Large-capacity heat storage device and heating system

Abstract

The present invention relates to the technical field of waste heat recovery, and discloses a large-capacity heat storage device and a heating system, including a mounting frame, a heat storage box is fixedly mounted on the inner side of the mounting frame, a heat storage water outlet distributor is connected to one side of the heat storage box, a heat storage return water distributor is connected to the other side of the heat storage box, a motor is fixedly mounted on the back of the heat storage box, a pulley A is mounted on the output end of the motor, a pulley B is rotatably connected to the inner wall of the heat storage box, and a transmission belt is sleeved on the surface of the pulley A and the pulley B. The present invention drives the heat storage element to move by a motor, so that it moves evenly in the heat storage box, increases the contact area and contact time between the heat storage element and the hot water, thereby improving the heat exchange efficiency, and because the heat storage element continuously moves in the heat storage box, it can more fully absorb the heat in the water, and the heat storage effect is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste heat recovery, and more specifically, to a large-capacity heat storage device and a heating system. Background Art

[0002] About 65% of the high-quality waste heat generated in the production processes of various industries such as urban waste incineration, landfill biogas, steel, metallurgy, chemical industry, sewage treatment, and power generation is basically recovered for power generation and industrial steam. However, the intermittent, unstable, and low-quality waste heat, which accounts for more than 85% of the total waste heat, has not been fully recovered and utilized due to the lack of mature heat storage process technology and engineering equipment. In particular, the technology and means for waste heat utilization are extremely lacking.

[0003] A patent application with publication number CN208720321U discloses a heat storage heating device and a heat storage heating system, including: a heat storage station, a first transmission channel, a heat storage box and a heat storage component; the heat storage station is connected to an external heat source to transmit the heat energy of the external heat source to the heat storage station, and the heat storage station is connected to the heat storage box through the first transmission channel to supply heat in the heat storage box; the heat storage component includes a plurality of liquid distribution components and a plurality of heat storage elements, the plurality of liquid distribution components are symmetrically arranged at both ends of the heat storage box, and the plurality of liquid distribution components are used to transmit fluid to the heat storage box. the first delivery channel is connected to the liquid distribution assembly through a first connector, a sealing portion is provided on the first connector, and a detection device is provided on the first connector, and the detection device is used to detect the pressure information of the first connector; a plurality of heat storage elements are arranged inside the heat storage box, and the plurality of heat storage elements are arranged to form an array or a stack, and a circulation channel for liquid flow is formed between the plurality of heat storage elements, and the heat storage elements can undergo a phase change relative to the temperature of the liquid flowing in the circulation channel, so that the heat storage elements can store or supply heat through the liquid flowing in the circulation channel.

[0004] However, in the prior art, the heat storage element is fixed in the heat storage box, resulting in limited contact area and contact time between the heat storage element and hot water, and low heat exchange efficiency. In addition, since the heat storage element is fixed in position, it cannot fully absorb the heat in the water, resulting in unsatisfactory heat storage effect and insufficient heat storage. When cold water is input, the fixed heat storage element cannot evenly release the stored heat, and the cold water heating effect is uneven and inefficient. Therefore, the fixed heat storage element is likely to cause local overheating or overcooling in the heat storage box, resulting in uneven temperature distribution, affecting stability and reliability. At the same time, since the heat storage element is fixed, it is easy to form areas of heat concentration or poor heat dissipation, resulting in local overheating or overcooling, affecting the normal operation and service life of the equipment.

[0005] To this end, the present invention provides a large-capacity heat storage device and a heating system. Summary of the invention

[0006] The present invention provides a large-capacity heat storage device and a heating system, which solve the technical problem in the related art that the heat storage element is fixed in the heat storage box, resulting in limited contact area and contact time between the heat storage element and hot water and low heat exchange efficiency.

[0007] The present invention provides a large-capacity heat storage device, comprising a mounting frame, a heat storage box is fixedly mounted on the inner side of the mounting frame, one side of the heat storage box is connected to a heat storage water outlet distributor, the other side of the heat storage box is connected to a heat storage water return distributor, a motor is fixedly mounted on the back side of the heat storage box, a pulley A is mounted on the output end of the motor, a pulley B is rotatably connected to the inner wall of the heat storage box, a transmission belt is sleeved on the surfaces of the pulley A and the pulley B, and a heat storage element is fixedly mounted on the surface of the transmission belt.

[0008] Preferably, the heat storage elements are arranged at equal distances on the surface of the transmission belt.

[0009] Preferably, the surfaces of the pulleys A and B are provided with saw teeth, and the inner side of the transmission belt is provided with grooves meshing with the saw teeth. Here, the transmission efficiency of the pulleys A and B and the transmission belt can be improved.

[0010] Preferably, the surface of the mounting frame is fixedly provided with mounting holes, and the mounting holes are evenly distributed at the four corners of the surface of the mounting frame. Here, the mounting frame can be conveniently installed.

