CN117113029A - Intelligent analysis method and system based on condenser equipment - Google Patents
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Abstract
The invention discloses an intelligent analysis method and system based on condenser equipment, in particular to the technical field of intelligent analysis of condensers, wherein the intelligent analysis method and system based on the condenser equipment is characterized in that target condenser equipment is determined through image recognition, real-time data and working parameters of the target condenser equipment are obtained through a sensor and stored in a database, the real-time data and the working parameters of the target condenser equipment are extracted from the database, the real-time data and the working parameters of the target condenser equipment are extracted, the extracted characteristic parameters are processed and then sent to a data analysis and calculation module, the performance index of the target condenser equipment is analyzed through a mathematical model, and the target condenser equipment at a high risk level is numbered and sent to a control module, so that a rapid condensation process is realized.
Description
Technical Field
The invention relates to the technical field of intelligent analysis of condensers, in particular to an intelligent analysis method and system based on condenser equipment.
Background
In the existing industrial production, in order to improve the production efficiency and the product quality, the materials are often required to be analyzed and detected, and the conventional analysis method has the problems of complex experimental operation, long analysis period, low precision and the like, so a new intelligent analysis method and system are required to improve the analysis efficiency and the accuracy, the conventional condenser device is usually used for condensing gas or steam into liquid so as to realize energy transfer and effective heat conduction, however, the conventional condenser device has some problems in the operation process, such as low energy efficiency, energy consumption waste, equipment failure and the like, because the working state of the condenser cannot be accurately monitored and analyzed.
Firstly, an analyte is required to be prepared and introduced into a system for subsequent analysis, the analyte is sprayed into a condenser through a sprayer, the condenser rapidly condenses the analyte from a gas phase into a liquid phase through controlling parameters such as temperature, pressure and the like, the condensation process can be realized by adjusting various parameters of the condenser, in the condensation process, a sensor monitors related parameters of the liquid phase in real time, such as temperature, pressure, flow rate and the like, parameter data acquired by the sensor are transmitted to a data processing module through a transmission line, the data processing module receives the parameter data acquired by the sensor and carries out real-time analysis and processing, the data processing module adopts an advanced intelligent algorithm to carry out rapid qualitative and quantitative analysis on the analyte according to the parameter data, and a user can check an analysis result through a user interface after the analysis is completed. The user interface provides an intuitively friendly operation interface, displays analysis results and supports further operations and analysis.
In order to solve the above-mentioned problems, in recent years, intelligent analysis methods and systems have been applied to condenser apparatuses, which monitor, analyze and predict the operation state of the condenser apparatus in real time using technologies such as sensors, data acquisition apparatuses and data analysis algorithms. However, the existing intelligent analysis method and system still have some defects, such as low data analysis precision, slow system response speed, untimely abnormal condition treatment and the like.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide an intelligent analysis method and system based on a condenser apparatus, so as to solve the above-mentioned problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent analysis system based on a condenser apparatus, comprising:
a data pre-collection module: the device comprises a device determining unit and a data acquisition unit;
the device determining unit determines a target condenser device through image recognition and sends the target condenser device to the data acquisition unit;
the data acquisition unit acquires real-time data and working parameters of the target condenser equipment through the sensor and stores the real-time data and the working parameters in the database;
and a data preprocessing module: extracting real-time data and working parameters of target condenser equipment from a database to perform feature extraction, processing the extracted feature parameters and then sending the processed feature parameters to a data analysis and calculation module;
and the data analysis and calculation module is used for: the device comprises a characteristic parameter calculation unit and a data analysis unit;
the characteristic parameter calculation unit calculates characteristic parameters of the target condenser equipment through a mathematical model to obtain a performance index of the target condenser equipment;
the data analysis unit analyzes the performance index of the target condenser equipment through a mathematical model to obtain the risk level of the target condenser, and sends the risk level to the state prediction module;
a state prediction module: analyzing the risk level of the failure of the target condenser equipment according to the performance evaluation coefficient of the target condenser, and sending the risk level to an early warning module;
and the early warning module is used for: performing early warning processing on target condenser equipment exceeding a risk index threshold value, and sending an early warning signal to a control module;
and the control module is used for: and the system is used for receiving the early warning signal sent by the early warning module and performing power-off treatment on the marked target condenser equipment.
