CN114692322A - Method, device and equipment for monitoring scaling of storage and heavy oil heat exchanger - Google Patents
Method, device and equipment for monitoring scaling of storage and heavy oil heat exchanger Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for monitoring scaling of a storage and a heavy oil heat exchanger, wherein the method comprises the following steps: collecting process data stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system; carrying out data correction on the process data through a data correction model; constructing a heat exchanger calculation model by using detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger through bringing in the checked process data; constructing a scaling prediction model based on a scaling critical theory; carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor; and (3) constructing a blockage state analysis model of the target heat exchanger by taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index, and determining the blockage degree of the tube pass of the target heat exchanger. The method avoids the distortion condition outside the training sample, and can effectively improve the accuracy of predicting the actual scaling degree of the heavy oil heat exchanger.
Description
Technical Field
The invention relates to the field of oil refining processing, in particular to a method, a device and equipment for monitoring scaling of a storage and a heavy oil heat exchanger.
Background
In the chemical production process, especially the petrochemical refining process, the heat exchange equipment is the most common and basic thermal equipment in production. In actual operation, various heat exchangers all have the scale deposit phenomenon of different degrees, reduce equipment heat transfer performance from this, have increased enterprise's energy consumption and indirect heating equipment maintenance cost, and at partial technological process, the heat exchanger can produce quite serious scale deposit problem even, blocks up the heat exchanger from this and then influences the operating cycle of deciding whole apparatus for producing even, becomes the bottleneck of the long period operation of device. Therefore, monitoring the scaling condition of the heat exchange equipment and accurately and reasonably predicting the scaling development condition become one of the research targets of long-period operation, energy conservation and consumption reduction of chemical devices.
In the prior art of the technical scheme for monitoring fouling and predicting fouling of heat exchange equipment, patent CN110490351A discloses a GA-RBF artificial neural network method, and a heat exchanger fouling growth prediction method is provided by training a neural network based on a sample.
The inventor finds that due to the fact that different process medium scaling mechanisms are different and actual conditions of the heat exchanger are different, the technical scheme of heat exchange equipment scaling monitoring and scaling prediction in the prior art is limited by the application conditions and the application range of the monitoring technology and the method, and accurate prediction of the actual scaling degree of the heavy oil heat exchanger is difficult to obtain.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art that is already known to a person skilled in the art.
Disclosure of Invention
The invention aims to improve the accuracy of the prediction of the actual scaling degree of a heavy oil heat exchanger.
The invention provides a heavy oil heat exchanger scaling monitoring method, which comprises the following steps:
s11, collecting process data including the material flow composition, inlet temperature, outlet temperature, flow and operation pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
s12, performing data correction on the process data through a data correction model to obtain checked reasonable data;
s13, constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger by taking in the checked process data;
s14, constructing a fouling prediction model based on a fouling critical theory;
s15, carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor;
s16, taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index, constructing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger.
In the present invention, the data correction of the process data by the data correction model to obtain the checked reasonable data includes:
s21, in the steady-state production process, for single target data, taking the data of the preset number collected before the current value as a sample, and adopting a Lauda method to remove significant errors;
s22, aiming at the measured data groups such as flow rate, temperature and the like, utilizing the energy conservation principle that the heat obtained by cold material flow is equal to the heat released by hot material flow, enabling the sum of squares of the difference of the measured values to be minimum, and carrying out data coordination by solving the least square solution of a constraint equation set;
the constraint equation set of the data coordination is as follows:
F(x'1,x'2...x'i)＝0
wherein, F represents a conservation constraint function, x 'represents a coordination value of measured data, x' is a measured value, and sigma is a measurement standard deviation.
In the invention, the building of the fouling prediction model based on the fouling critical theory comprises the following steps:
determining the content of asphaltene and colloid in heavy oil as factors having important influence on the generation of deposit type dirt, and on the basis of experimental tests, generating a fouling prediction model based on a fouling critical theory, wherein the fouling prediction model comprises the following steps:
X＝a*A+b*B
wherein, dRf(ii) dt is the fouling rate;
the term is the deposition term of fouling, Re is the Reynolds number, Pr is the Planck number, X is an easily deposited component influencing factor, wherein A is the asphaltene content in the heavy oil, B is the residual carbon content in the heavy oil, R is the gas constant, 8.314 kJ/mol.K, E is the reaction activation energy, kJ/mol, and Tf are the effective membrane layer temperatures respectively; gamma Re ηThe term is a scale inhibition term; alpha, beta, gamma, phi and activation energy E as well as a and b are constant terms, and the numerical values are obtained through regression on the basis of experimental data.