[0011] A large-capacity heat storage heating system includes the following modules:

[0012] Heat recovery module, based on industrial waste heat, selects organic working fluids suitable for low-temperature waste heat environment, and conducts thermodynamic analysis to determine evaporation temperature and condensation temperature, designs and installs evaporators, expanders, condensers and pumps in the ORC system, simulates and optimizes the thermal performance of multiple components, and generates high-pressure steam;

[0013] The heat energy conversion module, based on the high-pressure steam, introduces it into the expander to drive the mechanical operation and convert it into mechanical energy. The expanded steam enters the condenser, releases heat and re-liquefies, and is then pumped back to the evaporator for recycling to generate mechanical energy;

[0014] The large-capacity heat storage device, based on the mechanical energy, converts it into electrical energy through a generator and heats hot water, which is supplied to the heat storage and heating device to generate thermal energy reserves;

[0015] An intelligent control module, based on the thermal energy reserve, uses a genetic algorithm to adjust the operating parameters of multiple components of the ORC system, adopts real-time monitoring technology and feedback control algorithm, dynamically adjusts the operating status of the evaporator, expander, condenser, and pump, and generates optimized control parameters;

[0016] The tuning management module applies Bayesian optimization technology based on the optimization control parameters to adjust and optimize the heating parameters in real time by constructing a probability model of the heating parameters. It uses the Gaussian process as a proxy model to predict the performance of multiple parameter combinations, and through the Bayesian update mechanism, it cyclically optimizes the parameter combination to generate the optimal heating parameters.

[0017] Preferably, the high-pressure steam is specifically steam generated in the evaporator and delivered to the expander, the mechanical energy is specifically energy generated by the expander and used to drive the generator, the thermal energy reserve includes high-temperature hot water and stored heat in the heat storage material, the optimized control parameters specifically include the adjustment values ​​of the evaporator temperature, the expander pressure and the pump speed, and the optimal heating parameters include temperature, pressure and flow rate.

[0018] Preferably, the heat recovery module includes a working fluid selection submodule, a thermodynamic analysis submodule and a component design submodule;

[0019] The working fluid selection submodule selects an organic working fluid suitable for a low-temperature waste heat environment based on the temperature range of the industrial waste heat, performs thermodynamic analysis to determine working parameters, and generates optimal working fluid parameters;

[0020] The thermodynamic analysis submodule performs thermodynamic analysis based on the optimal working fluid parameters to determine the thermodynamic performance of the working fluid under various conditions, optimizes the heat exchange process of the working fluid in the evaporator and the condenser through simulation and calculation, and generates thermodynamic analysis results;

[0021] The component design submodule designs and optimizes the evaporator, expander, condenser and pump in the ORC system based on the thermal analysis results, simulates the thermal performance of multiple components through simulation tools, and generates high-pressure steam.

[0022] Preferably, the heat energy conversion module includes a steam conversion submodule, a mechanical energy generation submodule and a condensation circulation submodule;

[0023] The steam conversion submodule performs a heat energy conversion operation based on the high-pressure steam, introduces the high-pressure steam into an expander, and generates mechanical energy through the mechanical operation of the expander;

[0024] The mechanical energy generation submodule performs mechanical energy transmission operation based on the mechanical energy to drive the mechanical device to operate and generate useful mechanical output;

[0025] After the useful mechanical output is generated, the expanded steam of the condensation cycle submodule enters the condenser to release heat, and the steam is reliquefied into water. The liquefied water is then pumped back to the evaporator for recycling to generate mechanical energy.

[0026] Preferably, the intelligent control module includes a parameter adjustment submodule, a real-time monitoring submodule and a dynamic adjustment submodule;

[0027] The parameter adjustment submodule uses a genetic algorithm to adjust the working parameters of multiple components of the ORC system based on the thermal energy reserve, initializes the parameter population, performs fitness evaluation, selects a parameter combination with high cross fitness, generates a new parameter combination after mutation, and generates optimized working parameters;

[0028] The real-time monitoring submodule uses real-time monitoring technology based on the optimized working parameters to monitor the operating status of the evaporator, expander, condenser and pump components, acquires real-time operating data through sensor data collection, and generates real-time operating data;

[0029] The dynamic adjustment submodule applies a feedback control algorithm based on the real-time operation data to dynamically adjust the operating status of the evaporator, expander, condenser, and pump, adjusts the control parameters in real time according to the real-time monitoring results, optimizes the energy recovery efficiency, and generates optimized control parameters.

[0030] Preferably, the tuning management module includes a model building submodule, a parameter prediction submodule and a real-time optimization submodule;

[0031] The model building submodule uses a Bayesian optimization algorithm based on the optimization control parameters to build a probability model of the heating parameters to generate a probability model of the heating parameters;

[0032] The parameter prediction submodule uses a Gaussian process model based on the heating parameter probability model to predict the performance of multiple parameter combinations and generate parameter combination performance prediction results;

[0033] The real-time optimization submodule uses the Bayesian update mechanism to cyclically optimize the parameter combination based on the parameter combination performance prediction result, and performs real-time adjustment and optimization of the heating parameters to generate the optimal heating parameters.

[0034] The beneficial effects of the present invention are:

[0035] The large-capacity heat storage device described in the present invention drives the heat storage element to move by a motor, so that it moves evenly in the heat storage box, increasing the contact area and contact time between the heat storage element and the hot water, thereby improving the heat exchange efficiency. Moreover, since the heat storage element constantly moves in the heat storage box, it can more fully absorb the heat in the water, and the heat storage effect is significantly improved. When cold water is input, the moving heat storage element can release the stored heat more evenly, and the heating effect is more uniform, thereby improving the efficiency of cold water heating. The uniform movement of the heat storage element avoids local overheating or overcooling, ensuring that the temperature distribution in the entire heat storage box is more uniform, thereby improving stability and reliability.

