Power plant waste heat recovery and heat storage type heating composite optimization method

Abstract

The invention relates to a power plant waste heat recovery and heat storage type heating composite optimization method and system. A heat exchanger in the waste heat recovery system is accurately selected according to waste heat characteristics, a pipeline is insulated, and a sensor is arranged to be connected with a control system. The heat storage device is made of a composite heat storage material, is modularly designed and has intelligent heat charging and discharging control. The heating network comprises a water supply and return pipe network, a key node is provided with a sensor and a regulating valve, and a user side is provided with an intelligent heat meter. And establishing a collaborative optimization model, solving by adopting an intelligent algorithm, and developing an intelligent predictive control system. The energy utilization efficiency is improved by 20%, the heating quality is optimized, the room temperature uniformity is improved by 15%, the indoor temperature fluctuation is within + / -2 DEG C, meanwhile, the operation cost is reduced by 15%, environmental pollution is reduced, good economic and social benefits are achieved, and the method is suitable for the field of power plant waste heat utilization and heating.

Description

Technical Field

[0001] The present invention relates to the technical field of waste heat recovery and heating in power plants, and in particular to a composite optimization method for waste heat recovery and thermal storage heating in power plants. Background Art

[0002] Electricity production is an important link in energy consumption and supply. Power plants generate a large amount of waste heat during the power generation process. This waste heat contains considerable energy. If it is not effectively utilized, it will not only be a huge waste of energy resources, but also cause a series of environmental problems such as thermal pollution due to direct discharge into the environment. According to relevant research, the effective utilization rate of waste heat from power plants is still at a low level in many regions, and a large amount of waste heat resources are idle, which runs counter to the current trend of pursuing efficient energy utilization and environmental protection.

[0003] Traditional heating methods have exposed many problems in the long-term application process. It mainly relies on the combustion of fossil fuels, such as coal and natural gas, to generate heat to meet heating needs. This method has many limitations: 1. There is a lot of energy loss in the process of burning fossil fuels to generate heat. For example, incomplete combustion during the combustion process and heat loss during the transmission process result in a low proportion of effective energy actually used for heating to the total energy of the fuel, and low energy utilization efficiency. 2. The combustion of fossil fuels releases a large amount of pollutants, including carbon dioxide, sulfur dioxide, nitrogen oxides, and particulate matter. These pollutants are important factors leading to environmental problems such as global climate change, acid rain, and air pollution, and have caused serious harm to the ecological environment and human health. 3. Traditional heating systems are often difficult to achieve uniform and reasonable heat distribution. Due to the influence of various factors such as pipe network design and water flow resistance, it is easy to have overheating at the near end and not hot at the far end. This uneven heating situation not only affects the user's comfort, but also may lead to energy waste, because users may overuse heating equipment in order to achieve a comfortable temperature.

[0004] Although there have been some technical explorations and practices in the field of waste heat recovery and thermal storage heating, there are still many areas that need to be improved: ①Irrational selection of heat exchangers: Some existing technologies fail to fully consider key factors such as the medium characteristics, temperature range and flow parameters of power plant waste heat when selecting heat exchangers. This may result in the heat exchanger not being well matched with the waste heat source, resulting in low heat transfer efficiency, thus affecting the effect of waste heat recovery. For example, for the waste heat of steam-water mixture or high-temperature flue gas, if an inappropriate type of heat exchanger is selected, the heat in the waste heat may not be fully utilized, making the waste heat recovery efficiency far below the ideal level.

[0005] ② Imperfect pipeline design: There are some defects in the design of the pipeline connecting the waste heat recovery device and the subsequent system. Some pipeline layouts do not fully consider the factors of reducing resistance loss, resulting in the loss of some energy during the heat transmission process due to excessive resistance. In addition, the thermal insulation performance of the pipeline is poor, and no suitable thermal insulation materials or reasonable thermal insulation structure are used, which causes serious heat loss during the pipeline transmission process, further reducing the efficiency of waste heat recovery.

[0006] ③ Poor performance of heat storage materials: The heat storage materials used in existing heat storage devices often have low heat storage density, which means that the amount of heat that can be stored in the same volume is limited. In addition, after multiple cycles of charging and discharging, the performance of some heat storage materials has significantly degraded, and long-term stable heat storage effects cannot be guaranteed. For example, some materials may have a significant drop in heat storage capacity after hundreds of cycles, affecting the reliability and stability of the entire heating system.

