A multi-parameter collaborative operation optimization method for a large crude oil storage tank heating process

Abstract

The present application relates to a kind of large crude oil storage tank heating process multi-parameter collaborative operation optimization method, it includes: the physical parameter that influences the scientific, reasonable combination of crude oil heating of storage tank forms orthogonal table;By changing the outlet temperature of storage tank heating furnace, compressor valve opening degree realizes the controllable regulation of steam temperature, steam flow rate in heating coil;By adjusting electric push rod, frequency converter realizes the change of stirrer angle, rotation speed, realizes the controllable regulation of different parameter combinations in orthogonal table;The crude oil heating rate of different parameter combinations in the orthogonal table of storage tank is calculated and obtained;Establish the multi-parameter combination mathematical model of storage tank heating process;Solve the multi-parameter combination mathematical model of storage tank heating process, obtain the crude oil heating rate highest, the physical parameter combination of best heating effect in orthogonal table.The present application realizes the optimal collaborative heating mode of multiple physical quantities such as the stirring condition of stirrer in storage tank, heating environment in coil, energy consumption is low when oil production, and carbon emission is less.

Description

Technical fields:

[0001] This invention relates to the field of oil and gas storage and transportation technology, specifically to a multi-parameter collaborative operation optimization method for the heating process of a large crude oil storage tank. Background technology:

[0002] Large crude oil storage tanks, as the main storage facilities in crude oil depots, typically employ heating coils for heating to ensure safe and economical operation. However, in a single coil heating mode, the natural convection heat transfer efficiency caused by the crude oil itself inside the tank is low, resulting in inefficient energy utilization. By using a tank agitator to assist coil heating, the intensity of convective heat transfer within the tank is enhanced, significantly improving the overall heating effect. However, the coil-aggregator synergistic heating method exhibits a mutually reinforcing yet mutually restrictive relationship regarding heat transfer from the oil flow within the tank, reducing the overall heating efficiency and causing unnecessary energy waste. Therefore, it is necessary to understand the synergistic relationships among multiple parameters within waxy crude oil storage tanks under the coil-aggregator heating mode to find the optimal combination of operating parameters for the tank's heating effect.

[0003] For multi-parameter optimization of crude oil storage tanks, the crude oil heating rate is typically used as the objective function. A single-variable method is employed to optimize the physical parameters of the heating coils and agitators within the tank, thus obtaining their respective optimal operating parameters. However, this method neglects the impact of the synergistic effect between the coils and agitators on the crude oil heating rate. In practice, due to the limited convective heat transfer efficiency of crude oil within the tank, simply increasing the temperature and flow rate inside the coils cannot achieve a sustained linear increase in crude oil temperature, resulting in significant energy waste. Therefore, an auxiliary agitator is used to enhance the convective heat transfer intensity of the crude oil within the tank, improving the energy utilization efficiency of the coils and promoting overall crude oil temperature rise. However, the coil-agitator synergistic heating mode does not simply promote crude oil temperature rise. An unreasonable combination between the two can inhibit heat transfer within the tank and exacerbate heat loss at the tank boundary, creating a dynamic relationship where the coil heating environment and agitator stirring conditions are both mutually reinforcing and mutually restrictive. Therefore, it is necessary to establish a new multi-parameter synergistic optimization method for large crude oil storage tanks to achieve reasonable optimization of the combination of multiple physical quantity operating parameters within the tank.

[0004] In summary, current optimization methods for the coordinated operation of multiple physical quantities in the heating process of crude oil storage tanks have certain limitations and cannot scientifically and accurately optimize the coordinated operation of multiple parameters in the heating process of large crude oil storage tanks. Summary of the Invention:

[0005] The purpose of this invention is to provide a multi-parameter collaborative operation optimization method for the heating process of large crude oil storage tanks. This method addresses the problem that current optimization methods for the collaborative operation of multiple physical quantities in the heating process of crude oil storage tanks cannot scientifically and accurately optimize the reasonable operation of the multi-parameter collaborative operation of large crude oil storage tanks.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: This method for optimizing the multi-parameter coordinated operation of the heating process of a large crude oil storage tank includes the following steps;

[0007] Step 1: Scientifically and rationally combine the physical parameters affecting the temperature rise of crude oil in the storage tank to form an orthogonal array. The physical parameters include the steam temperature and steam flow rate inside the heating coil; the stirring direction and rotation speed of the agitator; and the storage tank is a large crude oil storage tank.

