transmission time is obtained by dividing the sum of the length of the main water supply pipe, the heat exchange delay of the heat exchange station, and the length of the return water pipe by the flow velocity in the pipe. The attenuation coefficient is determined by the heat loss coefficient of each pipe segment on the transmission path and the mixing and dilution ratio.

[0014] Furthermore, the ramp output adjustment function describes the change process of the output adjustment amount of the i-th boiler over time as follows: the output remains constant before the start of the load change, and increases linearly at a constant load change rate from the start of the load change until it reaches the temporary load change target amount of the boiler and remains constant thereafter; wherein, the temporary load change target amount is determined by the total load change amount allocated to each boiler according to the initial allocation ratio, and the start of the load change and the load change rate are used as decision variables to participate in the joint optimization solution.

[0015] Furthermore, the predicted time-domain response of the water collector mixing temperature includes: the output change of each boiler is sequentially passed through a series first-order inertial time delay stage corresponding to four levels of thermal inertial parameter vectors. The output of each stage is the result of the previous stage's output after being filtered by a first-order low-pass filter using the time constant corresponding to that stage. The final output after the four stages are connected in series is the time-domain curve of the boiler's outlet water temperature change. The time-domain response of the water collector mixing temperature is obtained by weighted superposition of the outlet water temperature changes of each boiler through the cross-boiler temperature transfer matrix. The secondary temperature disturbance caused by the output adjustment action of each boiler being fed back to the return water end of other boilers through the return water loop is calculated using the transmission time and attenuation coefficient in the cross-boiler thermal inertial coupling parameter matrix. Among them, the time constant of each stage is determined by the ratio of the metal heat storage capacity to the heat transfer coefficient of that stage.

[0016] Furthermore, the extraction method for the thermal coupling violation index is as follows: identify all local maxima and local minima in the time-domain response curve of the mixing temperature of the water collector, and take the maximum value among the absolute values ​​of the differences between adjacent maxima and minima as the peak-to-peak value of the oscillation; the extraction method for the hydraulic violation index is as follows: for the transient pressure time history curves of each key node in the pipeline network, calculate the over-limit amount where the pressure exceeds the upper limit of the pressure safety of the key node or falls below the lower limit of the pressure safety of the key node, and take the maximum value of the over-limit amount of all key nodes during the entire transition period; wherein, the upper limit and lower limit of the pressure safety of each key node are determined by the pipeline network design parameters and the pressure bearing specifications of the equipment.

[0017] Furthermore, the joint sensitivity matrix is ​​a 2×2N matrix. The first row is the partial derivative of the load change start time and load change rate of each boiler with respect to the thermal coupling violation index, and the second row is the partial derivative of the load change start time and load change rate of each boiler with respect to the hydraulic violation index. Each partial derivative is obtained by applying a small perturbation to the decision variables and then re-performing the thermal and hydraulic domain prediction calculations in a differential manner. In the joint optimization solution, the thermal coupling violation index and the hydraulic violation index are respectively subjected to mean normalization based on the range. The two types of violation indices are unified to the dimensionless interval and then weighted and summed to eliminate the influence of dimensional differences on the optimization objective function.

[0018] Furthermore, the coupling contribution pre-deduction correction is as follows: In each iteration of the optimization solution, the output equivalent corresponding to the expected temperature contribution transmitted to the i-th boiler side via the water collector through the adjustment action of other boilers is subtracted from the output adjustment amount of the i-th boiler, so as to obtain the corrected effective output adjustment amount of the i-th boiler; the output equivalent is obtained by multiplying the output adjustment amount of each other boiler by the corresponding attenuation coefficient in the cross-boiler thermal inertia coupling parameter matrix, and then multiplying by the ratio of the instantaneous contribution coefficient of the source boiler at the corresponding transmission delay moment to the instantaneous contribution coefficient of the i-th boiler at the current moment, and then summing them up.

[0019] Furthermore, before generating the joint control execution plan, a joint simulation verification step is also included: simultaneously performing cross-boiler thermal inertial coupling simulation and characteristic line method transient hydraulic simulation for the optimal load change start time and load change rate of each boiler, verifying whether the peak-to-peak value of the mixing temperature oscillation in the water collector is less than the allowable threshold for temperature oscillation, and whether the transient pressure of each node in the pipeline network is between the upper and lower pressure safety limits. If there are residual violations, the decision variables of the boiler with the largest violation contribution are fine-tuned and re-verified along the gradient direction provided by the joint sensitivity matrix, iterating until the thermal domain constraints and hydraulic domain constraints are simultaneously satisfied. During the load change execution process after the control command is issued, a real-time dual-domain monitoring and online correction step is also included: synchronously collecting the actual mixing temperature of the water collector and the actual pressure data of each key node in the pipeline network at a fixed period, comparing the deviation between the actual value and the predicted value at the corresponding time in the joint prediction result, and when the deviation exceeds the corresponding threshold, calculating the deviation correction direction using the joint sensitivity matrix and fine-tuning the output adjustment parameters of the boilers that have not yet been executed in real time, while using the residual data to incrementally update the attenuation coefficient and transmission time in the cross-boiler thermal inertial coupling parameter matrix online using the least squares method.

[0020] A hot water boiler is controlled by a computer, which is used in the aforementioned intelligent control method for hot water boilers. The computer includes:

[0021] The data acquisition module is used to acquire the current output value, outlet water temperature and outlet water flow of each hot water boiler operating in parallel, the total water supply temperature and total flow of the water collector, the real-time pressure data of each key node in the pipeline network, as well as the target load value and required response time, and integrate them to form a joint initial state dataset.

[0022] The thermal inertia coupling modeling module is used to extract multi-level thermal inertia parameter vectors for each hot water boiler, calculate the real-time contribution coefficient based on the proportion of water flow from each boiler, generate a cross-boiler temperature transfer matrix, and calculate the transmission time and attenuation coefficient to generate a cross-boiler thermal inertia coupling parameter matrix.

[0023] The unified parameter library construction module is used to calculate the transient hydraulic propagation parameter set of the pipeline network using the method of characteristics, and integrate it with the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix into a unified parameter library;

[0024] The joint prediction module is used to parameterize the variable load of each boiler into a ramp output adjustment function, and use a unified parameter library to predict the time domain response of the water collector mixing temperature and the transient pressure time history curves of each key node in the pipeline network, and generate joint prediction results.

[0025] The sensitivity analysis module is used to extract thermally coupled violation indicators and hydraulic violation indicators from the joint prediction results, calculate the partial derivatives of each decision variable with respect to the two types of violation indicators, and generate a joint sensitivity matrix.

[0026] The joint optimization solution module is used to input the joint sensitivity matrix into the optimization solver. With the goal of minimizing the weighted sum of the two types of violation indicators after normalization, it applies a coupling contribution pre-deduction correction to the output adjustment of each boiler, solves the optimal load change start time and load change rate of each boiler, and generates a joint control execution scheme.

[0027] The instruction issuance module is used to issue the load change rate curves of each boiler in the joint control execution plan to each boiler control system in a time sequence, and output the output control instructions of each boiler at each time.

