Waste heat recovery and distributed regulation method based on district energy network

Abstract

The application provides a waste heat recovery and distributed regulation method based on a district energy network, and relates to the technical field of district energy network regulation.The application obtains data of each heat source node and each heat consumption node in the district energy network, calculates available exergy values and minimum exergy demand values based on the second law of thermodynamics, further calculates exergy matching degree coefficients of each heat source node to each heat consumption node, and constructs an exergy matching degree matrix.Based on the exergy matching degree matrix, a minimum exergy loss minimization objective function is used to optimize a waste heat distribution path, and an optimal exergy flow distribution scheme is determined.Based on the optimal exergy flow distribution scheme, a distributed control system is used to monitor exergy flow changes and exergy loss conditions in real time, and flow distribution and temperature control parameters between nodes are dynamically adjusted based on exergy loss feedback information.The application solves the problems of low waste heat utilization efficiency, poor matching and regulation lag in the prior art through real-time optimization of waste heat distribution paths and temperature control parameters.

Description

Technical Field

[0001] This invention relates to the field of regional energy network regulation technology, and more specifically, to a waste heat recovery and distributed regulation method based on regional energy networks. Background Technology

[0002] With the escalating global energy crisis and the continuous advancement of energy conservation and emission reduction goals, regional energy networks, as an efficient integrated energy utilization model, have received widespread attention and research. Regional energy networks integrate multiple energy forms (such as electricity, heat, and cooling) and various energy supply equipment (such as boilers, heat pumps, and solar energy equipment) to achieve efficient energy transmission and distribution, thereby meeting the energy needs of users within a region. In this process, waste heat recovery and utilization is one of the crucial links in improving the efficiency of regional energy networks. Waste heat typically originates from industrial production processes, power generation equipment, and data centers, possessing wide distribution and potential utilization value. However, due to the uneven temperature and quality of waste heat, how to effectively achieve waste heat recovery and distributed control to maximize energy utilization remains a significant challenge in current research and engineering applications.

[0003] Existing waste heat recovery and regulation technologies in regional energy networks primarily rely on traditional heat balance analysis and simple flow control strategies. While these methods can achieve a certain degree of waste heat utilization, they still have significant shortcomings in energy flow optimization and resource matching efficiency. First, traditional methods often neglect the velocity flow law revealed by the second law of thermodynamics, while velocity is an important indicator of thermal energy quality, and its calculation and analysis can more accurately reflect the efficiency of energy utilization. Second, most existing waste heat allocation algorithms are mainly based on single-objective optimization, failing to fully consider the potential role of multi-objective optimization (such as minimizing velocity loss and maximizing heat demand satisfaction) in improving overall energy efficiency. Furthermore, existing technologies have weak dynamic feedback control capabilities for losses during waste heat transfer, making it difficult to adjust allocation strategies in real time to cope with complex operating conditions. These problems limit the depth and breadth of waste heat utilization in regional energy networks, leading to energy waste and inefficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a waste heat recovery and distributed control method based on a regional energy network, which can, to some extent, solve the problems of unreasonable heat distribution, low energy utilization efficiency, and lack of dynamic control capabilities.

[0005] According to one aspect of the present invention, a method for waste heat recovery and distributed control based on a regional energy network is provided, comprising: Data on each heat source node and each heat consumption node in the regional energy network are obtained, and the available energy value of each heat source node and the minimum energy demand value of each heat consumption node are calculated based on the second law of thermodynamics. Based on the available heat value and the heat demand baseline value, calculate the heat matching coefficient of each heat source node to each heat user node, and construct the heat matching matrix based on the heat matching coefficient; Based on the heat matching degree matrix, the waste heat allocation path is optimized by minimizing heat loss. The objective function determines the optimal heat flow allocation scheme through a multi-objective optimization algorithm. Based on the optimal flow allocation scheme, the distributed control system monitors the flow changes and flow loss on each transmission path in real time. When the actual flow loss deviates from the expected value, the flow allocation and temperature control parameters between each node are dynamically adjusted based on the flow loss feedback information.

[0006] Furthermore, a basic matching degree coefficient is defined based on the available cost value and the minimum cost requirement value, expressed by the formula: in, For heat source nodes For heat nodes The basic matching degree coefficient, For heat source nodes Available values, For heat nodes The minimum demand value, To avoid tiny positive numbers with a denominator of zero.

[0007] Furthermore, based on the aforementioned basic matching degree coefficient, a unified formula for calculating the comprehensive matching degree coefficient is established, considering both situations of supply surplus and supply shortage, as follows: in, For heat source nodes The residual heat temperature For heat nodes The required temperature, This is the temperature difference penalty coefficient. This is a temperature sensitivity index. To consider the matching degree coefficient of the missing amount, This is the penalty coefficient for shortfall.

[0008] Furthermore, based on the comprehensive matching degree coefficient, a matching degree matrix is ​​constructed, where the matrix elements are heat source nodes. For heat nodes The overall matching degree coefficient; Based on the matching degree matrix, the matching degree coefficient is transformed into a quantitative index of loss, the matching loss is calculated, and a mathematical model for waste heat network optimization is constructed by combining network transmission loss, multi-objective trade-offs and constraints.