[0008] Step 2: Controllable adjustment of the steam temperature and steam flow rate inside the heating coil is achieved by changing the outlet temperature of the storage tank heater and the opening of the compressor valve; the angle and rotation speed of the agitator are changed by adjusting the electric push rod and the frequency converter, thereby achieving controllable adjustment of different parameter combinations in the orthogonal table.

[0009] Step 3: Using temperature sensors placed on the guide columns inside the storage tank, obtain the changes in crude oil temperature at different liquid levels in the storage tank at different times under different parameter combinations in the orthogonal table. The average crude oil temperature measured on the guide columns represents the overall crude oil temperature in the storage tank. Calculate the crude oil heating rate for different parameter combinations in the orthogonal table of the storage tank.

[0010] Step 4: Establish a multi-parameter combined mathematical model of the tank heating process to obtain the functional relationship between the physical parameters and the crude oil heating rate;

[0011]

[0012] In the formula: x1 is the steam temperature, x2 is the steam flow rate, x3 is the stirring direction of the agitator, and x4 is the rotation speed; These are the regression coefficients; i1, i2, ..., i w d is the power coefficient; d is the degree of the polynomial; v is the rate of temperature increase of the crude oil in the storage tank.

[0013] Step 5: Solve the mathematical model of the multi-parameter combination of the tank heating process to obtain the combination of physical parameters in the orthogonal table that results in the highest crude oil heating rate and the best heating effect.

[0014] Step two in the above scheme is specifically as follows:

[0015] The steam temperature and steam flow rate inside the heating coil can be controlled and adjusted by changing the outlet temperature of the electric heater in the storage tank and the opening of the compressor valve. Specifically, the temperature setpoint can be gradually adjusted in the electric heater temperature control setting interface. When the outlet temperature approaches the setpoint, the heater control system will automatically adjust the heating power to achieve stable regulation of the steam temperature inside the heating coil. At the same time, the set flow rate value can be input through the compressor operation control interface. The control system will automatically calculate and adjust the valve opening based on the preset control algorithm and the actual flow rate signal fed back by the sensor to achieve precise control of the steam flow rate inside the heating coil.

[0016] The angle and rotation speed of the side-entry mixer can be changed by adjusting the electric push rod and frequency converter. Specifically, the mixing angle can be controlled by adjusting the length of the four inclined pull rods, and the motor speed can be controlled by the principle of electronic frequency conversion, thereby changing the rotation speed of the mixer.

[0017] In the above scheme, step four involves using a mathematical method of multivariate nonlinear regression to establish a multi-parameter combined mathematical model for solving the tank heating process, thereby obtaining the functional relationship between steam temperature, steam flow rate, stirring angle, rotation speed, and crude oil heating rate.

[0018] Step five of the above scheme involves using the interior point method to iteratively solve the multi-parameter combined mathematical model of the tank heating process based on the constraints of each physical parameter, in order to obtain the multi-physical synergistic mode with the highest crude oil heating rate and the best heating effect.

