CN112329959A - Intelligent operation and maintenance system and method for thermal equipment - Google Patents
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Abstract
The invention discloses an intelligent operation and maintenance system and method for thermodynamic equipment, wherein the method comprises the following steps: a thermodynamic system configuration modeling method; a modular companion simulation method; a thermodynamic system operation optimization method; a thermodynamic device performance degradation analysis method; provided is a thermal equipment state abnormity evaluation method. The method of the invention provides a comprehensive solution for intelligent operation and maintenance of the thermodynamic equipment, is beneficial to improving the operation management level of the thermodynamic system, and has obvious economic benefit and application value.
Description
Technical Field
The invention relates to an intelligent operation and maintenance technology of thermal equipment, in particular to an intelligent operation and maintenance system and method of the thermal equipment based on modularization companion simulation and system configuration optimization.
Background
At present, in the industrial fields of electric power, chemical industry, steel, metallurgy, cement and the like, large-scale equipment generally adopts working modes of regular operation, regular maintenance and fault maintenance. In the operation process, environmental parameters are changed all the time, equipment can be continuously reduced along with aging performance, the primary factor of operation regulation consideration is equipment safety, and the lack of personalized design aiming at a specific thermodynamic system causes that a unit is difficult to reach the optimal state when operating according to the regulation. In addition, the periodic maintenance may generate over-maintenance and under-maintenance, the over-maintenance will result in the decrease of the utilization rate of the equipment, the increase of the operation cost, the shortening of the service life of the equipment, etc., and the under-maintenance will result in the failure of the equipment, requiring the unplanned shutdown for maintenance, resulting in greater loss.
The visual condition maintenance (intelligent operation and maintenance for short) is a main approach to solve the above problems. The basic idea is to grasp the real-time working state of the equipment by strengthening and perfecting monitoring means, find problems in time and take corresponding countermeasures, so that some faults are effectively prevented before occurrence, and some serious faults can be controlled and eliminated when slight faults occur, thereby restraining the occurrence of serious faults, greatly reducing the fault rate, saving the maintenance cost, reducing the maintenance range, reducing the maintenance workload, improving the availability ratio of the equipment and changing the maintenance workload into passive mode to active mode. The problem that the repair cannot be carried out and the repair is carried out without the repair in the regular maintenance can be solved according to the condition. In addition, the operation rules can be continuously optimized according to the real-time working state of the equipment, the working performance of the system or the equipment is improved, and intelligent operation is realized.
However, for a corresponding system or device, the enhancement and perfection of monitoring and monitoring means imply a large increase in investment; most core problems are that many parameters are lack of effective monitoring instruments, for example, in a thermal power plant, the coal quality, the coal quantity, the exhaust steam humidity of a turbine, the gas leakage rate of a condenser, the ammonia escape rate and the like are difficult to measure and monitor in real time, which is a main obstacle restricting the intelligent operation and the visual maintenance of equipment.
The development of artificial intelligence technology provides a new approach to solve the above problems. Artificial intelligence is the use of machine learning to quantify historical experience from which to find rules (establish equations or models) for future decisions. In the traditional artificial intelligence, each learning target is taken as 1 black box, 1 group of data is 1 part in the black box, and the machine learning of the combined parts is not known in advance; if a problem occurs with one or some of the "parts", the end result will deviate from the actual target. When the method is used in the fields of pattern recognition, voice recognition and the like, because each group of data is independent, the global situation of the whole picture cannot be influenced by the problem of individual pixels. However, for industrial equipment, most parameters are interrelated, and the influence of each parameter is not 1 point, but 1 line or even 1 area; therefore, it is difficult to obtain reliable results using conventional artificial intelligence methods.
Disclosure of Invention
The invention mainly aims to provide an intelligent operation and maintenance system and method for thermodynamic equipment, which provide a comprehensive solution for the intelligent operation and maintenance of the thermodynamic equipment and improve the operation management level of the thermodynamic system through configuration modeling of the thermodynamic system, modularization accompanying simulation, operation optimization of the thermodynamic system, performance degradation analysis of the thermodynamic equipment and abnormal state evaluation of the thermodynamic equipment.
According to an aspect of the present invention, there is provided an intelligent operation and maintenance system for a thermal device, comprising:
the thermodynamic system configuration modeling module is used for thermodynamic system configuration modeling;
the modularized companion simulation module is used for modularized companion simulation;
the thermodynamic system operation optimization module is used for optimizing the operation of the thermodynamic system;
the thermal equipment performance degradation analysis module is used for performing performance degradation analysis on the thermal equipment;
and the thermal equipment state abnormity evaluation module is used for evaluating the thermal equipment state abnormity.