[0028] This invention solves the technical problem of conflicting control parameters when independently optimizing two types of constraints—thermal inertia coupling suppression and transient hydraulic safety of the pipeline network—in existing hot water boiler control methods. It addresses this by constructing a unified parameter library consisting of a cross-boiler temperature transfer matrix, a cross-boiler thermal inertia coupling parameter matrix, and a pipeline network transient hydraulic propagation parameter set. Furthermore, it achieves the technical effect of ensuring that the start time and rate of load change for each boiler simultaneously satisfy both the water collector mixing temperature oscillation suppression constraint and the pipeline network transient pressure safety constraint during the same control process. Through coupling contribution pre-deduction correction, it avoids overcompensation caused by the superposition of compensation actions when multiple boilers adjust their output simultaneously. Finally, through real-time dual-domain monitoring and online parameter correction, it continuously tracks actual response deviations and promptly corrects control parameters during the execution phase. Attached Figure Description

[0029] Figure 1 This is a flowchart of a hot water boiler and its intelligent control method provided in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram comparing the initial operating states of four boilers provided in an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the pressure distribution and safety range of key nodes in the pipeline network provided in an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of the transmission time and attenuation coefficient of the cross-boiler thermal inertial coupling parameters provided in an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram comparing the optimal load parameters of various boilers provided in the embodiments of the present invention;

[0034] Figure 6 This is a schematic diagram comparing the dual-domain violations of the initial and optimized schemes provided in this embodiment of the invention;

[0035] Figure 7 This is a schematic diagram of the joint sensitivity matrix provided in an embodiment of the present invention: the peak-to-peak sensitivity of thermal oscillation.

[0036] Figure 8 This is a schematic diagram of the real-time contribution coefficient and target load allocation of each boiler provided in an embodiment of the present invention;

[0037] Figure 9 This is a schematic diagram of the four-stage thermal inertia time constant of boiler 1 provided in an embodiment of the present invention. Detailed Implementation

[0038] In large-scale centralized heating systems, multiple hot water boilers operate in parallel and share a common water collector and main water supply pipe, delivering heat to various heat exchange stations through the pipeline network. When the heating load changes significantly, it is necessary to quickly coordinate and adjust the output of each boiler to keep the total supply water temperature after mixing in the water collector stable, while ensuring that the pressure and flow rate at each node in the pipeline network are within a safe range.

[0039] According to the embodiments of this implementation, in the control scenario of multiple hot water boilers operating in parallel with rapid load changes, there is a constraint conflict between thermal inertial coupling suppression and transient hydraulic safety of the pipeline network. Specifically, to eliminate cross-boiler thermal inertial coupling oscillations generated by each boiler through the water collector, the output adjustment actions of adjacent boilers need to be staggered and dispersed in time. However, this staggered timing may cause some boilers to be scheduled for concentrated load changes during periods when transient pressure waves have accumulated in the pipeline network, leading to water hammer risks. Conversely, when the load change rate of all boilers is strictly limited to suppress pipeline water hammer, the adjustment actions of each boiler tend to be synchronized in time, further exacerbating the thermal inertial coupling oscillations at the water collector. Existing intelligent control methods for hot water boilers typically perform independent thermal inertial compensation control on each boiler and calculate the output allocation scheme based on steady-state hydraulic assumptions, independently optimizing thermal inertial coordination and hydraulic safety. This cannot simultaneously satisfy both thermal inertial coupling suppression and transient hydraulic safety constraints of the pipeline network in the same control process.

[0040] It should be understood that the hot water boiler and its intelligent control method in this embodiment are executed in a computer system. This computer system is connected to the distributed control system of each boiler through a communication network, enabling it to acquire the operating parameters of each boiler, the water collector parameters, and the pipeline network parameters in real time, and to issue control commands to the control systems of each boiler. Pressure sensors and temperature sensors have been deployed in the pipeline network, covering key locations such as the water collector, the outlets of each boiler, the branch nodes of the pipeline network, and the inlets of each heat exchange station.

[0041] At least one embodiment of the present invention discloses a hot water boiler and its intelligent control method, such as Figure 1 As shown, it includes the following steps:

[0042] Step 1: Obtain the joint initial state dataset for the rapid load change scenario of hot water boiler group;

[0043] The system acquires the current output, outlet water temperature, and outlet flow rate of each hot water boiler operating in parallel; the total supply water temperature and flow rate after mixing in the water collector; real-time pressure data of key nodes in the pipeline network; and the target load value and required response time in the total load change command. This data is then integrated to form a joint initial state dataset for a rapid load change scenario of the hot water boiler group. Before integration, Z-score standardization is performed on the various sensor data in the joint initial state dataset to eliminate the influence of differences in the dimensions of different physical quantities on subsequent parameter calculations.

[0044] It should be noted that the aforementioned key nodes refer to nodes in the pipeline network topology that are sensitive to transient hydraulic responses, including the outlet of the water collector, the branch points of the main pipeline, the points where the pipe diameter changes abruptly, and the inlets of each heat exchange station. These locations are prone to pressure wave reflection and superposition when the pipeline network experiences rapid flow changes.

[0045] It should be noted that the above total load change command is a command issued by the heating dispatch system based on changes in outdoor temperature, user heat load forecasts, or operation optimization strategies. The target load value represents the total heat power output that the boiler group needs to achieve, and the required response time represents the allowable time from receiving the command to completing the load adjustment.

[0046] Step 2: Generate the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix;

[0047] For each hot water boiler, thermal inertia parameter vectors at four levels—radial heat transfer surface, convective heat transfer surface, economizer heat transfer surface, and boiler water—are extracted. A heat conservation model is performed on the mixing process of the water collector. Based on the proportion of each boiler's outlet water flow, the instantaneous contribution coefficient of each boiler's outlet water temperature change to the water collector's mixing temperature is calculated, generating a cross-boiler temperature transfer matrix. Furthermore, the transmission time and attenuation coefficient of each boiler's unit output adjustment fed back to the return water ends of other boilers via the water collector and return water pipeline are calculated, generating a cross-boiler thermal inertia coupling parameter matrix.

[0048] It should be noted that the aforementioned four-level thermal inertia parameter vectors refer to parameters extracted for each boiler through the four heat exchange stages sequentially along the flue gas flow direction. Specifically, the thermal inertia parameters for the furnace radiant heat exchange surface include the heat storage capacity of the furnace water-cooled wall metal and the radiative heat transfer coefficient; the thermal inertia parameters for the convective heat exchange surface include the heat storage capacity of the convective heat-receiving surface tube bundle metal and the convective heat transfer coefficient; the thermal inertia parameters for the economizer heat exchange surface include the heat storage capacity of the economizer tube bundle metal and the feedwater heating time constant; and the thermal inertia parameters for the boiler water body include the boiler body water volume and the water body heat capacity. The four-level thermal inertia parameter vectors for each boiler reflect the multi-stage thermal response time delay characteristics of the boiler from changes in combustion-side output to changes in outlet water temperature. The heat storage capacity of the furnace water-cooled wall metal, the heat storage capacity of the convective heating surface tube bundle metal, and the heat storage capacity of the economizer tube bundle metal are all obtained by multiplying the metal mass of the corresponding heat exchange surface by the specific heat capacity of the material. The radiative heat transfer coefficient and the convective heat transfer coefficient are obtained from boiler design data or online thermal tests. The boiler body water volume is determined by boiler structural parameters. The water heat capacity is obtained by multiplying the water volume by the density and specific heat capacity of water. The feedwater heating time constant is obtained by the ratio of the heat storage capacity of the economizer tube bundle metal to the feedwater side heat transfer coefficient. All of the above thermal inertia parameters can be obtained from boiler design documents, factory inspection reports, or online thermal tests and stored as known parameters in the joint initial state dataset.

