Multi-source waste heat clean heating system

Abstract

The invention relates to the technical field of energy conservation and environmental protection, and discloses a multi-source waste heat clean heating system, which constructs a topology engine based on heat supply network virtual nanotube modeling through a parameter analysis module, calculates the vacancy of a demand area and the schedulable total amount of a surplus area in combination with a heat supply vacancy formula, accurately judges the scheduling feasibility, and avoids blind shunting. In the regulation and control decision-making stage, the energy output quantity is controlled through a valve opening formula, meanwhile, the frequency of circulating pumps in surplus districts is instructed to be increased, scheduling lost energy is supplemented, and insufficient heat source supply is prevented; and the monitoring and early warning module tracks the water temperature of the surplus area in real time, pushes a valve opening degree reducing instruction if the water temperature is close to the normal lower limit, and dynamically avoids the water temperature fluctuation of the dispatched area. For example, when a cooling water zone of an air compressor is used as a surplus zone, the system can accurately control the residual heat output quantity, the requirement for zone heating is met, the heat dissipation and heating stability of the cooling water system is guaranteed, and the circulation for solving one problem and causing the other fault is broken through.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and environmental protection technology, and in particular to a multi-source waste heat clean heating system. Background Technology

[0002] For many years, lead-zinc mining and beneficiation enterprises have used two 10-ton coal-fired steam boilers for heating, one in operation and one on standby. These boilers primarily provide heating for the beneficiation plant's grinding and flotation workshop, crushing workshop, tailings workshop, main and auxiliary shafts, the mining building, office building, two dormitory buildings, and the canteen, covering a total heating area of ​​approximately 60,000 square meters. With the operation of these coal-fired boilers, the equipment has suffered from low efficiency, high operating costs, and poor heating performance. Furthermore, the plant's heating pipeline layout is unreasonable, resulting in poor condensate return and frequent leaks, posing significant safety hazards to the heating system. Simultaneously, with the construction of a new water treatment plant, mining building, and technical center, the heating area has increased, making the expansion of heat sources and the upgrading of the heating system an urgent priority for ensuring winter production and living conditions. Moreover, according to environmental protection requirements, coal-fired boilers are now obsolete technology, necessitating the search for clean, environmentally friendly, and energy-saving heating solutions.

[0003] The existing method utilizes the waste heat from concentrated or recycled water in water treatment plants, tailings slurry water, and air compressor cooling water as heat sources. New water treatment plants, tailings slurry heating stations, and air compressor cooling water heating stations are built to provide clean heating for the water treatment plant area, mining building area, and factory area. Specifically, heat exchangers are used to exchange heat between intermediate water and the corresponding heat source water, and then heat exchange is performed between the intermediate water and the user's heating return water before finally reaching the heating users.

[0004] The aforementioned and existing related technologies often suffer from the following drawbacks: When heating anomalies occur in a single area due to fluctuations in heat source temperature / flow, cross-area scheduling of waste heat resources becomes a key means to maximize the utilization rate of clean energy and ensure that the heating of the entire area meets standards. However, existing independent systems lack a dynamic balancing mechanism, and energy diversion overload can easily occur during scheduling. That is, the dispatched area may experience insufficient heat source replenishment due to waste heat output exceeding the safety redundancy, leading to fluctuations in heating water temperature, which in turn undermines its heating stability, creating a vicious cycle where solving one problem causes another to malfunction.

[0005] Therefore, a multi-source waste heat clean heating system is proposed that can integrate waste heat resources from various areas, optimize heating satisfaction and energy consumption by collecting parameters in real time, constructing a virtual heat network, and dynamically calculating energy deficit / surplus, thereby achieving stable heating throughout the entire area during cross-regional scheduling and maximizing the utilization of clean energy. Summary of the Invention

[0006] The technical problem to be solved by this invention is that the existing technology has the disadvantage that the waste heat output of the dispatched area exceeds the safety redundancy, resulting in insufficient heat source replenishment and causing fluctuations in heating water temperature. To address this, we propose a multi-source waste heat clean heating system.

