Method, device and equipment for monitoring fouling of storage device and heavy oil heat exchanger

Abstract

The present invention discloses a memory, a method, a device and an apparatus for monitoring fouling of a heavy oil heat exchanger, wherein the method comprises the steps of: collecting process data stored in an enterprise database; and collecting the test analysis data of a target system in an enterprise analysis system; performing data correction on the process data through a data correction model; constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by bringing in the verified process data; constructing a fouling prediction model based on the critical fouling theory; performing a correlation analysis on the fouling degree and the fouling generating factors, and obtaining the correlation coefficient of each fouling generating factor; constructing a blockage state analysis model of the target heat exchanger using the inlet and outlet pressure drop of the tube pass as an evaluation index and determining the blockage degree of the tube pass of the target heat exchanger. The present invention avoids distortion outside the training sample and can effectively improve the accuracy of the prediction of the actual fouling degree of the heavy oil heat exchanger.

Description

Technical Field

[0001] The invention relates to the field of oil refining, and in particular to a method, device and equipment for monitoring scaling of a storage device and a heavy oil heat exchanger. Background Art

[0002] In the chemical production process, especially in the petrochemical refining process, heat exchange equipment is the most common and basic thermal equipment in production. In actual operation, various heat exchangers have different degrees of scaling, which reduces the heat exchange performance of the equipment, increases the energy consumption of the enterprise and the maintenance cost of the heat exchange equipment. In some process, the heat exchanger may even have quite serious scaling problems, which will block the heat exchanger and affect or even determine the operation cycle of the entire production unit, becoming a bottleneck for the long-term operation of the unit. Therefore, monitoring the scaling of heat exchange equipment and accurately and reasonably predicting the development of scaling have become one of the research goals for the long-term operation and energy saving of chemical units.

[0003] In the prior art of technical solutions for heat exchange equipment fouling monitoring and scaling prediction, patent CN110490351A discloses a GA-RBF artificial neural network method, which proposes a heat exchanger fouling growth prediction method by training a neural network based on samples.

[0004] After research, the inventors found that due to the differences in scaling mechanisms of different process media and the wide variety of actual heat exchanger operating conditions, the technical solutions for scaling monitoring and scaling prediction of heat exchange equipment in the prior art are subject to the applicable conditions and scope of use of the monitoring technology and methods, and it is difficult to obtain an accurate prediction of the actual scaling degree of the heavy oil heat exchanger.

[0005] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0006] The object of the present invention is to improve the accuracy of predicting the actual fouling degree of a heavy oil heat exchanger.

[0007] The present invention provides a heavy oil heat exchanger fouling monitoring method, comprising the steps of:

[0008] S11, collecting process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and collecting the test analysis data of the target system in the enterprise analysis system;

[0009] S12, performing data correction on the process data through a data correction model to obtain reasonable data after correction;

[0010] S13, constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by inputting the verified process data;

[0011] S14. Construct a scaling prediction model based on scaling critical theory;

[0012] S15, performing correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor;

[0013] S16. Using the inlet and outlet pressure drop of the tube pass as an evaluation index, construct a blockage state analysis model of the target heat exchanger and determine the blockage degree of the tube pass of the target heat exchanger.

[0014] In the present invention, the process data is corrected by a data correction model to obtain corrected reasonable data, including:

[0015] S21. In a steady-state production process, for a single target data, a preset number of data collected before the current value is used as a sample, and the Laida method is used to eliminate significant errors;

[0016] S22. For the measurement data groups such as flow rate and temperature, the energy conservation principle is used, that is, the heat obtained by the cold flow is equal to the heat released by the hot flow, and the sum of squares of the differences between the measured values ​​is minimized. The data is coordinated by solving the least squares solution of the constraint equation group;

[0017] The constraint equations for data coordination are:

[0018] F(x'1,x'2...x' i )=0

[0019]

[0020] Where F represents the conservation constraint function, x' represents the coordination value of the measured data, x" is the measured value, and σ is the measurement standard deviation.

[0021] In the present invention, the scaling prediction model based on scaling critical theory is constructed, including:

[0022] The asphaltene and colloid content in heavy oil is determined to be a factor that has an important influence on the formation of deposition-type fouling. Based on experimental tests, the scaling prediction model based on the scaling critical theory is generated, which includes:

[0023]

[0024] X=a*A+b*B

[0025] Among them, dR f / dt is the scaling rate; The term is the fouling deposition term, Re is the Reynolds number, Pr is the Planck number, X is the influencing factor of the easily deposited component, where 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, Tf is the effective film layer temperature, γR e η The term is the inhibition term of fouling; α, β, γ, φ and activation energy E as well as a and b are all constant terms, and their values ​​are obtained through regression based on experimental data.