[0036] A large-capacity heat storage and heating system described in the present invention adopts organic Rankine cycle technology, which can efficiently recover and utilize industrial waste heat and significantly improve energy utilization. The heat recovery module ensures the efficient operation of the evaporator, expander, condenser and pump through thermodynamic analysis and precise design to generate high-pressure steam. The heat energy conversion module converts high-pressure steam into mechanical energy to achieve efficient energy transfer and utilization. The heat storage and heating device converts mechanical energy into electrical energy and heats hot water, stores thermal energy, and ensures the stability of heating. The intelligent control module adopts genetic algorithm and feedback control algorithm to adjust the operating parameters of each component of the system in real time to ensure the optimal operating state of the system under different working conditions. The tuning management module uses Bayesian optimization technology and Gaussian process agent model to adjust and optimize the heating parameters in real time to achieve continuous improvement of system performance. The design of the entire system makes energy utilization more efficient, stable and intelligent, achieves significant energy-saving effects, and reduces energy consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a stereogram of an embodiment of the present invention;

[0038] Figure 2 is a cross-sectional view of the present invention;

[0039] Figure 3 yes Figure 2 A partial enlarged view of the middle part;

[0040] Figure 4 is a rear view of the present invention;

[0041] Figure 5 It is a system flow chart of the present invention.

[0042] In the figure: 1. Installation frame; 2. Heat storage box; 3. Heat storage water outlet distributor; 4. Heat storage return water distributor; 5. Motor; 6. Pulley A; 7. Pulley B; 8. Drive belt; 9. Heat storage element; 10. Installation hole. DETAILED DESCRIPTION

[0043] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0044] Embodiment 1: Figures 1 to 4 As shown, a large capacity heat storage device according to an embodiment of the present invention is described, please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , including a mounting frame 1, the surface of the mounting frame 1 is fixedly installed with mounting holes 10, and the mounting holes 10 are evenly distributed at the four corners of the surface of the mounting frame 1. The mounting holes 10 can be fixed to the mounting frame 1 by bolts or screws, or can be fixed by riveting or welding to ensure the stability and installation convenience of the mounting frame 1. A heat storage box 2 is fixedly installed on the inner side of the mounting frame 1, and the heat storage box 2 is connected to the mounting frame 1 by welding or bolting to ensure that the heat storage box 2 does not move during operation and provide stable support. One side of the heat storage box 2 is connected to a heat storage water outlet distributor 3, and the other side of the heat storage box 2 is connected to a heat storage return water distributor 4. The connection between the heat storage water outlet distributor 3 and the heat storage return water distributor 4 can be connected by a flange connection to ensure the sealing of the water flow channel, prevent water leakage, and improve the reliability of system operation. A motor 5 is fixedly installed on the back of the heat storage box 2, and the motor 5 is fixed to the back of the heat storage box 2 by bolts, which is convenient for disassembly and maintenance of the motor 5. A pulley A6 is installed at the output end of the motor 5, and the pulley A6 is fixed to the output shaft of the motor 5 by a key connection or bolts to ensure the reliability of the transmission.

[0045] The inner wall of the heat storage box 2 is rotatably connected with a pulley B7, which can be installed on the inner wall of the heat storage box 2 through a bearing. The bearing can be a deep groove ball bearing or a tapered roller bearing to ensure the rotation flexibility and durability of the pulley B7. The surface of the pulley A6 and the pulley B7 is provided with a transmission belt 8, and the material of the transmission belt 8 can be selected from high temperature resistant rubber or polyurethane materials to ensure that it does not deform or break in a high temperature environment. The surface of the pulley A6 and the pulley B7 is provided with saw teeth, and the inner side of the transmission belt 8 is provided with a groove that meshes with the saw teeth. The design of the saw teeth and the groove can adopt precision processing technology to ensure the accuracy and stability of the meshing and prevent slipping during the transmission process. The surface of the transmission belt 8 is fixedly installed with a heat storage element 9, and the heat storage element 9 is arranged at equal distances on the surface of the transmission belt 8. The material of the heat storage element 9 can be selected from a high thermal conductivity metal such as copper or aluminum to ensure that the heat storage element 9 absorbs and releases heat in a short time and improves the heat exchange efficiency. The heat storage element 9 can be fixed on the surface of the transmission belt 8 by a clamp or a slot, so as to facilitate the installation and replacement of the heat storage element 9.

[0046] Embodiment 2: Figure 5 As shown, another embodiment of the present invention is: a large-capacity heat storage heating system, comprising the following modules:

[0047] Heat recovery module, based on industrial waste heat, selects organic working fluids suitable for low-temperature waste heat environment, and conducts thermodynamic analysis to determine evaporation temperature and condensation temperature, designs and installs evaporators, expanders, condensers and pumps in the ORC system, simulates and optimizes the thermal performance of multiple components, and generates high-pressure steam;

[0048] The heat energy conversion module is based on high-pressure steam, which is introduced into the expander to drive the machine to convert into mechanical energy. The expanded steam enters the condenser, releases heat and re-liquefies, and is then pumped back to the evaporator for recycling to generate mechanical energy.

[0049] Large-capacity heat storage device, based on mechanical energy, is converted into electrical energy through a generator and heats hot water, which is supplied to the heat storage and heating device to generate thermal energy reserves;

[0050] Intelligent control module, based on thermal energy reserve, uses genetic algorithm to adjust the working parameters of multiple components of ORC system, adopts real-time monitoring technology and feedback control algorithm to dynamically adjust the operating status of evaporator, expander, condenser and pump, and generate optimized control parameters;

[0051] The tuning management module applies Bayesian optimization technology based on the optimization control parameters. By constructing a probabilistic model of heating parameters, it adjusts and optimizes the heating parameters in real time. It uses the Gaussian process as a proxy model to predict the performance of multiple parameter combinations. Through the Bayesian update mechanism, it cyclically optimizes the parameter combination to generate the optimal heating parameters.

