Multi-parameter collaborative operation optimization method for heating process of large crude oil storage tank

Abstract

The invention relates to a multi-parameter collaborative operation optimization method in a heating process of a large crude oil storage tank. The method comprises the following steps of: scientifically and reasonably combining physical parameters which influence the temperature rise of crude oil in the storage tank to form an orthogonal table; controllable adjustment of the steam temperature and the steam flow speed in the heating coil is achieved by changing the outlet temperature of the storage tank heating furnace and the opening degree of a compressor valve. The angle and the rotating speed of the stirrer are changed by adjusting the electric push rod and the frequency converter, and controllable adjustment of different parameter combinations in an orthogonal table is achieved; calculating to obtain the crude oil heating rate of different parameter combinations in the storage tank orthogonal table; establishing a storage tank heating process multi-parameter combination mathematical model; and solving the multi-parameter combination mathematical model in the storage tank heating process to obtain a physical parameter combination with the highest crude oil heating rate and the best heating effect in the orthogonal table. The multi-physical-quantity optimal cooperative heating mode of the stirring condition of the stirrer in the storage tank, the heating environment in the coil pipe and the like is achieved, energy consumption is low during oil storage production, and carbon emission is small.

Description

Technical field:

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

[0002] As the main storage facility of crude oil depot, large crude oil storage tanks are usually heated by heating coils to ensure the safety and economic operation of the tanks. However, the natural convection heat transfer efficiency caused by the crude oil itself in the tank under the single heating mode of the coil is low, and energy cannot be effectively utilized. By using the tank agitator to assist the coil heating, the convection heat transfer intensity of the oil flow in the tank is enhanced, and the overall heating effect of the tank is greatly improved. However, the coil-stirring synergistic heating mode promotes but restricts the heat transfer of the oil flow in the tank, reduces the overall heating efficiency of the tank, and causes unnecessary energy waste. Therefore, it is necessary to master the synergistic law between multiple parameters inside the waxy crude oil storage tank under the coil-stirring heating mode, and seek the best operating parameter combination for tank heating effect.

[0003] For the optimization of multiple parameters inside crude oil storage tanks, the crude oil heating rate is usually used as the objective function, and the single variable method is used to optimize the physical parameters of the heating coil and the agitator inside the storage tank, so as to obtain the optimal operating parameters of the heating coil and the agitator. This method ignores the influence of the synergistic effect between the coil and the agitator inside the crude oil storage tank on the crude oil heating rate. In practice, due to the limited convective heat transfer efficiency of the crude oil in the tank, blindly increasing the temperature and flow rate inside the coil cannot make the crude oil in the tank rise continuously in a straight line, resulting in a large waste of coil energy. Therefore, an auxiliary agitator is used to enhance the convective heat transfer intensity of the crude oil in the tank, and the energy utilization efficiency of the coil is improved, which promotes the overall heating of the crude oil. However, the coil-agitator synergistic heating mode does not simply promote the heating of the crude oil in the tank. The unreasonable combination between the two will also inhibit the heat transfer of the crude oil in the tank, aggravate the heat loss at the boundary of the tank, and make the coil heating environment and the agitator stirring conditions have a dynamic relationship of mutual promotion and mutual restriction. Therefore, it is necessary to establish a new multi-parameter synergistic optimization method for large crude oil storage tanks to achieve the reasonable optimization of the combination of multi-physical quantity operating parameters inside the tank.

[0004] In summary, the current optimization of the coordinated operation of multiple physical quantities in the heating process of crude oil storage tanks has 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 object of the present invention is to provide an optimization method for the coordinated operation of multiple parameters in the heating process of large crude oil storage tanks. This optimization method for the coordinated operation of multiple parameters in the heating process of large crude oil storage tanks is used to solve the problem that the existing optimization methods for the coordinated operation of multiple physical quantities in the heating process of crude oil storage tanks cannot scientifically and accurately optimize the coordinated operation of multiple parameters in the heating process of large crude oil storage tanks.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows: This optimization method for the coordinated operation of multiple parameters in the heating process of large crude oil storage tanks includes the following steps;

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

[0008] Step 2: Realize the controllable adjustment of the steam temperature inside the heating coil and the steam flow rate by changing the outlet temperature of the storage tank heating furnace and the opening degree of the compressor valve; realize the change of the angle and rotation speed of the stirrer by adjusting the electric push rod and the frequency converter, so as to realize the controllable adjustment of different parameter combinations in the orthogonal table;