[0009] Furthermore, based on the aforementioned matching degree matrix, the matching loss between a single supply-demand pair is calculated, expressed by the formula: in, For heat source nodes Towards the heat node The matching loss between them For heat source nodes Towards the heat node The actual flow rate, For heat source nodes Towards the heat node The connection state variable, when a connection is established. ,otherwise .

[0010] Furthermore, the network transmission loss needs to be modeled using a transmission efficiency coefficient, expressed by the following formula: in, For heat source nodes Towards the heat node The transmission efficiency coefficient, This is the pipeline loss coefficient. For heat source nodes Towards the heat node Transmission distance between For heat source nodes Towards the heat node The overall heat transfer coefficient of the pipeline, For heat source nodes Towards the heat node The heat transfer area of ​​the pipe, For heat source nodes Towards the heat node Average temperature during transmission For ambient temperature, For heat source nodes Towards the heat node The actual situation.

[0011] Furthermore, based on the aforementioned transmission efficiency coefficient, a heat source node is established. Towards the heat node The transmission loss calculation model is expressed by the formula: in, Heat source node Towards the heat node Loss during transmission.

[0012] Furthermore, based on the aforementioned transmission loss and matching loss, the comprehensive loss coefficient is defined as: in, For heat source nodes Towards the heat node The combined loss coefficient between them To match the loss weight coefficients, This is the weighting coefficient for network transmission loss.

[0013] Furthermore, based on the aforementioned comprehensive loss coefficient, an objective function for minimizing the system loss is established, expressed as follows: in, The objective function value is to minimize the system loss. For the number of heat source nodes, This represents the number of hot nodes.

[0014] Furthermore, the solution to minimize the objective function adopts a hybrid strategy of branch and bound method of CPLEX solver and sequential quadratic programming. Initial parameters are set, and in each iteration, the proportion of efficient supply and demand pairs with a comprehensive loss coefficient below the threshold is increased, and the proportion of efficient supply and demand pairs with a comprehensive loss coefficient above the threshold is decreased, so that the objective function is gradually optimized from the initial value to the minimum value. When the improvement of the objective function in multiple consecutive iterations is less than a set value, the algorithm is considered to have converged, and the optimal flow allocation scheme is output.

[0015] Compared with existing technologies, this invention, by introducing the Δ analysis method based on the second law of thermodynamics, can more accurately assess the energy quality matching degree between heat source nodes and heat-consuming nodes, thereby improving the scientificity and rationality of waste heat allocation; by using the Δ loss minimization objective function for multi-objective optimization, it can significantly reduce energy loss during waste heat transfer and improve overall energy utilization efficiency; combined with the dynamic adjustment capability of the distributed control system, it can realize real-time optimization of waste heat allocation paths and temperature control parameters, enhancing the system's adaptability to complex dynamic operating conditions and effectively solving problems such as low waste heat utilization efficiency, poor matching, and control lag in existing technologies. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of a waste heat recovery and distributed control method based on a regional energy network according to an embodiment of the present invention. Detailed Implementation

[0017] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention; it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0018] As mentioned in the background section, existing technologies have three main problems: First, traditional waste heat recovery and distribution methods neglect the efficiency analysis in the second law of thermodynamics, making it impossible to accurately assess the quality of thermal energy and the matching relationship between nodes, resulting in low waste heat utilization efficiency; second, most existing waste heat distribution algorithms adopt single-objective optimization, failing to fully consider the multi-objective optimization requirements of minimizing efficiency loss and maximizing heat demand satisfaction, thus failing to achieve efficient resource utilization; and third, they lack dynamic feedback control capabilities, making it impossible to adjust in real time according to the actual efficiency loss during transmission, resulting in a lagging waste heat distribution strategy that is difficult to adapt to complex operating conditions.

[0019] Figure 1 This is a system block diagram of a waste heat recovery and distributed control method based on a regional energy network according to an embodiment of the present invention. Figure 1 As shown, the waste heat recovery and distributed control method based on regional energy networks includes: S1: Obtain data on each heat source node and each heat consumption node in the regional energy network, and calculate the available energy value of each heat source node and the minimum energy demand value of each heat consumption node based on the second law of thermodynamics.

[0020] In a regional energy network, heat source nodes include waste heat generating equipment such as blast furnace flue gas outlets in steel plants, reactor cooling systems in chemical plants, steam turbine exhaust systems in power plants, and industrial kiln flues. Data acquisition for each heat source node involves real-time collection of waste heat temperature, flow rate, and pressure data using monitoring equipment such as temperature sensors, flow meters, and pressure transmitters. Simultaneously, physical property parameters of the waste heat medium type, such as flue gas, steam, and hot water, are recorded. Static information such as the geographical coordinates, rated power, operating period, and maintenance cycle of each heat source node is obtained to establish a basic database of heat source nodes.

[0021] Each heat-consuming node encompasses residential heating systems, industrial furnaces, drying equipment, preheaters, and other heat-consuming equipment. Demand-side data is collected for each heat-consuming node, including heat temperature demand range, heat flow demand, heat usage time characteristic curves, daily heat load variation patterns, and seasonal heat consumption characteristics. Technical parameters such as rated heat load, thermal efficiency, allowable heating temperature range, and geographical coordinates of the heat-consuming equipment are obtained to form a database of heat-consuming node demand characteristics.