[0052] High-pressure steam specifically refers to the steam generated in the evaporator and transported to the expander, mechanical energy specifically refers to the energy generated by the expander and used to drive the generator, thermal energy reserves include high-temperature hot water and stored heat in heat storage materials, optimized control parameters specifically include the adjustment values ​​of evaporator temperature, expander pressure and pump speed, and optimal heating parameters include temperature, pressure and flow rate.

[0053] In the heat recovery module, based on industrial waste heat, organic working fluids suitable for low-temperature waste heat environments are selected, and thermodynamic analysis is performed to determine the evaporation temperature and condensation temperature. The specific implementation process includes using thermodynamic formulas to calculate the performance parameters of the working fluid at different temperatures and pressures to ensure the efficient operation of the working fluid in the system. Next, the evaporator, expander, condenser and pump in the ORC system are designed and installed, and the finite element analysis method is used to simulate the thermal performance of each component, and the heat exchange efficiency and working stability are optimized by adjusting the design parameters. During the simulation process, computational fluid dynamics (CFD) software is used to simulate fluid flow and heat transfer to ensure the efficient operation of each component under actual operating conditions. The generated high-pressure steam specifically refers to the steam generated in the evaporator and transported to the expander.

[0054] In the heat energy conversion module, high-pressure steam is introduced into the expander to drive the machine and convert it into mechanical energy. The specific process includes using the thermodynamic model of the expander to calculate the expansion process of the steam and determine the optimal expansion ratio and working parameters. The expanded steam enters the condenser, releases heat and re-liquefies, and is then pumped back to the evaporator for recycling. This process is optimized using thermodynamic cycle simulation software to ensure that the heat transfer efficiency of the steam in the expander and condenser is maximized. The generated mechanical energy specifically refers to the energy generated by the expander and used to drive the generator.

[0055] In a large-capacity thermal storage device, the mechanical energy is converted into electrical energy through a generator and heated to supply the thermal storage heating device. The specific process includes adjusting the output power of the generator using a motor control algorithm to ensure the stable output of electrical energy. Subsequently, the electrical energy is used to heat the hot water in the thermal storage device, and the working state of the heating element is adjusted by a temperature control algorithm to ensure that the hot water temperature reaches the set value. The generated thermal energy reserve includes high-temperature hot water and stored heat in the thermal storage material.

[0056] In the intelligent control module, the working parameters of multiple components of the ORC system are adjusted by using genetic algorithms based on thermal energy reserves. The specific process includes initializing the parameter population, evaluating the performance of each parameter combination through the fitness function, and using selection, crossover and mutation operations to generate new parameter combinations. Real-time monitoring technology and feedback control algorithms are used to dynamically adjust the operating status of the evaporator, expander, condenser, and pump. The specific process includes installing sensors to monitor the operating data of each component in real time and transmitting the data to the control center through the data acquisition system. The feedback control algorithm is used to analyze the operating data and adjust the operating parameters of each component in real time to ensure the optimal operating state of the system under different working conditions. The generated optimized control parameters include the adjustment values ​​of the evaporator temperature, expander pressure, and pump speed.

[0057] In the tuning management module, by applying Bayesian optimization technology based on optimized control parameters and constructing a probability model of heating parameters, real-time adjustment and optimization of heating parameters are performed. The specific process includes using the Bayesian optimization algorithm to initialize the probability model of heating parameters and continuously updating the model through historical data and real-time data. Gaussian process is used as a proxy model to predict the performance of multiple parameter combinations. The specific process includes training the Gaussian process model based on existing data to predict the performance of the system under different parameter combinations. Through the Bayesian update mechanism, the parameter combination is cyclically optimized. The specific process includes using the Bayesian update rule to adjust the parameter distribution in the probability model according to the newly collected data to find the optimal heating parameters. The generated optimal heating parameters include the optimal temperature, pressure and flow rate settings for each component of the system.

[0058] In this embodiment, the heat recovery module includes a working fluid selection submodule, a thermodynamic analysis submodule and a component design submodule;

[0059] The working fluid selection submodule selects organic working fluids suitable for low-temperature waste heat environments based on the temperature range of industrial waste heat, performs thermodynamic analysis to determine working parameters, and generates optimal working fluid parameters;

[0060] The thermodynamic analysis submodule performs thermodynamic analysis based on the optimal working fluid parameters to determine the thermal performance of the working fluid under various conditions. Through simulation and calculation, it optimizes the heat exchange process of the working fluid in the evaporator and condenser and generates thermodynamic analysis results.

[0061] The component design submodule designs and optimizes the evaporator, expander, condenser and pump in the ORC system based on the thermal analysis results. It simulates the thermal performance of multiple components through simulation tools to generate high-pressure steam.