[0009] Step 3: Through the temperature sensing probes arranged on the guiding columns in the storage tank, obtain the temperature change of crude oil at different liquid levels in the storage tank at different times under different parameter combinations in the orthogonal table, use the average value of the crude oil temperature measured on the guiding columns to represent the overall crude oil temperature of the storage tank, and calculate and obtain the crude oil temperature rise rate of different parameter combinations in the orthogonal table of the storage tank;

[0010] Step 4: Establish a mathematical model of multiple parameter combinations in the heating process of the storage tank to obtain the functional relationship between the physical parameters and the crude oil temperature rise rate;

[0011]

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

[0013] Step 5: Solve the mathematical model of multiple parameter combinations in the heating process of the storage tank to obtain the physical parameter combination with the highest crude oil temperature rise rate and the best heating effect in the orthogonal table.

[0014] In the above solution, step 2 is specifically as follows:

[0015] By changing the outlet temperature of the storage tank's electric heating furnace and the opening degree of the compressor valve, the controllable adjustment of the steam temperature and steam flow rate inside the heating coil is achieved. Among them, in the temperature control setting interface of the electric heating furnace, the temperature set value is gradually adjusted. When the outlet temperature approaches the set value, the control system of the heating furnace will automatically adjust the heating power to achieve the stable adjustment of the steam temperature inside the heating coil. At the same time, by inputting the set flow rate value through the compressor operation control interface, the control system will automatically calculate and adjust the opening degree of the valve according to the preset control algorithm and the actual flow rate signal feedback by the sensor to achieve the precise control of the steam flow rate inside the heating coil;

[0016] By adjusting the electric push rod and the frequency converter, the angle and rotation speed of the side-entry agitator are changed. Among them, by adjusting the lengths of the four inclined tie rods, the controllable adjustment of the stirring angle is achieved. At the same time, by the principle of electronic frequency conversion, the purpose of controlling the motor speed is achieved, and the change of the agitator rotation speed is realized.

[0017] In step four of the above solution, a mathematical method of multiple nonlinear regression is used to establish a mathematical model for solving the multi-parameter combination in the storage tank heating process, and the functional relationships between the steam temperature, steam flow rate, stirring angle, rotation speed and the crude oil heating rate are obtained.

[0018] In step five of the above solution, on the basis of imposing conditional constraints on each physical parameter, the interior point method is used to iteratively solve the multi-parameter combination mathematical model of the storage tank heating process, and the multi-physical collaborative mode with the highest crude oil heating rate and the best heating effect is obtained.

[0019] Beneficial effects:

[0020] The present invention comprehensively considers the influence of the interaction of multiple parameters such as the stirring conditions of the storage tank agitator and the internal heating environment of the coil on the crude oil heating rate. Based on mathematical methods such as orthogonal experiments, multiple nonlinear regression, and the interior point method, an optimization method for the collaborative operation of multiple physical quantities in the heating process of large crude oil storage tanks is established, breaking through the limitations of separately optimizing the operating parameters of the heating coil and the agitator in the past, and realizing the optimal collaborative heating mode of multiple physical quantities such as the stirring conditions of the agitator inside the storage tank and the internal heating environment of the coil, providing theoretical support for the oil depot production management to achieve the goals of reducing energy consumption and carbon emissions. Description of the drawings:

[0021] Figure 1 It is a schematic diagram of the positions of each measuring point inside the storage tank. Specific implementation manners:

[0022] The following further describes the present invention in conjunction with the drawings:

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

[0024] Step 1: Take a large crude oil storage tank as the research object. The temperature rise of the crude oil in the tank is comprehensively affected by the stirring conditions of the agitator outside the coil and the internal heating environment. Considering the interaction effects among multiple physical quantity parameters such as the internal heating environment of the coil and the stirring conditions of the agitator, through orthogonal experiments, scientific and reasonable combinations of the operating parameters of multiple physical quantities are formed to create an orthogonal table.