According to another aspect of the present invention, there is provided an intelligent operation and maintenance method for a thermal device, including:
a thermodynamic system configuration modeling method;
a modular companion simulation method;
a thermodynamic system operation optimization method;
a thermodynamic device performance degradation analysis method;
provided is a thermal equipment state abnormity evaluation method.
Further, the thermodynamic system configuration modeling method comprises the following steps:
the complex thermodynamic system is decomposed into a plurality of calculation modules with mathematical independence by utilizing a fundamental theory of thermodynamic simulation, the relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation, and various complex thermodynamic systems can be constructed in a modular diagram forming mode.
Still further, the modular companion simulation method comprises:
a thermodynamic simulation model of an equipment module is used as an initial framework, and a model is continuously updated in an iterative mode by means of machine learning of real-time and historical operating data to form an accompanying simulation model which gradually approaches the performance of actual operating equipment.
Furthermore, the machine learning takes the decomposed independent module as an object, and the module is internally provided with a main function
The relevance of the required parameters is determined by a high-precision simulation model, and the parameter relation which cannot be solved by the simulation model in the module is completed by machine learning such as regression analysis and artificial neural network.
Still further, the thermodynamic system operation optimization method comprises the following steps:
based on an equipment module accompanied with a simulation model and a system mass-heat balance equation, a gradient descent method, a particle swarm algorithm and a genetic algorithm are adopted to determine an optimal real-time combination matching scheme of each equipment parameter under any operation condition by taking the overall heat consumption rate, coal consumption rate, power consumption rate or other characteristic parameters of the system as optimization targets.
Still further, the method for analyzing the performance degradation of the thermal device comprises the following steps:
based on the equipment module and the accompanying simulation model, the change rule of the equipment performance parameters along with the time under different operation conditions and working conditions is obtained through methods of characteristic parameter evaluation and data mining, and the equipment performance degradation state and degradation trend data are determined.
Still further, the method for analyzing the performance degradation of the thermal device comprises the following steps:
and (3) utilizing equipment of the same model to analyze the benchmarks and the big data, evaluating whether the performance of the equipment is abnormal or not according to the performance degradation state and the degradation trend of the equipment, and realizing equipment fault early warning and visual condition maintenance according to the evaluation result and the FTA analysis methods.
The invention has the advantages that:
the basic idea of the method of the invention is as follows: discretizing and modularizing a complex industrial system and equipment, solving the problems of relevance and certainty by using the existing theoretical system, and solving the problem of uncertainty by using machine learning; the machine learning takes an equipment mechanism model as a framework, a large amount of expert knowledge is built in, so that the machine learning has low dimensionality, good real-time performance and high reliability, and the learning model has better knowledge accumulation capacity, extensibility and inheritance.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of an intelligent operation and maintenance method for a thermal device according to the present invention;
FIG. 2 is a schematic view of embodiment 1 of the present invention;
fig. 3 is a schematic diagram of embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
An intelligent operation and maintenance system for thermal equipment, comprising:
the thermodynamic system configuration modeling module is used for thermodynamic system configuration modeling;
the modularized companion simulation module is used for modularized companion simulation;
the thermodynamic system operation optimization module is used for optimizing the operation of the thermodynamic system;
the thermal equipment performance degradation analysis module is used for performing performance degradation analysis on the thermal equipment;
and the thermal equipment state abnormity evaluation module is used for evaluating the thermal equipment state abnormity.
Wherein, the thermodynamic system configuration modeling module and the modularization companion simulation are basic modules, and the thermodynamic system
The system operation optimization module, the thermal equipment performance degradation analysis module and the thermal equipment state abnormity evaluation module are functional modules.
Referring to fig. 1, an intelligent operation and maintenance method for a thermal device includes:
a thermodynamic system configuration modeling method;
a modular companion simulation method;
a thermodynamic system operation optimization method;
a thermodynamic device performance degradation analysis method;
provided is a thermal equipment state abnormity evaluation method.
The basic idea of the method of the invention is as follows: discretizing and modularizing a complex industrial system and equipment, solving the problems of relevance and certainty by using the existing theoretical system, and solving the problem of uncertainty by using machine learning; the machine learning takes an equipment mechanism model as a framework, a large amount of expert knowledge is built in, so that the machine learning has low dimensionality, good real-time performance and high reliability, and the learning model has better knowledge accumulation capacity, extensibility and inheritance.
