CN-122021322-A - Thermal management and life prediction method and equipment for waste heat recovery equipment

CN122021322ACN 122021322 ACN122021322 ACN 122021322ACN-122021322-A

Abstract

The application discloses a thermal management and life prediction method and equipment of waste heat recovery equipment. The method comprises the steps of firstly collecting relevant data of waste heat recovery equipment, integrating historical maintenance data, fault data and material attribute data of the equipment to form a multi-dimensional data set, constructing a digital twin body of the equipment based on the data set by fusing a multi-physical-field theory and a simulation technology, training by utilizing actual operation data of the equipment, the fault data and simulation data generated by the digital twin body to obtain a life prediction model, determining an optimized operation parameter based on a target optimization function aiming at improving the residual life and the waste heat recovery efficiency of the equipment under the safety constraint of the equipment structure, inputting the optimized operation parameter into the digital twin body to simulate so as to determine the corresponding waste heat recovery efficiency, predicting the residual life of the corresponding equipment based on a simulation result through the life prediction model, and if the waste heat recovery efficiency and the residual life of the equipment corresponding to the optimized operation parameter are better than the current state, controlling the equipment to operate according to the optimized operation parameter.

Inventors

FU HAILONG
WANG YALIN
NI MIN
YANG PEISHAN
WANG LONGFENG

Assignees

新疆准能化工有限公司
新疆天池能源有限责任公司

Dates

Publication Date: 20260512
Application Date: 20260203

Claims (10)