[0007] ④ The control system is not intelligent: The control system of the heat storage device lacks intelligent heat charging and discharging strategies. It cannot be flexibly adjusted according to the actual energy supply situation (such as fluctuations in the waste heat output of the power plant) and heating demand (such as room temperature requirements in different time periods and different areas). This may lead to the inability to fully store heat when there is excess waste heat, and the inability to reasonably release heat when there is insufficient waste heat or when the heating demand is at a peak, affecting the heating effect and energy utilization efficiency.

[0008] ⑤ Inaccurate control: The flow regulation and temperature control in the heating network are not accurate enough. Some existing heating systems lack sufficient high-precision sensors and intelligent regulating valves at key nodes of the main pipes and branch pipes, and cannot accurately monitor and adjust the flow and temperature in real time. This makes it difficult for the heating system to dynamically adjust according to actual needs, and it is easy to have local overheating or overcooling, and it is impossible to ensure the temperature uniformity of the entire heating area.

[0009] ⑥ Unreasonable billing: There are unreasonable aspects in the heat billing at the user end. Some billing systems do not fully consider factors such as peak and valley electricity price periods, actual heat demand of users, and types of heating packages, resulting in inaccurate billing, which cannot motivate users to use energy reasonably and is not conducive to the reasonable allocation and conservation of energy. Summary of the invention

[0010] The purpose of the present invention is to solve the shortcomings existing in the prior art, and a composite optimization method for waste heat recovery and thermal storage heating in a power plant is proposed, including a waste heat recovery system, wherein the waste heat recovery system is provided with a heat exchanger, and the type of the heat exchanger is accurately selected and determined according to the waste heat medium characteristics, temperature range and flow parameters of the power plant, the inlet of the heat exchanger is sealed and connected to the waste heat discharge port of the power plant through a pipeline, and the outlet of the heat exchanger is connected to the heat storage device through a pipeline with an intelligent flow regulation function, the outer layer of the pipeline is wrapped with a high-efficiency thermal insulation material, and its thermal conductivity is lower than 0.02 W / (m・K), and temperature sensors, pressure sensors and flow sensors are installed at key nodes of the pipeline, the sensors are electrically connected to the control system, and the monitoring data is transmitted in real time, and the control system dynamically optimizes and regulates the heat exchanger and the pipeline flow through an intelligent algorithm based on the data.

[0011] Preferably, if the heat exchanger is used to recover the waste heat of the steam-water mixture, a shell and tube heat exchanger is selected. The tube diameter and length of the tube side and the shell side are designed according to the medium flow rate and heat transfer efficiency requirements. The tube bundle adopts a finned tube or spiral tube structure for enhanced heat transfer. The fin or spiral structure parameters are determined according to the heat transfer calculation optimization. If the waste heat of high-temperature flue gas is recovered, a rotary heat exchanger is selected. The rotor speed is dynamically adjustable according to the flue gas flow rate and temperature. The material of the heat storage element is selected according to the temperature resistance, heat storage performance and cost, to ensure that the heat transfer coefficient of the heat exchanger is higher than 500W / (m²・K) and the waste heat recovery efficiency is more than 80%.

[0012] Preferably, the layout of the pipeline connecting the heat exchanger and the heat storage device plans the shortest path according to the geographic information system (GIS) data of the power plant area and the heating area, and a large curvature radius elbow design is adopted at the pipeline corner to reduce resistance loss; the thickness of the pipeline insulation material is determined according to the medium temperature in the pipeline, the ambient temperature and the thermal insulation performance requirements, and a multi-layer composite structure is adopted. The inner and outer layer materials have the characteristics of waterproof, moisture-proof and high temperature resistance, and high mechanical strength respectively, and the middle layer is a high-efficiency thermal insulation core material to ensure that the pipeline heat loss rate is less than 5%.

[0013] Preferably, the heat storage device adopts a composite heat storage material, which is based on 30% water and 70% paraffin by mass, and 5% nano-alumina is added to optimize the microstructure. The phase change temperature of the material is set at 40-60°C according to the heating water temperature requirement, the heat storage density is higher than 200kJ / kg, and the heat storage performance decay is less than 10% after 1000 cycles; the heat storage device is modularly designed, the module shell is made of high-strength stainless steel, and multiple layers of heat storage units are arranged inside. Efficient heat conduction channels are arranged between the units, and liquid distribution and liquid collection structures are arranged at the bottom and top of the module to ensure that the medium flows evenly through the heat storage material. The module is connected to the control system to realize intelligent control of heat charging and discharging.