[0022] Based on the second law of thermodynamics, a work done at each heat source node is calculated. According to the definition of work done, the work done is equal to the maximum useful work that the system can output during the process of reaching thermodynamic equilibrium with the environment. The usable work done at each heat source node is calculated using the following formula. : in, Waste heat mass flow rate, This is the residual heat enthalpy value. This is the enthalpy value under environmental conditions. For ambient temperature, The waste heat entropy value, This represents the environmental state entropy value.

[0023] A reverse energy consumption analysis method is used for each heat-consuming node, with the heat consumption process as the research object to calculate energy demand. Based on the process parameters such as the heat consumption temperature and flow rate requirements of the heat-consuming nodes, the minimum energy consumption value necessary to meet the heat consumption process requirements is determined through energy balance equations. The calculation formula is: in, The required temperature for the heat-using node, The specific heat capacity of the heat transfer medium.

[0024] Through the above analysis and calculation, we obtain the available energy value array for each heat source node and the minimum energy demand value array for each heat user node in the regional energy network.

[0025] S2: Based on the available heat value and the heat demand baseline value, calculate the heat matching coefficient of each heat source node to each heat user node, and construct the heat matching matrix based on the heat matching coefficient.

[0026] Based on the previously obtained available thermal efficiency values ​​for each heat source node and minimum thermal efficiency requirements for each heat consumption node, a thermal efficiency matching evaluation system is established. The thermal efficiency matching coefficient is calculated using a thermal quality adaptability analysis method, quantifying the degree of thermodynamic matching between the thermal output characteristics of the heat source node and the thermal consumption characteristics of the heat consumption node. First, the basic thermal efficiency matching coefficient is defined as the ratio of the available thermal efficiency value of the heat source node to the minimum thermal efficiency requirement value of the heat consumption node, and its calculation formula is: in, For heat source nodes For heat nodes The basic matching degree coefficient, For heat source nodes Available values, For heat nodes The minimum demand value, To avoid tiny positive numbers with a denominator of zero.

[0027] It should be noted that the basic matching degree coefficient only considers the numerical comparison of quantity and fails to reflect the importance of quality matching. Therefore, it is necessary to further establish a comprehensive matching degree coefficient.

[0028] The overall matching coefficient needs to consider two different matching scenarios simultaneously: a surplus of supply and a shortage of supply. If the available heat value at the heat source node is higher than the minimum heat demand value at the heat user node, although the heat supply is sufficient, a large temperature difference between the two will result in excess heat quality, leading to reduced system efficiency. In this case, a heat quality correction factor is introduced to penalize the heat waste caused by temperature mismatch. The formula is as follows: in, For heat source nodes For heat nodes The overall matching degree coefficient, For heat source nodes The residual heat temperature For heat nodes The required temperature, This is the temperature difference penalty coefficient. This is the temperature sensitivity index.

[0029] If the available heat value at the heat source node is lower than the minimum heat demand value at the heat consumption node, then there is a heat supply shortage problem. A heat shortage correction term needs to be introduced to reflect the negative impact of the supply shortage on the matching degree. The formula is as follows: in, To consider the matching degree coefficient of the missing amount, This is the penalty coefficient for shortfall.

[0030] Furthermore, based on the consideration of the two key factors of product quality matching and supply-demand balance, a unified formula for calculating the comprehensive product matching degree coefficient is established, expressed as: In the unified formula, the first term reflects the basic matching relationship of quantity. The second term, the exponential correction factor, addresses the problem of excess quantity. As the temperature difference increases, the value of this term decreases exponentially, thereby reducing the matching coefficient to reflect the waste of quantity. The third term, the linear correction factor, addresses the problem of insufficient quantity supply. As the proportion of quantity shortage increases, the value of this term decreases linearly, reflecting the negative impact of insufficient supply on the matching degree.

[0031] Based on the calculation results of the comprehensive matching degree coefficient between each heat source node and each heat consumption node, a matching degree matrix is ​​constructed, expressed by the formula: in, for × dimensional matching degree matrix, For the number of heat source nodes, The number of hot nodes, matrix elements For heat source nodes For heat nodes The overall matching degree coefficient.

[0032] The row vectors of the matching degree matrix represent heat source nodes. By analyzing the matching characteristic distribution of all heat-using nodes, heat source nodes can be identified. The optimal matching object. The column vector is represented using hot nodes. By analyzing the matching relationship with all heat source nodes, the heat-using nodes can be determined. The best source of heating.

[0033] To evaluate the matching performance of the entire system, a system matching degree index is defined based on the matching degree matrix. First, the statistical characteristic parameters of the matrix are calculated, expressed by the formula: in, The mean of the matching degree matrix is ​​_____. Let be the standard deviation of the matching degree matrix.

[0034] Therefore, the system's matching potential index is defined as: in, To match potential indicators to the system, To avoid tiny positive numbers with a denominator of zero.

[0035] The system's matching potential index quantitatively reflects the overall matching level and matching uniformity of the system through the numerical relationship between the mean and standard deviation. Specifically: If the mean of the matching degree matrix When the value is large, specifically when most elements in the matrix have values ​​close to or greater than 0.8, it indicates that more than 80% of the heat source nodes and heat-consuming nodes in the system can achieve effective matching, the overall matching level is in a good state, and the heat source quality and heat consumption demand are basically matched in terms of order of magnitude and temperature level.