[0007] To achieve the above objectives, this application adopts the following technical solution: a multi-source waste heat clean heating system, comprising: The multi-source waste heat unit includes at least three heating zones. Each heating zone uses a heat source to transfer circulating water to the heating users through an intermediate water source. The heating zones are connected by a heat network. The data acquisition module is used to collect monitoring parameters and heating zone priorities for each heating zone in real time. The parameter analysis module receives data transmitted from the data acquisition module and constructs a virtual heating network topology engine based on the virtual heating network modeling technology. At the same time, it combines the load sensitivity matrix to analyze the impact of waste heat source temperature / flow fluctuations on the heating load of each area, compares each parameter with the normal range, determines whether there are any abnormalities, and analyzes the problems corresponding to the abnormal parameters. Based on the cross-area scheduling and control mode and the multi-area simultaneous abnormal control mode, it calculates the energy deficit of the demand area and the total energy surplus of the surplus area according to the original formula. The regulation and decision-making module is used to introduce a distributed Q-learning algorithm based on the analysis results of the parameter analysis module, deploy management decision-making agents in each area, design a joint reward function, and update the regulation model parameters using an asynchronous parameter update mechanism; and generate specific regulation instructions according to the three-level regulation logic. The execution control module receives instructions from the regulation decision module and, based on the action space A of the network decision agent, controls the frequency of circulating pumps, the power of heat pump units, and the opening and closing of heat network interconnection valves in each area to perform specific regulation operations. The monitoring and early warning module is used to monitor parameter changes and control operation execution in each heating area in real time, and simultaneously track the scheduling response efficiency and pipeline heat loss data under the control of the distributed Q-learning algorithm; if the parameters are still abnormal for a long time after control, an early warning signal is triggered.

[0008] Furthermore, the heating area specifically uses heat source water pressurized by a liquid pump to heat the intermediate water in a heat exchanger. A flow meter and a temperature sensor are installed between the liquid pump and the heat exchanger. The flow meter is used to measure the flow rate of the concentrate, and the temperature sensor is used to measure the temperature of the concentrate. The intermediate water absorbs heat from the heat source water through the heat exchanger, and after gaining heat, it is pressurized by a water pump and sent to the evaporator of the heat pump unit to release heat. After releasing heat, it returns to the heat exchanger for circulation. The return water of the heating network is pressurized by a circulating pump and sent to the condenser of the heat pump unit for heating. A temperature sensor is installed between the heat pump unit and the heating users to measure the temperature of the heating water. The heated heating network water is then sent to each heating user.

[0009] Furthermore, the monitoring parameter is specifically the heat source temperature of the temperature sensor. Heating demand temperature from temperature sensors and the heat source flow of the flow meter .

[0010] Furthermore, the first-level control logic in the three-level control logic is specifically as follows: Data acquisition module: Real-time acquisition of heat source temperature, heat source flow rate and heating water temperature parameters for a single area, and transmission of the data to the parameter analysis module; Parameter analysis module: compares parameters to normal ranges, determines the type and cause of abnormalities, and generates parameter analysis results; Regulation and decision-making module: Based on parameter analysis results and the state space of the built-in management decision-making agent. Generate equipment adjustment instructions within the area; Execution control module: Receives adjustment commands, automatically adjusts the circulation pump frequency and heat pump power, and synchronously feeds back the operation results to the monitoring and early warning module during the execution process; Monitoring and early warning module: Tracks parameter changes in real time after regulation. If the heating water temperature returns to normal within 30 minutes, the regulation is deemed effective and the regulation data is recorded. If it is still abnormal after 30 minutes, an early warning for cross-regional regulation is triggered and the signal is pushed to the secondary regulation process.

[0011] Furthermore, the second-level control logic in the three-level control logic is specifically as follows: Data acquisition module: Expands the acquisition range, simultaneously collecting heat source temperature, flow rate, and heating water temperature data from both demand and surplus areas, and transmits them to the parameter analysis module in real time; Parameter analysis module: Confirms the heat supply deficit in the demand area, calculates the total energy surplus in the surplus area, determines the feasibility of dispatching, and ensures that the heating water temperature in the surplus area is not lower than the lower limit of the normal range after dispatching. Regulation and decision-making module: based on analysis results and joint reward function Generate cross-regional linkage instructions; The instruction is to open the heating network connection valve between the demand area and the surplus area, and adjust the valve opening according to the shortage. The valve opening is calculated according to the following formula.

[0012] in, For valve opening, To address the heating shortage in the demand areas, To ensure the total available energy supply in surplus areas, To correct the coefficient, the upper limit of the opening should not exceed 80% to avoid a sudden drop in heat source in the surplus area, and the lower limit should not be lower than 10% to ensure the minimum circulation flow. The instruction is to increase the frequency of circulating pumps in surplus areas to replenish energy lost due to scheduling and maintain their own heat supply. The instruction is to appropriately increase the power of heat pump units in the demand area to improve the heat exchange efficiency of the energy received and dispatched. The coordination server uses an asynchronous parameter update mechanism to aggregate the Q-tables of the two managed decision agents every 15 seconds and calculates the gradient. Update the regulatory model; Execution control module: Synchronously executes the switching of communication valves, frequency adjustment of circulating pumps in surplus areas, and power adjustment of heat pumps in demand areas, and provides real-time feedback on the status of each device; If the demand area is still abnormal after 1 hour of scheduling, a multi-area abnormality warning will be triggered, and the three-level control logic will be entered.

[0013] Furthermore, the system also includes a priority setting module, which is used to preset the heating priority of each area and supports manual adjustment of the priority order according to actual production and living needs, providing a priority basis for energy allocation when multiple areas are under abnormal control at the same time.