[0049] It should be noted that the above-mentioned instantaneous contribution coefficient is calculated based on the heat conservation equation of the water collector. When the boiler group includes When using a boiler, the mixing temperature of the water collector is... satisfy:

[0050]

[0051] in, For the first The boiler is at all times The outlet water temperature, For the first The boiler is at all times The instantaneous contribution coefficient, This represents the total number of boilers in the boiler group. Number the boiler. The instantaneous contribution coefficient is calculated as follows:

[0052]

[0053] in, For the first The boiler is at all times effluent mass flow rate, The boiler numbers are used for summation and iteration. The above instantaneous contribution coefficients... The ratio of dimensionless mass flow rates is the ratio of the mass flow rates of the same dimension, and the value is dimensionless and can be directly correlated with temperature. Multiplication is performed using a weighted summation operation. (Boiler temperature transfer matrix) for Matrix, elements of the temperature transfer matrix across boilers Indicates the first The effect of boiler outlet water temperature change on the first The degree of temperature impact felt by the boiler after mixing through the water collector, among which... Number the affected boilers. Diagonal elements. Off-diagonal elements ( ) indicates the first The effect of changes in the boiler outlet water temperature on the overall mixing temperature after mixing via the water collector is transmitted to the first stage through the return water pipeline. Temperature disturbance component on the return water side of the boiler.

[0054] It should be noted that the above-mentioned cross-boiler thermal inertia coupling parameter matrix for Matrix, elements of the cross-boiler thermal inertia coupling parameter matrix Transmission time and attenuation coefficient It consists of two parameters: transmission time. Indicates the first Temperature disturbances caused by changes in boiler output are mixed by the water collector and transmitted to the next stage via the main water supply pipe, heat exchange station, and return water pipeline. The time required for the boiler return water inlet, transmission time The value is obtained by dividing the sum of the length of the main supply pipe, the heat exchange time delay of the heat exchange station, and the length of the return pipe by the flow velocity in the pipe. The length and velocity of each pipe segment can be obtained from the pipe network topology parameters and the current flow measurement value. Attenuation coefficient This represents the attenuation ratio of the temperature disturbance along the transmission path due to heat dissipation and mixing / dilution in the piping, and is the attenuation coefficient. The range of values ​​is to The smaller the value, the greater the attenuation during the propagation process; the attenuation coefficient. The value is determined by the heat loss coefficient of each pipe segment along the transmission path and the mixing and dilution ratio. The heat loss coefficient is calculated from the pipe segment insulation parameters and the ambient temperature difference, while the mixing and dilution ratio is determined by the flow rate ratio at each confluence node. All of these parameters can be obtained from the pipeline network design data and current flow rate measurements. When hour, Indicates the first The output change of the boiler is fed back to its own return water end via the return water loop as a self-coupling parameter.

[0055] Furthermore, the aforementioned off-diagonal elements ( The physical meaning of ) is: the first When the outlet water temperature of the boiler changes by a unit, the mixed temperature disturbance component formed after mixing according to the flow ratio through the water collector is then transmitted through the main water supply pipe, heat exchange station, and return water pipeline to the next point. The ratio of the temperature effect at the boiler return water inlet to the original unit change. Off-diagonal elements. The calculation needs to be combined with the real-time contribution coefficient. Attenuation coefficient during transmission through return water pipeline ,Right now The instantaneous contribution coefficient Reflecting the effect of the water collector mixing process on the first Weighting and attenuation coefficient of boiler temperature disturbance This disturbance is reflected in the flow from the water collector to the first... Amplitude attenuation on the transmission path of the boiler return water end.

[0056] Step 3: Generate a set of transient hydraulic propagation parameters for the pipe network and construct a unified parameter library;

[0057] Based on the pipeline network topology and pipe segment parameters, the pressure wave propagation velocity of each pipe segment and the pressure wave arrival time from each boiler outlet to each key node of the pipeline network are calculated using the method of characteristics, generating a set of transient hydraulic propagation parameters for the pipeline network. This set of transient hydraulic propagation parameters is then combined with the cross-boiler temperature transfer matrix. and the thermal inertia coupling parameter matrix across boilers The system integrates these parameters to form a unified parameter library for the control of hot water boiler groups.

[0058] It should be noted that the method of characteristics is a conventional numerical method in transient hydraulic analysis of heating pipe networks. The inputs to the method of characteristics are the pipe network topology (including pipe segment connections, pipe segment lengths, pipe diameters, and pipe wall thicknesses), pipe segment parameters (including the elastic modulus of the pipe material, pipe wall roughness, and the bulk elastic modulus of water), and the boundary conditions of each node (including the boiler outlet flow rate and the heat exchange station inlet pressure). The output of the method of characteristics is the pressure wave propagation velocity within each pipe segment. and the pressure wave arrival time along the pipeline path from each boiler outlet to each critical node. subscript Indicates the pipe section number, subscript Indicates the boiler number, subscript Indicates the key node number.

[0059] It should be noted that the aforementioned set of transient hydraulic propagation parameters for the pipe network also includes pressure wave reflection coefficients and superposition coefficients for each pipe segment under different flow rate change rates. These are used to describe the reflection and superposition characteristics of pressure waves at pipe network branches and abrupt diameter changes. The pressure wave reflection coefficient and superposition coefficient are determined by the ratio of the characteristic impedances of the pipe segments on both sides of the branch or abrupt diameter change. The characteristic impedance is obtained by the reciprocal of the product of the pipe segment cross-sectional area and the pressure wave propagation velocity. The cross-sectional area of ​​the pipe segment and the pressure wave propagation velocity have been calculated using the method of characteristics and can be directly used for calculating the pressure wave reflection coefficient and superposition coefficient.

[0060] In this embodiment of the application, the unified parameter library will transfer the temperature across the boiler temperature transfer matrix. cross-boiler thermal inertia coupling parameter matrix The transient hydraulic propagation parameter set of the pipeline network is stored and indexed in the same data structure. The unified parameter library provides a synchronous access interface for thermal domain parameters and hydraulic domain parameters for subsequent joint prediction and joint optimization solutions, enabling thermal inertial coupling response calculation and transient hydraulic response calculation to share parameters in the same solution process, avoiding timing inconsistencies caused by the separate calls of the two types of parameters in independent optimization.

[0061] Step 4: Generate joint prediction results including the temperature oscillation amplitude of the water collector and the extreme pressure values ​​of the pipeline nodes;

[0062] The total load change is temporarily allocated to each boiler according to the initial distribution ratio. The temporary load change of each boiler is parameterized as a ramp output adjustment function with the start time and rate of load change as decision variables. The cross-boiler thermal inertia coupling parameter matrix is ​​then utilized. and cross-boiler temperature transfer matrix The system predicts the time-domain response of the mixing temperature in the water collector when each boiler adjusts its output according to the ramp output adjustment function. Simultaneously, it uses the method of characteristics to predict the transient pressure time-history curves of key nodes in the pipeline network during the transition period. By integrating the water collector temperature time-domain response and the pipeline network transient pressure time-history curves, a joint prediction result is generated, incorporating the water collector temperature oscillation amplitude and the extreme pressure values ​​of the pipeline network nodes.

[0063] It should be noted that the specific form of the above-mentioned slope output adjustment function is as follows: for the first... The output adjustment amount described by the ramp output adjustment function of the boiler. Represented as:

[0064]

[0065] in, For the first The start time of load change of the boiler. For the first The rate of change of load of a boiler (the change in output per unit time). For the first The target amount for temporary load shifting of the boiler. The variable is time. The start time of the variable load. and variable load rate These serve as decision variables in the subsequent joint optimization solution.

[0066] In the above slope output adjustment function, A medium-load rate With time difference The product dimension and The dimensions are consistent, all being units of thermal power. The dimensions of each piecewise expression are uniform, allowing for direct comparison and calculation.

[0067] Furthermore, the above-mentioned ramp output adjustment function describes the first... The boiler throughout the transition period Internal output adjustment over time The complete change process: at the start of the load change Previously, the output remained unchanged, starting from the moment of load change. Start at a constant rate Linear increase, up to time... When the target variable load is reached The output remains unchanged afterward, thus fully parameterizing the time-dimensional change in output as a decision variable, specifically the load initiation time. and variable load rate The function enables subsequent joint optimization solutions to control the starting position and duration slope of each boiler's output adjustment action on the time axis by adjusting these two decision variables.