[0026] In the present invention, the correlation analysis between the scaling degree and the scaling generating factors is performed to obtain the correlation coefficient of each scaling generating factor, including:

[0027] In combination with the target heat exchanger, according to scaling factors that can affect the scaling degree, including at least logistics temperature, flow rate, logistics composition and component content, and operating pressure, a correlation coefficient method is used to determine the correlation coefficient value of each scaling factor variable on scaling;

[0028] The correlation coefficient method calculation formula includes:

[0029]

[0030] Among them, r xy represents the sample correlation coefficient, S xy represents the sample covariance, S x represents the sample standard deviation of X, S y represents the sample standard deviation of y.

[0031] In the present invention, the inlet and outlet pressure drop of the tube pass is used as an evaluation index to construct a blockage state analysis model of the target heat exchanger and determine the blockage degree of the tube pass of the target heat exchanger, including:

[0032] S61. Construct a corresponding heat exchanger model using the structural parameters of the target heat exchanger, assuming that the number of blocked tubes of the target heat exchanger is Ni, where i is a value ranging from 0 to the total number of tubes in the tube bundle n, and obtain the tube-side pressure drop value Pi of the target heat exchanger by simulation calculation, and obtain the functional relationship between the pressure drop and the number of blocked tubes by fitting: N=f(P);

[0033] S62. According to the relationship of the blockage degree Φ: Φ=N / n; obtain a modified relationship of the blockage degree Φ: Φ=f(P) / n; the value of Φ is used to evaluate the blockage degree of the target heat exchanger tube.

[0034] In another aspect of the present invention, a method for evaluating fouling loss of a heavy oil heat exchanger is provided, comprising the steps of the above-mentioned method for monitoring fouling of a heavy oil heat exchanger, and

[0035] S17, generating economic loss data of the target heat exchanger fouling; the economic loss data includes additional gas consumption

[0036] In another aspect of the present invention, a heavy oil heat exchanger fouling monitoring device is provided, comprising:

[0037] A data acquisition unit is used to collect process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and to collect the test analysis data of the target system in the enterprise analysis system;

[0038] A data correction unit, used to perform data correction on the process data through a data correction model to obtain reasonable data after correction;

[0039] A fouling thermal resistance calculation unit is used to construct a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculate the fouling thermal resistance value of the target heat exchanger by bringing in the verified process data;

[0040] A prediction model building unit, used to build a scaling prediction model based on scaling critical theory;

[0041] A correlation coefficient calculation unit is used to perform correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor;

[0042] The analysis model building unit is used to build a blockage state analysis model of the target heat exchanger and determine the blockage degree of the target heat exchanger tube pass by taking the inlet and outlet pressure drop of the tube pass as an evaluation index.

[0043] In another aspect of the present invention, there is also provided a heavy oil heat exchanger fouling loss assessment device, comprising the above-mentioned heavy oil heat exchanger fouling monitoring device, and,

[0044] The economic loss calculation unit is used to generate economic loss data of fouling of the target heat exchanger; the economic loss data includes additional gas consumption.

[0045] In another aspect of the present invention, a memory is provided, comprising a software program, wherein the software program is suitable for a processor to execute the steps of the heavy oil heat exchanger fouling monitoring method or the heavy oil heat exchanger fouling loss assessment method.

[0046] On the other hand, an embodiment of the present invention further provides a heavy oil heat exchanger fouling monitoring device, which includes a computer program stored in a memory, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the methods described in the above aspects and achieves the same technical effects.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention forms a complete method for monitoring and predicting the fouling of heavy oil heat exchangers in refining enterprises through data collection, data correction, data calculation, in-depth analysis and other links, which can be more in line with the actual production situation. In this way, the fouling prediction model can be more consistent with the actual fouling situation of the heavy oil heat exchanger. Compared with the neural network prediction method in the prior art, the present invention avoids distortion outside the training sample, and thus can effectively improve the accuracy of the prediction of the actual fouling degree of the heavy oil heat exchanger.