The basic idea of the thermodynamic system configuration modeling method is as follows: the complex thermodynamic system is decomposed into calculation modules with mathematical independence by utilizing a fundamental theory of thermodynamic simulation, the relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation, and various complex thermodynamic systems can be constructed in a modular diagram forming mode.
The modularized adjoint simulation method takes a thermodynamic simulation model of an equipment module as an initial framework, and forms an adjoint simulation model gradually approaching the performance of actual operation equipment by continuously iterating and updating the model through machine learning of real-time and historical operation data; because the initial skeleton strictly depends on the physical mechanism and experimental data of the equipment, the blindness in the machine learning process can be solved, and the anti-interference capability of machine learning is improved.
The machine learning takes the decomposed independent module as an object, the relevance of main parameters in the module is determined by a high-precision simulation model, and the parameter relation which cannot be solved by the simulation model in the module is completed by machine learning such as regression analysis, artificial neural network and the like, so that the precision of the accompanying simulation model is improved, and the dimensionality of the machine learning is greatly reduced.
The thermodynamic system operation optimization method is based on an equipment module accompanied simulation model and a system quality-heat balance equation, adopts a gradient descent method, a particle swarm algorithm, a genetic algorithm and other nonlinear programming methods, takes the overall heat consumption rate, the coal consumption rate, the power consumption rate or other characteristic parameters of the system as optimization targets, determines an optimal real-time combination matching scheme of each equipment parameter under any operation condition, and deeply excavates the energy-saving potential of the system and optimizes the system operation.
The thermal equipment performance degradation analysis method is based on an equipment module accompanying simulation model, and the change rule of equipment performance parameters along with time under different operation conditions and working conditions is obtained through methods such as characteristic parameter evaluation, data mining and the like, so that the equipment performance degradation state and the degradation trend are determined, and basic data are provided for intelligent maintenance of equipment.
The abnormal state evaluation method of the thermal equipment is characterized in that standard and big data analysis and other methods are utilized by equipment of the same model, whether the equipment performance is abnormal or not is evaluated according to the equipment performance degradation state and the degradation trend, and equipment fault early warning and visual condition maintenance are realized according to the evaluation result and FTA analysis and other methods.
The general idea of the intelligent operation and maintenance method for the thermal equipment is as follows: discretizing and modularizing a complex industrial system and equipment, solving the problems of relevance and certainty by using the existing theoretical system, and solving the problem of uncertainty by using machine learning; the machine learning takes the equipment mechanism model as a framework, a large amount of expert knowledge is built in, so that the machine learning has low dimensionality, good real-time performance and high reliability, and the learning model has better knowledge accumulation capacity, extensibility and inheritance.
The intelligent operation and maintenance method of the thermal equipment is mainly suitable for the thermal equipment in the industrial fields of thermal power generation and poly-generation, distributed multi-combined supply, new energy systems, petrochemical industry, steel and nonferrous metal smelting and the like, and comprises a boiler, a turbine, a combustor or a combustion chamber, a coal mill, a pump, a fan, a heat pump, a refrigerator, various heat exchangers, a gasification furnace, a separator, an air separation device, a desulfurization device, a denitration device, an air cooling island, a condenser, a cooling tower, photovoltaic power generation, photo-thermal power generation, wind power generation and the like.
Example 1
An intelligent operation and maintenance method for industrial boiler cogeneration system equipment based on modularization companion simulation and system configuration optimization. The embodiment focuses on the intelligent operation and maintenance problem of the industrial boiler body.
First, a complex thermodynamic system is decomposed into individual calculation modules with mathematical independence by using a fundamental theory of thermodynamic simulation, and the thermodynamic system as shown in fig. 2 is established. The industrial boiler body is decomposed into an industrial boiler hearth 1, a coal hopper 2, a steam superheater 11, an evaporator 12, an economizer 13, a primary air preheater 14 and a secondary air preheater 15; the boiler auxiliary machine comprises a primary fan 16, a secondary fan 17, an induced draft fan 18 and a chimney 19; other thermal devices include steam turbine modules 3, 4, 5, regenerators 6, 7, feed water pump 8, heat exchange station 9 and generator 10. The relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation.
Secondly, according to the basic design and checking theory of each equipment module, a high-precision simulation model is established, wherein the high-precision simulation model comprises an industrial boiler hearth, a coal hopper, a steam superheater, an evaporator, an economizer, an air preheater, a fan, a chimney, a turbine, a heat regenerator, a pump, a heat exchange station and a generator.