[0014] Furthermore, the data acquisition module: collects parameters from all regions, confirms at least two demand regions and one core surplus region with normal parameters; Parameter Analysis Module: Calculates the heat supply deficit of each demand area, confirms the maximum dispatchable amount of the core surplus area, and allocates dispatchable energy to the core surplus area in combination with the area priority setting module. Regulation and decision-making module: Generates multi-region allocation instructions based on the size and priority of the shortage. The execution control module performs multiple sets of interconnected valve switching, circulating pump frequency adjustment, and heat pump power adjustment operations in stages to ensure that high-priority areas receive energy first. Monitoring and early warning module: Real-time monitoring of the temperature in the core surplus area. If it is lower than the lower limit of the normal range, the transmission volume of the low priority area will be reduced immediately. After the temperature of the high priority area returns to normal, a low priority scheduling signal will be pushed. After the temperature of all areas returns to normal, a scheduling end signal will be pushed, and the control module will close the redundant connecting valves, and all equipment will resume normal operation.

[0015] Furthermore, the aforementioned heat supply deficit The formula is as follows:

[0016] in, The specific heat capacity of water at constant pressure. For heating water density, This represents the actual flow rate of the circulating water in the heating network. For the target temperature of heating water and This refers to the actual temperature of the heating water.

[0017] Furthermore, the execution control module specifically instructs the priority opening of the heating network connection valves between the core surplus area and the high-priority demand area, and allocates the dispatchable quantity according to the following formula:

[0018] in, The amount of energy allocated to high-priority demand areas, The maximum dispatchable energy volume in the core surplus area; Once the heating water temperature in the high-priority area approaches the normal range, the instruction will open the connection valve between the core surplus area and the low-priority demand area, and allocate the dispatchable supply according to the following formula:

[0019] in, The amount of energy allocated to areas with low priority demand.

[0020] Furthermore, the execution control module specifically instructs the core surplus area circulation pump frequency to be increased to the upper limit, and each demand area to increase the heat pump power; simultaneously, based on the joint reward function... Optimize energy allocation to ensure maximum heating satisfaction and minimize pump energy consumption and temperature permeability.

[0021] The technical effects and advantages of this invention are as follows: In this invention, the parameter analysis module constructs a topology engine based on virtual grid modeling of the heating network. It calculates the heat shortage in demand areas and the total dispatchable amount in surplus areas using a heat supply deficit formula, accurately determining dispatch feasibility and avoiding blind diversion. During the control decision-making stage, energy output is controlled through valve opening formulas, while simultaneously increasing the frequency of circulating pumps in surplus areas to replenish lost energy and prevent insufficient heat supply. The monitoring and early warning module tracks the water temperature in surplus areas in real time; if it approaches the lower limit of normal, it pushes a command to reduce valve opening, dynamically avoiding water temperature fluctuations in the dispatched areas. For example, when the air compressor cooling water area is considered surplus, the system can accurately control the output of residual heat, satisfying heating needs in demand areas while ensuring the heat dissipation and heating stability of its own cooling water system, breaking the cycle of solving one problem leading to failure in another.

[0022] In this invention, the system also maximizes the stability of heating across the entire area and the utilization of clean energy. It employs a three-tiered, progressive control logic: when a single area experiences an anomaly, internal controls such as adjusting the circulation pump frequency and heat pump power quickly resolve the issue, reducing reliance on cross-area scheduling; during cross-area scheduling, a distributed Q-learning algorithm and joint reward function optimize heating satisfaction, pump energy consumption, and network heat uniformity, while coordinating with the server to asynchronously update model parameters every 15 seconds, improving control accuracy; when multiple areas experience anomalies, priority settings and energy allocation formulas prioritize heating for high-priority areas, gradually satisfying lower-priority needs. Attached Figure Description

[0023] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts: Figure 1 This is a flowchart of the clean heating process for a water treatment plant area according to the present invention; Figure 2 This is a flowchart of the clean heating process for the mining building area according to the present invention; Figure 3 This is a flowchart of the clean heating process for the factory area according to the present invention; Figure 4 This is a flowchart illustrating the clean heating scheduling process for the water treatment plant area, mining building area, factory area, and scheduling unit of the present invention. Figure 5 This is a flowchart of the scheduling unit of the present invention. Detailed Implementation