[0049] On the other hand, the present invention also realizes the loss assessment after fouling of heavy oil heat exchangers through correlation analysis, blockage analysis and fouling economic loss analysis, thereby expanding the availability and richness of heavy oil heat exchanger fouling monitoring and prediction, and more effectively meeting the application needs of enterprises.

[0050] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, and to make the above and other purposes, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the steps of the heavy oil heat exchanger fouling monitoring method of the present invention;

[0052] Figure 2 Schematic diagram of the steps of the method for evaluating fouling loss of a heavy oil heat exchanger according to the present invention;

[0053] Figure 3 A schematic diagram of the structure of the heavy oil heat exchanger fouling monitoring device of the present invention;

[0054] Figure 4 A schematic diagram of the structure of the heavy oil heat exchanger fouling loss assessment device of the present invention;

[0055] Figure 5 A schematic structural diagram of the heavy oil heat exchanger fouling monitoring device or the heavy oil heat exchanger fouling loss assessment device in the present invention. DETAILED DESCRIPTION

[0056] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0057] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0058] In this document, the terms "first", "second", etc. are used to distinguish two different elements or parts, and are not used to limit a specific position or relative relationship. In other words, in some embodiments, the terms "first", "second", etc. can also be interchangeable.

[0059] Embodiment 1

[0060] In order to improve the accuracy of prediction of actual fouling degree of heavy oil heat exchanger, Figure 1 As shown, in an embodiment of the present invention, a method for monitoring fouling of a heavy oil heat exchanger is provided, comprising the steps of:

[0061] S11, collecting process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and collecting the test analysis data of the target system in the enterprise analysis system;

[0062] In practical applications, the test and analysis data of the target system may specifically include asphaltene content, colloid content, residual carbon content, metal content, density, etc. These test and analysis data can be used for scaling correlation analysis to facilitate the discovery of the main factors affecting scaling in terms of system components.

[0063] S12, performing data correction on the process data through a data correction model to obtain reasonable data after correction;

[0064] The process data (also referred to as process production data) collected in step S11 may have errors due to various influencing factors such as unstable working conditions, instrument errors, pipeline vibrations, etc. Therefore, further data correction is required. In an embodiment of the present invention, the specific method of correcting the process data through a data correction model may be:

[0065] S21. In a steady-state production process, for a single target data, a preset number of data collected before the current value is used as a sample, and the Laida method is used to eliminate significant errors;

[0066] In practical applications, the preset number can be set according to the personal experience of technicians in this field or determined according to experimental results. In the embodiment of the present invention, the selected interval of the preset number is preferably between 25 and 35.

[0067] S22. For the measurement data groups such as flow rate and temperature, the energy conservation principle is used, that is, the heat obtained by the cold flow is equal to the heat released by the hot flow, and the sum of squares of the differences between the measured values ​​is minimized. The data is coordinated by solving the least squares solution of the constraint equation group;

[0068] The constraint equations for data coordination are:

[0069] F(x'1,x'2...x' i )=0

[0070]

[0071] Where F represents the conservation constraint function, x' represents the coordination value of the measured data, x" is the measured value, and σ is the measurement standard deviation.

[0072] S13, constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by inputting the verified process data;

[0073] For the target heat exchanger, the heat exchanger structural parameters, such as structural form, tube diameter, tube length, baffle style, etc., can be used to simulate and construct the theoretical model of the target heat exchanger. Combined with the specific structure of the heat exchanger, the heat exchanger calibration calculation method is used to iteratively calculate the fouling thermal resistance value under reasonable error. For the calculation of fouling thermal resistance, it can be based on the following principles:

[0074] The fouling thermal resistance of the target heat exchanger reflects the resistance of heat transfer from the hot flow side to the cold flow side of the target heat exchanger, and its basic definition is:

[0075]

[0076]

[0077]

[0078] Where R is the total thermal resistance, (m 2 ·K) / w; K is the total heat transfer coefficient, w / (m 2 ·K); K f is the total heat transfer coefficient under the polluted state, w / (m 2 ·K); K e is the total heat transfer coefficient under clean and polluted conditions, w / (m 2 ·K); R if and R of is the convection heat transfer resistance on both sides of the heat exchange tube under the contaminated state, (m 2 ·K) / w;R f1 and R f2 is the fouling thermal resistance on both sides of the heat exchange tube under the fouling state, (m2 ·K) / w;R ie and R oe is the convection heat transfer resistance on both sides of the heat exchange tube in the clean state, (m 2 ·K) / w;R w is the thermal resistance of the heat exchange tube, (m 2 ·K) / w.