In the present invention, the correlation analysis of the scaling degree and the scaling generation factors to obtain the correlation coefficient of each scaling generation factor includes:
determining a correlation coefficient value of each fouling generation factor variable on fouling generation by using a correlation coefficient method according to fouling generation factors which at least comprise stream temperature, stream velocity, stream composition and component content and operating pressure and can influence the fouling degree by combining the target heat exchanger;
the correlation coefficient method calculation formula comprises:
wherein r isxyRepresenting the sample correlation coefficient, SxyRepresents the sample covariance, SxSample standard deviation, S, for XySample standard deviations for y are indicated.
In the invention, the method for establishing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger by taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index comprises the following steps:
s61, constructing a corresponding heat exchanger model by using the structural parameters of the target heat exchanger, assuming that the number of blocked pipes of the target heat exchanger is Ni, wherein the value of i is 0 to the number n of main pipes of a pipe bundle, performing simulation calculation to obtain a pipe pass pressure drop value Pi of the target heat exchanger, and fitting to obtain a functional relation between the pressure drop and the number of the blocked pipes: n ═ f (p);
s62, according to the relation of the blockage degree phi: phi is N/N; obtaining a deformation relation phi ═ f (P)/n of the blockage degree phi; and the value of phi is used for evaluating the blockage degree of the target heat exchanger tube pass.
In another aspect of the present invention, there is also provided a method for evaluating fouling loss of a heavy oil heat exchanger, comprising the steps of the above-mentioned fouling monitoring method for a heavy oil heat exchanger, and,
s17, generating economic loss data of the target heat exchanger fouling; the economic loss data includes an extra consumption of gas
In another aspect of the present invention, there is provided a fouling monitoring device for a heavy oil heat exchanger, comprising:
the data acquisition unit is used for acquiring process data including the material flow composition, inlet temperature, outlet temperature, flow and operating pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
the data correction unit is used for carrying out data correction on the process data through a data correction model to obtain checked reasonable data;
the fouling thermal resistance calculation unit is used for constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger through the process data after the process data is brought into check;
the prediction model building unit is used for building a scaling prediction model based on a scaling critical theory;
the correlation coefficient calculation unit is used for carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain the correlation coefficient of each scaling generation factor;
and the analysis model construction unit is used for constructing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger by taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index.
In another aspect of the present invention, there is also provided a device for evaluating fouling loss of a heavy oil heat exchanger, comprising the above-mentioned fouling monitoring device for a heavy oil heat exchanger, and,
an economic loss calculation unit for generating economic loss data of the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
In another aspect of the present invention, there is also provided a memory comprising a software program adapted to execute the steps of the above-described heavy oil heat exchanger fouling monitoring method or the heavy oil heat exchanger fouling loss assessment method by a processor.
In another aspect of the embodiments of the present invention, there is also provided a fouling monitoring device for a heavy oil heat exchanger, including a computer program stored on a memory, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method in the above aspects, and achieves the same technical effects.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, a complete method for monitoring and predicting the scaling of the heavy oil heat exchanger of the refining enterprise is formed through a plurality of links such as data acquisition, data correction, data calculation, deep analysis and the like, so that the method can be more suitable for the actual condition of production. Therefore, the scaling prediction model can better accord with the actual scaling condition of the heavy oil heat exchanger, and compared with the neural network prediction method in the prior art, the method avoids the distortion condition outside a training sample, and further can effectively improve the accuracy of predicting the actual scaling degree of the heavy oil heat exchanger.