[0024] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0025] This invention provides a technical solution: a multi-source waste heat clean heating system, comprising a water treatment plant area, a mining building area, a factory area, and a scheduling unit for regulating the above three areas; Specifically, the clean heating methods in the water treatment plant area are as follows: Reference Figure 1 As shown, pressurized concentrated water at around 25°C from the water treatment plant is treated by a sludge remover and then pressurized by a circulating water pump to a water-to-water plate heat exchanger to heat the intermediate water. A flow meter and a temperature sensor are installed between the heat exchanger and the circulating water pump. The flow meter is an electromagnetic flow meter used to measure the flow rate of the concentrated water, and the temperature sensor is a platinum resistance temperature sensor used to measure the temperature of the concentrated water. The concentrated water after heat release is returned to the water treatment plant. Based on the above, the intermediate water acts as a heat transfer medium, absorbing heat from the concentrated water through the heat exchanger. After gaining heat, it is pressurized by the water pump and sent to the evaporator of the heat pump unit to release heat. After releasing heat, it returns to the heat exchanger for circulation. Based on the above, the return water from the heating network is treated by a dirt separator, pressurized by a circulating pump, and sent to the condenser of the heat pump unit for heating. A temperature sensor is installed between the heat pump unit and the heating users to measure the temperature of the heating water. The heated water from the heating network is then sent to each heating user. It should be further noted that variable frequency water supply with constant pressure is used, the water supply point is at the inlet header of the circulating water pump, and the water source is softened tap water. Specifically, the clean heating method for the mining building area is as follows: Reference Figure 2 As shown, the 38°C air compressor cooling water is treated by a dirt separator and then pressurized by a circulating water pump before entering a water-to-water plate heat exchanger to heat the intermediate water. A flow meter and a temperature sensor are installed between the heat exchanger and the circulating water pump. The flow meter is an electromagnetic flow meter used to measure the flow rate of the concentrate, and the temperature sensor is a platinum resistance temperature sensor used to measure the temperature of the concentrate. The cooled water after heat release returns to the cooling water system. Based on the above, after the intermediate water absorbs heat from the cooling water, it is pressurized by the water pump and sent to the evaporator of the heat pump unit to release heat. After releasing heat, it returns to the heat exchanger for circulation. Based on the above, the return water from the heating network is treated by a dirt separator, pressurized by a circulating pump, and sent to the condenser of the heat pump unit for heating. A temperature sensor is installed between the heat pump unit and the heating users to measure the temperature of the heating water. The heated water from the heating network is then sent to each user. It should be further noted that variable frequency water supply with constant pressure is used, the water supply point is at the inlet header of the circulating water pump, and the water source is softened tap water. Specifically, the clean heating methods for the factory area are as follows: Reference Figure 3 As shown, the 23°C slurry water taken from the 80m tailings thickener slurry water pipe before the shut-off valve first enters the high-level closed expansion buffer manifold. The effluent is pressurized by the slurry pump and enters the ore-water plate heat exchanger. A flow meter and a temperature sensor are installed at the connection pipe between the heat exchanger and the slurry pump. The flow meter is a wear-resistant electromagnetic flow meter used to measure the flow rate of the concentrate, and the temperature sensor is a platinum resistance temperature sensor used to measure the temperature of the concentrate. The slurry water after heat release returns to the pipeline after the shut-off valve and is transported to the tailings conveying pump station. Based on the above, after the intermediate water absorbs the heat from the slurry water, it is pressurized by a water pump and sent to the evaporator of the heat pump unit to release heat. After releasing heat, it returns to the heat exchanger for circulation. Based on the above, the return water from the heating network is treated by a dirt separator and then pressurized by a circulating pump before being sent to the condenser of the heat pump unit for heating. Temperature sensors are installed between the heat pump unit and the heating users to measure the temperature of the heating water. The heated water from the heating network is then sent to each user for circulation. It should be further noted that variable frequency water supply with constant pressure is adopted, and the water source is softened tap water, with the water supply point located at the inlet header of the circulating water pump.

[0026] Reference Figures 1-5 As shown in this implementation scheme: the scheduling unit includes a data acquisition module, used for: Real-time acquisition of temperature data from sensors in each area (heat source temperature) Heating demand temperature ), flow meter parameters (heat source flow) The system transmits data on the area priority to the parameter analysis module every 5 minutes. It also provides physical network data for virtual grid connection modeling of the heating network and data support for the construction of the agent state space of the distributed Q-learning algorithm. The temperature sensors include the heat source temperature and the heating water temperature.