[0062] In the working fluid selection submodule, based on the temperature range of industrial waste heat, organic working fluids suitable for low-temperature waste heat environment are selected, and thermodynamic analysis is performed to determine the working parameters. First, the temperature and pressure data of industrial waste heat are collected, and temperature sensors and pressure sensors are used to monitor the specific conditions of industrial waste heat in real time. Based on the collected data, organic working fluids suitable for efficient operation within this temperature range, such as propane and isobutane, are selected. Next, thermodynamic analysis is performed to calculate the physical properties of the working fluid at different temperatures and pressures, such as enthalpy, entropy, specific heat capacity, etc. The evaporation temperature and condensation temperature of the working fluid are determined using thermodynamic formulas and physical property databases. Mathematical models and calculation software, such as MATLAB or EES, are used to simulate the thermodynamic performance of the working fluid and optimize its thermodynamic performance under different operating conditions. This effect provides key data for the design and optimization of the system, ensuring that the selected working fluid has efficient thermodynamic performance in actual operation, and the generated files include the optimal working fluid parameter table and thermodynamic performance report.

[0063] In the thermodynamic analysis submodule, a detailed thermodynamic analysis is performed based on the optimal working fluid parameters to determine the thermodynamic performance of the working fluid under various conditions. First, the generated optimal working fluid parameters are used to calculate and simulate the thermodynamic performance of the working fluid under different operating conditions using thermodynamic analysis software such as Aspen Plus or ThermoCalc. The specific operations include establishing a thermodynamic model of the working fluid, inputting the physical parameters and operating conditions of the working fluid, and simulating the evaporation and condensation processes. Through simulation analysis, the heat transfer coefficient, pressure drop, and heat transfer efficiency of the working fluid at different temperatures, pressures, and flow rates are determined. Next, computational fluid dynamics (CFD) software, such as ANSYS Fluent, is used to simulate the working fluid flow and heat transfer process in the evaporator and condenser in detail. Through CFD simulation, the flow path and heat transfer process of the working fluid in the heat exchanger are optimized to ensure the efficient operation of the heat exchanger. The generated thermodynamic analysis results include the thermodynamic performance curves of the working fluid under different operating conditions, the optimized design parameters of the heat exchanger, and the report on the optimal operating conditions of the working fluid. This effect provides detailed thermodynamic performance data and optimization parameters for system design, ensuring efficient heat exchange and stable operation of the working fluid in actual operation. The generated files include thermal analysis reports and CFD simulation results.

[0064] In the component design submodule, the evaporator, expander, condenser and pump in the ORC system are designed and optimized based on the results of thermal analysis. First, the thermal analysis results generated by the thermal analysis submodule are used to perform preliminary designs of the evaporator, expander, condenser and pump using computer-aided design (CAD) software, such as SolidWorks or AutoCAD. The specific operations include designing the structure and size of each component according to the thermal performance of the working fluid to ensure that it can operate efficiently. Next, the finite element analysis (FEA) software, such as ANSYS or ABAQUS, is used to simulate the structural strength and thermal performance of each component and optimize its design parameters. Through FEA simulation, the stress distribution, deformation and heat conduction of each component under different operating conditions are analyzed to ensure that its structural strength and thermal performance meet the design requirements. Then, computational fluid dynamics (CFD) software is used to simulate the fluid flow and heat transfer process of each component in detail, optimize its internal flow path and heat transfer surface, and ensure an efficient heat exchange process. Finally, the simulation results are combined to optimize the design and generate high-pressure steam. The generated high-pressure steam specifically refers to the steam generated in the evaporator and supplied to the expander.

[0065] In this embodiment, the heat energy conversion module includes a steam conversion submodule, a mechanical energy generation submodule and a condensation circulation submodule;

[0066] The steam conversion submodule performs heat energy conversion operation based on high-pressure steam, introduces high-pressure steam into the expander, and generates mechanical energy through the mechanical operation of the expander;

[0067] The mechanical energy generation submodule performs mechanical energy transmission operations based on mechanical energy, drives the mechanical device to operate, and generates useful mechanical output;

[0068] Based on the useful mechanical output of the condensation cycle submodule, the expanded steam enters the condenser to release heat, and the steam is re-liquefied into water. The liquefied water is then pumped back to the evaporator for recycling and generation of mechanical energy.

[0069] In the steam conversion submodule, high-pressure steam is introduced into the expander through the heat energy conversion operation, and the data format of high-pressure steam is thermodynamic parameters such as temperature, pressure and flow. In this process, the enthalpy and entropy values ​​of the steam are calculated using thermodynamic analysis methods, and the thermal energy of the high-pressure steam is converted into mechanical energy through the working process of the expander. The specific operations include: when the high-pressure steam enters the expander, its initial pressure and temperature are recorded. Through the expansion process, the pressure and temperature of the steam gradually decrease. The blades in the expander rotate due to the push of the steam, and the internal energy of the steam is converted into the kinetic energy of the blades. The kinetic energy is then transmitted to the mechanical device through the shaft to generate mechanical energy. The temperature and pressure changes of the steam during the expansion process are monitored by real-time sensors, and the data is input into the thermodynamic model for calculation to generate a mechanical energy output file. This file records the speed of the expander, the output power and the final state of the steam.

[0070] In the mechanical energy generation submodule, based on the mechanical energy generated by the steam conversion submodule, mechanical energy transfer operations are performed to drive the mechanical device to operate and generate useful mechanical output. The data format of mechanical energy includes speed, torque and power, and the mechanical energy transfer process is analyzed by the dynamic model. First, the mechanical energy is transferred to the mechanical device through the shaft. The dynamic model calculates the energy loss during the shaft transfer process and optimizes the operating parameters of the mechanical device. The specific operations include: recording the speed and torque of the shaft, calculating the power output of the shaft through the dynamic equation, inputting the power data into the control system of the mechanical device, and the control system adjusts the operating state of the mechanical device according to the input power data to optimize its output efficiency. During the operation of the mechanical device, its output power and operating state are monitored in real time, and the monitoring data is fed back to the control system for further optimization. The final generated mechanical output file records the operating parameters and output power of the mechanical device.