[0068] It should be noted that, in the above-mentioned prediction of the mixing temperature time-domain response of the water collector, the output change of each boiler is first calculated through multi-level time delay response calculation corresponding to the four-level thermal inertia parameter vectors of that boiler to obtain the time-domain curve of the boiler's outlet water temperature change, and then through the cross-boiler temperature transfer matrix. By weighted superposition of the outlet water temperature changes of each boiler, the time-domain response of the mixing temperature of the water collector is obtained, and further obtained by cross-boiler thermal inertia coupling parameter matrix. Transmission time and attenuation coefficient Calculate the secondary temperature disturbances caused by the output adjustment actions of each boiler being fed back to the return water end of other boilers via the return water loop.

[0069] It should be noted that the specific method for calculating the above multi-level time delay response is as follows: for the first... Boiler output adjustment The thermal inertia successively passes through four levels of heat transfer elements: the furnace radiant heat transfer surface, the convective heat transfer surface, the economizer heat transfer surface, and the boiler water. Each level corresponds to a first-order inertial time delay element. The output of a level is the output of the previous level after a time constant. The result after first-order low-pass filtering, where For thermal inertia level numbering, time constant The value is determined by the ratio of the metal's heat storage capacity to its heat transfer coefficient at that level. The final output of the four levels connected in series is the [missing value]. Time-domain curve of boiler outlet water temperature variation This reflects the multi-stage thermal response time delay characteristics between changes in combustion-side output and changes in outlet water temperature. It should be noted that the above time constant... It is obtained by the ratio of the metal's heat storage capacity (in joules per Kelvin) to the heat transfer coefficient (in watts per Kelvin). The dimension is time, which is consistent with the dimension of the time constant of the first-order inertial time delay element, and can be directly used for time delay response calculation.

[0070] Furthermore, when using the method of characteristics to predict the transient pressure time history curves of key nodes in the pipeline network, the output change time series of each boiler determined by the ramp output adjustment function is used as input. The flow change process corresponding to each boiler outlet is converted into the boundary conditions of the method of characteristics. The calculation is advanced step by step along the characteristic line direction of each pipe segment in the pipeline network. At each time step, the pressure and flow are solved based on the compatibility equations at both ends of the pipe segment, thereby obtaining the transition period. Transient pressure time history curves of key internal nodes ,in Number the critical nodes. It covers the entire time range from the start of load change to the completion of output adjustment for all boilers. The change in outlet flow rate of each boiler is calculated by converting the output adjustment amount using boiler hydraulic characteristic parameters (including the ratio of boiler rated flow rate to rated output). This conversion factor is obtained from the boiler design data and stored in the joint initial state dataset.

[0071] Step 5: Perform a two-domain constraint test on the joint prediction results and generate a joint sensitivity matrix;

[0072] The joint prediction results were subjected to a dual-domain constraint test. In the thermal domain, the peak-to-peak value of the oscillation in the time-domain response of the water collector temperature was extracted as a thermal coupling violation index. In the hydraulic domain, the amount of transient pressure exceeding the upper safety limit or falling below the lower safety limit at key nodes of the pipeline network is extracted as a hydraulic violation indicator. Calculate the start time of the load change for each boiler. and variable load rate The joint sensitivity matrix is ​​generated by taking the partial derivatives of the two types of violation indicators. .

[0073] It should be noted that the above-mentioned thermal coupling violation indicators The calculation method is as follows: based on the predicted mixing temperature time-domain response curve of the water collector. In the process, all local maxima and local minima are identified, and the maximum difference between adjacent maxima and minima is taken as the peak-to-peak value of the oscillation, i.e.:

[0074]

[0075] in, The sequence number of the adjacent peak-valley pair. and The first For the times corresponding to adjacent peaks and valleys, and These are the temperature values ​​at the corresponding moments in the time-domain response curve of the water collector's mixing temperature; both are in the transition period. Extracted from the time-domain response curve of the mixing temperature of the water collector inside. The aforementioned thermal coupling violation indicators... It represents the difference in temperature at different times for the same physical quantity, with the dimension being temperature units, and the calculation is reasonable.

[0076] It should be noted that the above-mentioned hydraulic violation indicators The calculation method is as follows: for the first in the pipeline network Key nodes, transient pressure time history curves during the transition period In the middle, the computational pressure exceeded the safe limit. or below the safety lower limit If the limit is exceeded, take the maximum value of the exceeded limits of all critical nodes, that is:

[0077]

[0078] in, Number the critical nodes. For the first Pressure safety limit of each key node For the first The lower limit and upper limit of pressure safety for each key node. and lower limit of pressure safety All parameters are determined by the pipeline design parameters and equipment pressure specifications, and are read from the joint initial state dataset as known parameters before the control calculation. The first one predicted by the characteristic line method in step 4 Key nodes during the transition period Transient pressure time history curve within, These are time variables during the transition period. The aforementioned hydraulic violation indicators... All terms in the formula represent pressure quantities, have consistent dimensions, and are calculated reasonably.

[0079] It should be noted that the above joint sensitivity matrix for Matrix, joint sensitivity matrix The structure is as follows:

[0080]

[0081] The first row represents the thermally coupled sensitivity components, indicating the degree of influence of changes in each decision variable on the temperature oscillation amplitude of the water collector; the second row represents the hydraulic sensitivity components, indicating the degree of influence of changes in each decision variable on the pressure limit of the pipeline network. It should be noted that the joint sensitivity matrix... The dimensions of each element in the first row are temperature units divided by the corresponding decision variable units, while the dimensions of each element in the second row are pressure units divided by the corresponding decision variable units. Since the dimensions of the elements in the two rows are different, this will be addressed in the subsequent joint optimization solution by considering the thermal coupling violation index. and hydraulic violation indicators Both types of violation indicators are uniformly normalized to a dimensionless value by performing mean normalization based on the range. The intervals are then weighted and summed to eliminate the influence of dimensional differences on the objective function. Each partial derivative is obtained by applying a small perturbation to the parameters of the slope output adjustment function and then re-performing the thermal and hydraulic domain predictions, using a differential method.

[0082] In the embodiments of this application, the joint sensitivity matrix The sensitivity information of thermal domain constraints and hydraulic domain constraints for the same set of decision variables is compiled into a single matrix. This is achieved through a joint sensitivity matrix. It can directly identify which boiler load parameter adjustments affect the two types of constraints in the same or opposite directions, thus providing gradient direction information for subsequent joint optimization solutions. This allows the optimizer to simultaneously consider the satisfaction of the two types of constraints in a single iteration, rather than satisfying them alternately.

[0083] Step 6: Perform joint optimization to generate the optimal load change start time and load change rate curves for each boiler;

[0084] Joint sensitivity matrix The input sequence quadratic programming solver is used to calculate the normalized oscillation amplitude of the water collector mixing temperature. Minimize and exceed pipeline pressure limits Minimization is a dual objective, with the total load change and response time being hard constraints, and the start time of each boiler's load change being the limiting factor. and variable load rate For the decision variables, joint optimization is performed. During the solution process, a coupling contribution pre-deduction correction is applied to the output adjustment of each boiler, generating the optimal load change start time and load change rate curves for each boiler under the dual constraints of thermal coupling and hydraulic safety.