[0027] The parameter analysis module is used for: The system receives data transmitted from the data acquisition module and constructs a virtual heating network topology engine based on virtual pipe network modeling technology (mapping the physical pipe network into a graph structure of nodes (heat sources / users) and edges (potential connection paths). Simultaneously, it analyzes the impact of waste heat source temperature / flow fluctuations on the heating load of each area using a load sensitivity matrix, compares the normal range of each parameter to determine if any anomalies exist, and analyzes the problems corresponding to the abnormal parameters. Based on cross-area scheduling and control modes and multi-area simultaneous abnormal control modes, it calculates the energy deficit in demand areas and the total energy surplus in surplus areas using the original formula, providing a basis for control decisions. The control and decision-making module is used for: Based on the analysis results from the parameter analysis module, a distributed Q-learning algorithm is introduced to deploy a management decision-making agent (whose state space is...) in each region. Action space A = {opening / closing each connecting valve, adjusting the opening degree from 0% to 100%}); design a joint reward function. (Among them, temperature permeability) , This is the weighting coefficient for heating satisfaction. This is the pump energy consumption weighting coefficient. This is the weighting coefficient for temperature uniformity. This is a correction value for the deviation between the actual heating temperature and the target temperature. This involves optimizing heating satisfaction, pump energy consumption, and network thermal uniformity through real-time power monitoring of the circulating pump. Simultaneously, an asynchronous parameter update mechanism is employed (the coordination server aggregates the Q-tables of each agent every 15 seconds and calculates the gradient). θ represents the parameters of the Q-learning model. As a discount factor, The control model parameters are updated (for the Q value of the next state to action pair); specific control instructions (such as adjusting pump frequency, opening and closing valves, adjusting equipment power, etc.) are generated according to the three-level control logic, and the instructions are transmitted to the execution control module; at the same time, historical control data is stored for subsequent analysis and optimization. The execution control module is used for: Receive instructions from the control and decision-making module, and based on the action space A of the control decision-making agent, control the frequency of circulating pumps (concentrate pump, cooling water pump, slurry pump) in each area, the power of heat pump units, the opening and closing of the heat network connection valves (0%~100%), and the opening and closing of the water replenishment and pressure stabilization system valves, and execute specific control operations to ensure that the control instructions are implemented. The priority setting module is used for: The system presets heating priorities for different areas (factory area > mining building area > water treatment plant area), and allows manual adjustment of the priority order based on actual production and living needs, providing a priority basis for energy allocation when multiple areas are under abnormal control simultaneously. The monitoring and early warning module is used for: Real-time monitoring of parameter changes and control operation execution in each area, synchronously tracking scheduling response efficiency and pipeline heat loss data under distributed Q-learning algorithm control; if parameters remain abnormal for an extended period after control, or equipment malfunctions (such as pump overload or valve failure), an early warning signal is triggered to alert staff to intervene promptly.

[0028] Furthermore, the specific operations of the scheduling unit are as follows: Level 1: Self-regulation of the region, i.e., when a single region is abnormal, it relies on the closed-loop processing of internal modules.

[0029] First, the control mechanism is triggered: Data acquisition module: Real-time acquisition of heat source temperature (concentrate / cooling water / slurry water temperature), heat source flow rate (concentrate / cooling water / slurry water flow rate), and heating water temperature parameters in the area, transmitting data to the parameter analysis module every 5 minutes; Parameter analysis module: Compare parameters to normal ranges to determine the type and cause of abnormalities; If the temperature of the heating water is lower than the normal range, and the temperature of the heat source is normal but the flow rate of the heat source is lower than the normal range, it is determined that the heat source flow rate is insufficient, resulting in a shortage of heat supply. If the temperature of the heating water is higher than the normal range, and the temperature of the heat source is normal while the flow rate of the heat source is higher than the normal range, it is determined that the heat source flow rate is excessive, resulting in redundant heat supply. If the heat source flow rate is normal but the heating water temperature is abnormal, it is determined that the heat pump unit has insufficient or excessive heat exchange efficiency.

[0030] Then, control instructions are generated. The control decision module is based on the parameter analysis results and the state space of the control decision agent. Generate equipment adjustment instructions within the area; In response to insufficient heat source flow, the frequency of the circulating pumps (concentrate pump / cooling water pump / slurry pump) in that area was increased to improve the heat source flow. In response to excessive heat source flow, the frequency of the circulating pump in that area is reduced to decrease the heat source flow. To address the issue of heat pump efficiency, commands are given to increase / decrease the evaporator power of the heat pump unit. If there are fluctuations in system pressure, the valve of the water replenishment and pressure stabilization system will be opened / closed to replenish / reduce the amount of softened tap water. At this time, a pressure sensor can be installed in the system pipeline to detect the system pressure and replenish water in a timely manner.

[0031] Finally, regulation implementation and monitoring The execution control module receives control decision commands, automatically adjusts the circulation pump frequency, heat pump power, and water supply valve status, and synchronously feeds back the operation results to the monitoring and early warning module during the execution process; Monitoring and early warning module: Tracks parameter changes in real time after regulation. If the heating water temperature returns to normal within 30 minutes, the regulation is deemed effective and the regulation data is recorded. If it is still abnormal after 30 minutes, an early warning for cross-regional regulation is triggered, and the signal is pushed to the next level of regulation process.

[0032] Level 2: Cross-regional scheduling and control, meaning that single-region self-control is ineffective, and dual-region module collaboration is required.