[0079] In engineering applications, it is approximately considered that R if =R ie , R of =R oe , therefore, the fouling thermal resistance is:

[0080]

[0081] Using the logarithmic temperature difference method, the heat transfer coefficient is calculated as:

[0082]

[0083] ΔT1=T h,i -T c,o

[0084] ΔT2=T h,o -T c,i

[0085] Where q is the heat transfer flow rate of the heat exchanger, w; A is the heat transfer area of ​​the heat exchanger, m 2 ; T h,i is the inlet temperature of the heat exchanger hot flow, °C; T h,o is the outlet temperature of the heat exchanger hot flow, °C; T c,i is the inlet temperature of the cold stream of the heat exchanger, °C; T c,i is the outlet temperature of the cold stream of the heat exchanger, ℃.

[0086] The heat transfer flow calculation formula of the heat exchanger is:

[0087] q=m h ·Cp h ·(T h,i -T h,o )

[0088] Or q = m c ·Cp c ·(T c,o -T c,i )

[0089] Where m is the mass flow rate, kg / s; Cp is the specific heat capacity of the flow, J / (kg*℃).

[0090] S14. Construct a scaling prediction model based on scaling critical theory;

[0091] According to the analysis of the fouling mechanism of heavy oil, the asphaltene and colloid content in heavy oil has an important influence on the formation of deposition-type fouling. Therefore, based on experimental tests, by improving the existing fouling prediction model of heavy oil system, the fouling prediction model based on the critical fouling theory in the embodiment of the present invention can be generated.

[0092] In practical applications, this step may specifically include:

[0093] The asphaltene and colloid content in heavy oil is determined to be a factor that has an important influence on the formation of deposition-type fouling. Based on experimental tests, the scaling prediction model based on the scaling critical theory is generated, which includes:

[0094]

[0095] X=a*A+b*B

[0096] Among them, dR f / dt is the scaling rate; The term is the fouling deposition term, Re is the Reynolds number, Pr is the Planck number, X is the influencing factor of the easily deposited component, where 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, Tf is the effective film layer temperature, γR e η The term is the inhibition term of fouling; α, β, γ, φ and activation energy E as well as a and b are all constant terms, and their values ​​are obtained through regression based on experimental data.

[0097] S15, performing correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor;

[0098] Specifically, in this step, the correlation coefficient value of each scaling factor variable on scaling can be determined by using the correlation coefficient method based on the target heat exchanger and various scaling factors that can affect the scaling degree, including logistics temperature, flow rate, logistics composition and component content, and operating pressure;

[0099] The correlation coefficient method calculation formula includes:

[0100]

[0101] Among them, r xy represents the sample correlation coefficient, S xy represents the sample covariance, S x represents the sample standard deviation of X, S y represents the sample standard deviation of y.

[0102] Among them, the correlation coefficient rxy The calculated value is between 1 and -1, where 1 means that the two variables are completely linearly correlated, -1 means that the two variables are completely negatively correlated, and 0 means that the two variables are uncorrelated. xy The closer the data is to 0, the weaker the correlation is.

[0103] S16. Using the inlet and outlet pressure drop of the tube pass as an evaluation index, construct a blockage state analysis model of the target heat exchanger and determine the blockage degree of the tube pass of the target heat exchanger.

[0104] In practical applications, this step may specifically include:

[0105] S61, constructing a corresponding heat exchanger model using the structural parameters of the target heat exchanger, assuming that the number of blocked tubes of the target heat exchanger is N i , where i ranges from 0 to the total number of tubes in the tube bundle n, and the tube side pressure drop value P of the target heat exchanger is obtained by simulation calculation i , and the functional relationship between pressure drop and number of blocked pipes is obtained by fitting: N = f(P);

[0106] S62. According to the relationship of the blockage degree Φ: Φ=N / n; obtain a modified relationship of the blockage degree Φ: Φ=f(P) / n; the value of Φ is used to evaluate the blockage degree of the target heat exchanger tube.

[0107] In summary, in the embodiment of the present invention, through data collection, data correction, data calculation, in-depth analysis and other links, a complete method for monitoring and predicting the fouling of heavy oil heat exchangers in refining enterprises is formed, which can be more in line with the actual production situation. In this way, the fouling prediction model can be more consistent with the actual fouling situation of the heavy oil heat exchanger. Compared with the neural network prediction method in the prior art, the present invention avoids distortion outside the training sample, and thus can effectively improve the accuracy of the prediction of the actual fouling degree of the heavy oil heat exchanger.