[0071] In the condensation cycle submodule, based on the useful mechanical output generated by the mechanical energy generation submodule, the expanded steam enters the condenser, performs heat release operation, and the steam is re-liquefied into water. The data format of steam condensation includes the pressure, temperature and flow of steam, and the condensation process is analyzed by the heat transfer model. The specific operations include: the expanded low-pressure steam enters the condenser, the initial temperature and pressure of the steam are recorded, and the condenser absorbs the heat of the steam through the cooling medium, so that the steam temperature is reduced and gradually liquefied. The temperature and pressure changes in the condenser are monitored by real-time sensors, and the data are input into the heat transfer model to calculate the condensation efficiency. The liquefied water is pumped back to the evaporator for recycling, and the operating state of the pump is optimized by the fluid dynamics model to ensure that the flow and pressure of the liquefied water meet the requirements of the evaporator. The generated circulating liquefied water file records the temperature, pressure and flow of the liquefied water. Throughout the process, the efficiency of heat release and water liquefaction directly affects the overall performance of the system, ensuring efficient thermal energy conversion and utilization.

[0072] In this embodiment, the intelligent control module includes a parameter adjustment submodule, a real-time monitoring submodule and a dynamic adjustment submodule;

[0073] The parameter adjustment submodule uses genetic algorithms to adjust the working parameters of multiple components of the ORC system based on thermal energy reserves, initializes the parameter population, performs fitness evaluation, selects parameter combinations with high cross fitness, generates new parameter combinations after mutation, and generates optimized working parameters;

[0074] The real-time monitoring submodule uses real-time monitoring technology based on optimized working parameters to monitor the operating status of the evaporator, expander, condenser and pump components, obtains real-time operating data through sensor data collection, and generates real-time operating data;

[0075] The dynamic adjustment submodule applies feedback control algorithm based on real-time operation data to dynamically adjust the operating status of the evaporator, expander, condenser and pump, adjusts the control parameters in real time according to the real-time monitoring results, optimizes the energy recovery efficiency, and generates optimized control parameters.

[0076] In the parameter adjustment submodule, the working parameters of multiple components of the ORC system are adjusted by using a genetic algorithm based on thermal energy reserves. The specific execution process includes the following steps: First, initialize the parameter population and define each individual as a parameter combination, including parameters such as evaporator temperature, expander pressure, and pump speed. Create the initial population by using a random generation method, and set parameters such as population size and number of iterations. Then, evaluate the fitness of each individual, use the thermal energy conversion efficiency as the fitness function, and calculate the fitness value of each individual by simulating the operation of the ORC system. Select individuals with high fitness according to the fitness value for crossover operation, and generate new individuals by using a single-point crossover or multi-point crossover method. Subsequently, perform a mutation operation on the new individuals to increase the diversity of the population by randomly changing the parameter values. Repeat the selection, crossover, and mutation operations until the preset number of iterations or fitness convergence is reached. Finally, select the individual with the highest fitness as the optimized working parameter. The generated optimized working parameters include specific adjustment values ​​such as evaporator temperature, expander pressure, and pump speed.

[0077] In the real-time monitoring submodule, the operating status of the evaporator, expander, condenser and pump components is monitored by using real-time monitoring technology based on optimized working parameters. The specific implementation process includes the following steps: First, install monitoring equipment such as temperature sensors, pressure sensors and flow sensors to ensure that the monitoring points cover the key parts of the evaporator, expander, condenser and pump. The sensor obtains the operating data of each component in real time through the data acquisition system, such as parameters such as temperature, pressure and flow. The data acquisition system uses high-frequency sampling technology to ensure that the acquired data has high accuracy and timeliness. Subsequently, the collected data is transmitted to the central control system for data storage and processing. The central control system uses data processing algorithms to analyze the real-time operating data, including data filtering, outlier detection and trend analysis, to ensure the accuracy and reliability of the data. The generated real-time operating data includes specific operating parameters such as temperature, pressure and flow of each component.

[0078] In the dynamic adjustment submodule, the operating status of the evaporator, expander, condenser, and pump is dynamically adjusted by applying the feedback control algorithm based on the real-time operating data. The specific execution process includes the following steps: First, the central control system calculates the deviation between the current operating status of each component and the optimized working parameters based on the real-time operating data. The PID control algorithm is used for dynamic adjustment. The PID control algorithm calculates the adjustment amount through the joint adjustment of the three parts of proportion, integration, and differentiation to ensure that the operating parameters of each component quickly return to the optimized working parameters. The specific operations include: adjusting the heating power for the evaporator; adjusting the inlet pressure and speed for the expander; adjusting the cooling water flow for the condenser; and adjusting the pump speed and flow for the pump. The response of each component is monitored in real time. If there is still a deviation after the adjustment, continue to adjust until each parameter reaches the optimized working parameters. The generated optimized control parameters include specific control values ​​such as the heating power of the evaporator, the inlet pressure and speed of the expander, and the cooling water flow and pump speed of the condenser.