[0036] If the standard deviation of the matching degree matrix When the value is small, it is specifically represented by the ratio of the standard deviation to the mean. A value less than 0.3 indicates that the numerical distribution of the matching degree between each heat source node and heat consumption node combination is close, the difference between the maximum and minimum matching degree is controlled within 60% of the mean, the system matching performance is evenly distributed, and the extreme differentiation of individual supply and demand combinations with matching degree exceeding 1.5 while other combinations are below 0.5 is avoided.

[0037] If both conditions are met >0.8 and If the numerical condition is <0.3, then the system's matching potential index A value exceeding 0.75 indicates that the system possesses the thermodynamic basis for establishing an efficient network, and at least 75% of the possible connection schemes can achieve a performance level with network utilization efficiency exceeding 75%.

[0038] If the value of an element in the heat matching degree matrix is ​​in the range of 0.9-1.1, it indicates that the available heat value of the corresponding heat source node and the minimum heat demand value of the heat user node are not more than 10% apart, and the relative deviation between the heat source temperature and the demand temperature is not more than 15%. At this time, the quality degradation loss in the heat transfer process is controlled within 5%, and the heat supply and demand imbalance loss is controlled within 2%.

[0039] If the matrix element value exceeds 1.3, it indicates that the heat supply capacity of the heat source node exceeds the heat demand of the heat user node by more than 30% or the heat source temperature is more than 25% higher than the demand temperature, indicating a problem of excess heat quality. In actual use, this will result in more than 15% heat waste.

[0040] If the matrix element value is less than 0.7, it indicates that the heat supply capacity of the heat source node is less than 70% of the heat demand of the heat user node, or that temperature differences cause a serious mismatch in heat quality. The heat transfer efficiency of this supply and demand combination is less than 70%, and it is necessary to improve the system heat utilization efficiency to an acceptable level of more than 85% in subsequent path optimization by using multi-heat source coordinated heating or reconfiguring a highly matched supply and demand pair.

[0041] S3: Based on the heat matching degree matrix, the heat loss minimization objective function is used to optimize the waste heat allocation path. The objective function determines the optimal heat flow allocation scheme through a multi-objective optimization algorithm.

[0042] Based on the matching degree matrix To construct a system loss minimization objective function for waste heat allocation path optimization, it is necessary to convert the loss matching coefficient into a loss quantification index and establish a comprehensive optimization model in conjunction with network transmission loss.

[0043] By organically combining matching loss quantification, network transmission loss modeling, comprehensive objective function establishment, and constraint setting, a unified optimization framework is formed, and the objective function is constructed.

[0044] Specifically, the entire construction process starts from the matching degree matrix, identifies the physical essence of matching loss, namely, the waste phenomenon caused by quality mismatch and supply-demand imbalance. Based on the comprehensive matching degree coefficient in the matching degree matrix, the matching loss is expressed as the difference between the ideal matching state and the actual matching state. When the heat source node and the heat consumption node are perfectly matched, there is no matching loss; when A deviation from 1 results in a corresponding mismatch loss. The mismatch loss between a single supply-demand pair is calculated using the following formula: in, For heat source nodes Towards the heat node The matching loss between them For heat source nodes Towards the heat node The actual flow rate, For heat source nodes Towards the heat node The connection state variable, when a connection is established. ,otherwise .

[0045] Building upon the quantification of transmission matching loss, this paper further considers the degradation of transmission quality during network transmission. Network transmission loss primarily stems from temperature drop, pressure drop, and heat loss during pipeline transmission, and is closely related to transmission distance, pipeline characteristics, environmental conditions, and transmission flow rate. Quantifying transmission loss requires establishing a transmission efficiency coefficient model, expressed by the formula: in, For heat source nodes Towards the heat node The transmission efficiency coefficient, This is the pipeline loss coefficient. For heat source nodes Towards the heat node Transmission distance between For heat source nodes Towards the heat node The overall heat transfer coefficient of the pipeline, For heat source nodes Towards the heat node The heat transfer area of ​​the pipe, For heat source nodes Towards the heat node Average temperature during transmission The ambient temperature.

[0046] Based on the transmission efficiency coefficient, heat source nodes are established. Towards the heat node The transmission loss calculation model is expressed by the formula: in, Heat source node Towards the heat node Loss during transmission.

[0047] By integrating the matching loss and network transmission loss, and by introducing a weighting factor to balance the relative importance of the two types of losses, a system loss minimization objective function is constructed.

[0048] To simplify the expression, the comprehensive loss coefficient is defined as: in, For heat source nodes Towards the heat node The combined loss coefficient between them To match the loss weight coefficients, This is the weighting coefficient for network transmission loss.

[0049] Based on the comprehensive loss coefficient, an objective function for minimizing the system loss is established, expressed by the formula: in, The objective function value is to minimize the system loss. For the number of heat source nodes, This represents the number of hot nodes.

[0050] Furthermore, the optimization solution of the objective function needs to satisfy the system's physical constraints and operational constraints, including the supply capacity constraints of heat source nodes, the demand satisfaction constraints of heat user nodes, the non-negativity constraints of heat flow, the connection state constraints, and the consistency constraints between heat flow and connection state.