[0033] First, the scheduling condition determination. Data acquisition module: Expands the acquisition range and simultaneously collects heat source temperature, flow rate, and heating water temperature data from demand areas (areas where self-regulation is ineffective) and surplus areas (areas with normal parameters and heating water temperature above the upper limit), and transmits them to the parameter analysis module in real time. Parameter analysis module: Confirms the heating shortage in the demand area (e.g., heating water temperature is 2°C lower than normal). Calculate the total energy surplus in the surplus area (e.g., if the heating water temperature is 1°C higher than the normal range, the total amount of circulating water in the heating network can be dispatched). Determine the feasibility of scheduling and ensure that the temperature of heating water in surplus areas does not fall below the lower limit of the normal range after scheduling.

[0034] Then, the scheduling instructions are generated. The regulation decision module is based on the analysis results and the joint reward function. Generate cross-regional linkage instructions: The instruction opens the heating network connection valve between the demand area and the surplus area, and adjusts the valve opening according to the deficit; if the deficit is large, the valve is opened larger, and if the deficit is small, the valve is opened smaller. Heat supply deficit The calculation focuses on the difference between the target heat supply required by the heating system and the actual output heat supply. It is derived based on the temperature difference, flow rate, and thermophysical properties of the heating water. The formula is as follows:

[0035] in, The specific heat capacity of water at constant pressure. For heating water density, This represents the actual flow rate of the circulating water in the heating network. For the target temperature of heating water and Based on the actual temperature of the heating water, the parameter analysis module calculates the shortage in the demand area using this formula, and then combines it with the total energy surplus in the surplus area to determine the feasibility of scheduling. The valve opening is calculated using the following formula:

[0036] in, For valve opening, To address the heating shortage in the demand areas, To ensure the total available energy supply in surplus areas, The correction factor (value 0.8-1.0, preset according to the heat loss characteristics of the pipeline) is set, and the upper limit of the opening is no more than 80% to avoid a sudden drop in heat source in the surplus area, and the lower limit is no less than 10% to ensure the minimum circulation flow. The instruction is to increase the frequency of circulating pumps in surplus areas to replenish energy lost due to scheduling and maintain their own heat supply. The instruction is to appropriately increase the power of heat pump units in the demand area to improve the heat exchange efficiency of the energy received and dispatched. The coordination server uses an asynchronous parameter update mechanism to aggregate the Q-tables of the two managed decision agents every 15 seconds and calculates the gradient. Update the regulatory model.

[0037] Finally, scheduling execution and dynamic monitoring Execution control module: Synchronously executes the switching of communication valves, frequency adjustment of circulating pumps in surplus areas, and power adjustment of heat pumps in demand areas, and provides real-time feedback on the status of each device; Monitoring and early warning module: If the temperature of the heating water in the surplus area is close to the lower limit of the normal range, a signal to reduce the opening of the connecting valve will be pushed. The control decision module will generate a corresponding instruction and execute the control module to make adjustments. If the heating water temperature in the demand area returns to normal, a signal to close the communication valve will be sent to stop dispatching. If the demand area is still abnormal after 1 hour of scheduling, a multi-area abnormality warning will be triggered, and the third level of control will be activated.

[0038] Level 3: Simultaneous abnormal control in multiple areas First, abnormal scenario identification and priority confirmation. Data acquisition module: Collects parameters for all regions, confirming at least two regions with demand and one core region with normal parameters and surplus capacity.

[0039] Parameter analysis module: Calculates the heat supply deficit in each demand area (e.g., 30% deficit in the factory area, 20% deficit in the water treatment plant area), and confirms the maximum dispatchable amount in the core surplus area.

[0040] The formula for the heat supply deficit is as follows:

[0041] in, The specific heat capacity of water at constant pressure. For heating water density, This represents the actual flow rate of the circulating water in the heating network. For the target temperature of heating water and This refers to the actual temperature of the heating water. The parameter analysis module calculates the shortage of each demand area using the heat supply shortage formula, and combines the priority setting module's priority of factory area > mining building area > water treatment plant area to allocate dispatchable energy in the core surplus area. Priority setting module: Calls preset priorities. If there is a temporary production need, the priority can be manually adjusted (e.g., the priority can be temporarily increased when there is a high density of people in the mining building during winter).

[0042] Then, multi-directional scheduling instructions are generated. The control and decision-making module combines the size of the shortfall and priority to generate multi-region allocation instructions: The instruction prioritizes opening the heating network connection valves (opening degree 0%~100%) between the core surplus area and the high-priority demand area, and allocates the dispatchable capacity according to the following formula:

[0043] in, The amount of energy allocated to high-priority demand areas, The maximum dispatchable energy volume in the core surplus area; Once the heating water temperature in the high-priority area approaches the normal range, the connecting valve between the core surplus area and the low-priority demand area will be opened (opening degree 0%~100%), and the dispatchable supply will be allocated according to the following formula:

[0044] in, The amount of energy allocated to low-priority demand areas; The frequency of the circulating pump in the spare area of ​​the command core is increased to the upper limit, and the power of the heat pump in each demand area is increased. Meanwhile, based on the joint reward function Optimize energy allocation to ensure maximum heating satisfaction and minimize pump energy consumption and temperature permeability.