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

[0026] First, determine each parameter and its value range, and clarify the physical parameters to be studied and the value range of each physical parameter. In the study of the collaborative optimization of multiple parameters during the heating process of the storage tank, the physical parameters include the internal steam temperature x1 of the coil, steam flow rate x2, stirring direction x3 of the agitator, and rotation speed x4, and each physical parameter has 3 values. Among them, the value range of x1 is: x 11 、x 12 、x 13 ; the value range of x2 is: x 21 、x 22 、x 23 ; the value range of x3 is: x 31 、x 32 、x 33 ; the value range of x4 is: x 41 、x 42 、x 43 .

[0027] Secondly, select a suitable orthogonal table L n (m k ) according to the number of parameters and the number of values. Here, L represents the orthogonal table, the subscript n represents the number of experiments, m represents the number of values of the parameter, and k represents the number of physical parameters arranged. For four physical parameters with three values for each parameter, the L9(3 4 ) orthogonal table can be selected, as shown below:

[0028]

[0029] Step 2: Based on the obtained orthogonal table of different parameter combinations, controllably adjust the internal heating environment (steam temperature, steam flow rate) of the heating coil by changing the outlet temperature of the storage tank heating furnace and the opening of the compressor valve; change the angle and rotation speed of the side-entry agitator by adjusting the electric push rod and frequency converter, thereby achieving controllable adjustment of different parameter combinations.

[0030] By changing the outlet temperature of the storage tank's electric heating furnace and the opening degree of the compressor valve, the internal heating environment (steam temperature, steam flow rate) of the heating coil can be controllably adjusted. Among them, enter the temperature control setting interface of the electric heating furnace through the operation panel (such as a touch screen or a control panel with buttons), and gradually adjust the temperature setting value. When the outlet temperature approaches the set value, the control system of the heating furnace will automatically adjust the heating power to achieve stable adjustment of the steam temperature inside the heating coil. At the same time, for compressors equipped with an automatic control system, by inputting the set flow rate value or relevant control parameters through the operation control interface, the control system will automatically calculate and adjust the opening degree of the valve according to the preset control algorithm and the actual flow rate signal feedback by the sensor to achieve precise control of the steam flow rate inside the heating coil.

[0031] By adjusting the electric push rod and the frequency converter, the angle and rotation speed of the side-entry agitator can be changed. Among them, start the motor to drive the stirring shaft to rotate. After stirring for a period of time, loosen the fixing bolts, pull the first connecting rod and the second connecting rod, and by adjusting the length of the inclined pull rod, the spherical seal can be rotated around the installation base plate, driving the stirring shaft and the agitator to rotate. After adjustment, tighten the four fixing bolts to fix the moving rod. According to the different rotation angles of the stirring shaft, adjust the lengths of the four inclined pull rods to achieve controllable adjustment of the stirring angle. At the same time, by the principle of electronic frequency conversion, the purpose of controlling the motor speed is achieved, thereby changing the rotation speed of the agitator.

[0032] Step 3: Through the temperature sensors arranged on the guiding columns in the tank, learn about the temperature changes of the crude oil at different liquid levels in the storage tank at different times under different parameter combination modes. Take the average value of the crude oil temperature measured on the guiding columns as the overall crude oil temperature of the storage tank, and use the crude oil heating rate as the evaluation index of the heating effect.

[0033] The temperature sensors arranged on the guiding columns inside the storage tank are used to monitor the temperature changes of the crude oil inside the storage tank in real time. Obtain the initial temperature of the crude oil at each measuring point under the heating of different parameter collaborative combination modes of the coil - agitator and the final temperature of the crude oil at each measuring point inside the storage tank after 3 days of heating. Take the average value of 5 test results as the final measurement result. Among them, the measuring points of the temperature sensors on the guiding columns are respectively recorded as: points A, B, C, and D, as Figure 1 shown. Take the average value of each measuring point as the overall temperature rise of the crude oil in the tank, and use the crude oil heating rate as the evaluation index of the heating effect.

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

[0035] Crude oil heating rate v:

[0036]

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

[0038] The faster the heating rate of the crude oil in the tank, the higher the temperature of the heated crude oil within the specified time. Different rotational speeds of the stirrer blades, stirring directions, and heating conditions of the coiled pipe dynamic heat source will result in different heating rates of the crude oil.

[0039] Step 4: Adopt the mathematical method of multiple nonlinear regression to establish a mathematical model for solving the combination of multiple physical quantities during the heating process of the storage tank, and obtain the functional relationship between operating parameters such as steam temperature, steam flow rate, stirring angle, rotational speed, etc. and the heating rate of the crude oil.