[0051] The supply capacity constraint of the heat source node is expressed as: The requirement to satisfy constraints using hot nodes can be expressed as follows: The non-negativity constraint on flow rate is expressed as: Connection state constraints are represented as follows: The consistency constraint between traffic and connection state is expressed as: in, It is a sufficiently large positive number used for connection state constraints, and is usually taken as the maximum available quantity of all heat source nodes.

[0052] Furthermore, the optimal flow allocation scheme is obtained based on the objective function of minimizing system loss. Specifically: First, based on the comprehensive matching coefficient in the matching matrix... Accurately calculate the mismatch loss coefficient for each supply-demand pair, when When the value is in the range of 0.9-1.1, the corresponding matching loss coefficient is (1- An absolute value less than 0.1 indicates that the supply-demand combination's mismatch loss rate is less than 10%, and the contribution of this combination to unit flow loss in the objective function is... If the value is below ×0.1, then this combination is judged as a high-efficiency transmission path and priority is given to allocating traffic.

[0053] when When the value exceeds 1.3 or falls below 0.7, the absolute value of the corresponding supply-demand matching loss coefficient exceeds 0.3, indicating that the supply-demand combination's supply-demand matching loss rate is higher than 30%. In the objective function, the contribution value of this combination's unit supply flow loss is... For values ​​above ×0.3, such combinations are marked as high-loss paths and their traffic allocation is minimized to within 10% of the total demand during the search process.

[0054] Meanwhile, the transmission efficiency coefficient According to the specific transmission distance Pipeline loss coefficient Calculate the transmission loss coefficient for each connection path using heat loss parameters, if the transmission distance... For 1-2 kilometers and pipeline loss coefficient When it is 0.02-0.05 / km, The value is 0.90-0.96, and the transmission loss coefficient (1- The value is 0.04-0.10, and the unit flow transmission loss of this path in the objective function is... ×0.04 to ×0.10. If the transmission distance For 5-8 kilometers or pipeline loss coefficient When it is 0.10-0.15 / km, The values ​​are 0.65-0.80, the transmission loss coefficient is 0.20-0.35, and the transmission loss per unit volume of traffic for this path is [value missing] in the objective function. ×0.20 to ×0.35.

[0055] The overall loss coefficient is obtained through weighting coefficients. and The matching loss and transmission loss are weighted and fused according to specific values, with weight coefficients... and It establishes a two-level optimization model based on the principle of minimizing the total system loss to dynamically determine the optimal weight combination. Specifically, it uses the particle swarm optimization algorithm within the feasible region of the weight coefficients. , , The internal search finds the weight combination that minimizes the total loss of the system.

[0056] The optimization algorithm for the objective function employs a hybrid solution strategy combining the CPLEX solver's branch and bound method with sequential quadratic programming. The initial iteration step size is set to 0.01, and the convergence tolerance is set to... The maximum number of iterations is limited to 5000. The strategy involves systematically adjusting the flow distribution of each supply-demand pair by increasing the flow of supply-demand pairs with a comprehensive loss coefficient less than 0.10 by 5%-15% and decreasing the flow of supply-demand pairs with a comprehensive loss coefficient greater than 0.25 by 10%-30% in each iteration. The objective function is gradually reduced from its initial value to its optimal value. The algorithm is considered converged when the improvement in the objective function is less than 0.001% after 50 consecutive iterations. The optimal flow allocation scheme obtained when the optimization algorithm reaches the convergence condition is... Precisely provide each heat source node To each heat-using node The transmission capacity is calculated with a precision of 0.01kW. A physical connection is established when the power is ≥0.1kW. A power consumption of less than 0.1kW is considered a disconnection.

[0057] The optimal solution ultimately achieves total heat loss controlled within 12%-18% of the initial heat supply, with supply and demand combinations of heat matching coefficients ranging from 0.9 to 1.1 allocated to 65%-75% of the total heat flow, and connections with transmission distances of 1-3 kilometers handling 60%-80% of the total heat transmission. The overall heat utilization efficiency of the system reaches 82%-88%, providing precise engineering design parameters for the selection of DN50-DN300 pipe diameters, 0.5-5.0 m / s flow velocity control, and 15-45℃ temperature drop operation strategies for the waste heat network.

[0058] S4: Based on the optimal flow allocation scheme, the distributed control system monitors the flow changes and flow loss on each transmission path in real time. When the actual flow loss deviates from the expected value, the flow allocation and temperature control parameters between each node are dynamically adjusted based on the flow loss feedback information.

[0059] Based on the above optimal flow allocation scheme, the flow changes and flow loss on each transmission path are monitored in real time through a distributed control system.

[0060] The distributed control system installs temperature sensors, flow sensors, pressure sensors, and heat meters at each heat source node and heat consumption node. The temperature sensors are Pt100 platinum resistance thermometers with a measurement accuracy of ±0.1℃ and a response time of less than 30 seconds. One set is installed on each of the water supply and return pipes to monitor temperature difference changes. The flow sensor uses an electromagnetic flow meter or an ultrasonic flow meter, with a measurement accuracy of ±0.5% and a range covering 10%-120% of the design flow rate, enabling real-time monitoring of mass flow rate along each transmission path. .

[0061] Pressure sensors are installed at key locations with a measurement accuracy of ±0.25%FS, and are used to monitor system pressure distribution and identify changes in pipeline resistance.