[0085] It should be noted that sequential quadratic programming is a conventional optimization algorithm, and the input to sequential quadratic programming is the joint sensitivity matrix. Given the provided gradient information, the bi-objective weighted objective function, and the constraints on each decision variable, the output of the sequential quadratic programming is the optimal load change start time for each boiler. and variable load rate The optimization objective and constraints are in the following form:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] in, and These are the weighting coefficients for thermal domain targets and hydraulic domain targets, respectively. and These are the thermal coupling violation index and the hydraulic violation index after mean normalization based on the range, respectively. This represents the total load change. To meet response time requirements, and The first The lower and upper limits of the load change rate of the boiler. This represents the total number of boilers in the boiler group. The boiler is numbered, with the lower limit of the load variation rate included. The upper limit of the load change rate is determined by the minimum stable regulation rate of the boiler combustion regulation system. The lower limit of the load change rate is determined jointly by the boiler thermal stress safety limit and the maximum response rate of the combustion equipment. and upper limit of variable load rate All parameters are read as known parameters from the joint initial state dataset. The sequential quadratic programming solver utilizes the joint sensitivity matrix in each iteration. The provided gradient information is used to construct a quadratic subproblem for solving, gradually approaching the optimal solution.

[0092] Furthermore, the above objective function In the normalized thermal coupling violation index and normalized hydraulic violation indicators All are scalar indices obtained by extracting the time-domain response results predicted in step 4, including the normalized thermal coupling violation index. Time-domain response curve of water collector mixing temperature During the transition period The peak-to-peak value of the oscillation within the range is normalized to obtain the normalized hydraulic violation index. Transient pressure time history curves of key nodes in the pipeline network During the transition period The maximum internal pressure is obtained by normalization of the limit, and both implicitly depend on the decision variable at the start time of the load. and variable load rate The determined output adjustment sequence ensures that the objective function fully reflects the coordination effect of the load change actions of each boiler in the time dimension.

[0093] It should be noted that the aforementioned coupling contribution pre-deduction correction refers to adjusting the coefficient of performance in each iteration of the optimization solution. The output adjustment of each boiler is corrected. Specifically, when multiple boilers adjust their output simultaneously, the first... The adjustment action of the boiler is transmitted through the water collector coupling at the first... The boiler generates a temperature contribution to the control target. The coupling contribution pre-subtraction correction is applied from the first... The output adjustment of the boiler is deducted from the adjustment actions of other boilers and transmitted to the first boiler via the water collector. The output equivalent corresponding to the expected temperature contribution on the boiler side, i.e., the first... The effective output adjustment amount of the boiler after correction for:

[0094]

[0095] in, and These are the cross-boiler thermal inertia coupling parameter matrices. The Middle The boiler is for the first The attenuation coefficient and transmission time of the boiler. For the first The boiler is at all times The instantaneous contribution coefficient, For the first The boiler is at all times The instantaneous contribution coefficient, To sum and iterate through the other boiler numbers, the contribution coefficient is calculated instantly. and instantaneous contribution coefficient All from the cross-boiler temperature transfer matrix In the above correction formula, the attenuation coefficient... The dimensionless attenuation coefficient is and Same dimensions, instantaneous contribution coefficient With real-time contribution coefficient All are dimensionless flow ratios, so the dimensions of the correction term and the quantity being corrected are consistent, allowing for direct subtraction. The pre-deduction correction for coupling contribution ensures that the actual output adjustment of each boiler already takes into account the coupling contribution of other boilers, avoiding overcompensation or resonance amplification caused by the superposition of compensation actions from multiple boilers.

[0096] Furthermore, the aforementioned weighting coefficients and The determination method is as follows: Before the control calculation begins, assess the relative priority of thermal risk and hydraulic risk based on the current operating status of the heating system. When the current pressure margin of the pipeline network is small, appropriately increase the weighting coefficient. When historical data on water collector temperature fluctuations indicate a high risk of coupled oscillations, the weighting coefficient should be appropriately increased. Weighting coefficient and weighting coefficients The sum satisfies The specific values ​​are set as configurable parameters by the operators before the control calculation based on the system status and stored in the joint initial state dataset.

[0097] In this embodiment, to improve the reliability of the joint optimization solution, after obtaining the optimal load change start time and load change rate curves for each boiler in step 6, the following joint simulation verification step is also included: Simultaneously perform cross-boiler thermal-inertial coupling simulation and characteristic line method transient hydraulic simulation on the optimal load change start time and load change rate curves for each boiler to obtain the time-domain response of the water collector mixing temperature and the transient pressure time history curves of each key node in the pipeline network during the verification stage. Then, verify whether the peak-to-peak value of the water collector mixing temperature oscillation is less than the allowable temperature oscillation threshold, and whether the transient pressure of each node in the pipeline network is between the upper and lower pressure safety limits. The allowable temperature oscillation threshold is the upper limit of the water collector outlet temperature fluctuation specified in the heating system operation regulations, and the upper and lower pressure safety limits are the same as those in step 5. and The values ​​are consistent and are all known parameters. If the verification passes, the optimal load change start time and load change rate curve for each boiler will be determined as the execution scheme for the hot water boiler and its intelligent control. If residual violations exist, the joint sensitivity matrix will be used. The provided gradient direction is used to fine-tune the load change start time and load change rate of the boiler that contributes the most to the violation, and the joint simulation verification is re-executed. The process is iterated until the thermal domain constraints and hydraulic domain constraints are satisfied at the same time, and a verified hot water boiler and its intelligent control execution scheme are generated.

[0098] Step 7: Issue the hot water boiler and its intelligent control implementation plan and output control commands;

[0099] The load change rate curves of each boiler in the hot water boiler and its intelligent control execution scheme are sent to the boiler control system in time sequence, and the output control commands of each boiler at each time are output.

[0100] In this embodiment, to ensure the dynamic safety of the control process, the load change execution process after the control command is issued in step 7 also includes the following real-time dual-domain monitoring and online correction steps: The actual mixing temperature of the water collector and the actual pressure data of each key node in the pipeline network are collected synchronously at fixed intervals, and the actual values ​​are compared with the predicted values ​​at the corresponding time in the joint prediction results. When the mixing temperature deviation of the water collector exceeds the temperature deviation threshold or the pressure deviation of any key node in the pipeline network exceeds the pressure deviation threshold, the joint sensitivity matrix is ​​used... The deviation correction direction is calculated, and the output adjustment parameters of boilers that have not yet been fully executed are fine-tuned in real time. Corrected control commands are generated and sent to the corresponding boiler control systems. The temperature deviation threshold and pressure deviation threshold are the preset upper limits of allowable deviation for the water collector mixing temperature and the upper limits of allowable deviation for the pipeline node pressure, respectively. These thresholds are determined based on the heating system operating procedures and sensor measurement accuracy, and are configured as known parameters in the computer system before the control calculation. Simultaneously, the residual data between the collected actual and predicted values ​​is used to correct the cross-boiler thermal inertia coupling parameter matrix online. attenuation coefficient in and transmission time This makes the prediction calculations for subsequent cycles closer to the actual system response.

[0101] In the embodiments of this application, the above-mentioned online correction of the cross-boiler thermal inertia coupling parameter matrix The intermediate parameter method is as follows: In each acquisition cycle, the residual between the actual mixing temperature and the predicted mixing temperature of the water collector is calculated. Based on the known timing of the current output adjustment actions of each boiler, the least squares method is used to calculate the thermal inertia coupling parameter matrix across boilers. attenuation coefficient in and transmission time Incremental updates are performed, with the update magnitude limited by the preset upper limit of the parameter correction step size, to avoid large jumps in parameters due to measurement noise in a single acquisition cycle.

[0102] The hot water boiler and its intelligent control method provided in this embodiment address the constraint conflict between thermal inertia coupling suppression and transient hydraulic safety of the pipeline network when multiple hot water boilers are connected in parallel and undergo rapid load changes. The following technical means are used to achieve the coordinated satisfaction of the two types of constraints.