And thirdly, calling real-time operation data and historical operation data of each equipment module, taking the module simulation model as a framework, and continuously iterating and updating the model through machine learning methods such as regression analysis and artificial neural network to form an accompanying simulation model gradually approaching the performance of the actual operation equipment.
Fourthly, taking the comprehensive coal consumption rate or the optimal economic benefit of the system as an optimization target, adopting a non-linear programming method such as a particle swarm algorithm, a genetic algorithm and the like, determining an optimal combination matching scheme of each equipment parameter under any operation condition by utilizing an equipment module along with a simulation model and a system quality-heat balance equation, and directly regulating and controlling the equipment or giving an operation prompt.
Fifthly, determining a change curve of heat exchange coefficients of heat exchanger modules such as a steam superheater, an evaporator, an economizer, an air preheater and the like along with time by using the equipment module and a simulation model, and determining a soot blowing threshold; and intelligently blowing soot according to the real-time running state.
Sixthly, determining the change curve of the working performance of equipment modules such as a steam turbine, a fan, a pump and the like along with time by using the equipment modules and the simulation model, determining the performance degradation state of the equipment, and performing visual maintenance or making a maintenance plan.
Example 2
An intelligent operation and maintenance method for equipment of a direct air-cooling cogeneration system based on modularization accompanying simulation and system configuration optimization. The embodiment focuses on the intelligent operation and maintenance problem of the air cooling island.
First, a complex thermodynamic system is decomposed into individual calculation modules with mathematical independence by using a fundamental theory of thermodynamic simulation, and the thermodynamic system as shown in fig. 3 is established. The air cooling island is divided into 6 modules (more air cooling units are possible) according to the heat exchange units, and the serial numbers are respectively 11-16; other thermal plants include a steam generator 1, steam turbine modules 3, 4, 5, regenerators 6, 7, feed water pump 8, heat exchange station 9 and generator 10. The relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation.
Secondly, according to the basic design and checking theory of each equipment module, a high-precision simulation model is established, wherein the high-precision simulation model comprises an air-cooled heat exchanger, a turbine, a heat regenerator, a pump, a heat exchange station and a generator.
And thirdly, calling real-time operation data and historical operation data of each equipment module, taking the module simulation model as a framework, and continuously iterating and updating the model through machine learning methods such as regression analysis and artificial neural network to form an accompanying simulation model gradually approaching the performance of the actual operation equipment.
Fourthly, determining the relation between the dirty state of the heat exchanger and the heat exchange coefficient by using the equipment module along with the simulation model, constructing a dirty index system of the air cooling unit, and establishing an air cooling island dirty prediction model; evaluating the dirt degree of each air cooling unit according to the real-time monitoring parameters, and cleaning according to the conditions; only the units needing cleaning are cleaned, and the water consumption of the air cooling unit is greatly reduced under the condition of ensuring the efficient operation of the unit.
Fifthly, determining the relationship between the temperature reduction rate and the environmental parameters and the operating parameters by using the equipment module and the simulation model, establishing an anti-freezing prediction model under the cold condition in winter, and extracting a model characteristic value as a basis for constructing an anti-freezing logic of the air cooling island; and evaluating the icing risk of each air cooling unit according to the real-time monitoring parameters, and realizing the differential anti-freezing control of each air cooling unit.
And fifthly, taking the heat consumption rate or the coal consumption rate as an optimization target, adopting a non-linear programming method such as a particle swarm algorithm, a genetic algorithm and the like, determining the optimal rotating speed of the fan of each air cooling unit under any operation condition and environment parameter by using an equipment module along with a simulation model and a system mass-heat balance equation under the constraint conditions of freezing prevention, pollution prevention, environment change, equipment failure and the like, and directly regulating and controlling the fan or giving an operation prompt.