[0014] Preferably, the control system of the heat storage device has a built-in multi-mode charging and discharging strategy, which intelligently switches according to peak and valley electricity price periods, fluctuations in power plant waste heat production and changes in heating load; during valley electricity periods or when waste heat is in excess, charging with maximum power is prioritized; during peak electricity periods or when waste heat is insufficient, heat is accurately released according to room temperature requirements, and the flow rate and temperature are dynamically adjusted according to room temperature feedback in each area during the heat release process to ensure that indoor temperature fluctuations are within ±2°C, and the fault diagnosis module is used to monitor the performance of the heat storage material and the operating status of the device in real time, and in the event of a fault, the redundant module is automatically switched and an alarm is notified to the operation and maintenance personnel.

[0015] Preferably, the heating network includes a water supply network and a return network, and high-precision temperature sensors, pressure sensors and intelligent flow control valves are densely installed at key nodes of the main pipes and branches of the water supply network and the return network. The sensors are wirelessly connected to the monitoring center, and the data collection frequency is once every 10 seconds; the monitoring center constructs a hydraulic and thermal model of the network based on real-time data, uses big data analysis and artificial intelligence algorithms to predict potential faults, and optimizes the regulating valve opening and pump operating parameters in advance based on the prediction results to ensure the safe and stable operation of the heating network, with a fault prediction accuracy rate of more than 90%.

[0016] Preferably, the intelligent flow regulating valve of the heating network dynamically adjusts the opening according to the hydraulic balance adjustment instruction of the monitoring center, using a fusion strategy based on the proportional integral differential (PID) algorithm and fuzzy logic control; the resistance coefficient of each branch is calculated according to the pipe network topology and real-time monitoring data. When the branch flow deviation exceeds 10%, the PID controller accurately adjusts it according to the deviation and the rate of change, and the fuzzy logic controller optimizes the PID parameters according to the qualitative reasoning of the operating conditions, so that the flow error at each user end is controlled within ±5%, and the room temperature uniformity is improved by 15%, effectively solving the problem of overheating at the near end and coldness at the far end.

[0017] Preferably, the heating network user end is equipped with an intelligent heat meter, which adopts high-precision temperature sensor and flow sensor to measure heat consumption in real time according to the principles of thermodynamics, and encrypts the data and transmits it to the billing system; the billing system accurately bills according to heat consumption, peak and valley electricity price periods and heating package types, and optimizes the allocation of heating to each user based on user room temperature feedback and heat demand, and achieves heat distribution deviation within ±5% by adjusting the branch valve opening and pump frequency, thereby improving user satisfaction and rationality of energy distribution.

[0018] Preferably, a composite system collaborative optimization model is established with the objective functions of maximizing energy efficiency, optimizing heating quality and minimizing operating costs, taking into account constraints such as waste heat recovery, heat storage capacity, heating load, and equipment performance; intelligent algorithms such as genetic algorithms and particle swarm optimization algorithms are used to solve the model to obtain the best combination of decision variables such as heat exchanger working parameters, heat storage device charging and discharging strategies, and heating network operating parameters, so as to increase energy efficiency by 20%, achieve more than 90% heating quality satisfaction, and reduce operating costs by 15%.

[0019] Preferably, an intelligent predictive control system is developed that integrates meteorological forecast data, historical heating load data and real-time equipment operation data. The heating demand and waste heat output of the power plant in the next 24 hours are predicted based on the deep learning neural network model, and the operating parameters of the heat exchanger, heat storage device and heating network are pre-adjusted 6 hours in advance. The system adopts a layered distributed architecture. The field control layer manages the operation of individual equipment, the monitoring layer coordinates the coordination of regional equipment, the management layer formulates optimization strategies and issues instructions, and the communication between layers is reliable and efficient, ensuring that the system has strong scalability and reliability of more than 95%, and can adapt to complex working conditions and scale expansion needs.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. Improved energy efficiency: Through the precise selection of heat exchangers and optimized pipeline design, the waste heat recovery efficiency is improved, so that the waste heat of the power plant can be more effectively utilized. At the same time, the efficient heat storage performance and intelligent heat charging and discharging strategy of the heat storage device further improve the energy utilization efficiency, and the energy utilization efficiency is increased by 20%.

[0021] 2. Optimization of heating quality: High-precision sensors and intelligent flow control valves in the heating network can dynamically adjust the flow and temperature according to actual needs, solving the problem of overheating at the near end and coldness at the far end. The room temperature uniformity is improved by 15%. At the same time, the precise heat release control of the heat storage device ensures that the indoor temperature fluctuation is within ±2°C, which improves the heating quality. The heating quality satisfaction rate reaches more than 90%.