On the other hand, the invention also realizes the loss evaluation of the heavy oil heat exchanger after scaling through correlation analysis, blockage analysis and scaling economic loss analysis, thereby expanding the usability and richness of scaling monitoring and prediction of the heavy oil heat exchanger and more effectively meeting the application requirements of enterprises.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood and to make the technical means implementable in accordance with the contents of the description, and to make the above and other objects, technical features, and advantages of the present invention more comprehensible, one or more preferred embodiments are described below in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic illustration of the steps of a method of monitoring fouling of a heavy oil heat exchanger according to the present invention;
FIG. 2 is a schematic diagram of the steps of the heavy oil heat exchanger fouling loss assessment method described in the present invention;
FIG. 3 is a schematic structural diagram of a fouling monitoring device for a heavy oil heat exchanger according to the present invention;
FIG. 4 is a schematic structural view of a fouling loss evaluation device for a heavy oil heat exchanger according to the present invention;
FIG. 5 is a schematic structural diagram of a fouling monitoring device or a fouling loss evaluation device for a heavy oil heat exchanger according to the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
In this document, the terms "first", "second", etc. are used to distinguish two different elements or portions, and are not used to define a particular position or relative relationship. In other words, the terms "first," "second," and the like may also be interchanged with one another in some embodiments.
Example one
In order to improve the accuracy of predicting the actual fouling degree of the heavy oil heat exchanger, as shown in fig. 1, in an embodiment of the present invention, a fouling monitoring method for a heavy oil heat exchanger is provided, which includes the steps of:
s11, collecting process data including the material flow composition, inlet temperature, outlet temperature, flow and operation pressure of the cold material flow and the hot material flow of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
in practical applications, the assay analysis data of the target system may specifically include asphaltene content, colloid content, residual carbon content, metal content, density, and the like, and these assay analysis data may be used for scale correlation analysis, so as to find out the main factors affecting the scale in the aspect of system components.
S12, performing data correction on the process data through a data correction model to obtain checked reasonable data;
in step S11, the collected process data (which may also be referred to as process production data) may have errors due to various influences such as unstable operating conditions, instrument errors, and pipeline vibrations, and therefore, further data correction is required, in the embodiment of the present invention, a specific way of performing data correction on the process data through a data correction model may be:
s21, in the steady-state production process, for single target data, taking the data of the preset number collected before the current value as a sample, and adopting a Lauda method to remove significant errors;
in practical applications, the predetermined number may be set according to personal experience of a person skilled in the art or determined according to experimental results, and in the embodiment of the present invention, the selected interval of the predetermined number is preferably between 25 and 35.
S22, aiming at the measured data groups such as flow rate, temperature and the like, utilizing the energy conservation principle that the heat obtained by cold material flow is equal to the heat released by hot material flow, enabling the sum of squares of the difference of the measured values to be minimum, and carrying out data coordination by solving the least square solution of a constraint equation set;
the constraint equation set of the data coordination is as follows:
F(x'1,x'2...x'i)＝0
wherein, F represents a conservation constraint function, x 'represents a coordination value of measured data, x' is a measured value, and sigma is a measurement standard deviation.
S13, constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger by taking in the checked process data;
aiming at a target heat exchanger, a theoretical model of the target heat exchanger can be constructed in a simulation mode by utilizing heat exchanger structure parameters such as structure form, pipe diameter, pipe length, baffle plate pattern and the like, and a dirt thermal resistance value under a reasonable error is obtained through iterative calculation by combining a heat exchanger specific structure and a heat exchanger checking calculation method; for the fouling resistance calculation, the following principles can be specifically used:
the target heat exchanger fouling resistance reflects the resistance of the target heat exchanger hot stream side to heat transfer to the cold stream side, which is defined essentially as:
wherein R is the total thermal resistance, (m)2K)/w; k is the total heat transfer coefficient, w/(m)2·K)；KfIs the total heat transfer coefficient in the contaminated state, w/(m)2·K)；KeIs the total heat transfer coefficient in a clean pollution state, w/(m)2·K)；RifAnd RofIs thermal resistance of convective heat transfer at two sides of the heat exchange tube under a pollution state (m)2·K)/w；Rf1And Rf2Is the dirt thermal resistance at two sides of the heat exchange tube in a pollution state (m)2·K)/w；RieAnd RoeIs the thermal resistance of convection heat transfer at two sides of the heat exchange tube under the clean state, (m)2·K)/w；RwIs the heat conduction resistance of the heat exchange tube, (m)2·K)/w。
In engineering applications, R is considered approximatelyif＝Rie，Rof＝RoeThus, the fouling resistance is:
by using a logarithmic temperature difference method, the heat transfer coefficient calculation formula is as follows:
ΔT1＝Th,i-Tc,o
ΔT2＝Th,o-Tc,i
in the formula, q is the heat transfer flow of the heat exchanger, w; a is the heat exchange area of the heat exchanger, m2；Th,iIs the inlet temperature, deg.C, of the heat exchanger hot stream; t ish,oThe outlet temperature of the heat flow of the heat exchanger is DEG C; t isc,iThe inlet temperature of the cold stream of the heat exchanger is at DEG C; t isc,iIs the outlet temperature of the cold stream of the heat exchanger, DEG C.