[0045] Finally, scheduling execution and end-to-end monitoring. The execution control module performs multiple sets of interconnected valve switching, circulating pump frequency adjustment, and heat pump power adjustment operations in stages to ensure that high-priority areas receive energy first. Monitoring and early warning module: Real-time monitoring of temperature in core surplus areas; if it falls below the lower limit of the normal range, immediately reduce the delivery volume to low-priority areas. Once the temperature in the high-priority area returns to normal, a switchover signal for the low-priority scheduling will be pushed. Once the temperature in all areas returns to normal, a dispatch end signal is sent, the execution control module closes any unnecessary connecting valves, and all equipment resumes normal operation.

[0046] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A multi-source waste heat clean heating system, characterized in that, include: The multi-source waste heat unit includes at least three heating zones. Each heating zone uses a heat source to transfer circulating water to the heating users through an intermediate water source. The heating zones are connected by a heat network. The data acquisition module is used to collect monitoring parameters and heating area priority for each heating area in real time. The parameter analysis module is used to receive data transmitted from the data acquisition module and build a virtual heating network topology engine based on the virtual heating network pipe modeling technology. At the same time, it combines the load sensitivity matrix to analyze the impact of waste heat source parameter fluctuations on the heating load of each area, compares each parameter with the normal range, determines whether there are any abnormalities, and analyzes the problems corresponding to the abnormal parameters. Based on the cross-regional scheduling and control mode and the multi-regional simultaneous abnormal control mode, the energy deficit of the demand area and the total energy surplus of the surplus area are calculated according to the original formula. The regulation and decision-making module is used to introduce a distributed Q-learning algorithm based on the analysis results of the parameter analysis module, deploy management decision-making agents in each area, design a joint reward function, and update the regulation model parameters using an asynchronous parameter update mechanism; and generate specific regulation instructions according to the three-level regulation logic. The execution control module receives instructions from the regulation decision module and, based on the action space A of the network decision agent, controls the frequency of circulating pumps, the power of heat pump units, and the opening and closing of heat network interconnection valves in each area to perform specific regulation operations. The monitoring and early warning module is used to monitor parameter changes and control operation execution in each heating area in real time, and simultaneously track the scheduling response efficiency and pipeline heat loss data under the control of the distributed Q-learning algorithm. If the parameters remain abnormal for an extended period after adjustment, a warning signal will be triggered.

2. The multi-source waste heat clean heating system according to claim 1, characterized in that: Specifically, the heating area uses heat source water, pressurized by a liquid pump, to be sent to a heat exchanger to heat intermediate water. A flow meter and a temperature sensor are installed between the liquid pump and the heat exchanger. The flow meter measures the flow rate of the concentrate, and the temperature sensor measures the temperature of the concentrate. The intermediate water absorbs heat from the heat source water through the heat exchanger, and after gaining heat, it is pressurized by a water pump and sent to the evaporator of the heat pump unit to release heat. After releasing heat, it returns to the heat exchanger for circulation. The return water of the heating network is pressurized by a circulating pump and sent to the condenser of the heat pump unit for heating. A temperature sensor is installed between the heat pump unit and the heating users to measure the temperature of the heating water. The heated heating network water is then sent to each heating user.

3. The multi-source waste heat clean heating system according to claim 2, characterized in that: The monitoring parameters specifically include the heat source temperature of the temperature sensor, the heating demand temperature of the temperature sensor, and the heat source flow rate of the flow meter.

4. The multi-source waste heat clean heating system according to claim 3, characterized in that: The first-level control logic in the three-level control logic is specifically as follows: Data acquisition module: Real-time acquisition of heat source temperature, heat source flow rate and heating water temperature parameters for a single area, and transmission of the data to the parameter analysis module; Parameter analysis module: Compare parameters to normal ranges, determine the type and cause of abnormalities, and generate parameter analysis results; Regulation and decision-making module: Based on parameter analysis results and the state space of the built-in management decision-making agent. Generate equipment adjustment instructions within the area; Execution control module: Receives adjustment commands, automatically adjusts the circulation pump frequency and heat pump power, and synchronously feeds back the operation results to the monitoring and early warning module during the execution process; Monitoring and early warning module: Tracks parameter changes in real time after regulation. If the heating water temperature returns to normal within 30 minutes, the regulation is deemed effective and the regulation data is recorded. If the situation remains abnormal after 30 minutes, a cross-regional control warning will be triggered, and the signal will be pushed to the secondary control process.