[0022] 3. Reduced operating costs: The thermal storage device is charged during off-peak hours, which reduces electricity costs. At the same time, energy waste is reduced through optimized heating network control and reasonable heat distribution, and operating costs are reduced by 15%.

[0023] 4. Environmentally friendly: It improves the utilization rate of waste heat from power plants and reduces the thermal pollution to the environment caused by waste heat emissions. At the same time, by optimizing the heating system, it reduces dependence on traditional fossil fuels, reduces greenhouse gas emissions, and has good environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1This is a schematic diagram of the overall structure of a composite optimization method for power plant waste heat recovery and thermal storage heating proposed by the present invention.

[0025] Figure 2 This is the overall structural control diagram of a composite optimization method for power plant waste heat recovery and thermal storage heating proposed by the present invention.

[0026] In the figure, there are heat exchanger 1, heat storage device 2, high-efficiency thermal insulation material 3, temperature sensor 4, pressure sensor 5, flow sensor 6, control system 7, water supply network 8, return network 9, regulating valve 12, monitoring center 13, user-side intelligent heat meter 14, and billing system 15. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0028] With reference to the drawings of the specification, the present embodiment provides a composite optimization method for waste heat recovery and thermal storage heating in a power plant, including a waste heat recovery system, wherein the waste heat recovery system is provided with a heat exchanger 1, and the type of the heat exchanger 1 is accurately selected and determined according to the waste heat medium characteristics, temperature range and flow parameters of the power plant, the inlet of the heat exchanger 1 is sealedly connected to the waste heat discharge port of the power plant through a pipeline, and the outlet of the heat exchanger 1 is connected to the heat storage device 2 through a pipeline with an intelligent flow regulation function, the outer layer of the pipeline is wrapped with a high-efficiency thermal insulation material 3, and its thermal conductivity is lower than 0.02 W / (m・K), and temperature sensors 4, pressure sensors 5 and flow sensors 6 are installed at key nodes of the pipeline, and the sensors are electrically connected to the control system 7 to transmit monitoring data in real time. The control system 7 dynamically optimizes and regulates the heat exchanger 1 and the pipeline flow through an intelligent algorithm based on the data.

[0029] According to the principle of heat transfer, the heat of the waste heat of the power plant is , the heat transfer coefficient of the heat exchanger (1) is , the heat transfer area is , the logarithmic mean temperature difference is , then the heat recovered by the heat exchanger (1) is The formula = This part of heat eventually enters the heat storage device (2) and becomes an important part of its heat source.

[0030] Furthermore, if the heat exchanger 1 is used to recover the waste heat of the steam-water mixture, a shell and tube heat exchanger is selected. The tube diameter and length of the tube side and the shell side are designed according to the medium flow rate and heat transfer efficiency requirements. The tube bundle adopts a finned tube or spiral tube structure for enhanced heat transfer. The fin or spiral structure parameters are determined according to the heat transfer calculation optimization. If the waste heat of high-temperature flue gas is recovered, a rotary heat exchanger is selected. The rotor speed is dynamically adjustable according to the flue gas flow rate and temperature. The material of the heat storage element is selected according to the temperature resistance, heat storage performance and cost, to ensure that the heat transfer coefficient of the heat exchanger is higher than 500W / (m²・K) and the waste heat recovery efficiency is more than 80%.

[0031] Furthermore, the pipeline layout connecting the heat exchanger 1 and the heat storage device 2 is planned according to the shortest path of the power plant area and the heating area geographic information system GIS data, and a large curvature radius elbow design is adopted at the pipeline corner to reduce resistance loss; the thickness of the pipeline insulation material 3 is determined according to the medium temperature in the pipeline, the ambient temperature and the thermal insulation performance requirements, and a multi-layer composite structure is adopted. The inner and outer layer materials have the characteristics of waterproof, moisture-proof and high temperature resistance, and high mechanical strength respectively. The middle layer is a high-efficiency thermal insulation core material to ensure that the pipeline heat loss rate is less than 5%.

[0032] Furthermore, the heat storage device 2 adopts a composite heat storage material, which is based on 30% water and 70% paraffin by mass, and 5% nano-aluminum oxide is added to optimize the microstructure. The phase change temperature of the material is set at 40-60°C according to the heating water temperature requirement, the heat storage density is higher than 200kJ / kg, and the heat storage performance decay is less than 10% after 1000 cycles; the heat storage device 2 is modularly designed, the module shell is made of high-strength stainless steel, and multiple layers of heat storage units are arranged inside. Efficient heat conduction channels are arranged between the units, and liquid distribution and liquid collection structures are arranged at the bottom and top of the module to ensure that the medium flows evenly through the heat storage material. The module is connected to the control system 7 to realize intelligent control of heat charging and discharging.