[0019] Beneficial effects:

[0020] This invention comprehensively considers the influence of the interaction of multiple parameters, such as the stirring conditions of the tank agitator and the internal heating environment of the coil, on the crude oil heating rate. Based on mathematical methods such as orthogonal experiments, multivariate nonlinear regression, and interior point method, it establishes a multi-physical quantity coordinated operation optimization method for the heating process of large crude oil storage tanks. This method breaks through the limitations of optimizing the operating parameters of the heating coil and agitator separately in the past, and realizes the optimal coordinated heating mode of multiple physical quantities, such as the stirring conditions of the agitator inside the tank and the internal heating environment of the coil. This provides theoretical support for oil depot production management to achieve the goal of reducing energy consumption and carbon emissions. Attached image description:

[0021] Figure 1 This is a schematic diagram showing the locations of various measuring points inside the storage tank. Detailed implementation method:

[0022] The present invention will be further described below with reference to the accompanying drawings:

[0023] This multi-parameter collaborative optimization method for the heating process of large crude oil storage tanks includes the following:

[0024] Step 1: Taking a large crude oil storage tank as the research object, the temperature rise of crude oil inside the tank is affected by the stirring conditions of the external agitator of the coil and the internal heating environment. Considering the influence of the interaction between multiple physical parameters such as the internal heating environment of the coil and the stirring conditions of the agitator, an orthogonal table is formed by scientifically and reasonably combining multiple physical parameters through orthogonal experiments.

[0025] Orthogonal experimental design combines physical parameters within different value ranges to evaluate the impact of each parameter on the rate of crude oil heating under different value ranges, making the parameter combinations more scientific and reasonable. The specific steps are as follows:

[0026] First, determine the parameters and their value ranges, clarifying the physical parameters to be studied and the value range of each physical parameter. In the study of multi-parameter synergistic optimization of the tank heating process, the physical parameters include the steam temperature inside the coil (x1), steam flow rate (x2), agitator stirring direction (x3), and rotation speed (x4), and each physical parameter has three possible values. Specifically, x1 has the following values: 11 x 12 x 13 x2 can take the following values: x 21 x 22 x 23 x3 can take the following values: x 31 x 32 x 33 x4 can take the following values: x 41 x 42 x 43 .

[0027] Secondly, select an appropriate orthogonal array L based on the number of parameters and the number of possible values. n (m k ), where L represents an orthogonal array, the subscript n represents the number of trials, m represents the number of parameter values, and k represents the number of physical parameters arranged. For four physical parameters, each with three values, L9(3) can be selected. 4 An orthogonal array is shown below:

[0028]

[0029] Step 2: Based on the orthogonal array of different combinations of parameters, the heating environment (steam temperature and steam flow rate) inside the heating coil is controllably adjusted by changing the outlet temperature of the storage tank heater and the opening of the compressor valve; the angle and rotation speed of the side-entry agitator are changed by adjusting the electric push rod and the frequency converter, thereby achieving controllable adjustment of different parameter combinations.

[0030] The heating environment (steam temperature and steam flow rate) inside the heating coil can be controlled and adjusted by changing the outlet temperature of the electric heater in the storage tank and the valve opening of the compressor. Specifically, the temperature control setting interface of the electric heater is accessed through an operation panel (such as a touchscreen or a control panel with buttons). The temperature setpoint is gradually adjusted, and when the outlet temperature approaches the setpoint, the heater's control system automatically adjusts the heating power to achieve stable regulation of the steam temperature inside the heating coil. Simultaneously, for compressors equipped with automatic control systems, the set flow rate value or relevant control parameters are input through the operation control interface. The control system automatically calculates and adjusts the valve opening based on a preset control algorithm and the actual flow rate signal fed back from sensors, achieving precise control of the steam flow rate inside the heating coil.

[0031] The angle and rotation speed of the side-entry agitator are changed by adjusting the electric push rod and frequency converter. Specifically, starting the motor drives the agitator shaft to rotate. After a period of agitation at one end, the fixing bolts are loosened, and the first and second connecting rods are pulled. By adjusting the length of the diagonal tie rods, the spherical seal rotates around the mounting base plate, driving the agitator shaft and agitator to rotate. After adjustment, the four fixing bolts are tightened to secure the moving rod. The lengths of the four diagonal tie rods are adjusted according to the rotation angle of the agitator shaft, thus achieving controllable adjustment of the agitator angle. Simultaneously, the motor speed is controlled through electronic frequency conversion, thereby changing the agitator's rotation speed.