Preferably, the target condenser device comprises a condenser, a compressor and at least one condensing fan, wherein the condenser is composed of a steam inlet, a heat exchange tube, a tube plate, a front water chamber, a rear water chamber, a cooling water inlet, a cooling water outlet and a condensed water collecting tank.
Preferably, the collecting the real-time data of the target condenser device specifically includes the following steps:
a1, selecting two target condensers with the same type, wherein one target condenser is an unused target condenser, and the other target condenser is a target condenser using H time, which are respectively marked as a 1 ，a 2 ；
A2, A is a 1 Put into use, a is measured by a sensor 1 The temperature of the heat transfer end is w, the cooling water temperature is b, and the pressure is p;
a3, pair a 2 Power is supplied, and a is measured by a sensor 2 The temperature of the heat transfer end is w ', the cooling water temperature is b ', and the pressure is p ';
and A4, calculating the temperature difference Deltaw of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference Deltap through a formula.
Preferably, the calculating Δw, Δb, and Δp sensitivity to fouling changes specifically includes:
converting Δw, Δb, and Δp into vector representations, respectively, i.e., Δw=f (R f ，Z 1 )，Δb＝f(R f ，Z 2 )，Δp＝f(R f ，Z 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein R is f Expressed as thermal resistance to fouling, Z 1 、Z 2 And Z 3 Expressed as effects Deltaw, deltab, respectively, andother influencing factors of Δp;
when R is f Variation DeltaR f Then the formula Δw+Δw' =f (R f +ΔR f ，Z 1 ) If the above formula is established by Taylor expansion, there isWherein->Expressed as a function f for variable R f Partial derivative of>Expressed as a function f for Z 1 Is then formed by DeltaR f The change in Δw caused by the change can be expressed as +.>Let Δw sensitivity to fouling change +.> Similarly, the sensitivity of Δb and Δp to fouling changes is α and β, then +.>
Preferably, the determining the feature vector of the dirt change by Δw, Δb, and Δp sensitivity to the dirt change specifically includes:
from the above, it can be seen that the sensitivity of Δw, Δb and Δp to fouling changes is respectively usedAlpha and beta, represented byAnd +.>It can be seen that to determine the sensitivity of Δw, Δb, and Δp to fouling changes, the key is to solve the partial derivative of the output variable with respect to the input variable +.>
The BP neural network can approximate any nonlinear function with arbitrary precision, meanwhile, after the BP network is trained, the network expression is a first-order continuous function of an output variable relative to an input variable, the output variable has partial differentiation relative to the input variable, and for the characteristic, the BP network is utilized to model the relation between each performance index and dirt, and then the corresponding partial differentiation, namely f (R) f ，Z 1 )＝R f ^2+Z 1 The method comprises the steps of carrying out a first treatment on the surface of the For R f Conduct derivation to obtainThe temperature difference Deltaw of the output heat transfer section is set as a dirt characteristic variable.
Preferably, the scale heat resistance is used for describing the scale degree of the condenser, and specifically comprises the following steps:
by the formulaMeasuring the thermal resistance of the dirt, wherein R f Expressed as thermal resistance to fouling, T R Expressed as the interface temperature between the heat exchange tube wall and dirt, T' expressed as the interface temperature between the fluid and dirt, u expressed as the heat flux density, the heat exchange surface wall temperature T measured by installing a heat sensor on the heat exchange tube wall R By installing temperature sensors at the inlet of the cooling water and the outlet of the cooling water, the cooling water inlet temperature of the pipe section is measured to be c 1 The temperature of the cooling water outlet of the pipe section is c 2 The length of the pipe section is l, the inner diameter of the pipe section is d, measured by a measuring tool, by the formula +.>Calculating the heat flux density, wherein F is expressed as the volume flow of cooling water, < >>θ is expressed as the thickness of the fouling layer, +.>c p Expressed as the specific heat capacity, Δp, of the cooling water at constant pressure 1 And Δp 2 Expressed as the flow pressure drop of the cooling water before and after the change in the fouling layer thickness.