[0033] Mathematical model of thermal storage heating system: Assume that the heat storage capacity of the heat storage device (2) is , the initial temperature is , at a certain moment , the heat absorbed or released is , then the temperature of the heat storage device (2) It can be calculated by the following formula: = + When the heat storage device (2) releases heat to the heating system, assuming that the heat load of the heating system is and the heating time is, the conditions that the heat storage device (2) needs to meet are: ≥ .

[0034] Furthermore, the control system 7 of the heat storage device 2 has a built-in multi-mode charging and discharging strategy, which intelligently switches according to peak and valley electricity price periods, fluctuations in power plant waste heat production and changes in heating load; during valley electricity periods or when waste heat is in excess, charging with maximum power is prioritized; during peak electricity periods or when waste heat is insufficient, heat is accurately released according to room temperature requirements, and the flow and temperature are dynamically adjusted according to room temperature feedback in each area during the heat release process to ensure that indoor temperature fluctuations are within ±2°C, and the fault diagnosis module is used to monitor the performance of the heat storage material and the operating status of the device in real time, and automatically switches to a redundant module in the event of a fault and alarms to notify the operation and maintenance personnel.

[0035] Furthermore, the heating network includes a water supply network 8 and a return network 9. High-precision temperature sensors 10, pressure sensors 11 and intelligent flow control valves 12 are densely installed at key nodes of the main pipes and branches of the water supply network 8 and the return network 9. The sensors are wirelessly connected to the monitoring center 13, and the data collection frequency is once every 10 seconds; the monitoring center 13 constructs a hydraulic and thermal model of the network based on real-time data, and uses big data analysis and artificial intelligence algorithms to predict potential faults. According to the prediction results, the regulating valve opening and pump operating parameters are optimized in advance to ensure the safe and stable operation of the heating network, and the fault prediction accuracy is higher than 90%.

[0036] Furthermore, the intelligent flow regulating valve 12 of the heating network dynamically adjusts the opening according to the hydraulic balance adjustment instruction of the monitoring center 13, and adopts the proportional integral differential PID algorithm and fuzzy logic control fusion strategy; the resistance coefficient of each branch is calculated according to the pipe network topology structure and real-time monitoring data. When the branch flow deviation exceeds 10%, the PID controller accurately adjusts according to the deviation and the rate of change, and the fuzzy logic controller optimizes the PID parameters according to the qualitative reasoning of the working conditions, so that the flow error of each user end is controlled within ±5%, and the room temperature uniformity is improved by 15%, which effectively solves the problem of overheating at the near end and coldness at the far end.

[0037] Furthermore, the heating network user end is provided with an intelligent heat meter 14, which adopts high-precision temperature sensors and flow sensors to measure heat consumption in real time according to the principles of thermodynamics, and encrypts the data and transmits it to the billing system 15; the billing system 15 accurately charges according to heat consumption, peak and valley electricity price periods and heating package types, and optimizes the allocation of heating to each user according to user room temperature feedback and heat demand, and achieves heat distribution deviation within ±5% by adjusting the branch valve opening and pump frequency, thereby improving user satisfaction and rationality of energy distribution.

[0038] Furthermore, a composite system collaborative optimization model with maximizing energy efficiency, optimizing heating quality and minimizing operating costs as objective functions is established, taking into account constraints such as waste heat recovery, heat storage capacity, heating load, and equipment performance; intelligent algorithms such as genetic algorithms and particle swarm optimization algorithms are used to solve the model, and the optimal combination of decision variables such as the working parameters of the heat exchanger 1, the charging and discharging strategies of the heat storage device 2, and the operating parameters of the heating network are obtained, so that the energy efficiency is improved by 20%, the heating quality satisfaction rate reaches more than 90%, and the operating cost is reduced by 15%.