[0032] Step 3: By using temperature sensors placed on the guide columns inside the tank, the temperature changes of crude oil at different liquid levels in the tank at different times under different parameter combinations are known. The average value of the crude oil temperature measured on the guide columns represents the overall crude oil temperature of the tank, and the crude oil heating rate is used as the evaluation index of the heating effect.

[0033] The temperature changes of crude oil inside the storage tank are monitored in real time by temperature probes placed on guide columns inside the tank. The initial temperature of crude oil at each measuring point under different combined heating modes of the coil-agitator is obtained, as well as the final temperature of crude oil at each measuring point inside the tank after 3 days of heating. The average of 5 test results is taken as the final measurement result. The measuring points of the temperature probes on the guide columns are labeled A, B, C, and D. Figure 1 As shown in the figure. The average value of each measuring point is taken as the overall temperature rise of the crude oil in the tank, and the rate of crude oil temperature rise is used as the evaluation index of the heating effect.

[0034] The ratio of the temperature rise of crude oil in the tank to the heating time is defined as the heating rate of crude oil in the tank, which reflects the change of crude oil temperature in the tank over time.

[0035] Crude oil heating rate v:

[0036]

[0037] In the formula, The final average temperature of the crude oil in the tank after heating, in °C; τ is the initial average temperature of the crude oil in the tank before heating, in °C; τ is the heating time in the tank, in hours; q is the total number of measuring points, in units; (t s ) sta Let be the initial temperature of the crude oil at the s-th measuring point, in K; (t) s ) end Let be the final temperature of the crude oil after heating at the s-th measuring point, in K.

[0038] The faster the crude oil in the tank heats up, the higher the temperature of the crude oil will be within a specified time. The rate of crude oil heating will vary depending on the speed of the agitator blades, the direction of stirring, and the heating conditions of the coil heat source.

[0039] Step 4: Using the mathematical method of multivariate nonlinear regression, establish a mathematical model combining multiple physical quantities to solve the heating process of the storage tank, and obtain the functional relationship between operating parameters such as steam temperature, steam flow rate, stirring angle, and rotation speed and the rate of crude oil heating.

[0040] Based on the orthogonal array of various physical quantity parameters, the crude oil heating rate was used as the evaluation index of the tank heating effect to obtain the field measured data, as shown below:

[0041]

[0042] Using a mathematical method of multiple nonlinear regression, a multi-parameter combined model of the heating process of waxy crude oil in tank storage was further established to obtain the functional relationship between parameters such as stirring direction, rotation speed, steam temperature, steam flow rate and crude oil heating rate.

[0043]

[0044] Where v is the measured rate of crude oil heating in the storage tank, in °C / h; Represents the regression coefficients of the constructed model; i1, i2, ..., i w represents the power coefficients of the parameters in the regression model; d represents the degree of the polynomial.

[0045] Given initial values ​​for the regression model, the corresponding regression coefficients are obtained through iterative solving. And the power coefficients i1, i2, ..., i of the parameters w Based on this, the parameter values ​​are input into the multi-parameter combined model of the heating process of waxy crude oil tank storage to obtain the calculated value of the crude oil heating rate in the tank.

[0046] Mean squared error (MSE) is used as the loss function to evaluate the difference between the calculated and measured data of the crude oil heating rate in the storage tank. A mean squared error close to 0 indicates that the smaller the difference between the model's calculation results and the measured values, the more accurate the model is. The mean squared error is defined as:

[0047]

[0048] Among them, v z The measured value of the crude oil heating rate in the z-th group of storage tanks is given in °C / h. This is the calculated value of the crude oil heating rate in the z-th group of storage tanks, in °C / h.