[0079] In this embodiment, the tuning management module includes a model building submodule, a parameter prediction submodule and a real-time optimization submodule;

[0080] The model building submodule is based on the optimization control parameters and adopts the Bayesian optimization algorithm to build a probability model of heating parameters to generate a probability model of heating parameters;

[0081] The parameter prediction submodule uses the Gaussian process model based on the heating parameter probability model to predict the performance of multiple parameter combinations and generate parameter combination performance prediction results;

[0082] The real-time optimization submodule adopts the Bayesian update mechanism based on the parameter combination performance prediction results, cyclically optimizes the parameter combination, adjusts and optimizes the heating parameters in real time, and generates the optimal heating parameters.

[0083] In the model building submodule, the Bayesian optimization algorithm is used to build the model by optimizing the data format of the control parameters including temperature, pressure, flow, etc. The specific operation is to construct a probability model of the heating parameters, first input the optimized control parameters into the Bayesian optimization algorithm, and the algorithm calculates the probability distribution of the heating parameters by combining prior knowledge and observation data. In this process, the initial parameters are set to the optimal values ​​set according to experience and historical data, and then the probability distribution of the parameters is continuously updated through the Bayesian formula so that the model can reflect the actual situation. By iterating the input parameters and observation results multiple times, the probability distribution of each heating parameter under different conditions is calculated to generate a probability model of the heating parameters. The model file contains the distribution of each heating parameter and the corresponding probability value, which can intuitively display the change trend of each parameter and provide data support for subsequent parameter prediction and optimization.

[0084] In the parameter prediction submodule, the data format of the heating parameter probability model includes the probability distribution of each parameter and historical observation data, and the Gaussian process model is used for prediction. The specific operation is based on the heating parameter probability model, and the probability distribution of each parameter is input into the Gaussian process model. The Gaussian process model calculates the covariance matrix of different parameter combinations through the kernel function, thereby predicting the performance of each parameter combination. The choice of kernel function is set according to the actual situation of the heating system. Generally, radial basis function or exponential kernel function is selected to ensure that the model can accurately reflect the nonlinear relationship between parameters. Through the calculation of the Gaussian process model, the system performance under different parameter combinations is predicted, and the combinations are sorted and evaluated to generate parameter combination performance prediction results. The result file records the predicted performance values ​​of each parameter combination, including thermal efficiency, system stability and energy utilization, etc., providing evaluation basis and data support for real-time optimization.

[0085] In the real-time optimization submodule, the data format of the parameter combination performance prediction results includes the performance value and priority of each parameter combination, and the Bayesian update mechanism is used for cyclic optimization. The specific operation is to input the performance value of each parameter combination into the Bayesian update mechanism based on the parameter combination performance prediction results. The Bayesian update mechanism optimizes the optimal heating parameter combination by continuously adjusting the weight and probability distribution of the parameter combination. During the optimization process, the operating status and performance of the system are monitored in real time, and the observed data are fed back to the Bayesian update mechanism to update the probability distribution of the parameters to ensure the real-time and accuracy of the optimization process. After multiple iterations, the optimal heating parameters are finally generated. The parameter file records the optimized values ​​of each heating parameter, including evaporation temperature, condensation temperature, flow rate and pressure, etc., which can ensure the optimal performance of the heating system under different operating conditions and provide guarantee for the efficient operation of the system.

[0086] How it works

[0087] The hot water in the heat storage box 2 is circulated by the water flowing in and out of the heat storage outlet distributor 3 and the heat storage return distributor 4. The heat storage element 9 arranged in the heat storage box 2 can absorb the heat in the water for storage, and when cold water is input, it can release heat to heat the cold water. The motor 5 can also drive the pulley A6 to rotate, and the pulley A6 can then drive the transmission belt 8 to move, and the transmission belt 8 can then drive the heat storage element 9 to move, so that the heat storage element 9 can move evenly in the heat storage box 2, so that the heat storage element 9 can be more fully in contact with the hot water, thereby improving the heat storage effect. The present invention drives the storage element 9 by the motor 5. The movement of the heat element 9 causes it to move evenly in the heat storage tank 2, increasing the contact area and contact time between the heat storage element 9 and the hot water, thereby improving the heat exchange efficiency. Moreover, since the heat storage element 9 continuously moves in the heat storage tank 2, it can more fully absorb the heat in the water, and the heat storage effect is significantly improved. When cold water is input, the moving heat storage element 9 can release the stored heat more evenly, and the heating effect is more uniform, thereby improving the efficiency of cold water heating. The uniform movement of the heat storage element 9 avoids local overheating or overcooling, thereby ensuring that the temperature distribution in the entire heat storage tank 2 is more uniform, thereby improving stability and reliability.

[0088] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A large-capacity heat storage device, characterized in that: The invention comprises a mounting frame (1), a heat storage box (2) is fixedly mounted on the inner side of the mounting frame (1), one side of the heat storage box (2) is connected to a heat storage water outlet distributor (3), the other side of the heat storage box (2) is connected to a heat storage water return distributor (4), a motor (5) is fixedly mounted on the back side of the heat storage box (2), a pulley A (6) is mounted on the output end of the motor (5), a pulley B (7) is rotatably connected to the inner wall of the heat storage box (2), the pulley A (6) and the pulley B (7) are rotatably connected to each other, and the pulley A (6) and the pulley B (7) are rotatably connected to each other. A transmission belt (8) is sleeved on the surface of the transmission belt (7), a heat storage element (9) is fixedly mounted on the surface of the transmission belt (8), and the heat storage elements (9) are arranged at equal distances on the surface of the transmission belt (8), saw teeth are mounted on the surfaces of the pulley A (6) and the pulley B (7), and a groove meshing with the saw teeth is formed on the inner side of the transmission belt (8), and mounting holes (10) are fixedly mounted on the surface of the mounting frame (1), and the mounting holes (10) are evenly distributed at the four corners of the surface of the mounting frame (1).