The heat transfer flow calculation formula of the heat exchanger is as follows:
q＝mh·Cph·(Th,i-Th,o)
or q ═ mc·Cpc·(Tc,o-Tc,i)
Wherein m is mass flow, kg/s; cp is the specific heat capacity of the stream, J/(kg).
S14, constructing a scaling prediction model based on a scaling critical theory;
according to the analysis of the heavy oil scaling mechanism, the content of asphaltenes and colloids in the heavy oil has an important influence on the generation of the deposition type scale, so that the scaling prediction model based on the scaling critical theory in the embodiment of the invention can be generated by improving the existing scaling prediction model of the heavy oil system on the basis of experimental tests.
In practical application, the step may specifically include:
determining the content of asphaltene and colloid in heavy oil as factors having important influence on the generation of deposit type dirt, and on the basis of experimental tests, generating a fouling prediction model based on a fouling critical theory, wherein the fouling prediction model comprises the following steps:
X＝a*A+b*B
wherein, dRf(ii) dt is the fouling rate;
the term is the deposition term of fouling, Re is the Reynolds number, Pr is the Planck number, X is an easily deposited component influencing factor, wherein A is the asphaltene content in the heavy oil, B is the residual carbon content in the heavy oil, R is the gas constant, 8.314 kJ/mol.K, E is the reaction activation energy, kJ/mol, and Tf are the effective membrane layer temperatures respectively; gamma Re ηThe term is a scale inhibition term; alpha, beta, gamma, phi and activation energy E as well as a and b are constant terms, and the numerical values are obtained through regression on the basis of experimental data.
S15, carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor;
specifically, in the step, a correlation coefficient value of each fouling generation factor variable on fouling generation can be determined by using a correlation coefficient method according to various fouling generation factors which can influence the fouling degree and comprise material flow temperature, flow rate, material flow composition, component content, operation pressure and the like by combining a target heat exchanger;
the correlation coefficient method calculation formula comprises:
wherein r isxyRepresents the sample correlation coefficient, SxyRepresents the sample covariance, SxSample standard deviation, S, for XySample standard deviations for y are indicated.
Wherein the correlation coefficient rxyThe calculated value of (a) is between 1 and-1, where 1 indicates that the two variables are completely linearly related, -1 indicates that the two variables are completely negatively related, and 0 indicates that the two variables are not related. Coefficient of correlation rxyThe closer the data of (1) is to 0, the weaker the correlation is.
S16, taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index, constructing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger.
In practical applications, the step may specifically include:
s61, constructing a corresponding heat exchanger model by using the structural parameters of the target heat exchanger, and assuming that the number of blocked pipes of the target heat exchanger is NiWherein the value of i is 0 to the number n of main pipes of the pipe bundle, and the tube pass pressure drop value P of the target heat exchanger is obtained through simulation calculationiAnd fitting to obtain a functional relation between the pressure drop and the number of the blocked pipes: n ═ f (p);
s62, according to the relation of the blockage degree phi: phi is N/N; obtaining a deformation relation phi ═ f (P)/n of the blockage degree phi; and the value of phi is used for evaluating the blockage degree of the target heat exchanger tube pass.
In conclusion, in the embodiment of the invention, a complete method for monitoring and predicting the scaling of the heavy oil heat exchanger of the refinery enterprise is formed through a plurality of links such as data acquisition, data correction, data calculation and deep analysis, and the method can be more suitable for the actual production situation. Therefore, the scaling prediction model can better accord with the actual scaling condition of the heavy oil heat exchanger, and compared with the neural network prediction method in the prior art, the method avoids the distortion condition outside a training sample, and further can effectively improve the accuracy of predicting the actual scaling degree of the heavy oil heat exchanger.