[0040] Based on obtaining the orthogonal table of the parameter combinations of each physical quantity, taking the heating rate of the crude oil as the evaluation index of the heating effect of the storage tank, the on-site measured data are as follows:

[0041]

[0042] Adopt the mathematical method of multiple nonlinear regression to further establish a multi-parameter combination model for the heating process of the waxy crude oil storage tank, and obtain the functional relationship between parameters such as stirring direction, rotational speed, steam temperature, steam flow rate, etc. and the heating rate of the crude oil.

[0043]

[0044] Among them, v is the measured value of the heating rate of the crude oil in the storage tank, in °C / h; represents the regression coefficient of the established model; i1, i2,..., i w are the power coefficients of the parameters in the regression model; d represents the degree of the polynomial.

[0045] After giving the initial value of the regression model, through iterative solution, the corresponding regression coefficient and the power coefficients i1, i2,..., i of the parameters w are obtained. On this basis, substituting the parameter values into the established multi-parameter combination model for the heating process of the waxy crude oil storage tank, the calculated value of the heating rate of the crude oil in the storage tank is obtained.

[0046] The mean squared error (MSE) is used as the loss function to evaluate the gap between the calculated value of the crude oil heating rate in the storage tank and the measured data. The closer the mean squared error is to 0, the smaller the gap between the model calculation result and the measured value, and the more accurate the model. The mean squared error is defined as:

[0047]

[0048] where v z is the measured value of the crude oil heating rate of the z-th storage tank, °C / h; is the calculated value of the crude oil heating rate of the z-th storage tank, °C / h.

[0049] Meanwhile, the coefficient of determination R 2 is used as the explanatory ability of the regression model for the overall fluctuation of the data. The closer R 2 is to 1, the better the fitting degree of the model to the data, that is, the higher the proportion of the data fluctuation part that the model can explain in the total fluctuation. It can be used to represent the functional relationship between parameters such as stirring direction, rotation speed, steam temperature, and steam flow rate and the crude oil heating rate. R 2 is calculated as shown in the following formula:

[0050]

[0051] where SS tot is the total error; SS res is the random error SS res ; is the average measured value of the crude oil heating rate, °C / h.

[0052] Combining the use of the mean squared error MSE and the coefficient of determination R 2 , these two indicators can more comprehensively evaluate the performance of the model, and obtain a multi-parameter combination model for the wax-containing crude oil tank storage heating process with the smallest mean squared error and the highest coefficient of determination, as shown in the following formula:

[0053]

[0054] Step Five: On the basis of imposing conditional constraints on each physical quantity parameter, the interior point method is used to iteratively solve the multi-physical quantity combination mathematical model of the storage tank heating process, and obtain a multi-physical collaborative mode with the highest crude oil heating rate and the best heating effect.

[0055] The interior point method is used to solve the minimum value problem in the constrained optimization problem. Therefore, when solving the maximum value problem of the model heating rate, the original objective function can be first transformed into finding the minimum value of - .

[0056] For the inequality condition constraint problem, a barrier function needs to be introduced:

[0057]

[0058] Construct an approximately unconstrained optimization function:

[0059]

[0060] where g l (x) is the inequality constraint condition, g l (x) ≤ 0 (l = 1, 2, …, p); μ is the barrier parameter, μ > 0.

[0061] Given the initial values (x1)0, (x2)0, …, (x w )0 and the initial barrier parameter μ0, solve the unconstrained optimization problem through iteration That is to obtain the combination value of physical parameters with the optimal heating effect of the storage tank and the highest crude oil heating rate: x1, x2, …, x w .