Preferably, the determining the ash deposition amount of the condenser through calculation specifically includes:
after the compressor is started, every H, the pressures of the air outlet and the air supply outlet of the condenser are detected and respectively set as p 1 ，p 3 ，……，p n And p 2 ，p 4 ，……，p n+1 The average pressure difference between the air outlet and the air supply outlet of the condenser isThe ambient temperature of the condenser device is measured by a temperature detection sensor to be t w The surface temperature of the condenser is t a Δp, t w And t a Substitution formula d=Δp (t a -t w ) δ; where δ is denoted as a weight affecting the amount of ash deposited on the condenser and D is denoted as the amount of ash deposited on the condenser.
Preferably, the analyzing the risk level of the target condenser apparatus by the performance index of the condenser specifically includes:
the heat transfer area of the condenser is s measured by a sensor 1 The heat flux density is u, T R expressed as the interface temperature between the heat exchanger tube wall and the fouling, T' expressed as the interface temperature between the fluid and the foulingDegree, temperature difference Δt=t' -T R The method comprises the steps of carrying out a first treatment on the surface of the Will be Δ T, u, and s 1 Substitution formula-> Where h is denoted as the performance index of the target condenser device, D is denoted as the ash deposit amount of the condenser, h=1 is set as the risk threshold of the target condenser device, h<1 is indicated as target condenser apparatus at low risk level, h>1 indicates that the target condenser device is at a high risk level, the target condenser device at high risk is automatically numbered and sent to the control module.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent analysis method based on condenser equipment, which uses the intelligent analysis system based on condenser equipment, comprises the following steps:
s1, determining target condenser equipment through image recognition, acquiring real-time data and working parameters of the target condenser equipment through a sensor, and storing the real-time data and the working parameters in a database;
s2, extracting real-time data and working parameters of the target condenser equipment from a database, extracting features of the real-time data and the working parameters of the target condenser equipment, processing the extracted features and sending the processed features to a data analysis and calculation module;
s3, calculating the temperature difference delta w of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference delta p through a formula, and calculating the sensitivity to dirt change according to the delta w, the delta b and the delta pα, and β;
s4, through a formulaThe thermal resistance of the scale was measured by the formula d=Δp (t a -t w ) Delta calculation of target condenser deviceAsh deposition amount of R f And D, analyzing through a mathematical model to obtain the performance index of the target condenser equipment;
s5, judging the risk level of the target condenser equipment through the performance index of the target condenser equipment, and numbering the target condenser equipment at the high risk level and sending the number to the control module.
The invention has the technical effects and advantages that:
1. the invention utilizes the condenser equipment to convert the analyte from the gas phase to the liquid phase, realizes the rapid condensation process, greatly shortens the analysis time and improves the analysis efficiency compared with the traditional analysis method.
2. By adopting the advanced intelligent algorithm and the accurate sensor, the invention can carry out accurate qualitative and quantitative analysis on the analyte, has more accurate analysis result and can meet the requirements of different industries on analysis precision.
3. The invention can perform function expansion and customization according to the requirements of different industries and application fields, and can adjust the working parameters of the condenser according to specific analysis requirements so as to adapt to different sample types and analysis conditions.
Drawings
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Fig. 2 is a flow chart of the operation of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an intelligent analysis system based on condenser equipment, which comprises a data pre-collection module, a data pre-processing module, a data analysis and calculation module, a state prediction module, an early warning module and a control module.
The data pre-collection module is connected with the data pre-processing module, the data pre-processing module is connected with the data analysis and calculation module, the data analysis and calculation module is connected with the state prediction module, the state prediction module is connected with the early warning module, and the early warning module is connected with the control module.
The data pre-collection module comprises an equipment determining unit and a data acquisition unit;
the device determining unit determines a target condenser device through image recognition and sends the target condenser device to the data acquisition unit;
the data acquisition unit obtains real-time data and working parameters of the target condenser equipment through the sensor and stores the real-time data and the working parameters in the database.