[0049] Meanwhile, by using the goodness-of-fit R... 2 As a regression model's ability to explain the overall fluctuations in the data, R... 2 The closer the value is to 1, the better the model fits the data; that is, the higher the proportion of data fluctuations the model can explain. Ra can be used to represent the functional relationship between parameters such as stirring direction, rotation speed, steam temperature, and steam flow rate and the rate of crude oil heating. 2 The calculation is shown in the following formula:

[0050]

[0051] Among them, SS tot Total error; SS res For random error SS res ; This represents the measured average rate of increase in crude oil temperature. ℃ / h.

[0052] Combined use of mean squared error (MSE) and goodness of fit (R²) 2 These two indicators can more comprehensively evaluate the model's performance, yielding a multi-parameter combined model for the heating process of waxy crude oil tank storage with the smallest mean square error and the highest goodness of fit, as shown in the following equation:

[0053]

[0054] Step 5: Based on the conditional constraints on each physical quantity parameter, the interior point method is used to iteratively solve the mathematical model of the multi-physical quantity combination in the tank heating process, and obtain the multi-physical synergistic mode with the highest crude oil heating rate and the best heating effect.

[0055] Interior point methods are used to solve minimum problems in constrained optimization problems. Therefore, when solving the problem of maximizing the heating rate of a model, the original objective function can be first... Transform into finding - The minimum value.

[0056] For problems with inequality constraints, a barrier function needs to be introduced:

[0057]

[0058] Construct an approximately unconstrained optimization function:

[0059]

[0060] Among them, g l (x) represents the inequality constraint, g l (x)≤0(l=1,2,…,p);μ is the obstacle parameter,μ>0.

[0061] Given initial values ​​(x1)0, (x2)0, ..., (x... w Given initial obstacle parameters μ0, an unconstrained optimization problem is solved iteratively. Right now The optimal combination of physical parameters for tank heating and the highest rate of crude oil temperature increase is obtained: x1, x2, ..., x w .

[0062] Calculation of relative error ε. Comparison of measured field data v with calculated data. The relative error ε is:

[0063]

[0064] When ε≤0.05, the accuracy of the model calculation results is verified.

[0065] To make the above-mentioned contents of this invention more apparent and understandable, a storage tank in Daqing Oilfield is used as the research object to optimize the synergistic effect of multiple parameters during the heating process of the storage tank, and the details are as follows:

[0066] A 100,000 cubic meter floating roof storage tank in an oil depot of Daqing Oilfield has a bottom diameter of 80 meters and a wall height of 20 meters. Three sets of three-bladed propeller agitators are evenly positioned on the tank wall at a distance of 1.2 meters from the bottom. This invention, based on mathematical methods such as orthogonal experiments, multivariate nonlinear regression, and the interior point method, establishes a multi-physical quantity synergistic optimization model for the heating process of a large crude oil storage tank. This model achieves the optimal synergistic heating mode for multiple physical quantities, including the agitator conditions inside the tank and the heating environment inside the coils. The specific method steps are as follows:

[0067] Step 1: Taking a large crude oil storage tank as the research object, the temperature rise of crude oil inside the tank is affected by the stirring conditions of the external agitator of the coil and the internal heating environment. Considering the influence of the interaction between multiple physical parameters such as the internal heating environment of the coil and the stirring conditions of the agitator, an orthogonal table is formed by scientifically and reasonably combining multiple physical parameters through orthogonal experiments.

[0068] In the study of multi-parameter synergistic optimization of the tank heating process, the physical parameters include the steam temperature inside the coil (x1), steam flow rate (x2), stirring direction of the agitator (x3), and rotation speed (x4). Specifically, x1 takes values ​​of 413K, 393K, and 373K; x2 takes values ​​of 8.5m / s, 7.5m / s, and 6.5m / s; x3 takes values ​​of 90°, 60°, and 30°; and x4 takes values ​​of 420r / min, 360r / min, and 300r / min. This generates L9(3)... 4 An orthogonal array is shown below:

[0069]

[0070]

[0071] Step 2: Based on the orthogonal array of different combinations of parameters, the heating environment (steam temperature and steam flow rate) inside the heating coil is controllably adjusted by changing the outlet temperature of the storage tank heater and the opening of the compressor valve; the angle and rotation speed of the side-entry agitator are changed by adjusting the electric push rod and the frequency converter, thereby achieving controllable adjustment of different parameter combinations.