[0062] Calculation of relative error ε. The on-site measured data v and the 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 content of the present invention more obvious and understandable, the following takes a storage tank in Daqing Oilfield as the research object, optimizes the multi-parameter synergistic effect during the heating process of the storage tank, and the detailed description is as follows:

[0066] A 100,000-cubic-meter floating roof storage tank in an oil depot in Daqing Oilfield, with a bottom diameter of 80 m and a tank wall height of 20 m. Three groups of three-blade propeller agitators are evenly located at the tank wall position 1.2 m away from the bottom of the tank. Based on mathematical methods such as orthogonal experiments, multiple non-linear regression, and interior point method, the present invention establishes a multi-physical quantity collaborative optimization model for the heating process of large crude oil storage tanks, and realizes the optimal collaborative heating mode of multiple physical quantities such as the stirring conditions of the agitators inside the storage tank and the heating environment inside the coil pipes. The specific method steps are as follows:

[0067] Step 1: Take the large crude oil storage tank as the research object. The heating of the crude oil in the tank is comprehensively affected by the stirring conditions of the agitator outside the coil pipe and the internal heating environment. Considering the interaction effects among multi-physical quantity parameters such as the internal heating environment of the coil pipe and the stirring conditions of the agitator, scientifically and reasonably combine the operating parameters of multiple physical quantities through orthogonal experiments to form an orthogonal table.

[0068] In the study of the multi-parameter collaborative optimization of the storage tank heating process, the physical parameters include the steam temperature x1 inside the coil, the steam flow rate x2, the stirring direction x3 of the stirrer, and the rotation speed x4. Among them, the value range of x1 is: 413K, 393K, 373K; the value range of x2 is: 8.5m / s, 7.5m / s, 6.5m / s; the value range of x3 is: 90°, 60°, 30°; the value range of x4 is: 420r / min, 360r / min, 300r / min, and an L9(3 4 ) orthogonal array is generated as follows:

[0069]

[0070]

[0071] Step 2: On the basis of obtaining the orthogonal array of different parameter combinations, the controllable adjustment of the heating environment (steam temperature, steam flow rate) inside the heating coil is achieved by changing the outlet temperature of the storage tank heating furnace and the opening degree of the compressor valve; the angle and rotation speed of the side-entry stirrer are changed by adjusting the electric push rod and the frequency converter, so as to achieve the controllable adjustment of different parameter combinations.

[0072] Step 3: Through the temperature sensors arranged on the guiding columns in the tank, the temperature changes of the crude oil at different liquid levels in the storage tank at different times under different parameter combination modes are obtained. The average value of the crude oil temperature measured on the guiding columns represents the overall crude oil temperature of the storage tank, and the crude oil heating rate is used as the evaluation index of the heating effect.

[0073] Through on-site tests on different parameter combination conditions inside the storage tank, the initial temperatures of each measuring point of the temperature sensors on the guiding columns inside the storage tank and the final temperatures after 3 days of heating under different parameter combinations are obtained. Among them, the selected measuring point positions are: Point A: 5.46m; Point B: 9.72m; Point C: 15.33m; Point D: 18.81m.

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

[0075] It is obtained that the initial average temperature of the crude oil is:

[0076]

[0077] The final average temperature of the crude oil after heating is

[0078]

[0079] Thus, it can be obtained that the heating rate v1 of the crude oil is:

[0080]

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

[0082]

[0083] Step 4: Adopt the mathematical method of multiple nonlinear regression to establish a combined regression mathematical model for solving multiple physical quantities during the heating process of the storage tank, and obtain the functional relationship between the operating parameters such as the internal steam temperature x1 of the coil, the steam flow rate x2, the stirring direction x3 of the stirrer, and the stirring speed x4 and the heating rate of the crude oil. Among them, the heating rate of the crude oil is used as the dependent variable, and the steam temperature x1, the steam flow rate x2, the stirring direction x3 of the stirrer, and the rotation speed x4 are used as independent variables.

[0084] Combined mathematical model of multiple physical quantities during the heating process of the storage tank:

[0085]

[0086] After multiple nonlinear regression, the respective coefficient and power coefficient are obtained as:

[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 , and the coefficients of the remaining terms are all 0.

[0088] Substitute the multi-parameter values back into the multi-physical combined mathematical regression model for the heating process of the built storage tank to obtain the calculated value of the crude oil heating rate as shown in the following table:

[0089]

[0090] Use the mean square error (MSE) as the loss function to evaluate the gap between the calculated value of the crude oil heating rate in the storage tank and the measured data. The closer the mean square error is to 0, the smaller the gap between the model calculation result and the measured value, and the more accurate the model.

[0091]

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

[0093] Through the goodness of fit R 2 as the explanatory ability of the regression model for the overall fluctuation of the data, the closer R 2 is to 1, the better the fitting degree of the model to the data, that is, the higher the proportion of the data fluctuation part that the model can explain in the total fluctuation.