In one possible design, the target condenser apparatus includes a condenser, a compressor, and at least one condensing fan, the condenser being comprised of a steam inlet, a heat exchange tube, a tube sheet, a front water chamber, a rear water chamber, a cooling water inlet, a cooling water outlet, and a condensate header.
Further, the real-time data of the target condenser equipment is collected, and the specific steps are as follows:
a1, selecting two target condensers with the same type, wherein one target condenser is an unused target condenser, and the other target condenser is a target condenser using H time, which are respectively marked as a 1 ，a 2 ；
A2, A is a 1 Put into use, a is measured by a sensor 1 The temperature of the heat transfer end is w, the cooling water temperature is b, and the pressure is p;
a3, pair a 2 Power is supplied, and a is measured by a sensor 2 The temperature of the heat transfer end is w ', the cooling water temperature is b ', and the pressure is p ';
and A4, calculating the temperature difference Deltaw of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference Deltap through a formula.
The data preprocessing module extracts real-time data and working parameters of the target condenser equipment from the database to perform feature extraction, processes the extracted feature parameters and sends the processed feature parameters to the data analysis and calculation module.
In one possible design, the calculating Δw, Δb, and Δp sensitivity to fouling changes specifically includes:
converting Δw, Δb, and Δp into vector representations, respectively, i.e., Δw=f (R f ，Z 1 )，Δb＝f(R f ，Z 2 )，Δp＝f(R f ，Z 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein R is f Expressed as thermal resistance to fouling, Z 1 、Z 2 And Z 3 Represented as other influencing factors affecting aw, Δb, and Δp, respectively;
when R is f Variation DeltaR f Then the formula Δw+Δw' =f (R f +ΔR f ，Z 1 ) If the above formula is established by Taylor expansion, there isWherein->Expressed as a function f for variable R f Partial derivative of>Expressed as a function f for Z 1 Is then formed by DeltaR f The change in Δw caused by the change can be expressed as +.>Let Δw sensitivity to fouling change +.> Similarly, the sensitivity of Δb and Δp to fouling changes is α and β, then +.>
The data analysis and calculation module comprises a characteristic parameter calculation unit and a data analysis unit;
the characteristic parameter calculation unit calculates characteristic parameters of the target condenser equipment through a mathematical model to obtain a performance index of the target condenser equipment;
the data analysis unit analyzes the performance index of the target condenser equipment through a mathematical model to obtain the risk level of the target condenser, and sends the risk level to the state prediction module.
In one possible design, the determining the feature vector of the dirt change by the sensitivity of Δw, Δb, and Δp to the dirt change specifically includes:
from the above, it can be seen that the sensitivity of Δw, Δb and Δp to fouling changes is respectively usedAlpha and beta, represented byAnd +.>It can be seen that to determine the sensitivity of Δw, Δb, and Δp to fouling changes, the key is to solve the partial derivative of the output variable with respect to the input variable +.>
The BP neural network can approximate any nonlinear function with arbitrary precision, meanwhile, after the BP network is trained, the network expression is a first-order continuous function of an output variable relative to an input variable, the output variable has partial differentiation relative to the input variable, and for the characteristic, the BP network is utilized to model the relation between each performance index and dirt, and then the corresponding partial differentiation, namely f (R) f ，Z 1 )＝R f ^2+Z 1 The method comprises the steps of carrying out a first treatment on the surface of the For R f Conduct derivation to obtainThe temperature difference Deltaw of the output heat transfer section is set as a dirt characteristic variable.