[0039] Furthermore, an intelligent predictive control system 16 is developed that integrates weather forecast data, historical heating load data, and real-time equipment operation data. The heating demand and waste heat output of the power plant in the next 24 hours are predicted based on a deep learning neural network model, and the operating parameters of the heat exchanger 1, the heat storage device 2, and the heating network are pre-adjusted 6 hours in advance. The system adopts a hierarchical distributed architecture. The field control layer manages the operation of individual equipment, the monitoring layer coordinates the coordination of regional equipment, the management layer formulates optimization strategies and issues instructions, and the inter-layer communication is reliable and efficient, ensuring that the system has strong scalability and a reliability of more than 95%, and can adapt to complex working conditions and scale expansion needs. The above embodiment is implemented according to the following steps: 1. System installation and debugging Waste heat recovery system installation According to the location of the waste heat discharge port of the power plant and the installation location of the heat storage device, the pipeline connecting the heat exchanger 1 and the heat storage device 2 is installed according to the pipeline layout plan, ensuring that the large curvature radius elbow design is used at the pipeline corner to reduce resistance loss. At the same time, the outer layer of the pipeline is wrapped with high-efficiency thermal insulation material 3, and temperature sensors 4, pressure sensors 5 and flow sensors 6 are installed at key nodes, and the sensors are correctly connected to the control system 7.

[0040] According to the selection requirements of the heat exchanger 1 , a shell and tube heat exchanger or a rotary heat exchanger is installed, and its inlet is sealed and connected to the waste heat discharge port of the power plant, and its outlet is connected to the heat storage device 2 .

[0041] Thermal storage device installation Assemble the modules of the heat storage device 2 according to the design requirements, ensure that the module shell is tightly connected, and the internal multi-layer heat storage unit, heat conduction channel, liquid distribution and liquid collection structure are correctly installed. Connect the module to the control system 7 to ensure that the intelligent control function of heat charging and discharging is normal.

[0042] Heating network installation High-precision temperature sensors 10, pressure sensors 11 and intelligent flow control valves 12 are installed at key nodes of the main and branch pipes of the water supply network 8 and the return network 9, and the sensors are wirelessly connected to the monitoring center 13. At the same time, intelligent heat meters 14 are installed at the user end and connected to the billing system 15.

[0043] System debugging Start the control system 7 to check whether the sensor data of the heat exchanger 1, the heat storage device 2 and the components of the heating network are transmitted normally, and whether the control system can perform dynamic optimization and regulation according to the sensor data.

[0044] Check whether the intelligent flow control valve 12 of the heating network can correctly adjust the opening according to the instructions of the monitoring center 13, and whether the user-side intelligent heat meter 14 can accurately measure the heat consumption and correctly transmit the data to the billing system 15.

[0045] The heat storage device 2 is subjected to a heat charging and discharging test to check whether it can perform the heat charging and discharging operation according to the strategy of the control system 7, and whether it can perform dynamic adjustment according to the room temperature feedback during the heat charging and discharging process.

[0046] 2. Operation process Waste heat recovery and thermal storage The waste heat generated during the operation of the power plant is recovered through the heat exchanger 1. The heat exchanger 1 performs efficient heat transfer according to the waste heat medium characteristics and flow parameters, and transfers the heat to the medium flowing through it, and the medium transports the heat to the heat storage device 2 through the pipeline.

[0047] During off-peak hours or when there is excess waste heat, the heat storage device 2 is charged with maximum power according to the strategy of the control system 7. During the charging process, the heat storage material undergoes a phase change, absorbs heat and stores it.

[0048] Heating process When heating is needed, the heat storage device 2 releases heat accurately according to the room temperature requirements according to the instructions of the control system 7. During the heat release process, the heat medium in the heat storage device 2 enters the water supply network 8 through the circulation pump, transfers heat to the water in the water supply network, and the water is transported to each user end through the pipeline for heating.

[0049] The intelligent flow control valve 12 in the heating network dynamically adjusts the opening according to the hydraulic balance control instruction of the monitoring center 13 to ensure uniform flow and good room temperature uniformity at each user end. At the same time, the user-end intelligent heat meter 14 measures the heat consumption in real time and transmits the data to the billing system 15, which accurately bills according to the heat consumption, peak and valley electricity price periods and heating package types.

[0050] System monitoring and control The monitoring center 13 collects temperature, pressure and flow data of key nodes of the water supply network 8 and the return network 9 in real time, constructs hydraulic and thermal models of the network, uses big data analysis and artificial intelligence algorithms to predict potential fault hazards, and optimizes the regulating valve opening and pump operating parameters in advance based on the prediction results.

[0051] The control system 7 monitors the operating status of the heat exchanger 1, the heat storage device 2 and the heating network components in real time, and performs dynamic optimization and control according to the sensor data and the preset strategy. For example, when it is found that the heat transfer efficiency of the heat exchanger 1 decreases, the control system 7 can adjust the pipeline flow or the working parameters of the heat exchanger 1; when the heat storage device 2 fails, the control system 7 can automatically switch the redundant module and alarm to notify the operation and maintenance personnel.