5. A multi-source waste heat clean heating system according to claim 4, characterized in that: The The second-level control logic within the three-level control logic is specifically as follows: Data acquisition module: Expands the acquisition range, simultaneously collecting heat source temperature, flow rate, and heating water temperature data from both demand and surplus areas, and transmits them to the parameter analysis module in real time; Parameter analysis module: Confirms the heat supply deficit in the demand area, calculates the total energy surplus in the surplus area, determines the feasibility of dispatching, and ensures that the heating water temperature in the surplus area is not lower than the lower limit of the normal range after dispatching. Regulation and decision-making module: based on analysis results and joint reward function Temperature permeability , This is the weighting coefficient for heating satisfaction. This is the pump energy consumption weighting coefficient. This is the weighting coefficient for temperature uniformity. This is a correction value for the deviation between the actual heating temperature and the target temperature. Generate cross-region linkage commands for the real-time power of the circulating pump; The instruction is to open the heating network connection valve between the demand area and the surplus area, and adjust the valve opening according to the shortage. The valve opening is calculated according to the following formula. in, For valve opening, To address the heating shortage in the demand areas, To ensure the total available energy supply in surplus areas, To correct the coefficient, the upper limit of the opening should not exceed 80% to avoid a sudden drop in heat source in the surplus area, and the lower limit should not be lower than 10% to ensure the minimum circulation flow. The instruction is to increase the frequency of circulating pumps in surplus areas to replenish energy lost due to scheduling and maintain their own heat supply. The instruction is to appropriately increase the power of heat pump units in the demand area to improve the heat exchange efficiency of the energy received and dispatched. The coordination server uses an asynchronous parameter update mechanism to aggregate the Q-tables of the two managed decision agents every 15 seconds and calculates the gradient. θ represents the parameters of the Q-learning model. As a discount factor, Update the control model for the Q-value of the next state-to-action pair; Execution control module: Synchronously executes the switching of communication valves, frequency adjustment of circulating pumps in surplus areas, and power adjustment of heat pumps in demand areas, and provides real-time feedback on the status of each device; If the demand area is still abnormal after 1 hour of scheduling, a multi-area abnormality warning will be triggered, and the three-level control logic will be entered.

6. A multi-source waste heat clean heating system according to claim 5, characterized in that: It also includes a priority setting module, which is used to preset the heating priority of a region and supports manual adjustment of the priority order according to actual production and living needs, providing a priority basis for energy allocation when multiple regions are under abnormal control at the same time.

7. A multi-source waste heat clean heating system according to claim 6, characterized in that: Data acquisition module: Collect parameters for all regions, and confirm at least two regions with demand and one core region with normal parameters and surplus capacity; Parameter Analysis Module: Calculates the heat supply deficit of each demand area, confirms the maximum dispatchable amount of the core surplus area, and allocates dispatchable energy to the core surplus area in combination with the area priority setting module. Regulation and decision-making module: Generates multi-region allocation instructions based on the size and priority of the shortage. The execution control module performs multiple sets of interconnected valve switching, circulating pump frequency adjustment, and heat pump power adjustment operations in stages to ensure that high-priority areas receive energy first. Monitoring and early warning module: Real-time monitoring of the temperature in the core surplus area. If it is lower than the lower limit of the normal range, the transmission volume of the low priority area will be reduced immediately. After the temperature of the high priority area returns to normal, a low priority scheduling signal will be pushed. After the temperature of all areas returns to normal, a scheduling end signal will be pushed, and the control module will close the redundant connecting valves, and all equipment will resume normal operation.

8. A multi-source waste heat clean heating system according to claim 5 or 7, characterized in that: The formula for the heat supply deficit is as follows: in, The specific heat capacity of water at constant pressure. For heating water density, This represents the actual flow rate of the circulating water in the heating network. For the target temperature of heating water and This refers to the actual temperature of the heating water.

9. A multi-source waste heat clean heating system according to claim 7, characterized in that: Specifically, the execution control module instructs the priority opening of the heating network connection valves between the core surplus area and the high-priority demand area, and allocates the dispatchable quantity according to the following formula: in, The amount of energy allocated to high-priority demand areas, The maximum dispatchable energy volume in the core surplus area; Once the heating water temperature in the high-priority area approaches the normal range, the instruction will open the connection valve between the core surplus area and the low-priority demand area, and allocate the dispatchable supply according to the following formula: in, The amount of energy allocated to areas with low priority demand.

10. A multi-source waste heat clean heating system according to claim 7, characterized in that: Specifically, the execution control module also instructs the circulation pump frequency in the core surplus area to be increased to the upper limit, and the heat pump power in each demand area to be increased; at the same time, it optimizes energy allocation based on the joint reward function to ensure maximum heating satisfaction and minimum pump energy consumption and temperature penetration rate.

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