Further, the method for describing the fouling degree of the condenser through the fouling thermal resistance specifically comprises the following steps:
by the formulaMeasuring the thermal resistance of the dirt, wherein R f Expressed as thermal resistance to fouling, T R Expressed as the interface temperature between the heat exchange tube wall and dirt, T' expressed as the interface temperature between the fluid and dirt, u expressed as the heat flux density, the heat exchange surface wall temperature T measured by installing a heat sensor on the heat exchange tube wall R By installing temperature sensors at the inlet of the cooling water and the outlet of the cooling water, the cooling water inlet temperature of the pipe section is measured to be c 1 The temperature of the cooling water outlet of the pipe section is c 2 The length of the pipe section is l, the inner diameter of the pipe section is d, measured by a measuring tool, by the formula +.>Calculating the heat flux density, wherein F is expressed as the volume flow of cooling water, < >>θ is expressed as the thickness of the fouling layer, +.>c p Expressed as the specific heat capacity, Δp, of the cooling water at constant pressure 1 And Δp 2 Expressed as the flow pressure drop of the cooling water before and after the change in the fouling layer thickness.
As a preferred application of the present invention, the determining the ash deposition amount of the condenser by calculation specifically includes:
after the compressor is started, every H, the pressures of the air outlet and the air supply outlet of the condenser are detected and respectively set as p 1 ，p 3 ，……，p n And p 2 ，p 4 ，……，p n+1 ThenThe average pressure difference between the air outlet and the air supply outlet of the condenser isThe ambient temperature of the condenser device is measured by a temperature detection sensor to be t w The surface temperature of the condenser is t a Δp, t w And t a Substitution formula d=Δp (t a -t w ) δ; where δ is denoted as a weight affecting the amount of ash deposited on the condenser and D is denoted as the amount of ash deposited on the condenser.
The state prediction module: and analyzing the risk level of the failure of the target condenser equipment according to the performance evaluation coefficient of the target condenser, and sending the risk level to an early warning module.
In one possible design, the analyzing the risk level of the target condenser apparatus by the performance index of the condenser specifically includes:
the heat transfer area of the condenser is s measured by a sensor 1 The heat flux density is u, T R expressed as the interface temperature between the heat exchanger tube wall and the fouling, T 'expressed as the interface temperature between the fluid and the fouling, temperature difference Δt=t' -T R The method comprises the steps of carrying out a first treatment on the surface of the Will be Δ T, u, and s 1 Substitution formula-> Where h is denoted as the performance index of the target condenser device, D is denoted as the ash deposit amount of the condenser, h=1 is set as the risk threshold of the target condenser device, h<1 is indicated as target condenser apparatus at low risk level, h>1 is indicated as the target condenser device being at a high risk level, the target condenser device at high risk is automatically numbered and sent to the control module。
The control module is used for receiving the early warning signal sent by the early warning module and carrying out power-off treatment on the marked target condenser equipment.
As shown in fig. 2, the present embodiment provides an intelligent analysis method based on a condenser apparatus, including the following steps:
s1, determining target condenser equipment through image recognition, acquiring real-time data and working parameters of the target condenser equipment through a sensor, and storing the real-time data and the working parameters in a database;
s2, extracting real-time data and working parameters of the target condenser equipment from a database, extracting features of the real-time data and the working parameters of the target condenser equipment, processing the extracted features and sending the processed features to a data analysis and calculation module;
s3, calculating the temperature difference delta w of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference delta p through a formula, and calculating the sensitivity to dirt change according to the delta w, the delta b and the delta pα, and β;
s4, through a formulaThe thermal resistance of the scale was measured by the formula d=Δp (t a -t w ) Calculating the ash deposition amount of the target condenser equipment, and adding R to the calculated ash deposition amount f And D, analyzing through a mathematical model to obtain the performance index of the target condenser equipment;
s5, judging the risk level of the target condenser equipment through the performance index of the target condenser equipment, and numbering the target condenser equipment at the high risk level and sending the number to the control module.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. An intelligent analysis system based on a condenser apparatus, comprising:
a data pre-collection module: the device comprises a device determining unit and a data acquisition unit;
the device determining unit determines a target condenser device through image recognition and sends the target condenser device to the data acquisition unit;
the data acquisition unit acquires real-time data and working parameters of the target condenser equipment through the sensor and stores the real-time data and the working parameters in the database;
and a data preprocessing module: extracting real-time data and working parameters of target condenser equipment from a database to perform feature extraction, processing the extracted feature parameters and then sending the processed feature parameters to a data analysis and calculation module;
and the data analysis and calculation module is used for: the device comprises a characteristic parameter calculation unit and a data analysis unit;
the characteristic parameter calculation unit calculates characteristic parameters of the target condenser equipment through a mathematical model to obtain a performance index of the target condenser equipment;
the data analysis unit analyzes the performance index of the target condenser equipment through a mathematical model to obtain the risk level of the target condenser, and sends the risk level to the state prediction module;
a state prediction module: analyzing the risk level of the failure of the target condenser equipment according to the performance evaluation coefficient of the target condenser, and sending the risk level to an early warning module;
and the early warning module is used for: performing early warning processing on target condenser equipment exceeding a risk index threshold value, and sending an early warning signal to a control module;
and the control module is used for: and the system is used for receiving the early warning signal sent by the early warning module and performing power-off treatment on the marked target condenser equipment.