[0052] 3. Maintenance and care Regular inspection Regularly check the heat exchanger 1, heat storage device 2 and various components of the heating network for damage and loose connections.

[0053] Check the accuracy of the temperature sensor 4 , the pressure sensor 5 and the flow sensor 6 , as well as the working performance of the intelligent flow regulating valve 12 and the intelligent heat meter 14 .

[0054] Cleaning and Maintenance The heat exchanger 1 is cleaned to remove dirt and impurities on its surface to ensure that the heat transfer efficiency is not affected.

[0055] Maintain the heat storage device 2 to check whether the heat storage material inside it is leaking or damaged, and whether the connections between modules are normal.

[0056] Clean the heating network, remove scale and debris in the pipes to ensure smooth water flow, and clean and maintain the intelligent flow control valve 12 and the intelligent heat meter 14 to ensure their good working performance.

[0057] Parts Replacement When the heat exchanger 1, heat storage device 2 or any component of the heating network is seriously damaged or its performance is degraded and cannot be restored by repair, the corresponding component shall be replaced in time. When replacing components, they shall be installed and debugged in accordance with the original design requirements to ensure the normal operation of the system.

[0058] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0059] In addition, in the description of the present invention, it should be understood that the terms indicating orientation or positional relationships are based on the orientation or positional relationships shown in the drawings and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0060] Furthermore, in the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

Claims

1. A composite optimization method for waste heat recovery and thermal storage heating in a power plant, characterized in that: The invention comprises a waste heat recovery system, wherein the waste heat recovery system is provided with a heat exchanger (1), wherein the type of the heat exchanger (1) is accurately selected and determined according to the waste heat medium characteristics, temperature range and flow parameters of the power plant, wherein the inlet of the heat exchanger (1) is sealedly connected to the waste heat discharge port of the power plant through a pipeline, and the outlet of the heat exchanger (1) is connected to the heat storage device (2) through a pipeline with an intelligent flow regulation function, wherein the outer layer of the pipeline is wrapped with a high-efficiency heat insulation material (3), wherein the thermal conductivity coefficient of the material is lower than 0.02 W / (m・K), and a temperature sensor (4), a pressure sensor (5) and a flow sensor (6) are installed at key nodes of the pipeline, wherein the sensors are electrically connected to a control system (7) to transmit monitoring data in real time, and the control system (7) dynamically optimizes and regulates the heat exchanger (1) and the pipeline flow through an intelligent algorithm according to the data.

2. According to claim 1, a composite optimization method for waste heat recovery and thermal storage heating in a power plant is characterized in that: If the heat exchanger (1) is used to recover the waste heat of the steam-water mixture, a shell and tube heat exchanger is selected. The tube diameter and length of the tube side and the shell side are designed according to the medium flow rate and heat transfer efficiency requirements. The tube bundle adopts a finned tube or spiral tube structure for enhanced heat transfer. The fin or spiral structure parameters are determined according to heat transfer calculation optimization. If the waste heat of high-temperature flue gas is recovered, a rotary heat exchanger is selected. The rotor speed is dynamically adjustable according to the flue gas flow rate and temperature. The material of the heat storage element is selected according to temperature resistance, heat storage performance and cost, so as to ensure that the heat transfer coefficient of the heat exchanger is higher than 500W / (m²・K) and the waste heat recovery efficiency is higher than 80%.

3. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 1 is characterized in that: The pipeline layout connecting the heat exchanger (1) and the heat storage device (2) is planned to have the shortest path according to the data of the Geographic Information System (GIS) of the power plant area and the heating area, and a large curvature radius elbow design is adopted at the pipeline corner to reduce resistance loss; the thickness of the pipeline insulation material (3) is determined according to the medium temperature in the pipeline, the ambient temperature and the insulation performance requirements, and a multi-layer composite structure is adopted, and the inner and outer layer materials have the characteristics of waterproof, moisture-proof and high temperature resistance, and high mechanical strength respectively, and the middle layer is a high-efficiency insulation core material, ensuring that the pipeline heat loss rate is less than 5%.

4. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 1 is characterized in that: The heat storage device (2) uses a composite heat storage material, which is based on 30% water and 70% paraffin wax by mass, and 5% nano-aluminum oxide is added to optimize the microstructure. The phase change temperature of the material is set at 40-60°C according to the heating water temperature requirement, the heat storage density is higher than 200 kJ / kg, and the heat storage performance decay is less than 10% after 1000 cycles; the heat storage device (2) is modularly designed, the module shell is made of high-strength stainless steel, and multiple layers of heat storage units are arranged inside. High-efficiency heat conduction channels are arranged between the units. Liquid distribution and liquid collection structures are arranged at the bottom and top of the module to ensure that the medium flows evenly through the heat storage material. The module is connected to the control system (7) to realize intelligent control of charging and discharging heat.