[0108] Embodiment 2

[0109] On the basis of Example 1, the embodiment of the present invention can realize the loss assessment of fouling of heavy oil heat exchanger by adding corresponding steps, that is, in the embodiment of the present invention, a method for assessing the loss of fouling of heavy oil heat exchanger is provided, which includes, in addition to the heavy oil heat exchanger fouling monitoring method as described in Example 1, also includes: generating economic loss data of fouling of the target heat exchanger; the economic loss data includes additional gas consumption.

[0110] like Figure 2 As shown, the complete steps of the embodiment of the present invention are:

[0111] S11, collecting process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and collecting the test analysis data of the target system in the enterprise analysis system;

[0112] S12, performing data correction on the process data through a data correction model to obtain reasonable data after correction;

[0113] S13, constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by inputting the verified process data;

[0114] S14. Construct a scaling prediction model based on scaling critical theory;

[0115] S15, performing correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor;

[0116] S16, using the inlet and outlet pressure drop 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;

[0117] S17, generating economic loss data of the target heat exchanger due to scaling; the economic loss data includes additional gas consumption.

[0118] Wherein steps S11 to S16 refer to the description in the first embodiment and will not be described in detail here;

[0119] Regarding the generation of the economic loss data of fouling of the target heat exchanger in step S17 in the embodiment of the present invention, the specific steps may be:

[0120] S71, calculating the temperature rise loss of the cold flow caused by scaling of the target heat exchanger according to the relationship: ΔT=T2-T1;

[0121] Wherein, T1 is the actual outlet temperature of the cold stream of the target heat exchanger; T2 is the outlet temperature of the cold stream calculated by using the heat exchanger model when there is no scaling.

[0122] S72, according to the relationship: Q = ΔT*m 物 *Cp 物 / q 瓦斯 , calculate the additional gas consumption due to temperature rise loss;

[0123] Where Q is the additional gas consumption; m is the mass flow of cold flow; Cp 物 is the mass specific heat capacity of the cold stream; q 瓦斯 It is the calorific value per unit mass of gas.

[0124] S73, calculating the economic loss caused by scaling of the target heat exchanger according to the relationship: M = ∑Q*u;

[0125] Among them, u is the gas price; M is the total economic loss caused by scaling.

[0126] In summary, in the embodiment of the present invention, through multiple links such as data collection, data correction, data calculation, and in-depth analysis, a complete method for monitoring and predicting the fouling of heavy oil heat exchangers in refining enterprises is formed, which can be more in line with the actual production situation. In this way, the fouling prediction model can be more consistent with the actual fouling situation of the heavy oil heat exchanger. Compared with the neural network prediction method in the prior art, the present invention avoids distortion outside the training sample, and thus can effectively improve the accuracy of the prediction of the actual degree of fouling of the heavy oil heat exchanger. Furthermore, the embodiment of the present invention also realizes the loss assessment of the heavy oil heat exchanger after fouling through correlation analysis, blockage analysis, and fouling economic loss analysis, thereby expanding the availability and richness of fouling monitoring and prediction of heavy oil heat exchangers, and can more effectively meet the application needs of enterprises.

[0127] Embodiment 3

[0128] In another aspect of the embodiment of the present invention, a heavy oil heat exchanger fouling monitoring device is also provided. Figure 3 The structural schematic diagram of the heavy oil heat exchanger fouling monitoring device provided in the embodiment of the present invention is shown. The heavy oil heat exchanger fouling monitoring device is Figure 1 The device corresponding to the heavy oil heat exchanger fouling monitoring method described in the corresponding embodiment, that is, implemented by means of a virtual device Figure 1 In the corresponding embodiment of the heavy oil heat exchanger fouling monitoring method, each virtual module constituting the heavy oil heat exchanger fouling monitoring device can be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the heavy oil heat exchanger fouling monitoring device in the embodiment of the present invention includes:

[0129] The data acquisition unit 01 is used to collect process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and to collect the test analysis data of the target system in the enterprise analysis system;

[0130] The data correction unit 02 is used to perform data correction on the process data through a data correction model to obtain reasonable data after verification;

[0131] The fouling thermal resistance calculation unit 03 is used to construct a heat exchanger calculation model using the detailed structural parameters of the target heat exchanger, and calculate the fouling thermal resistance value of the target heat exchanger by bringing in the verified process data;

[0132] The prediction model building unit 04 is used to build a scaling prediction model based on scaling critical theory;

[0133] The correlation coefficient calculation unit 05 is used to perform correlation analysis on the scaling degree and scaling generation factors to obtain the correlation coefficient of each scaling generation factor;

[0134] The analysis model building unit 06 is used to build a blockage state analysis model of the target heat exchanger by taking the inlet and outlet pressure drop of the tube as an evaluation index.