[0103] First, by extracting multi-level thermal inertia parameter vectors from each boiler and establishing a heat conservation model for the water collector, a cross-boiler temperature transfer matrix and a cross-boiler thermal inertia coupling parameter matrix are generated. This explicitly quantifies the temperature coupling effect generated by the output adjustment actions of each boiler through the water collector and the secondary disturbance characteristics fed back through the return water loop. Simultaneously, the transient hydraulic propagation parameters of the pipe network are calculated using the method of characteristics, describing the propagation time and superposition characteristics of pressure waves induced by rapid load changes in the pipe network. These two types of parameters are integrated into a unified parameter library, enabling the calculation of thermal domain response and hydraulic domain response to be performed on the same data basis.

[0104] Secondly, through the joint sensitivity matrix The influence of each boiler decision variable on thermally coupled violation indicators and hydraulic violation indicators is uniformly arranged. After normalizing the two types of violation indicators to eliminate dimensional differences, they are input into a sequential quadratic programming solver for joint optimization. This ensures that the start time and rate of load change of each boiler are simultaneously constrained by the water collector mixing temperature oscillation suppression constraint and the pipeline transient pressure safety constraint during the same solution process. Therefore, it overcomes the problem of conflicting control parameters generated by the two types of constraints when they are independently optimized in existing methods.

[0105] Furthermore, the coupling contribution pre-deduction correction subtracts the output equivalent corresponding to the expected temperature contribution transmitted through the water collector from the output adjustment of each boiler. Therefore, when multiple boilers adjust their output synchronously, the compensation actions of each boiler will not result in superimposed overcompensation or resonance amplification due to the failure to consider the coupling transmission between them. The joint simulation verification simultaneously validated the optimization solution in the thermal and hydraulic domains. Real-time dual-domain monitoring and online parameter correction continuously tracked the deviation between the actual response and the predicted value during the execution phase and promptly corrected the control parameters and the parameters of the cross-boiler thermal inertia coupling parameter matrix, thus ensuring the dynamic safety of the control process under actual operating condition disturbances.

[0106] A centralized heating station in a northern city (code: Heat Source Station GH-07) is equipped with four parallel hot water boilers (numbered: Boiler 1 to Boiler 4), each with a rated thermal power of 12 MW. They share a common water collector and supply heat to six heat exchange stations via a main water supply pipe. On a certain day in December 20XX at 06:15, the heating dispatch system issued a rapid load increase command due to a sudden drop in outdoor temperature (from -8℃ to -15℃): requiring the total thermal power of the boiler group to increase from 32 MW to 44 MW within 300 seconds, i.e., a total load increase of 12 MW. Currently, the water flow rates of the boilers are uneven, and the outlet pressure margin of the water collector is at a moderate level. Historical data shows that this station has experienced excessive temperature oscillations in the water collector during similar load increase operations; therefore, the heat domain weight is appropriately high.

[0107] like Figures 2-9 As shown, the method in this embodiment operates as follows:

[0108] During the stage of acquiring the joint initial state dataset, the control computer system synchronously collects the current operating status of the four boilers, water collector parameters, pressure of key nodes in the pipeline network, and dispatch command parameters from various sensors and the dispatch system, and integrates them into a joint initial state dataset. After the data acquisition is completed, the data from each sensor is Z-score standardized to eliminate the differences in the dimensions of different physical quantities such as temperature (°C), pressure (MPa), and flow rate (kg / s).

[0109] Table 1 Joint Initial State Dataset (Raw Collected Values):

[0110]

[0111] The pipeline network has six key nodes (nodes M1 to M6). M1 is the outlet of the water collector; M2 and M3 are branches of the main pipeline; M4 is a point of abrupt change in pipe diameter; and M5 and M6 are the inlets of the heat exchange stations. The current pressure and safety range of each node are as follows:

[0112] Table 2 Initial Pressure and Safety Range of Key Nodes in the Pipeline Network:

[0113]

[0114] In the stage of generating the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix, the instantaneous contribution coefficient is first calculated based on the current outflow mass flow rate of each boiler. The total flow rate is 372.0 kg / s, and the contribution coefficients of each boiler are as follows:

[0115]

[0116] Subsequently, the attenuation coefficient is calculated by combining the pipeline topology and pipeline heat dissipation parameters, and the elements of the cross-boiler temperature transfer matrix are calculated according to the off-diagonal element formula. The diagonal elements are equal to the corresponding contribution coefficients, forming a 4×4 cross-boiler temperature transfer matrix.

[0117] Taking the temperature transfer element from boiler 1 to boiler 2 as an example, the attenuation coefficient is determined to be 0.71 based on pipeline heat dissipation and mixing dilution. Therefore:

[0118]

[0119] Each element in the cross-boiler thermal inertia coupling parameter matrix consists of the transmission time (s) and the attenuation coefficient. Some key elements are as follows:

[0120] Table 3 Key elements of the cross-boiler thermal inertia coupling parameter matrix:

[0121]

[0122] In the stage of generating the transient hydraulic propagation parameter set of the pipeline network, based on the pipeline network topology (a total of 8 main pipe segments, numbered S1 to S8) and the material parameters of the pipe segments, the pressure wave propagation velocity of each pipe segment and the pressure wave arrival time from each boiler outlet to each key node are calculated using the method of characteristics.

[0123] Taking the pressure wave arrival time from boiler 1 outlet to node M4 as an example, this path passes through pipe segments S1, S2, and S4, with lengths of 180 m, 240 m, and 150 m respectively. The corresponding pressure wave propagation velocities are 1180 m / s, 1150 m / s, and 1200 m / s respectively. Therefore:

[0124]

[0125] After integrating the transient hydraulic propagation parameter set of the pipeline network with the cross-boiler temperature transfer matrix and thermal inertia coupling parameter matrix, the unified parameter library contains all parameters in the thermal and hydraulic domains, providing a synchronous access interface for subsequent joint prediction.

[0126] During the joint prediction phase, the total load increase of 12 MW was temporarily allocated to four boilers according to the initial output ratio: Boiler 1 was temporarily allocated 3.19 MW, Boiler 2 3.00 MW, Boiler 3 2.93 MW, and Boiler 4 2.88 MW. Each boiler was assigned the same load change start time (0 s) and the same load change rate (2.4 MW / min) as the initial scheme for the ramp output adjustment function.

[0127] Taking boiler 1 as an example, the ramp output adjustment function is described as follows: starting from 0 s, the output increases linearly at 2.4 MW / min, reaching the target load increase of 3.19 MW at 79.8 s and then remaining constant. The completion time is calculated as follows:

[0128]

[0129] Multi-level time delay response calculations were performed using four-level thermal inertia parameters. The time constants for each level of boiler 1 are as follows:

[0130] Table 4. Time constants of boiler 1 thermal inertia level:

[0131]

[0132] Under the initial synchronous load change scheme, the output adjustment actions of the four boilers are completely synchronized. The time domain response of the mixing temperature of the water collector shows obvious oscillation between approximately 85 s and 220 s, with a predicted peak value of 2.8℃. At the same time, due to the synchronous and significant increase in the flow rate of the four boilers, the transient pressure at node M4 shows a superimposed peak value at approximately 1.2 s, with a predicted maximum over-limit of 0.038 MPa (exceeding the safe pressure limit of node M4 by 0.85 MPa).

[0133] In the dual-domain constraint test and joint sensitivity matrix generation stage, small perturbations are applied to each decision variable (the perturbation amount at the start of the load change is 5 s, and the perturbation amount at the load change rate is 0.1 MW / min). The partial derivatives are calculated by the difference method to form a 2×8 joint sensitivity matrix.