1. A method of thermal management and life prediction for a waste heat recovery device, the method comprising: collecting operation state data, environment data and structure state data of the waste heat recovery equipment, and integrating equipment history maintenance data, fault data and material attribute data of the waste heat recovery equipment to form a multi-dimensional data set; Based on the multi-dimensional data set, a digital twin body of the waste heat recovery device is constructed by fusing a multi-physical field theory and a simulation technology; training to obtain a life prediction model based on actual operation data of the waste heat recovery equipment, fault data of the waste heat recovery equipment and simulation data generated by the digital twin body, wherein the actual operation data, the fault data and the simulation data are data in the same time period; determining an optimized operation parameter of the waste heat recovery equipment based on a target optimization function under the structural safety constraint of the waste heat recovery equipment, wherein the target optimization function is constructed with the aim of improving the residual life and the waste heat recovery efficiency of the equipment under the structural safety constraint; inputting the optimized operation parameters into the digital twin body for simulation, and determining the waste heat recovery efficiency corresponding to the optimized operation parameters; predicting the residual life of the equipment corresponding to the optimized operation parameter based on the simulation result of the digital twin body by utilizing the life prediction model; And if the waste heat recovery efficiency and the equipment residual life corresponding to the optimized operation parameters are better than the waste heat recovery efficiency and the equipment residual life of the current waste heat recovery equipment, controlling the waste heat recovery equipment to operate according to the optimized operation parameters.
2. The method of claim 1, wherein the collecting operational status data, environmental data, and structural status data of the waste heat recovery device comprises: The operation state data and the structure state data are collected through a sensor, wherein the sensor is deployed at a key part of the waste heat recovery equipment, the key part refers to an area affecting the waste heat recovery efficiency, the structure safety or the operation stability of the waste heat recovery equipment, and the key part at least comprises any one of a heating surface, a medium flow passage, a shell, a flue gas inlet and outlet end surface and a heat exchange executing mechanism of the equipment; And acquiring the environmental data through an industrial control system interface, wherein the environmental data refer to external associated data influencing the heat recovery heat efficiency, structural corrosion or heat load balance of the heat recovery equipment.
3. The method of claim 1, wherein the fusing multiple physical field theory and simulation techniques to construct a digital twin body of the waste heat recovery device based on the multi-dimensional dataset comprises: Based on a three-dimensional structure model of the waste heat recovery equipment, based on the multi-dimensional data set, fusing a multi-physical field theory and a simulation technology, and constructing the digital twin body through multi-field coupling simulation; The multi-physical-field theory at least comprises any one of heat transfer science, fluid mechanics and material mechanics, the simulation technology at least comprises any one of finite element simulation technology and computational fluid dynamics technology, the digital twin body is used for mapping internal physical-field distribution and performance attenuation processes of the waste heat recovery equipment in real time based on the multi-dimensional data set, the internal physical-field distribution at least comprises any one of a temperature field, a flow field and a stress field, and the performance attenuation processes at least comprise any one of scaling, corrosion and creep damage.
4. The method of claim 1, wherein after constructing the digital twin, the method further comprises: selecting actual operation parameters of the waste heat recovery equipment in a preset time period, and inputting the actual operation parameters into the digital twin body for simulation to obtain simulation data of the digital twin body; If the deviation of the operation state data and the structure state data corresponding to the simulation data and the actual operation data is larger than or equal to a preset threshold value, the heat conduction coefficient, the flow field resistance coefficient and the material fatigue parameter of the digital twin body are adjusted, And repeating calibration until the deviation between the simulation data and the actual operation data is smaller than the preset threshold value, so that the simulation state of the digital twin body is close to the waste heat recovery equipment.
5. The method of claim 1, wherein the training to obtain a life prediction model based on actual operational data of the waste heat recovery device, fault data of the waste heat recovery device, and simulation data generated by the digital twin body comprises: Dividing actual operation data, fault data and simulation data in the same time period into a training set, a verification set and a test set according to a preset proportion; Constructing a model frame of the life prediction model by adopting a long-period memory network and combining an attention mechanism; And taking medium temperature, pressure, flow, structural stress and scaling thickness as input characteristics, taking the residual life of the waste heat recovery equipment as an output label, and carrying out iterative training through a counter-propagation algorithm until the prediction error of the model is smaller than the model prediction error standard, so as to obtain the life prediction model.
6. The method of claim 1, wherein the objective optimization function is a weighted summation function that maximizes plant remaining life and maximizes waste heat recovery efficiency as a dual objective; The expression of the objective optimization function is f (X) =alpha.L (X) +beta.E (X); Wherein X is an operation parameter to be optimized, alpha and beta are weight coefficients, alpha+beta=1, alpha and beta are E (0, 1), L (X) is a residual life function of equipment corresponding to the operation parameter X to be optimized, E (X) is a waste heat recovery efficiency function corresponding to the operation parameter X to be optimized; The structural safety constraint is expressed as sigma (X) which is equal to or less than sigma 0, wherein sigma (X) is equipment structural stress corresponding to the operation parameter X to be optimized, sigma 0 is a preset safety threshold, and the preset safety threshold is determined at least according to any one of equipment material properties, design standards and industry safety specifications.
7. The method according to claim 1, wherein the method further comprises: and updating the life prediction model by using actual operation data of the waste heat recovery equipment, fault data of the waste heat recovery equipment and simulation data of the digital twin body in the preset period every time the preset period passes.
8. A thermal management and life prediction apparatus for a waste heat recovery device, the apparatus comprising: the acquisition module is used for acquiring running state data, environment data and structural state data of the waste heat recovery equipment, integrating equipment history maintenance data, fault data and material attribute data of the waste heat recovery equipment, and forming a multi-dimensional data set; the construction module is used for constructing a digital twin body of the waste heat recovery device based on the multi-dimensional data set and by fusing a multi-physical field theory and a simulation technology; The device comprises a training module, a life prediction module and a life prediction module, wherein the training module is used for training to obtain a life prediction model based on actual operation data of the waste heat recovery device, fault data of the waste heat recovery device and simulation data generated by the digital twin body, wherein the actual operation data, the fault data and the simulation data are data in the same time period; The optimizing module is used for determining the optimized operation parameters of the waste heat recovery equipment based on a target optimizing function, wherein the target optimizing function is constructed by taking the improvement of the residual life of the equipment and the waste heat recovery efficiency as targets under the constraint of the structural safety of the waste heat recovery equipment; The simulation module is used for inputting the optimized operation parameters into the digital twin body for simulation, and determining the waste heat recovery efficiency corresponding to the optimized operation parameters; And the operation module is used for controlling the waste heat recovery equipment to operate according to the optimized operation parameters if the waste heat recovery efficiency and the equipment residual life corresponding to the optimized operation parameters are better than the waste heat recovery efficiency and the equipment residual life of the current waste heat recovery equipment.
9. A control apparatus comprising a processor and a memory, the memory for storing a program, instructions or code, the processor for executing the program, instructions or code in the memory to perform the thermal management and life prediction method of the waste heat recovery device of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored, which is loaded by a processor to perform the thermal management and life prediction method of the waste heat recovery device according to any one of claims 1-7.