[0135] Since the working principle and beneficial effects of the heavy oil heat exchanger fouling monitoring device in the embodiment of the present invention have been Figure 1 The corresponding heavy oil heat exchanger fouling monitoring embodiments are also described and explained, so they can be cross-referenced and will not be repeated here.

[0136] Embodiment 4

[0137] In another aspect of the embodiment of the present invention, a device for evaluating fouling loss of a heavy oil heat exchanger is provided. Figure 4 The structural schematic diagram of the heavy oil heat exchanger fouling loss assessment device provided in the embodiment of the present invention is shown, and the heavy oil heat exchanger fouling loss assessment device is a device corresponding to the heavy oil heat exchanger fouling loss assessment method in the second embodiment, that is, the heavy oil heat exchanger fouling loss assessment method in the second embodiment is implemented by a virtual device, and each virtual module constituting the heavy oil heat exchanger fouling loss assessment device can be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the heavy oil heat exchanger fouling loss assessment device in the embodiment of the present invention includes:

[0138] The data acquisition unit 01 is used to collect process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and to collect the test analysis data of the target system in the enterprise analysis system;

[0139] The data correction unit 02 is used to perform data correction on the process data through a data correction model to obtain reasonable data after verification;

[0140] The fouling thermal resistance calculation unit 03 is used to construct a heat exchanger calculation model using the detailed structural parameters of the target heat exchanger, and calculate the fouling thermal resistance value of the target heat exchanger by bringing in the verified process data;

[0141] The prediction model building unit 04 is used to build a scaling prediction model based on scaling critical theory;

[0142] The correlation coefficient calculation unit 05 is used to perform correlation analysis on the scaling degree and scaling generation factors to obtain the correlation coefficient of each scaling generation factor;

[0143] The analysis model building unit 06 is used to build a blockage state analysis model of the target heat exchanger by taking the inlet and outlet pressure drop of the tube as an evaluation index.

[0144] The economic loss calculation unit 07 is used to generate economic loss data of fouling of the target heat exchanger; the economic loss data includes additional gas consumption.

[0145] Since the working principle and beneficial effects of the heavy oil heat exchanger fouling loss assessment device in the embodiment of the present invention have been recorded and described in the embodiment of the heavy oil heat exchanger fouling loss assessment method in Example 2, they can be referenced to each other and will not be repeated here.

[0146] Embodiment 5

[0147] In an embodiment of the present invention, a memory is further provided, wherein the memory includes a software program, and the software program is suitable for a processor to execute each step of the heavy oil heat exchanger fouling monitoring method in embodiment one.

[0148] The embodiments of the present invention can be implemented in the form of a software program, that is, by writing a software program (and an instruction set) for implementing the heavy oil heat exchanger fouling monitoring method in Example 1, or the heavy oil heat exchanger fouling loss assessment method in Example 2, and the software program is stored in a storage device, and the storage device is provided in a computer device, so that the software program can be called by the processor of the computer device to achieve the purpose of the embodiments of the present invention.

[0149] Embodiment 6

[0150] In an embodiment of the present invention, a heavy oil heat exchanger fouling monitoring device or a heavy oil heat exchanger fouling loss assessment device is also provided. The heavy oil heat exchanger fouling monitoring device or the heavy oil heat exchanger fouling loss assessment device includes a memory including a corresponding computer program product. When the program instructions included in the computer program product are executed by a computer, the computer can execute the heavy oil heat exchanger fouling monitoring method or the heavy oil heat exchanger fouling loss assessment method described in the above aspects and achieve the same technical effects.

[0151] Figure 5 The hardware structure diagram of the heavy oil heat exchanger fouling monitoring device or the heavy oil heat exchanger fouling loss assessment device in the embodiment of the present invention is as follows: Figure 5 As shown in FIG. 6 , the device includes one or more processors 610, a bus 630, and a memory 620. Taking one processor 610 as an example, the device may further include: an input device 640 and an output device 650.