Example two
On the basis of the first embodiment, the embodiment of the present invention may implement loss evaluation on fouling of the heavy oil heat exchanger by adding corresponding steps, that is, in the embodiment of the present invention, there is provided a method for evaluating fouling loss of a heavy oil heat exchanger, which includes, in addition to the method for monitoring fouling of a heavy oil heat exchanger as described in the first embodiment, further: generating economic loss data for the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
As shown in fig. 2, the complete steps of the embodiment of the present invention are:
s11, collecting process data including the material flow composition, inlet temperature, outlet temperature, flow and operation pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
s12, performing data correction on the process data through a data correction model to obtain checked reasonable data;
s13, constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger by taking the checked process data;
s14, constructing a fouling prediction model based on a fouling critical theory;
s15, carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor;
s16, constructing a blockage state analysis model of the target heat exchanger by taking the pressure drop of an inlet and an outlet of a tube pass as an evaluation index, and determining the blockage degree of the tube pass of the target heat exchanger;
s17, generating economic loss data of the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
Please refer to the description in the first embodiment for steps S11 to S16, which are not repeated herein;
with respect to generating economic loss data of the target heat exchanger fouling in step S17 in the embodiment of the present invention, the specific steps may be:
s71, according to the relation: (ii) Δ T-T2-T1, calculating the loss in cold stream temperature rise due to fouling of the target heat exchanger;
wherein T1 is the cold stream actual outlet temperature of the target heat exchanger; t2 is the cold stream outlet temperature calculated using the heat exchanger model in the case of no fouling.
S72, according to the relation: q ═ Δ T ═ mArticle (A)*CpArticle (A)/qGas (es)Calculating the extra consumption of the gas caused by temperature rise loss;
wherein Q is the extra consumption of gas; the m is the mass flow of the cold material flow; cpArticle (A)Is the mass specific heat capacity of the cold stream; q. q ofGas (es)Is the unit mass heat value of the gas.
S73, according to the relation: calculating the economic loss caused by the target heat exchanger fouling;
wherein u is the gas price; m is the sum of the economic losses due to fouling.
In summary, in the embodiment of the invention, a complete method for monitoring and predicting the scaling of the heavy oil heat exchanger of the refinery enterprise is formed through a plurality of links such as data acquisition, data correction, data calculation and deep analysis, and the method can be more suitable for the actual production situation. Therefore, the scaling prediction model can better accord with the actual scaling condition of the heavy oil heat exchanger, and compared with the neural network prediction method in the prior art, the method avoids the distortion condition outside a training sample, and further can effectively improve the accuracy of predicting the actual scaling degree of the heavy oil heat exchanger. Furthermore, the embodiment of the invention realizes the loss evaluation of the heavy oil heat exchanger after scaling through correlation analysis, blockage analysis and scaling economic loss analysis, thereby expanding the availability and richness of scaling monitoring and prediction of the heavy oil heat exchanger and more effectively meeting the application requirements of enterprises.
EXAMPLE III
On the other side of the embodiment of the present invention, there is further provided a fouling monitoring device for a heavy oil heat exchanger, fig. 3 is a schematic structural diagram of the fouling monitoring device for a heavy oil heat exchanger provided in the embodiment of the present invention, the fouling monitoring device for a heavy oil heat exchanger is a device corresponding to the fouling monitoring method for a heavy oil heat exchanger in the embodiment corresponding to fig. 1, that is, the fouling monitoring method for a heavy oil heat exchanger in the embodiment corresponding to fig. 1 is implemented by using a virtual device, and each virtual module constituting the fouling monitoring device for a heavy oil heat exchanger can be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the fouling monitoring device for the heavy oil heat exchanger in the embodiment of the invention comprises:
the data acquisition unit 01 is used for acquiring process data including the material flow composition, inlet temperature, outlet temperature, flow and operating pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
the data correction unit 02 is used for performing data correction on the process data through a data correction model to obtain checked reasonable data;
the fouling thermal resistance calculation unit 03 is used for constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger through the process data after checking;
the prediction model construction unit 04 is used for constructing a scaling prediction model based on a scaling critical theory;
the correlation coefficient calculation unit 05 is used for performing correlation analysis on the fouling degree and the fouling generation factors to obtain correlation coefficients of the fouling generation factors;
the analysis model construction unit 06 is configured to construct a blockage state analysis model of the target heat exchanger by using the tube pass inlet/outlet pressure drop as an evaluation index.