Description

Thermal management and life prediction method and equipment for waste heat recovery equipment Technical Field The application relates to the technical field of thermal power generation, in particular to a thermal management and service life prediction method and equipment of waste heat recovery equipment. Background In the industrial energy saving and intelligent transformation process, waste heat recovery equipment (such as a waste heat boiler, a heat exchanger and the like) is widely applied to the fields of chemical industry, energy, metallurgy and the like, and is core equipment for improving the energy utilization rate. The equipment is in a complex working condition of high temperature, high pressure and medium corrosion for a long time, and the heat management efficiency and the service life of the equipment are directly related to the production benefit and the operation safety, so that the related optimization and prediction have become key requirements of industrial management. At present, the prior art mainly collects equipment operation data and historical operation and maintenance records through a sensor and an industrial control system, adopts a machine learning model driven by pure data to estimate the residual service life of equipment, optimizes thermal management to maximize recovery efficiency as a single target, and adjusts operation parameters through experience or a simple algorithm. However, pure data-driven predictive models lack physical mechanism support, are sensitive to data noise, have large predictive deviations, and cannot trace back the root cause of the fault. In addition, in the prior art, the heat management and the life prediction are mutually independent, the structural safety and the material loss constraint are not considered, and the equipment attenuation is easy to accelerate in the pursuing of high load efficiency. Disclosure of Invention Based on the problems, the application provides a heat management and life prediction method and equipment for waste heat recovery equipment. The embodiment of the application discloses the following technical scheme: In a first aspect, an embodiment of the present application provides a method for thermal management and lifetime prediction of a waste heat recovery device, where the method includes: Collecting operation state data, environment data and structure state data of the waste heat recovery equipment, and integrating equipment history maintenance data, fault data and material attribute data of the waste heat recovery equipment to form a multi-dimensional data set; Based on a multi-dimensional dataset, a digital twin body of the waste heat recovery device is constructed by fusing a multi-physical field theory and a simulation technology; Training to obtain a life prediction model based on actual operation data of the waste heat recovery equipment, fault data of the waste heat recovery equipment and simulation data generated by a digital twin body, wherein the actual operation data, the fault data and the simulation data are data in the same time period; Under the structural safety constraint of the waste heat recovery equipment, determining the optimized operation parameters of the waste heat recovery equipment based on a target optimization function, wherein the target optimization function is constructed by taking the improvement of the residual life of the equipment and the waste heat recovery efficiency as targets under the structural safety constraint; Inputting the optimized operation parameters into a digital twin body for simulation, and determining the residual heat recovery efficiency corresponding to the optimized operation parameters; and if the waste heat recovery efficiency and the equipment residual life corresponding to the optimized operation parameters are better than those of the current waste heat recovery equipment, controlling the waste heat recovery equipment to operate according to the optimized operation parameters. In one possible implementation, collecting the operation state data, the environment data, and the structure state data of the waste heat recovery device includes: A sensor is deployed at a key part of the waste heat recovery equipment to acquire operation state data and structure state data, wherein the key part refers to an area affecting the waste heat recovery efficiency, the structure safety or the operation stability of the waste heat recovery equipment, and at least comprises any one of a heating surface, a medium flow passage, a shell, a flue gas inlet and outlet end face and a heat exchange executing mechanism of the equipment; environmental data is acquired through an industrial control system interface, wherein the environmental data refers to external associated data affecting waste heat recovery heat efficiency, structural corrosion or heat load balance of waste heat recovery equipment. In one possible implementation, based on the multi-dimensional data set, a digital twin