2. A large-capacity heat storage heating system, characterized in that: Includes the following modules: Heat recovery module, based on industrial waste heat, selects organic working fluids suitable for low-temperature waste heat environment, and conducts thermodynamic analysis to determine evaporation temperature and condensation temperature, designs and installs evaporators, expanders, condensers and pumps in the ORC system, simulates and optimizes the thermal performance of multiple components, and generates high-pressure steam; The heat energy conversion module, based on the high-pressure steam, introduces it into the expander to drive the mechanical operation and convert it into mechanical energy. The expanded steam enters the condenser, releases heat and re-liquefies, and is then pumped back to the evaporator for recycling to generate mechanical energy; The large-capacity heat storage device as claimed in claim 1, based on the mechanical energy, is converted into electrical energy by a generator and heated with hot water, which is supplied to the heat storage and heating device to generate thermal energy reserves; An intelligent control module, based on the thermal energy reserve, uses a genetic algorithm to adjust the operating parameters of multiple components of the ORC system, adopts real-time monitoring technology and feedback control algorithm, dynamically adjusts the operating status of the evaporator, expander, condenser, and pump, and generates optimized control parameters; The tuning management module applies Bayesian optimization technology based on the optimization control parameters to adjust and optimize the heating parameters in real time by constructing a probability model of the heating parameters. It uses the Gaussian process as a proxy model to predict the performance of multiple parameter combinations, and through the Bayesian update mechanism, it cyclically optimizes the parameter combination to generate the optimal heating parameters.

3. A large-capacity heat storage heating system according to claim 2, characterized in that: The high-pressure steam is specifically the steam generated in the evaporator and transported to the expander, the mechanical energy is specifically the energy generated by the expander and used to drive the generator, the thermal energy reserve includes high-temperature hot water and stored heat in the heat storage material, the optimized control parameters specifically include the adjustment values ​​of the evaporator temperature, the expander pressure and the pump speed, and the optimal heating parameters include temperature, pressure and flow rate.

4. A large-capacity heat storage heating system according to claim 2, characterized in that: The heat recovery module includes a working fluid selection submodule, a thermodynamic analysis submodule and a component design submodule; The working fluid selection submodule selects an organic working fluid suitable for a low-temperature waste heat environment based on the temperature range of the industrial waste heat, performs thermodynamic analysis to determine working parameters, and generates optimal working fluid parameters; The thermodynamic analysis submodule performs thermodynamic analysis based on the optimal working fluid parameters to determine the thermodynamic performance of the working fluid under various conditions, optimizes the heat exchange process of the working fluid in the evaporator and the condenser through simulation and calculation, and generates thermodynamic analysis results; The component design submodule designs and optimizes the evaporator, expander, condenser and pump in the ORC system based on the thermal analysis results, simulates the thermal performance of multiple components through simulation tools, and generates high-pressure steam.

5. A large-capacity heat storage heating system according to claim 2, characterized in that: The heat energy conversion module includes a steam conversion submodule, a mechanical energy generation submodule and a condensation circulation submodule; The steam conversion submodule performs a heat energy conversion operation based on the high-pressure steam, introduces the high-pressure steam into an expander, and generates mechanical energy through the mechanical operation of the expander; The mechanical energy generation submodule performs mechanical energy transmission operation based on the mechanical energy to drive the mechanical device to operate and generate useful mechanical output; After the useful mechanical output is generated, the expanded steam of the condensation cycle submodule enters the condenser to release heat, and the steam is reliquefied into water. The liquefied water is then pumped back to the evaporator for recycling to generate mechanical energy.

6. A large-capacity heat storage heating system according to claim 2, characterized in that: The intelligent control module includes a parameter adjustment submodule, a real-time monitoring submodule and a dynamic adjustment submodule; The parameter adjustment submodule uses a genetic algorithm to adjust the working parameters of multiple components of the ORC system based on the thermal energy reserve, initializes the parameter population, performs fitness evaluation, selects a parameter combination with high cross fitness, generates a new parameter combination after mutation, and generates optimized working parameters; The real-time monitoring submodule uses real-time monitoring technology based on the optimized working parameters to monitor the operating status of the evaporator, expander, condenser and pump components, acquires real-time operating data through sensor data collection, and generates real-time operating data; The dynamic adjustment submodule applies a feedback control algorithm based on the real-time operation data to dynamically adjust the operating status of the evaporator, expander, condenser, and pump, adjusts the control parameters in real time according to the real-time monitoring results, optimizes the energy recovery efficiency, and generates optimized control parameters.

7. A large-capacity heat storage heating system according to claim 2, characterized in that: The tuning management module includes a model building submodule, a parameter prediction submodule and a real-time optimization submodule; The model building submodule uses a Bayesian optimization algorithm based on the optimization control parameters to build a probability model of the heating parameters to generate a probability model of the heating parameters; The parameter prediction submodule uses a Gaussian process model based on the heating parameter probability model to predict the performance of multiple parameter combinations and generate parameter combination performance prediction results; The real-time optimization submodule adopts a Bayesian update mechanism based on the parameter combination performance prediction result, cyclically optimizes the parameter combination, performs real-time adjustment and optimization of the heating parameters, and generates the optimal heating parameters.

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