Since the working principle and the beneficial effects of the fouling monitoring device for the heavy oil heat exchanger in the embodiment of the invention have been recorded and described in the embodiment of fouling monitoring for the heavy oil heat exchanger corresponding to fig. 1, they can be referred to each other and are not described herein again.
Example four
In another aspect of the embodiment of the present invention, a device for evaluating a fouling loss of a heavy oil heat exchanger is further provided, and fig. 4 is a schematic structural diagram of the device for evaluating a fouling loss of a heavy oil heat exchanger according to the embodiment of the present invention, where the device for evaluating a fouling loss of a heavy oil heat exchanger is a device corresponding to the method for evaluating a fouling loss of a heavy oil heat exchanger according to the second embodiment, that is, the method for evaluating a fouling loss of a heavy oil heat exchanger according to the second embodiment is implemented by using a virtual device, and each virtual module constituting the device for evaluating a fouling loss of a heavy oil heat exchanger may be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the fouling loss evaluation device for the heavy oil heat exchanger in the embodiment of the invention comprises:
the data acquisition unit 01 is used for acquiring process data including the material flow composition, inlet temperature, outlet temperature, flow and operating pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
the data correction unit 02 is used for performing data correction on the process data through a data correction model to obtain checked reasonable data;
the fouling thermal resistance calculation unit 03 is used for constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger through the process data after checking;
the prediction model construction unit 04 is used for constructing a scaling prediction model based on a scaling critical theory;
the correlation coefficient calculation unit 05 is used for performing correlation analysis on the fouling degree and the fouling generation factors to obtain correlation coefficients of the fouling generation factors;
the analysis model construction unit 06 is configured to construct a blockage state analysis model of the target heat exchanger by using the tube pass inlet/outlet pressure drop as an evaluation index.
The economic loss calculation unit 07 is used for generating economic loss data of the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
Since the working principle and the beneficial effects of the heavy oil heat exchanger scaling loss evaluation device in the embodiment of the invention have been described and illustrated in the embodiment of the heavy oil heat exchanger scaling loss evaluation method in the second embodiment, they can be referred to each other and are not repeated herein.
EXAMPLE five
In an embodiment of the present invention, there is further provided a memory, wherein the memory includes a software program, and the software program is adapted to enable the processor to execute the steps of the fouling monitoring method for the heavy oil heat exchanger in the first embodiment.
The embodiment of the present invention can be implemented by way of a software program, that is, by writing a software program (and an instruction set) for implementing each step of the method for monitoring fouling of a heavy oil heat exchanger in the first embodiment or the method for evaluating fouling loss of a heavy oil heat exchanger in the second embodiment, the software program is stored in a storage device, and the storage device is disposed in a computer device, so that the software program can be invoked by a processor of the computer device to implement the purpose of the embodiment of the present invention.
EXAMPLE six
In an embodiment of the present invention, a scaling monitoring device for a heavy oil heat exchanger, or a scaling loss evaluation device for a heavy oil heat exchanger is further provided, where a memory included in the scaling monitoring device for a heavy oil heat exchanger, or the scaling loss evaluation device for a heavy oil heat exchanger includes a corresponding computer program product, and when a program instruction included in the computer program product is executed by a computer, the computer may execute the scaling monitoring method for a heavy oil heat exchanger, or the scaling loss evaluation method for a heavy oil heat exchanger, according to the above aspects, and achieve the same technical effects.
Fig. 5 is a schematic diagram of a hardware configuration of a heavy oil heat exchanger fouling monitoring device, or a heavy oil heat exchanger fouling loss evaluation device, as an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the device includes one or more processors 610, a bus 630, and a memory 620. Taking one processor 610 as an example, the apparatus may further include: input device 640, output device 650.
The processor 610, memory 620, input device 640, and output device 650 may be connected by a bus or other means, with fig. 5 exemplified by a bus connection.
The memory 620, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device, i.e., the processing method of the above-described method embodiment, by executing the non-transitory software programs, instructions and modules stored in the memory 620.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 620 optionally includes memory located remotely from the processor 610, which may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 640 may receive input numeric or character information and generate a signal input. The output device 650 may include a display device such as a display screen.