[0134] Taking the partial derivative of the peak-to-peak value of the thermal oscillation with respect to the start time of the load change of boiler 1 as an example: if the start time of the load change of boiler 1 is delayed by 5 seconds and the thermal prediction is re-executed, the peak-to-peak value of the oscillation decreases from 2.8℃ to 2.63℃, then:

[0135]

[0136] The elements of the joint sensitivity matrix are as follows:

[0137] Table 5 Joint Sensitivity Matrix:

[0138]

[0139] The first row of the matrix shows that delaying the start time of load changes for each boiler (negative partial derivative) can reduce thermally coupled oscillations, while increasing the load change rate (positive partial derivative) will exacerbate the oscillations. The second row shows that delaying the start time also helps alleviate pressure overruns. The two types of constraints respond in the same direction to the adjustment of the start time, but their responses to the load change rate are both positive. Therefore, the two types of constraints cooperate in reducing the rate and there is no conflict. After normalization, both the thermal domain violation and the hydraulic domain violation are mapped to the interval [0, 1], eliminating dimensional differences.

[0140] In the joint optimization solution stage, the sequential quadratic programming solver constructs a weighted objective function with a thermal domain weight of 0.65 and a hydraulic domain weight of 0.35. It iteratively solves the problem under hard constraints (total load increase of 12 MW, response time of 300 s, and load change rate of each boiler between 0.8 and 3.6 MW / min), and applies a coupling contribution pre-deduction correction to the output adjustment of each boiler in each iteration.

[0141] Taking the correction of boiler 2 at time 60 s as an example (at this time, boiler 1 has started to change load for 35 s, and the load increase of boiler 1 at time 25 s is 1.0 MW):

[0142]

[0143] Since 60−190<0, this term is zero, and the correction only takes effect within the transmission delay coverage area, reflecting the physical characteristics of delay coupling.

[0144] After 12 iterations, the solver converged, and the optimal variable load parameters for each boiler are as follows:

[0145] Table 6. Results of the joint optimization solution:

[0146]

[0147] Boiler 1 starts first, and Boiler 3 starts last. The start times of the load changes of each boiler are staggered and dispersed, which avoids the synchronous superposition of the flow changes of the four boilers in the pipeline network. At the same time, the load change rate is controlled within a safe range, and the latest completion time of 145.6 s meets the 300 s response time requirement.

[0148] The joint simulation verification results show that the peak-to-peak value of the mixing temperature oscillation of the water collector in the optimized scheme is reduced to 0.7℃, the maximum pressure of node M4 is 0.831 MPa, which is lower than the safety limit of 0.85 MPa, the pressure over-limit in the hydraulic domain is 0 (no over-limit), and the constraints in the thermal domain and the hydraulic domain are both verified, confirming it as a joint control execution scheme.

[0149] During the control command issuance and real-time monitoring phase, the control computer synchronously monitors the mixing temperature of the water collector and the pressure of six key nodes with a fixed acquisition cycle of 30 seconds. In the 90-second acquisition cycle, the actual mixing temperature of the water collector is 0.4℃ higher than the predicted value (exceeding the temperature deviation threshold of 0.3℃). The system uses the joint sensitivity matrix to calculate the correction direction, fine-tuning the load change rate of boiler 3 from 2.6 MW / min to 2.45 MW / min, and generating a corrected control command to be issued to the boiler 3 control system. Simultaneously, the thermal inertia coupling parameter matrix is ​​incrementally updated using the least squares method based on the residual data of this cycle, correcting the attenuation coefficient of the path from boiler 1 to boiler 3 from 0.68 to 0.71, making the predictions for subsequent cycles closer to the actual response.

[0150] The data flow throughout the implementation process reflects a complete logical chain: the flow data collected in step 1 (Table 1) directly drives the calculation of the real-time contribution coefficient in step 2, thereby generating the temperature transfer matrix and the thermal inertia coupling parameter matrix (Table 3); the pressure wave arrival time calculated based on the pipeline topology in step 3, together with the thermal domain parameters in step 2, constitutes a unified parameter library; step 4 uses the initial allocation scheme and the four-level time constant (Table 4) as inputs to predict the dual-domain violation of the initial scheme, providing a benchmark for the sensitivity calculation in step 5 (Table 5); step 6 uses the sensitivity matrix to guide the optimization direction and outputs the optimal variable load parameters with time staggering (Table 6), where the pre-deduction correction of the coupling contribution depends on the attenuation coefficient and transmission time parameters in step 2; step 7 feeds back the real-time deviation to the parameter library during the execution phase, forming a closed-loop correction to ensure the dual safety of the thermal and hydraulic domains throughout the entire control process.

[0151] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for intelligent control of a hot water boiler, characterized in that, Includes the following steps: Acquire the current output value, outlet water temperature and outlet water flow of each hot water boiler operating in parallel, the total water supply temperature and total flow of the water collector, the real-time pressure data of each key node in the pipeline network, as well as the target load value and required response time, and integrate them to form a joint initial state dataset. For each hot water boiler, extract multi-level thermal inertia parameter vectors, calculate the real-time contribution coefficient based on the proportion of water output flow of each boiler, generate a cross-boiler temperature transfer matrix, and calculate the transmission time and attenuation coefficient of the output adjustment value of each boiler fed back to the return water end of other boilers through the water collector and return water pipeline, and generate a cross-boiler thermal inertia coupling parameter matrix. The transient hydraulic propagation parameter set of the pipeline network is calculated using the method of characteristics and integrated with the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix into a unified parameter library; The variable load of each boiler is parameterized into a ramp output adjustment function with the start time of variable load and the variable load rate as decision variables. The time-domain response of the mixing temperature of the water collector and the transient pressure time history curves of each key node in the pipeline network are predicted using a unified parameter library to generate joint prediction results. The thermally coupled violation index and the hydraulic violation index are extracted from the joint prediction results. The partial derivatives of each decision variable with respect to the two types of violation indexes are calculated to generate a joint sensitivity matrix. The joint sensitivity matrix is ​​input into the optimization solver. With the goal of minimizing the weighted sum of the two types of violation indicators after normalization, and with the constraints of total load satisfaction and response time satisfaction, a coupling contribution pre-deduction correction is applied to the output adjustment of each boiler. The optimal load change start time and load change rate of each boiler are solved, and a joint control execution scheme is generated and sent to the control system of each boiler. The multi-level thermal inertia parameter vector includes thermal inertia parameters at four levels sequentially along the flue gas flow direction: furnace radiant heat transfer surface, convective heat transfer surface, economizer heat transfer surface, and boiler water. Specifically, the thermal inertia parameters of the furnace radiant heat transfer surface include the heat storage capacity of the furnace water-cooled wall metal and the radiative heat transfer coefficient; the thermal inertia parameters of the convective heat transfer surface include the heat storage capacity of the convective heat-receiving surface tube bundle metal and the convective heat transfer coefficient; the thermal inertia parameters of the economizer heat transfer surface include the heat storage capacity of the economizer tube bundle metal and the feedwater heating time constant; and the thermal inertia parameters of the boiler water include the boiler body water volume and the water body heat capacity. The feedwater heating time constant is obtained by the ratio of the economizer tube bundle metal heat storage capacity to the feedwater side heat transfer coefficient. The instantaneous contribution coefficient is the ratio of the effluent mass flow rate of the i-th boiler at time t to the sum of the effluent mass flow rates of all boilers in the boiler group at that time; the cross-boiler temperature transfer matrix is ​​an N×N matrix, whose diagonal elements are the instantaneous contribution coefficients of the corresponding boilers, and whose off-diagonal elements are the product of the instantaneous contribution coefficient of the source boiler and the attenuation coefficient on the transmission path from the water collector to the return water end of the affected boiler, where N is the total number of boilers in the boiler group; the cross-boiler thermal inertia coupling parameter matrix is ​​an N×N matrix, whose elements consist of two parameters: transmission time and attenuation coefficient. The transmission time is obtained by dividing the sum of the length of the main water supply pipe, the heat exchange delay of the heat exchange station, and the length of the return water pipe by the flow velocity in the pipe. The attenuation coefficient is determined by the heat loss coefficient of each pipe segment on the transmission path and the mixing and dilution ratio.