[0152] The processor 610, the memory 620, the input device 640 and the output device 650 may be connected via a bus or other means. Figure 5 Take the example of connecting via a bus.

[0153] The memory 620 is a non-transitory computer-readable storage medium that can 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 by running the non-transitory software programs, instructions, and modules stored in the memory 620, that is, the processing method of the above method embodiment is implemented.

[0154] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data, etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely arranged relative to the processor 610, and these remote memories may be connected to the processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] The input device 640 can receive input digital or character information and generate signal input. The output device 650 can include a display device such as a display screen.

[0156] The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, perform:

[0157] S11, collecting process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and collecting the test analysis data of the target system in the enterprise analysis system;

[0158] S12, performing data correction on the process data through a data correction model to obtain reasonable data after correction;

[0159] S13, constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by inputting the verified process data;

[0160] S14. Construct a scaling prediction model based on scaling critical theory;

[0161] S15, performing correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor;

[0162] S16, using the inlet and outlet pressure drop of the tube pass as an evaluation index, constructing a blockage state analysis model of the target heat exchanger;

[0163] Additionally, the steps may include:

[0164] S17, generating economic loss data of the target heat exchanger due to scaling; the economic loss data includes additional gas consumption.

[0165] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0166] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage device, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage device includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), ReRAM, MRAM, PCM, NAND Flash, NOR Flash, Memristor, disk or optical disk and other media that can store program codes.

[0170] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring fouling of a heavy oil heat exchanger, characterized in that: Includes steps: S11, collecting process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and, collecting the test and analysis data of the target system in the enterprise's analysis system; S12, performing data correction on the process data through a data correction model to obtain reasonable data after correction, including: S21. In a steady-state production process, for a single target data, a preset number of data collected before the current value is used as a sample, and the Laida method is used to eliminate significant errors; S22. For the measurement data groups such as flow rate and temperature, the energy conservation principle is used, that is, the heat obtained by the cold flow is equal to the heat released by the hot flow, and the sum of squares of the differences between the measured values ​​is minimized. The data is coordinated by solving the least squares solution of the constraint equation group; The constraint equations for data coordination are: F(x'1,x'2...x'i)=0 Where F represents the conservation constraint function, x' represents the coordination value of the measured data, x" is the measured value, and σ is the measurement standard deviation; S13, constructing a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculating the fouling thermal resistance value of the target heat exchanger by inputting the verified process data; S14. Construct a scaling prediction model based on scaling critical theory; S15, performing correlation analysis on the scaling degree and scaling generating factors to obtain the correlation coefficient of each scaling generating factor; S16. Using the inlet and outlet pressure drop of the tube pass as an evaluation index, construct a blockage state analysis model of the target heat exchanger and determine 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, characterized in that: The scaling prediction model based on scaling critical theory is constructed, including: The asphaltene and colloid content in heavy oil is determined to be a factor that has an important influence on the formation of deposition-type fouling. Based on experimental tests, the scaling prediction model based on the scaling critical theory is generated, which includes: X=a*A+b*B Among them, dR f / dt is the scaling rate; The term is the fouling deposition term, Re is the Reynolds number, Pr is the Planck number, X is the influencing factor of the easily deposited component, where 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, Tf is the effective film layer temperature, γR e η The term is the inhibition term of dirt; α, β, γ, The activation energy E as well as a and b are all constant terms, and their values ​​are obtained through regression based on experimental data.

3. The method for monitoring fouling of a heavy oil heat exchanger according to claim 1, characterized in that: The correlation analysis between the scaling degree and the scaling generating factors is performed to obtain the correlation coefficient of each scaling generating factor, including: In combination with the target heat exchanger, according to scaling factors that can affect the scaling degree, including at least logistics temperature, flow rate, logistics composition and component content, and operating pressure, a correlation coefficient method is used to determine the correlation coefficient value of each scaling factor variable on scaling; The correlation coefficient method calculation formula includes: Among them, r xy represents the sample correlation coefficient, S xy represents the sample covariance, S x represents the sample standard deviation of X, S y represents the sample standard deviation of y.