[0072] Step 3: By using temperature sensors placed on the guide columns inside the tank, the temperature changes of crude oil at different liquid levels in the tank at different times under different parameter combinations are known. The average value of the crude oil temperature measured on the guide columns represents the overall crude oil temperature of the tank, and the crude oil heating rate is used as the evaluation index of the heating effect.

[0073] By conducting on-site tests under different parameter combinations inside the storage tank, the initial temperature of each measuring point of the guide column temperature probe inside the storage tank and the final temperature after heating for 3 days were obtained under different parameter combinations. The selected measuring point locations are: point A: 5.46m; point B: 9.72m; point C: 15.33m; point D: 18.81m.

[0074] Taking the tank parameter combination as follows: x1 = 413K, x2 = 8.5m / s, x3 = 90°, x4 = 420r / min, the initial crude oil temperatures at each measuring point in the tank are as follows: (t A ) sta =304K, (t B ) sta =304.8K, (t C ) sta =305.5K, (t D ) sta =304.9K; Final temperature of crude oil after heating at various measuring points in the storage tank: (t) A ) end =305.1K, (t B ) end=306.1K, (t C ) end =306.2K, (t D ) end =306.1K.

[0075] The initial average temperature of crude oil was obtained. for:

[0076]

[0077] Final average temperature of crude oil after heating for

[0078]

[0079] Therefore, the rate of increase in crude oil temperature, v1, can be obtained as:

[0080]

[0081] Similarly, the crude oil heating rate under other different combinations of storage tank parameters can be obtained, as shown in the table below:

[0082]

[0083] Step 4: Using the mathematical method of multiple nonlinear regression, establish a mathematical model of multi-physical quantity combination regression to solve the heating process of the storage tank, and obtain the functional relationship between the operating parameters such as the steam temperature x1 inside the coil, the steam flow rate x2, the stirring direction of the agitator x3, and the stirring speed x4 and the crude oil heating rate. Among them, the crude oil heating rate is used as the dependent variable, and the steam temperature x1, the steam flow rate x2, the stirring direction of the agitator x3, and the rotation speed x4 are used as independent variables.

[0084] Mathematical model of multiple physical quantities combination in the tank heating process:

[0085]

[0086] After multivariate nonlinear regression, the coefficients and power coefficients of each term are obtained as follows:

[0087] b 0000 =0.342, b 1100 = -7.0727e -5 b 1010 =2.2972e -7 b 1001 =-2.1041e -7 b 0110 =7.3261e -6 b 0101 =1.0801e -5 b 0011 =2.4915e-6 b 1000 =-0.002, b 0100 =0.026, b 0010 = -6.5776e -4 b 2000 =4.1085e -6 b 0200 = -1.2622e -4 b 0020 =3.3897e -6 The coefficients of all other terms are 0.

[0088] The multi-parameter values ​​are then fed back into the established mathematical regression model combining multiple physical quantities of the tank heating process to obtain the calculated value of the crude oil heating rate. As shown in the table below:

[0089]

[0090] The mean squared error (MSE) was used as the loss function to evaluate the difference between the calculated value and the measured data of the crude oil heating rate in the storage tank. A mean squared error close to 0 indicates that the smaller the difference between the model calculation result and the measured value, the more accurate the model is.

[0091]

[0092] The mean square error (MSE) value is close to 0, indicating that the difference between the calculated value of the crude oil heating rate in the storage tank and the measured data is small.

[0093] By goodness-of-fit R 2 As a regression model's ability to explain the overall fluctuations in the data, R... 2 The closer a value is to 1, the better the model fits the data; that is, the higher the proportion of data fluctuations that the model can explain relative to the total fluctuations.