[0062] The heat meter integrates temperature and flow measurement functions, directly calculating the heat transfer power of each node. The heat transfer power is calculated by multiplying the mass flow rate of the heat transfer medium by the specific heat capacity of the heat transfer medium and then by the difference between the supply water temperature and the return water temperature. The specific heat capacity of the heat transfer medium is determined according to the medium type. The supply water temperature and return water temperature are obtained in real time by temperature sensors installed on the corresponding pipes.

[0063] Based on real-time collected temperature, flow rate, and heat data, the distributed control system calculates the scour rate on each transmission path in real time through the scour calculation module. The calculation method is to multiply the mass flow rate of the heat transfer medium by the scour density per unit mass of the heat transfer medium. The scour density consists of two parts: the first part is the specific heat capacity of the heat transfer medium multiplied by the difference between the supply water temperature and the ambient reference temperature; the second part is the ambient reference temperature multiplied by the specific heat capacity of the heat transfer medium and then multiplied by the natural logarithm of the ratio of the supply water temperature to the ambient reference temperature. Finally, the scour density is equal to the first part minus the second part. The ambient reference temperature is obtained in real time through an outdoor ambient temperature sensor.

[0064] Simultaneously, the flow loss rate is calculated, which is equal to the inlet flow rate of the transmission path minus the outlet flow rate. The inlet flow rate is measured at the beginning of the transmission path, and the outlet flow rate is measured at the end of the transmission path. By comparing it in real time with the theoretical flow value in the optimal flow allocation scheme, the flow deviation and flow loss deviation on each transmission path are identified. The flow deviation is equal to the measured flow rate minus the theoretical optimal flow rate, and the flow loss deviation is equal to the measured flow loss rate minus the expected flow loss rate.

[0065] When the actual loss deviates from the expected value, the specific judgment criteria are that the loss deviation of a single transmission path exceeds 15% of the expected value or the total loss deviation of the system exceeds 10% of the expected value. The distributed control system then activates the loss feedback control mechanism to make dynamic adjustments.

[0066] The loss feedback control mechanism first analyzes the causes of deviation through the loss diagnosis module: If the actual heat loss of a certain transmission path exceeds 20% of the expected value, it is judged as a decrease in transmission heat loss efficiency. Possible causes include deterioration of pipeline insulation performance, excessively high or low flow rate of heat transfer medium, abnormal changes in ambient temperature, etc. At this time, the control system adjusts the flow distribution ratio of that path to transfer the flow of the excess heat loss portion to an alternative path with lower heat loss. The flow adjustment range is determined according to the degree of heat loss deviation. When the heat loss deviation is 20%-30%, the flow is reduced by 10%-20%. When the heat loss deviation exceeds 50%, the flow is reduced by 30%-50% or the path is temporarily shut down.

[0067] If the temperature matching loss at a heat source node or heat-consuming node increases significantly, it indicates a change in the supply-demand temperature matching degree. The control system optimizes the temperature matching effect through specific temperature control parameter adjustment strategies. The specific adjustment process includes three steps: temperature deviation identification, temperature setpoint correction, and multi-objective temperature optimization. Specifically: Temperature deviation identification determines the adjustment direction by calculating the deviation between the actual water supply temperature and the theoretical optimal demand temperature of each heat-consuming node. When the water supply temperature is more than 5 degrees Celsius higher than the optimal demand temperature, it is judged as overheating leading to increased temperature matching loss. When the water supply temperature is more than 3 degrees Celsius lower than the optimal demand temperature, it is judged as insufficient heating requiring a temperature increase. Temperature setpoint correction calculates a new optimal water supply temperature setpoint based on the increment of temperature matching loss. The correction formula considers the current water supply temperature, the weighted average of the demand temperatures of each heat-consuming node, and the temperature loss sensitivity coefficient. The weighting coefficient is determined according to the heat load and quality requirements of each heat-consuming node. The temperature loss sensitivity coefficient is obtained by fitting historical operating data and reflects the degree of influence of temperature changes on temperature loss. Multi-objective temperature optimization, while meeting the quality requirements of the main heat-using nodes, also takes into account the needs of secondary heat-using nodes and minimizes the total system loss. The optimization algorithm uses a weighted summation method to merge multiple objective functions into a single objective function. The weight coefficients of the main heat-using nodes are set to 0.6 to 0.8, and the weight coefficients of the secondary heat-using nodes are set to 0.2 to 0.4.

[0068] For heat source nodes, when the deviation between the supply water temperature and the heat demand temperature exceeds a set threshold, the control system adjusts the heat source outlet temperature setpoint by 50%-80% of the deviation. The adjustment process is divided into two stages: temperature regulation actuator control and temperature regulation effect tracking. The temperature control actuator controls the outlet temperature by operating the temperature control valve of the heat source equipment, the burner power control device, or the electric heater power control module. The control process adopts a segmented control strategy. When the temperature deviation is within the range of 3 to 8 degrees Celsius, the adjustment range is 60% of the deviation each time. When the temperature deviation is within the range of 8 to 15 degrees Celsius, the adjustment range is 70% of the deviation each time. When the temperature deviation exceeds 15 degrees Celsius, the adjustment range is 80% of the deviation each time. After each adjustment, wait 10 to 20 minutes to observe the temperature change trend before making the next adjustment.