The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform:
s11, collecting process data including the material flow composition, inlet temperature, outlet temperature, flow and operation pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
s12, performing data correction on the process data through a data correction model to obtain checked reasonable data;
s13, constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger by taking in the checked process data;
s14, constructing a fouling prediction model based on a fouling critical theory;
s15, carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor;
s16, constructing a blockage state analysis model of the target heat exchanger by taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index;
further, the method may further include the steps of:
s17, generating economic loss data of the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage device and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage device includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a ReRAM, an MRAM, a PCM, a NAND Flash, a NOR Flash, a Memory, a magnetic disk, an optical disk, or other various media that can store program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (11)
1. A method for monitoring the scale of a heavy oil heat exchanger is characterized by comprising the following steps:
s11, collecting process data including the material flow composition, inlet temperature, outlet temperature, flow and operation pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
s12, performing data correction on the process data through a data correction model to obtain checked reasonable data;
s13, constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating to obtain the fouling thermal resistance value of the target heat exchanger by taking in the checked process data;
s14, constructing a scaling prediction model based on a scaling critical theory;
s15, carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain a correlation coefficient of each scaling generation factor;
s16, taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index, constructing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger.
2. The method for monitoring fouling of a heavy oil heat exchanger according to claim 1, wherein the step of performing data correction on the process data through a data correction model to obtain corrected reasonable data comprises:
s21, in the steady-state production process, for single target data, taking the data of the preset number collected before the current value as a sample, and adopting a Lauda method to remove significant errors;
s22, aiming at the measured data groups of flow, temperature and the like, utilizing the energy conservation principle that the heat obtained by cold material flow is equal to the heat released by hot material flow, enabling the square sum of the difference of the measured values to be minimum, and carrying out data coordination by solving the least square solution of a constraint equation set;
the constraint equation set of the data coordination is as follows:
F(x'1,x'2...x'i)＝0
wherein, F represents a conservation constraint function, x 'represents a coordination value of measured data, x' is a measured value, and sigma is a measurement standard deviation.
3. The heavy oil heat exchanger fouling monitoring method of claim 1, wherein the constructing of the fouling prediction model based on the fouling criticality theory comprises:
determining the content of asphaltene and colloid in heavy oil as factors having important influence on the generation of deposit type dirt, and on the basis of experimental tests, generating a fouling prediction model based on a fouling critical theory, wherein the fouling prediction model comprises the following steps:
X＝a*A+b*B
wherein, dRf(ii) dt is the fouling rate;
the term is the deposition term of fouling, Re is the Reynolds number, Pr is the Planck number, X is an easily deposited component influencing factor, wherein A is the asphaltene content in the heavy oil, B is the residual carbon content in the heavy oil, R is the gas constant, 8.314 kJ/mol.K, E is the reaction activation energy, kJ/mol, and Tf are the effective membrane layer temperatures respectively; gamma Re ηThe term is a scale inhibition term; alpha, beta, gamma, phi and activation energy E as well as a and b are constant terms, and the numerical values are obtained by regression on the basis of experimental data.
4. The method for monitoring fouling of a heavy oil heat exchanger according to claim 1, wherein the correlation analysis of the fouling degree and the fouling generation factors to obtain the correlation coefficient of each fouling generation factor comprises:
determining a correlation coefficient value of each fouling generation factor variable on fouling generation by using a correlation coefficient method according to fouling generation factors which at least comprise stream temperature, stream velocity, stream composition and component content and operating pressure and can influence the fouling degree by combining the target heat exchanger;
the correlation coefficient method calculation formula comprises:
wherein r isxyRepresenting the sample correlation coefficient, SxyRepresents the sample covariance, SxSample standard deviation, S, for XySample standard deviations for y are indicated.
5. The heavy oil heat exchanger fouling monitoring method according to claim 1, wherein the establishing of the plugging state analysis model of the target heat exchanger and the determination of the plugging degree of the tube pass of the target heat exchanger by using the tube pass inlet-outlet pressure drop as an evaluation index comprises:
s61, constructing a corresponding heat exchanger model by using the structural parameters of the target heat exchanger, and assuming that the number of blocked pipes of the target heat exchanger is NiWherein the value of i is 0 to the number n of main pipes of the pipe bundle, and the tube pass pressure drop value P of the target heat exchanger is obtained through simulation calculationiAnd fitting to obtain a functional relation between the pressure drop and the number of the blocked pipes: n ═ f (p);
s62, according to the relational expression of the blockage degree phi: phi is N/N; obtaining a deformation relation phi ═ f (P)/n of the blockage degree phi; and the value of phi is used for evaluating the blockage degree of the target heat exchanger tube pass.