[0094] Random Error SS res calculate:

[0095]

[0096] Average rate of warming of crude oil calculate:

[0097]

[0098] Total error SS tot calculate:

[0099]

[0100] Therefore, the goodness of fit R 2 for:

[0101]

[0102] The goodness of fit R was calculated. 2 A value close to 1 indicates that the model fits the data well, meaning that the model can explain a high proportion of the total data fluctuations.

[0103] By combining mean squared error (MSE) and goodness of fit (R²) 2 Two metrics were used to comprehensively evaluate the performance of the established model, yielding a mean squared error of 2.122e. -7 A multi-parameter combined regression mathematical model for the heating process of waxy crude oil tank storage with a good fit of 0.99 is presented.

[0104] The multi-physical quantity combined regression mathematical model for the tank heating process is as follows:

[0105]

[0106] Step 5: Based on the conditional constraints on each physical quantity parameter, the interior point method is used to iteratively solve the mathematical model of the multi-physical quantity combination in the tank heating process, and obtain the multi-physical synergistic mode with the highest crude oil heating rate and the best heating effect.

[0107] The constraints on the physical parameters of the storage tank are as follows:

[0108] 373≤x1≤413;

[0109] 6.5 ≤ x² ≤ 8.5;

[0110] 30≤x3≤90;

[0111] 300≤x4≤420.

[0112] Therefore, the constraints can be rewritten in the standard form of inequality constraints:

[0113] g1(x) = x1 - 413 ≤ 0;

[0114] g2(x) = 373 - x1 ≤ 0;

[0115] g3(x) = x² - 8.5 ≤ 0;

[0116] g4(x) = 6.5 - x² ≤ 0;

[0117] g5(x) = x³ - 90 ≤ 0;

[0118] g6(x) = 30 - x3 ≤ 0;

[0119] g7(x) = x⁴ - 420 ≤ 0;

[0120] g8(x) = 300 - x4 ≤ 0.

[0121] For problems with inequality constraints, a barrier function needs to be introduced:

[0122]

[0123] Construct an approximately unconstrained optimization function:

[0124]

[0125] Given initial values ​​(x1)0, (x2)0, ..., (x... w Based on 0 and the initial obstacle parameter μ0, the unconstrained optimization problem is solved iteratively.

[0126] The initial values ​​of each parameter are: (x1)0 = 373, (x2)0 = 6.5, (x3)0 = 30, (x4)0 = 300; the initial obstacle parameter μ0 = 0.1.

[0127] Calculations show that when the combined physical operating parameters of the storage tank are: x1 = 413, x2 = 6.5, x3 = 30, x4 = 333, The crude oil reaches its maximum heating rate, resulting in optimal heating effect.

[0128] Based on the optimal combination calculation results, the steam temperature inside the heating coil was adjusted to 413K and the steam flow rate was 6.5m / s by adjusting the outlet temperature of the crude oil storage tank heater and the opening of the compressor valve; the side-entry agitator angle was adjusted to 30° and the rotation speed was adjusted to 333r / min by adjusting the electric push rod and the frequency converter. Field tests were then conducted under this combination.

[0129] The average temperature change of crude oil before and after three days of heating in the storage tank was determined by measuring points on the temperature probes on the guide columns. Monitoring revealed that the initial average temperature of the crude oil was... The final average temperature of the crude oil after heating is The final on-site measured crude oil heating rate was:

[0130]

[0131] Field measured data v and calculated data The relative error ε is:

[0132] The accuracy of the model's calculation results was verified.

[0133] Under the combination of tank parameters x1=413, x2=6.5, x3=30, x4=333, the overall temperature rise rate of crude oil was increased by 11.59% compared with the maximum value of the overall temperature rise rate of crude oil under other parameter combination modes.