[0069] Temperature regulation effect tracking evaluates the regulation effect by monitoring the rate and stability of temperature change at the heat source outlet. When the rate of temperature change exceeds 2 degrees Celsius per minute, it is judged that the regulation is too aggressive and the regulation amplitude needs to be reduced. When the rate of temperature change is less than 0.5 degrees Celsius per minute, it is judged that the regulation response is too slow and the regulation amplitude needs to be increased or the working status of the actuator needs to be checked.

[0070] Simultaneously, by optimizing the flow distribution, the heat source's flow rate is preferentially allocated to heating nodes with more matching temperature requirements. The priority allocation strategy is based on the temperature matching index of each heating node. The temperature matching index is equal to the ratio of the heating node's required temperature to the heat source's supply water temperature. The closer the ratio is to 1.0, the better the matching degree. The heat source flow rate is allocated in order of matching index from high to low to ensure that heating nodes with high matching degree receive sufficient heat supply first.

[0071] For heat-consuming nodes, when an abnormally high or low return water temperature is detected, the control system adjusts the opening of the flow control valve at that node using a precise flow control strategy to ensure that the heat-consuming equipment obtains suitable heat quality and heat exchange effect. The flow control valve opening control strategy includes three specific steps: abnormal return water temperature diagnosis, opening adjustment calculation, and verification of adjustment effect. The diagnosis of abnormal return water temperature is to identify the type of abnormality by comparing the actual return water temperature with the theoretical return water temperature. When the return water temperature is more than 5 degrees Celsius higher than the theoretical value, it is judged as insufficient heat exchange, and the flow rate needs to be reduced to enhance heat exchange. When the return water temperature is more than 3 degrees Celsius lower than the theoretical value, it is judged as excessive heat exchange, and the flow rate needs to be increased to reduce the heat exchange intensity.

[0072] The valve opening adjustment calculation is based on the return water temperature deviation and the heat exchanger characteristic curve to determine the adjustment range of the valve opening. The adjustment range adopts the proportional-integral control algorithm, with the proportional coefficient set to 0.8 to 1.5 and the integral time constant set to 300 to 600 seconds. When the return water temperature deviation is 5 to 10 degrees Celsius, the valve opening adjustment range is 10% to 20% of the current opening. When the return water temperature deviation is 10 to 20 degrees Celsius, the valve opening adjustment range is 20% to 40% of the current opening.

[0073] The effect of the adjustment is evaluated by continuously monitoring the change in the return water temperature after adjustment. The return water temperature should change towards the target value within 15 to 30 minutes after adjustment. When the reduction in return water temperature deviation reaches more than 60% of the deviation before adjustment, the adjustment is considered effective. When the reduction in deviation is less than 30%, the adjustment range needs to be further increased or the operating status of the heat-using equipment needs to be checked.

[0074] The dynamic adjustment process of the entire heat loss feedback control mechanism adopts a hierarchical and progressive control strategy, covering multiple control links such as transmission path flow allocation adjustment, heat source node temperature control adjustment, and heat-consuming node flow regulation. The first layer is local adjustment, which responds quickly to the heat loss deviation of a single transmission path or node, with an adjustment time constant of 5-15 minutes. The second layer is regional coordination. When multiple adjacent paths or nodes experience heat loss anomalies at the same time, the control system re-optimizes the heat flow allocation scheme within the region, with an adjustment time constant of 15-45 minutes. The third layer is global re-optimization. When the total heat loss deviation of the system continues to exceed the threshold, the control system initiates the recalculation and update of the entire network heat flow allocation scheme, with an adjustment time constant of 45-120 minutes.

[0075] In practical operation, the distributed control system uses the PID control algorithm to achieve precise adjustment of flow distribution and temperature control parameters. The PID parameters are tuned according to the dynamic characteristics of the system, with the proportional coefficient ranging from 0.8 to 2.5, the integral time parameter ranging from 300 seconds to 1800 seconds, and the derivative time parameter ranging from 60 seconds to 300 seconds, ensuring the speed and stability of the control response. After the waste heat feedback control action is executed, the system continuously monitors the adjustment effect and evaluates the control effect by comparing the change in waste heat before and after the adjustment. When the waste heat reduction reaches more than 80% of the expected target, the adjustment is considered effective and the current control parameters are maintained. When the waste heat reduction is less than 50% of the expected target, the adjustment intensity is further increased, including increasing the opening adjustment range of the flow regulating valve, increasing the correction range of the temperature setpoint, and shortening the execution interval of the control action. For example, the original flow adjustment range of 10% to 20% is increased to 25% to 40%, the original temperature adjustment range of 50% to 80% is increased to 80% to 100%, and the original adjustment interval of 10 to 20 minutes is shortened to 5 to 10 minutes, so as to ensure the efficient utilization of waste heat resources and the economic operation of the system.