4. The method for monitoring fouling of a heavy oil heat exchanger according to claim 1, characterized in that: The method of using the inlet and outlet pressure drop 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 comprises: S61, constructing a corresponding heat exchanger model using the structural parameters of the target heat exchanger, assuming that the number of blocked tubes of the target heat exchanger is N i , where i ranges from 0 to the total number of tubes in the tube bundle n, and the tube side pressure drop value P of the target heat exchanger is obtained by simulation calculation i , and the functional relationship between pressure drop and number of blocked pipes is obtained by fitting: N = f(P); S62. According to the relationship of the blockage degree Φ: Φ=N / n; obtain a modified relationship of the blockage degree Φ: Φ=f(P) / n; the value of Φ is used to evaluate the blockage degree of the target heat exchanger tube.

5. A method for evaluating fouling loss of a heavy oil heat exchanger, characterized in that: The method comprises the heavy oil heat exchanger fouling monitoring method as claimed in any one of claims 1 to 4, and S17, generating economic loss data of the target heat exchanger due to scaling; the economic loss data includes additional gas consumption.

6. The method for evaluating fouling loss of a heavy oil heat exchanger according to claim 5, characterized in that: The generating of the economic loss data of the fouling of the target heat exchanger comprises: S71, calculating the temperature rise loss of the cold flow caused by scaling of the target heat exchanger according to the relationship: ΔT=T2-T1; Wherein, T1 is the actual outlet temperature of the cold stream of the target heat exchanger; T2 is the outlet temperature of the cold stream calculated by using the heat exchanger model when there is no scaling; S72, according to the relationship: Q = ΔT*m 物 *Cp 物 / q 瓦斯 , calculate the additional gas consumption due to temperature rise loss; Where Q is the additional gas consumption; m is the mass flow of cold flow; Cp 物 is the mass specific heat capacity of the cold stream; q 瓦斯 is the calorific value per unit mass of gas; S73, calculating the economic loss caused by scaling of the target heat exchanger according to the relationship: M = ∑Q*u; Among them, u is the gas price; M is the total economic loss caused by scaling.

7. A heavy oil heat exchanger fouling monitoring device, characterized in that: include: A data acquisition unit is used to collect process data stored in the enterprise database, including the logistics composition, inlet temperature, outlet temperature, flow rate and operating pressure of the cold and hot logistics of the target heat exchanger; and, collecting the test and analysis data of the target system in the enterprise's analysis system; The data correction unit is used to correct the process data through the data correction model to obtain reasonable data after verification, including: S21. In a steady-state production process, for a single target data, a preset number of data collected before the current value is used as a sample, and the Laida method is used to eliminate significant errors; S22. For the measurement data groups such as flow rate and temperature, the energy conservation principle is used, that is, the heat obtained by the cold flow is equal to the heat released by the hot flow, and the sum of squares of the differences between the measured values ​​is minimized. The data is coordinated by solving the least squares solution of the constraint equation group; The constraint equations for data coordination are: F(x'1,x'2...x'i)=0 Where F represents the conservation constraint function, x' represents the coordination value of the measured data, x" is the measured value, and σ is the measurement standard deviation; A fouling thermal resistance calculation unit is used to construct a heat exchanger calculation model using detailed structural parameters of the target heat exchanger, and calculate the fouling thermal resistance value of the target heat exchanger by bringing in the verified process data; A prediction model building unit, used to build a scaling prediction model based on scaling critical theory; A correlation coefficient calculation unit, used to perform correlation analysis on the scaling degree and scaling generating factors, and obtain the correlation coefficient of each scaling generating factor; The analysis model building unit is used to build a blockage state analysis model of the target heat exchanger and determine the blockage degree of the target heat exchanger tube pass by taking the inlet and outlet pressure drop of the tube pass as an evaluation index.

8. A heavy oil heat exchanger fouling loss assessment device, characterized in that: The device comprises a heavy oil heat exchanger fouling monitoring device as claimed in claim 7, and The economic loss calculation unit is used to generate economic loss data of fouling of the target heat exchanger; the economic loss data includes additional gas consumption.

9. A memory, characterized in that: The method comprises a software program, wherein the software program is suitable for executing the steps of the heavy oil heat exchanger fouling monitoring method according to any one of claims 1 to 4 or the heavy oil heat exchanger fouling loss assessment method according to claim 5 by a processor.

10. A heavy oil heat exchanger fouling monitoring device, or a heavy oil heat exchanger fouling loss assessment device, characterized in that: comprising a bus, a processor and a memory as claimed in claim 9; The bus is used to connect the memory and the processor; The processor is configured to execute an instruction set in the memory.

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