2. The intelligent analysis system of claim 1, wherein the target condenser device comprises a condenser, a compressor and at least one condensing fan, the condenser is composed of a steam inlet, a heat exchange tube, a tube plate, a front water chamber, a rear water chamber, a cooling water inlet, a cooling water outlet and a condensed water collecting tank.
3. The intelligent analysis system based on a condenser apparatus according to claim 1, wherein the real-time data of the target condenser apparatus is collected, and the specific steps are as follows:
a1, selecting two target condensers with the same type, wherein one target condenser is an unused target condenser, and the other target condenser is a target condenser using H time, which are respectively marked as a 1 ，a 2 ；
A2, A is a 1 Put into use, a is measured by a sensor 1 The temperature of the heat transfer end is w, the cooling water temperature is b, and the pressure is p;
a3, pair a 2 Power is supplied, and a is measured by a sensor 2 The temperature of the heat transfer end is w ', the cooling water temperature is b ', and the pressure is p ';
and A4, calculating the temperature difference Deltaw of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference Deltap through a formula.
4. A condenser apparatus-based intelligent analysis system according to claim 3, wherein the calculation of Δw, Δb, and Δp sensitivity to fouling variations comprises:
converting Δw, Δb, and Δp into vector representations, respectively, i.e., Δw=f (R f ，Z 1 )，Δb＝f(R f ，Z 2 ) And Δp=f (R f ，Z 3 ) Wherein R is f Expressed as thermal resistance to fouling, Z 1 、Z 2 Z is as follows 3 Represented as other influencing factors affecting aw, Δb, and Δp, respectively;
when R is f Variation DeltaR f Then the formula Δw+Δw' =f (R f +ΔR f ，Z 1 ) If the above formula is established by Taylor expansion, there isWherein->Expressed as a function f for variable R f Partial derivative of>Expressed as a function f for Z 1 Is then formed by DeltaR f The change in Δw caused by the change can be expressed asLet Δw sensitivity to fouling change +.> Similarly, the sensitivity of Δb and Δp to fouling changes is α and β, then +.>
5. The intelligent analysis system based on a condenser apparatus according to claim 4, wherein the feature vector of the fouling change is determined by the sensitivity of Δw, Δb, and Δp to the fouling change, in particular comprising:
from the above, it can be seen that the sensitivity of Δw, Δb and Δp to fouling changes is respectively usedAlpha and beta, represented byAnd +.>It can be seen that to determine the sensitivity of Δw, Δb, and Δp to fouling changes, the key is to solve the partial derivative of the output variable with respect to the input variable +.>
The BP neural network can approximate any nonlinear function with arbitrary precision, meanwhile, after the BP network is trained, the network expression is a first-order continuous function of an output variable relative to an input variable, the output variable has partial differentiation relative to the input variable, and for the characteristic, the BP network is utilized to model the relation between each performance index and dirt, and then the corresponding partial differentiation, namely f (R) f ，Z 1 )＝R f ^2+Z 1 The method comprises the steps of carrying out a first treatment on the surface of the For R f Conduct derivation to obtainThe temperature difference Deltaw of the output heat transfer section is set as a dirt characteristic variable.