6. A method for evaluating fouling loss of a heavy oil heat exchanger, comprising the method for monitoring fouling of a heavy oil heat exchanger according to any one of claims 1 to 5, and,
s17, generating economic loss data of the target heat exchanger fouling; the economic loss data includes an additional consumption of gas.
7. The heavy oil heat exchanger fouling loss evaluation method of claim 6, wherein the generating economic loss data for the target heat exchanger fouling comprises:
s71, according to the relation: (ii) Δ T-T2-T1, calculating the loss in cold stream temperature rise due to fouling of the target heat exchanger;
wherein T1 is the cold stream actual outlet temperature of the target heat exchanger; t2 is the cold stream outlet temperature calculated using the heat exchanger model in the case of no fouling.
S72, according to the relation: q ═ Δ T ═ mArticle (A)*CpArticle (A)/qGas (es)Calculating the extra consumption of the gas caused by temperature rise loss;
wherein Q is the extra consumption of gas; the m is the mass flow of the cold material flow; cpArticle (A)Is the mass specific heat capacity of the cold stream; q. q.sGas (es)Is the unit mass heat value of the gas.
S73, according to the relation: calculating the economic loss caused by the target heat exchanger fouling;
wherein u is the gas price; m is the sum of the economic losses due to fouling.
8. A fouling monitoring device for a weighted oil heat exchanger, comprising:
the data acquisition unit is used for acquiring process data including the material flow composition, inlet temperature, outlet temperature, flow and operating pressure of the cold and hot material flows of the target heat exchanger, which are stored in an enterprise database; collecting the assay analysis data of a target material system in the enterprise analysis system;
the data correction unit is used for carrying out data correction on the process data through a data correction model to obtain checked reasonable data;
the fouling thermal resistance calculation unit is used for constructing a heat exchanger calculation model by using the detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger through the process data after the process data is brought into check;
the prediction model building unit is used for building a scaling prediction model based on a scaling critical theory;
the correlation coefficient calculation unit is used for carrying out correlation analysis on the scaling degree and the scaling generation factors to obtain the correlation coefficient of each scaling generation factor;
and the analysis model construction unit is used for constructing a blockage state analysis model of the target heat exchanger and determining the blockage degree of the tube pass of the target heat exchanger by taking the pressure drop of the inlet and the outlet of the tube pass as an evaluation index.
9. An apparatus for evaluating fouling loss of a heavy oil heat exchanger, comprising the fouling monitoring apparatus for a heavy oil heat exchanger according to claim 8, and,
an economic loss calculation unit for generating economic loss data of the target heat exchanger fouling; the economic loss data includes gas additional consumption.
10. A memory comprising a software program adapted to execute the steps of the heavy oil heat exchanger fouling monitoring method of any one of claims 1 to 5, or the heavy oil heat exchanger fouling loss assessment method of claim 5, by a processor.
11. A heavy oil heat exchanger fouling monitoring apparatus, or a heavy oil heat exchanger fouling loss assessment apparatus, comprising a bus, a processor, and the memory as claimed in claim 9;
the bus is used for connecting the memory and the processor;
the processor is configured to execute a set of instructions in the memory.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115060870A (en) * | 2022-08-11 | 2022-09-16 | 中国长江三峡集团有限公司 | Geothermic fluid scaling prediction method and device and laboratory reaction equipment |
| CN115127848A (en) * | 2022-08-31 | 2022-09-30 | 山东汇通工业制造有限公司 | Heat exchanger pipeline blockage detection method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115060870A (en) * | 2022-08-11 | 2022-09-16 | 中国长江三峡集团有限公司 | Geothermic fluid scaling prediction method and device and laboratory reaction equipment |
| CN115060870B (en) * | 2022-08-11 | 2022-11-29 | 中国长江三峡集团有限公司 | Geofluorine fluid scaling prediction method and device and laboratory reaction equipment |
| CN115127848A (en) * | 2022-08-31 | 2022-09-30 | 山东汇通工业制造有限公司 | Heat exchanger pipeline blockage detection method |
| CN115127848B (en) * | 2022-08-31 | 2022-11-11 | 山东汇通工业制造有限公司 | Heat exchanger pipeline blockage detection method |
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