[0076] In summary, the waste heat recovery and distributed control method based on the regional energy network of this invention has been clarified. By introducing the Δ analysis method based on the second law of thermodynamics, it can more accurately assess the energy quality matching degree between heat source nodes and heat-consuming nodes, thereby improving the scientificity and rationality of waste heat allocation. By using the Δ loss minimization objective function for multi-objective optimization, it can significantly reduce energy loss during waste heat transmission and improve overall energy utilization efficiency. Combined with the dynamic adjustment capability of the distributed control system, it can realize real-time optimization of waste heat allocation path and temperature control parameters, enhance the system's adaptability to complex dynamic operating conditions, and effectively solve the problems of low waste heat utilization efficiency, poor matching, and control lag in the prior art.

Claims

1. A waste heat recovery and distributed control method based on regional energy networks, characterized in that, include: Data on each heat source node and each heat consumption node in the regional energy network are obtained, and the available energy value of each heat source node and the minimum energy demand value of each heat consumption node are calculated based on the second law of thermodynamics. Based on the available heat value and the heat demand baseline value, calculate the heat matching coefficient of each heat source node to each heat user node, and construct the heat matching matrix based on the heat matching coefficient. Based on the heat matching degree matrix, the waste heat allocation path is optimized by minimizing heat loss. The objective function determines the optimal heat flow allocation scheme through a multi-objective optimization algorithm. Based on the optimal flow allocation scheme, the distributed control system monitors the flow changes and flow loss on each transmission path in real time. When the actual flow loss deviates from the expected value, the flow allocation and temperature control parameters between each node are dynamically adjusted based on the flow loss feedback information.

2. The waste heat recovery and distributed control method based on a regional energy network according to claim 1, characterized in that, The basic matching degree coefficient is defined based on the available value and the minimum value required, and is expressed by the following formula: in, For heat source nodes For heat nodes The basic matching degree coefficient, For heat source nodes Available values, For heat nodes The minimum demand value, To avoid tiny positive numbers with a denominator of zero.

3. The waste heat recovery and distributed control method based on a regional energy network according to claim 2, characterized in that, Based on the aforementioned basic matching degree coefficient, a unified formula for calculating the comprehensive matching degree coefficient is established, considering both situations of supply surplus and supply shortage. This formula is expressed as: in, For heat source nodes The residual heat temperature For heat nodes The required temperature, This is the temperature difference penalty coefficient. This is a temperature sensitivity index. To consider the matching degree coefficient of the missing amount, This is the penalty coefficient for shortfall.

4. The waste heat recovery and distributed control method based on a regional energy network according to claim 3, characterized in that, Based on the comprehensive heat source matching coefficient, a heat source matching matrix is ​​constructed, where the matrix elements are heat source nodes. For heat nodes The overall matching degree coefficient; Based on the matching degree matrix, the matching degree coefficient is transformed into a quantitative index of loss, the matching loss is calculated, and a mathematical model for waste heat network optimization is constructed by combining network transmission loss, multi-objective trade-offs and constraints.

5. The waste heat recovery and distributed control method based on a regional energy network according to claim 4, characterized in that, Based on the aforementioned matching degree matrix, the matching loss between a single supply and demand pair is calculated, expressed by the formula: in, For heat source nodes Towards the heat node The matching loss between them For heat source nodes Towards the heat node The actual flow rate, For heat source nodes Towards the heat node The connection state variable, when a connection is established. ,otherwise .

6. The waste heat recovery and distributed control method based on a regional energy network according to claim 5, characterized in that, The network transmission loss requires the establishment of a transmission efficiency coefficient model, expressed by the formula: in, For heat source nodes Towards the heat node The transmission efficiency coefficient, This is the pipeline loss coefficient. For heat source nodes Towards the heat node Transmission distance between For heat source nodes Towards the heat node The overall heat transfer coefficient of the pipeline, For heat source nodes Towards the heat node The heat transfer area of ​​the pipe, For heat source nodes Towards the heat node Average temperature during transmission For ambient temperature, For heat source nodes Towards the heat node The actual situation.

7. The waste heat recovery and distributed control method based on a regional energy network according to claim 6, characterized in that, Based on the aforementioned transmission efficiency coefficient, a heat source node is established. Towards the heat node The transmission loss calculation model is expressed by the formula: in, Heat source node Towards the heat node Loss during transmission.

8. The waste heat recovery and distributed control method based on a regional energy network according to claim 7, characterized in that, Based on the aforementioned transmission loss and matching loss, the comprehensive loss coefficient is defined as: in, For heat source nodes Towards the heat node The combined loss coefficient between them To match the loss weight coefficients, This is the weighting coefficient for network transmission loss.

9. The waste heat recovery and distributed control method based on a regional energy network according to claim 8, characterized in that, Based on the aforementioned comprehensive loss coefficient, an objective function for minimizing the system loss is established, expressed by the following formula: in, The objective function value is to minimize the system loss. For the number of heat source nodes, This represents the number of hot nodes.

10. The waste heat recovery and distributed control method based on a regional energy network according to claim 9, characterized in that, The objective function is solved by a hybrid strategy of branch and bound method of CPLEX solver and sequential quadratic programming. Initial parameters are set, and in each iteration, the proportion of efficient supply and demand pairs with comprehensive loss coefficient below the threshold is increased, and the proportion of inefficient supply and demand pairs with comprehensive loss coefficient above the threshold is decreased, so that the objective function is gradually optimized from the initial value to the minimum value. When the improvement of the objective function in multiple consecutive iterations is less than a set value, the algorithm is considered to have converged, and the optimal flow allocation scheme is output.

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