6. The intelligent analysis system based on a condenser apparatus according to claim 1, characterized in that the condenser junction fouling degree is described by fouling thermal resistance, in particular comprising:
by the formulaMeasuring the thermal resistance of the dirt, wherein R f Expressed as thermal resistance to fouling, T R Expressed as the interface temperature between the heat exchange tube wall and dirt, T' expressed as the interface temperature between the fluid and dirt, u expressed as the heat flux density, the heat exchange surface wall temperature T measured by installing a heat sensor on the heat exchange tube wall R By installing temperature sensors at the inlet of the cooling water and the outlet of the cooling water, the cooling water inlet temperature of the pipe section is measured to be c 1 The temperature of the cooling water outlet of the pipe section is c 2 The length of the pipe section is l, the inner diameter of the pipe section is d, measured by a measuring tool, by the formula +.>Calculating the heat flux density, wherein F is expressed as the volume flow of cooling water, < >>θ is expressed as the thickness of the fouling layer, +.>c p Expressed as the specific heat capacity, Δp, of the cooling water at constant pressure 1 And Δp 2 Expressed as the flow pressure drop of the cooling water before and after the change in the fouling layer thickness.
7. The intelligent analysis system based on a condenser apparatus according to claim 1, wherein the determination of the ash deposit amount of the condenser by calculation comprises:
after the compressor is started, every H, the pressures of the air outlet and the air supply outlet of the condenser are detected and respectively set as p 1 ，p 3 ，……，p n And p 2 ，p 4 ，……，p n+1 The average pressure difference between the air outlet and the air supply outlet of the condenser isThe ambient temperature of the condenser device is measured by a temperature detection sensor to be t w The surface temperature of the condenser is t a Δp, t w And t a Substitution formula d=Δp (t a -t w ) δ; where δ is denoted as a weight affecting the amount of ash deposited on the condenser and D is denoted as the amount of ash deposited on the condenser.
8. Intelligent analysis system based on a condenser device according to any of claims 6, 7, characterized in that the risk level of the target condenser device is analyzed by the performance index of the condenser, in particular comprising:
sensing of condenser by sensorA thermal area s 1 The heat flux isT R Expressed as the interface temperature between the heat exchanger tube wall and the fouling, T 'expressed as the interface temperature between the fluid and the fouling, temperature difference Δt=t' -T R The method comprises the steps of carrying out a first treatment on the surface of the Will be Δ T, u, and s 1 Substitution formula->Where h is denoted as the performance index of the target condenser device, D is denoted as the ash deposit amount of the condenser, h=1 is set as the risk threshold of the target condenser device, h<1 is indicated as target condenser apparatus at low risk level, h>1 indicates that the target condenser device is at a high risk level, the target condenser device at high risk is automatically numbered and sent to the control module.
9. A condenser apparatus-based intelligent analysis method using a condenser apparatus-based intelligent analysis system according to any one of claims 1 to 8, comprising the steps of:
s1, determining target condenser equipment through image recognition, acquiring real-time data and working parameters of the target condenser equipment through a sensor, and storing the real-time data and the working parameters in a database;
s2, extracting real-time data and working parameters of the target condenser equipment from a database, extracting features of the real-time data and the working parameters of the target condenser equipment, processing the extracted features and sending the processed features to a data analysis and calculation module;
s3, calculating the temperature difference delta w of the heat transfer section of the target condenser, the cooling water Wen Sheng b and the pressure difference delta p through a formula, and calculating the sensitivity to dirt change according to the delta w, the delta b and the delta pα, and β;
s4, through a formulaThe thermal resistance of the scale was measured by the formula d=Δp (t a -t w ) Calculating the ash deposition amount of the target condenser equipment, and adding R to the calculated ash deposition amount f And D, analyzing through a mathematical model to obtain the performance index of the target condenser equipment;
s5, judging the risk level of the target condenser equipment through the performance index of the target condenser equipment, and numbering the target condenser equipment at the high risk level and sending the number to the control module.
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| US4390058A (en) * | 1979-12-05 | 1983-06-28 | Hitachi, Ltd. | Method of monitoring condenser performance and system therefor |
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