2. The intelligent control method for hot water boilers according to claim 1, characterized in that, The ramp output adjustment function describes the change of the output adjustment amount of the i-th boiler over time as follows: the output remains constant before the start of the load change, and increases linearly at a constant load change rate from the start of the load change until it reaches the temporary load change target amount of the boiler, after which it remains constant; wherein, the temporary load change target amount is determined by the total load change amount allocated to each boiler according to the initial allocation ratio, and the start time of the load change and the load change rate are used as decision variables to participate in the joint optimization solution.

3. The intelligent control method for hot water boilers according to claim 1, characterized in that, The predicted time-domain response of the water collector mixing temperature includes: the output change of each boiler is sequentially passed through a series of first-order inertial time delay stages corresponding to four levels of thermal inertial parameter vectors. The output of each stage is the result of the previous stage's output after being filtered by a first-order low-pass filter using the time constant corresponding to that stage. The final output after the four stages are connected in series is the time-domain curve of the boiler's outlet water temperature change. The time-domain response of the water collector mixing temperature is obtained by weighted superposition of the outlet water temperature changes of each boiler through the cross-boiler temperature transfer matrix. The secondary temperature disturbance caused by the output adjustment action of each boiler being fed back to the return water end of other boilers through the return water loop is calculated using the transmission time and attenuation coefficient in the cross-boiler thermal inertial coupling parameter matrix. The time constant of each stage is determined by the ratio of the metal heat storage capacity to the heat transfer coefficient of that stage.

4. The intelligent control method for hot water boilers according to claim 1, characterized in that, The extraction method for the thermal coupling violation index is as follows: identify all local maxima and local minima in the time-domain response curve of the mixing temperature of the water collector, and take the maximum value among the absolute values ​​of the differences between adjacent maxima and minima as the peak-to-peak value of the oscillation; the extraction method for the hydraulic violation index is as follows: calculate the over-limit amount when the pressure exceeds the upper limit of the pressure safety of the key node or falls below the lower limit of the pressure safety of the key node for the transient pressure time history curve of each key node, and take the maximum value of the over-limit amount of all key nodes during the entire transition period; wherein, the upper limit and lower limit of the pressure safety of each key node are determined by the pipeline design parameters and the pressure bearing specifications of the equipment.

5. The intelligent control method for hot water boilers according to claim 1, characterized in that, The joint sensitivity matrix is ​​a 2×2N matrix. The first row is the partial derivative of the load change start time and load change rate of each boiler with respect to the thermal coupling violation index. The second row is the partial derivative of the load change start time and load change rate of each boiler with respect to the hydraulic violation index. Each partial derivative is obtained by applying a small perturbation to the decision variables and then re-performing the thermal and hydraulic domain prediction calculations in a differential manner. In the joint optimization solution, the thermal coupling violation index and the hydraulic violation index are respectively subjected to mean normalization based on the range. The two types of violation indices are unified to the dimensionless interval and then weighted and summed to eliminate the influence of dimensional differences on the optimization objective function.

6. The intelligent control method for hot water boilers according to claim 1, characterized in that, The coupling contribution pre-deduction correction is as follows: In each iteration of the optimization solution, the output equivalent corresponding to the expected temperature contribution transmitted to the i-th boiler side via the water collector through the adjustment action of other boilers is subtracted from the output adjustment amount of the i-th boiler, so as to obtain the effective output adjustment amount of the i-th boiler after correction; the output equivalent is obtained by multiplying the output adjustment amount of each other boiler by the corresponding attenuation coefficient in the cross-boiler thermal inertia coupling parameter matrix, and then multiplying by the ratio of the instantaneous contribution coefficient of the source boiler at the corresponding transmission delay moment to the instantaneous contribution coefficient of the i-th boiler at the current moment, and then summing them up.

7. The intelligent control method for hot water boilers according to claim 1, characterized in that, Before generating the joint control execution plan, a joint simulation verification step is also included: simultaneously performing cross-boiler thermal inertial coupling simulation and characteristic line method transient hydraulic simulation for the optimal load change start time and load change rate of each boiler, verifying whether the peak-to-peak value of the mixing temperature oscillation in the water collector is less than the allowable threshold for temperature oscillation, and whether the transient pressure of each node in the pipeline network is between the upper and lower pressure safety limits. If there are residual violations, the decision variables of the boiler with the largest violation contribution are fine-tuned and re-verified along the gradient direction provided by the joint sensitivity matrix, iterating until the thermal domain constraints and hydraulic domain constraints are simultaneously satisfied. During the load change execution process after the control command is issued, a real-time dual-domain monitoring and online correction step is also included: synchronously collecting the actual mixing temperature of the water collector and the actual pressure data of each key node in the pipeline network at a fixed period, comparing the deviation between the actual value and the predicted value at the corresponding time in the joint prediction result, and when the deviation exceeds the corresponding threshold, calculating the deviation correction direction using the joint sensitivity matrix and fine-tuning the output adjustment parameters of the boilers that have not yet been executed in real time, while using the residual data to incrementally update the attenuation coefficient and transmission time in the cross-boiler thermal inertial coupling parameter matrix online using the least squares method.

8. A hot water boiler controlled by a computer, wherein the computer is used to execute the intelligent control method for the hot water boiler according to any one of claims 1 to 7, characterized in that, Computers include: The data acquisition module is used to acquire the current output value, outlet water temperature and outlet water flow of each hot water boiler operating in parallel, the total water supply temperature and total flow of the water collector, the real-time pressure data of each key node in the pipeline network, as well as the target load value and required response time, and integrate them to form a joint initial state dataset. The thermal inertia coupling modeling module is used to extract multi-level thermal inertia parameter vectors for each hot water boiler, calculate the real-time contribution coefficient based on the proportion of water flow from each boiler, generate a cross-boiler temperature transfer matrix, and calculate the transmission time and attenuation coefficient to generate a cross-boiler thermal inertia coupling parameter matrix. The unified parameter library construction module is used to calculate the transient hydraulic propagation parameter set of the pipeline network using the method of characteristics, and integrate it with the cross-boiler temperature transfer matrix and the cross-boiler thermal inertia coupling parameter matrix into a unified parameter library; The joint prediction module is used to parameterize the variable load of each boiler into a ramp output adjustment function, and use a unified parameter library to predict the time domain response of the water collector mixing temperature and the transient pressure time history curves of each key node in the pipeline network, and generate joint prediction results. The sensitivity analysis module is used to extract thermally coupled violation indicators and hydraulic violation indicators from the joint prediction results, calculate the partial derivatives of each decision variable with respect to the two types of violation indicators, and generate a joint sensitivity matrix. The joint optimization solution module is used to input the joint sensitivity matrix into the optimization solver. With the goal of minimizing the weighted sum of the two types of violation indicators after normalization, it applies a coupling contribution pre-deduction correction to the output adjustment of each boiler, solves the optimal load change start time and load change rate of each boiler, and generates a joint control execution scheme. The instruction issuance module is used to issue the load change rate curves of each boiler in the joint control execution plan to each boiler control system in a time sequence, and output the output control instructions of each boiler at each time.

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