[0134] In summary, this multi-parameter synergistic optimization method for the heating process of large crude oil storage tanks integrates the optimization of the tank agitator's stirring conditions and the internal heating environment of the coils to consider the impact on the heating effect of crude oil in the tank. It breaks through the limitations of the previous method of optimizing the operating parameters of the heating coils and agitators separately, and realizes the optimal synergistic heating mode of multiple physical quantities such as the stirring conditions of the agitator inside the tank and the internal heating environment of the coils. This provides theoretical support for oil depot production management to achieve the goals of reducing energy consumption and carbon emissions.

Claims

1. A method for optimizing the multi-parameter coordinated operation of a heating process in a large crude oil storage tank, characterized in that... Includes the following steps: Step 1: Scientifically and rationally combine the physical parameters affecting the temperature rise of crude oil in the storage tank to form an orthogonal array. The physical parameters include the steam temperature and steam flow rate inside the heating coil; the stirring direction and rotation speed of the agitator; and the storage tank is a large crude oil storage tank. Step 2: Controllable adjustment of the steam temperature and steam flow rate inside the heating coil is achieved by changing the outlet temperature of the storage tank heater and the opening of the compressor valve; the angle and rotation speed of the agitator are changed by adjusting the electric push rod and the frequency converter, thereby achieving controllable adjustment of different parameter combinations in the orthogonal table. Step 3: Using temperature sensors placed on the guide columns inside the storage tank, obtain the changes in crude oil temperature at different liquid levels in the storage tank at different times under different parameter combinations in the orthogonal table. The average crude oil temperature measured on the guide columns represents the overall crude oil temperature in the storage tank. Calculate the crude oil heating rate for different parameter combinations in the orthogonal table of the storage tank. Step 4: Establish a multi-parameter combined mathematical model of the tank heating process to obtain the functional relationship between the physical parameters and the crude oil heating rate; In the formula: x1 is the steam temperature, x2 is the steam flow rate, x3 is the stirring direction of the agitator, and x4 is the rotation speed; These are the regression coefficients; i1, i2, ..., i w d is the power coefficient; d is the degree of the polynomial; v is the rate of temperature increase of the crude oil in the storage tank. Step 5: Solve the mathematical model of the multi-parameter combination of the tank heating process to obtain the combination of physical parameters in the orthogonal table that results in the highest crude oil heating rate and the best heating effect.

2. The multi-parameter collaborative operation optimization method for the heating process of a large crude oil storage tank according to claim 1, characterized in that: Step two specifically involves: The steam temperature and steam flow rate inside the heating coil can be controlled and adjusted by changing the outlet temperature of the electric heater in the storage tank and the opening of the compressor valve. Specifically, the temperature setpoint can be gradually adjusted in the electric heater temperature control setting interface. When the outlet temperature approaches the setpoint, the heater control system will automatically adjust the heating power to achieve stable regulation of the steam temperature inside the heating coil. At the same time, the set flow rate value can be input through the compressor operation control interface. The control system will automatically calculate and adjust the valve opening based on the preset control algorithm and the actual flow rate signal fed back by the sensor to achieve precise control of the steam flow rate inside the heating coil. The angle and rotation speed of the side-entry mixer can be changed by adjusting the electric push rod and frequency converter. Specifically, the mixing angle can be controlled by adjusting the length of the four inclined pull rods, and the motor speed can be controlled by the principle of electronic frequency conversion, thereby changing the rotation speed of the mixer.

3. The multi-parameter collaborative operation optimization method for the heating process of large crude oil storage tanks according to claim 2, characterized in that: Step four involves using a mathematical method of multivariate nonlinear regression to establish a multi-parameter combined mathematical model for solving the tank heating process, thereby obtaining the functional relationship between steam temperature, steam flow rate, stirring angle, rotation speed, and crude oil heating rate.

4. The multi-parameter collaborative operation optimization method for the heating process of a large crude oil storage tank according to claim 3, characterized in that: Step five involves: based on the constraints of each physical parameter, using the interior point method to iteratively solve the multi-parameter combined mathematical model of the tank heating process, thereby obtaining the multi-physical synergistic mode with the highest crude oil heating rate and the best heating effect.

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