The invention discloses an intelligent operation and maintenance system and method for thermal equipment. With reference to fig. 1 to 3, the basic idea and embodiment of the method is: the method is characterized in that a thermodynamic simulation basic theory is utilized, a complex thermodynamic system is decomposed into calculation modules with mathematical independence, the relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation, and the relevance of main parameters in the modules is determined by an accompanying simulation model which is established by taking an equipment high-precision simulation model as a framework for real-time and historical operation data machine learning. On the basis, the optimal real-time combination matching scheme of each equipment parameter under any operation condition is determined through analyzing the overall performance of the thermodynamic system, the energy-saving potential of the system is deeply excavated, and the operation of the system is optimized; the change rule of the equipment performance parameters along with time under different operation conditions and working conditions can be obtained through analyzing the equipment performance, the equipment performance degradation state and the degradation trend are determined, and an equipment intelligent maintenance, fault early warning and visual condition maintenance system is further established. The invention provides a comprehensive solution for intelligent operation and maintenance of the thermodynamic equipment, is beneficial to improving the operation management level of the thermodynamic system, and has obvious economic benefit and application value.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. An intelligent operation and maintenance system for thermal equipment is characterized by comprising:
the thermodynamic system configuration modeling module is used for thermodynamic system configuration modeling;
the modularized companion simulation module is used for modularized companion simulation;
the thermodynamic system operation optimization module is used for optimizing the operation of the thermodynamic system;
the thermal equipment performance degradation analysis module is used for performing performance degradation analysis on the thermal equipment;
and the thermal equipment state abnormity evaluation module is used for evaluating the thermal equipment state abnormity.
2. An intelligent operation and maintenance method for thermal equipment is characterized by comprising the following steps:
a thermodynamic system configuration modeling method;
a modular companion simulation method;
a thermodynamic system operation optimization method;
a thermodynamic device performance degradation analysis method;
provided is a thermal equipment state abnormity evaluation method.
3. The intelligent operation and maintenance method for thermal equipment according to claim 2, wherein the method is characterized in that
The thermodynamic system configuration modeling method comprises the following steps:
the complex thermodynamic system is decomposed into a plurality of calculation modules with mathematical independence by utilizing a fundamental theory of thermodynamic simulation, the relevance of each parameter between different modules is determined by a thermodynamic system mass-heat balance equation, and various complex thermodynamic systems can be constructed in a modular diagram forming mode.
4. The intelligent operation and maintenance method for thermal equipment according to claim 2, wherein the method is characterized in that
The modularized companion simulation method comprises the following steps:
a thermodynamic simulation model of an equipment module is used as an initial framework, and a model is continuously updated in an iterative mode by means of machine learning of real-time and historical operating data to form an accompanying simulation model which gradually approaches the performance of actual operating equipment.
5. The intelligent operation and maintenance method for thermal equipment according to claim 4, wherein the method is characterized in that
The machine learning is implemented by taking the decomposed independent module as an object, the relevance of main parameters in the module is determined by a high-precision simulation model, and the parameter relation which cannot be solved by the simulation model in the module is implemented by the machine learning such as regression analysis and an artificial neural network.
6. The intelligent operation and maintenance method for thermal equipment according to claim 2, wherein the method is characterized in that
The thermodynamic system operation optimization method comprises the following steps:
based on an equipment module accompanied with a simulation model and a system mass-heat balance equation, a gradient descent method, a particle swarm algorithm and a genetic algorithm are adopted to determine an optimal real-time combination matching scheme of each equipment parameter under any operation condition by taking the overall heat consumption rate, coal consumption rate, power consumption rate or other characteristic parameters of the system as optimization targets.
7. The intelligent operation and maintenance method for thermal equipment according to claim 2, wherein the method is characterized in that
The method for analyzing the performance degradation of the thermodynamic equipment comprises the following steps:
based on the equipment module and the accompanying simulation model, the change rule of the equipment performance parameters along with the time under different operation conditions and working conditions is obtained through methods of characteristic parameter evaluation and data mining, and the equipment performance degradation state and degradation trend data are determined.
8. The intelligent operation and maintenance method for thermal equipment according to claim 2, wherein the method is characterized in that
The method for analyzing the performance degradation of the thermodynamic equipment comprises the following steps:
and (3) utilizing equipment of the same model to analyze the benchmarks and the big data, evaluating whether the performance of the equipment is abnormal or not according to the performance degradation state and the degradation trend of the equipment, and realizing equipment fault early warning and visual condition maintenance according to the evaluation result and the FTA analysis methods.
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| CN116579762A (en) * | 2023-04-14 | 2023-08-11 | 广州林旺空调工程有限公司 | Intelligent operation and maintenance platform for cooling tower |
| CN116579762B (en) * | 2023-04-14 | 2023-10-20 | 广州林旺空调工程有限公司 | Intelligent operation and maintenance platform for cooling tower |
| CN117764561A (en) * | 2024-02-21 | 2024-03-26 | 临沂明振仪表科技有限公司 | Intelligent operation and maintenance management system for thermodynamic equipment based on Internet of things |
| CN117764561B (en) * | 2024-02-21 | 2024-05-14 | 临沂明振仪表科技有限公司 | Intelligent operation and maintenance management system for thermodynamic equipment based on Internet of things |
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