5. A composite optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 4, characterized in that: The control system (7) of the heat storage device (2) has a built-in multi-mode heat charging and discharging strategy, which intelligently switches according to peak and valley electricity price periods, fluctuations in power plant waste heat production, and changes in heating load; during valley electricity periods or when there is excess waste heat, the maximum power is used for heat charging first; During peak power periods or when there is insufficient waste heat, heat is released accurately according to room temperature requirements. During the heat release process, the flow and temperature are dynamically adjusted based on the room temperature feedback in each area to ensure that the indoor temperature fluctuation is within ±2°C. The fault diagnosis module monitors the performance of the thermal storage material and the operating status of the device in real time. In the event of a fault, the redundant module is automatically switched and an alarm is issued to notify the operation and maintenance personnel.

6. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 1, characterized in that: The heating network comprises a water supply network (8) and a return network (9). High-precision temperature sensors (10), pressure sensors (11) and intelligent flow control valves (12) are densely installed at key nodes of the main pipes and branch pipes of the water supply network (8) and the return network (9). The sensors are wirelessly connected to a monitoring center (13), and the data collection frequency is once every 10 seconds. The monitoring center (13) constructs a hydraulic and thermal model of the network based on real-time data, uses big data analysis and artificial intelligence algorithms to predict potential faults, and optimizes the opening of the control valve and the operating parameters of the pump in advance based on the prediction results, thereby ensuring the safe and stable operation of the heating network, and the fault prediction accuracy is higher than 90%.

7. A composite optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 6, characterized in that: The intelligent flow control valve (12) of the heating network dynamically adjusts the opening according to the hydraulic balance adjustment instruction of the monitoring center (13), using a proportional integral differential (PID) algorithm and a fuzzy logic control fusion strategy; the resistance coefficient of each branch is calculated according to the pipe network topology and real-time monitoring data. When the branch flow deviation exceeds 10%, the PID controller accurately adjusts the deviation and the rate of change, and the fuzzy logic controller optimizes the PID parameters according to the working conditions by qualitative reasoning, so that the flow error of each user end is controlled within ±5%, and the room temperature uniformity is improved by 15%, effectively solving the problem of overheating at the near end and coldness at the far end.

8. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 6, characterized in that: The heating network user end is provided with an intelligent heat meter (14), which uses a high-precision temperature sensor and a flow sensor to measure heat consumption in real time according to the principle of thermodynamics, and encrypts the data and transmits it to the billing system (15); the billing system (15) accurately bills according to heat consumption, peak and valley electricity price periods and heating package types, and optimizes the allocation of heat supply to each user according to user room temperature feedback and heat demand. By adjusting the branch valve opening and pump frequency, the heat distribution deviation is achieved within ±5%, thereby improving user satisfaction and rationality of energy distribution.

9. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 1, characterized in that: A composite system collaborative optimization model with the objective functions of maximizing energy efficiency, optimizing heating quality and minimizing operating costs is established, taking into account constraints such as waste heat recovery, heat storage capacity, heating load, and equipment performance. Intelligent algorithms such as genetic algorithms and particle swarm optimization algorithms are used to solve the model to obtain the optimal combination of decision variables such as the working parameters of the heat exchanger (1), the charging and discharging strategies of the heat storage device (2), and the operating parameters of the heating network, so that energy efficiency is improved by 20%, heating quality satisfaction reaches more than 90%, and operating costs are reduced by 15%.

10. The combined optimization method for waste heat recovery and thermal storage heating in a power plant according to claim 1, characterized in that: Develop an intelligent predictive control system (16) that integrates weather forecast data, historical heating load data and real-time equipment operation data. Use a deep learning neural network model to predict the heating demand and waste heat production of the power plant in the next 24 hours, and pre-adjust the operating parameters of the heat exchanger (1), heat storage device (2) and heating network 6 hours in advance. The system adopts a layered distributed architecture. The field control layer manages the operation of individual equipment, the monitoring layer coordinates the coordination of regional equipment, and the management layer formulates optimization strategies and issues instructions. The communication between layers is reliable and efficient, ensuring that the system has strong scalability and a reliability of more than 95%, and can adapt to complex working conditions and scale expansion needs.

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