[0094] Random error SS res Calculate:

[0095]

[0096] Average heating rate of crude oil Calculate:

[0097]

[0098] Total error SS tot Calculate:

[0099]

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

[0101]

[0102] After calculation, the goodness of fit R 2 is close to 1, indicating that the model has a good fitting degree for the data, that is, the proportion of the data fluctuation part that the model can explain in the total fluctuation is relatively high.

[0103] By combining the mean square error MSE and the goodness of fit R 2 Two indicators are used to comprehensively evaluate the performance of the established model, and the mean square error is 2.122e -7 A multi-parameter combined regression mathematical model for the heating process of a paraffin-containing crude oil storage tank with a goodness of fit of 0.99 and good fitting performance is obtained.

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

[0105]

[0106] Step 5: On the basis of imposing conditional constraints on each physical quantity parameter, the interior point method is used to iteratively solve the multi-physical quantity combined mathematical model for the storage tank heating process, and a multi-physical cooperation mode with the highest crude oil heating rate and the best heating effect is obtained.

[0107] The constraint conditions for each physical quantity parameter of the storage tank are respectively:

[0108] 373 ≤ x1 ≤ 413;

[0109] 6.5 ≤ x2 ≤ 8.5;

[0110] 30 ≤ x3 ≤ 90;

[0111] 300 ≤ x4 ≤ 420.

[0112] Thus, the constraint conditions 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 the inequality-constrained problem, it is necessary to introduce a barrier function:

[0122]

[0123] Construct an approximate unconstrained optimization function:

[0124]

[0125] Based on the given initial values \((x_1)_0\), \((x_2)_0\), …, \((x w )_0\) and the initial barrier parameter \(\mu_0\), solve the unconstrained optimization problem by iteration

[0126] Among them, the initial values of each parameter are: \((x_1)_0 = 373\), \((x_2)_0 = 6.5\), \((x_3)_0 = 30\), \((x_4)_0 = 300\); the initial barrier parameter \(\mu_0 = 0.1\).

[0127] After calculation, when the multi-physical operating parameter combination of the storage tank is: \(x_1 = 413\), \(x_2 = 6.5\), \(x_3 = 30\), \(x_4 = 333\), The heating rate of the crude oil reaches the maximum, and the heating effect is optimal.

[0128] Based on the obtained optimal combination calculation results, adjust the outlet temperature of the heating furnace of the crude oil storage tank and the opening of the compressor valve to make the steam temperature inside the heating coil 413K and the steam flow rate 6.5m / s; adjust the electric push rod and the frequency converter to make the angle of the side-in type agitator 30° and the rotation speed 333r / min, and conduct on-site tests under this combination.

[0129] Through the measuring points on the temperature-sensing probe of the storage tank guiding column, the change in the average temperature of the crude oil before and after 3 days of heating of the storage tank is known. After monitoring, the initial average temperature of the crude oil is The final average temperature of the crude oil after heating is The final on-site measured heating rate of the crude oil is

[0130] The on-site measured data \(v\) and the calculated data The relative error \(\varepsilon\) is:

[0131] Verify the accuracy of the model calculation results.

[0132] Under the combination of the storage tank parameters \(x_1 = 413\), \(x_2 = 6.5\), \(x_3 = 30\), \(x_4 = 333\), the overall heating rate of the crude oil is increased by 11.59% compared with the maximum value of the overall heating rate of the crude oil under other parameter combination modes.

[0133] Overall, this multi-parameter collaborative optimization method for the heating process of large crude oil storage tanks comprehensively considers the influence of the stirring conditions of the storage tank agitator and the optimization of the internal heating environment of the coil on the heating effect of the crude oil in the storage tank, breaking through the limitations of separately optimizing the operating parameters of the heating coil and the agitator in the past, and realizing the optimal collaborative heating mode of multiple physical quantities such as the stirring conditions of the agitator inside the storage tank and the internal heating environment of the coil, providing theoretical support for the oil depot production management to achieve the goals of reducing energy consumption and carbon emissions.

Claims

1. A multi-parameter coordinated operation optimization method for the heating process of a large crude oil storage tank, characterized in that The steps include: Step 1: Scientifically and reasonably combine the physical parameters that affect the temperature rise of crude oil in the storage tank to form an orthogonal table. The physical parameters include the steam temperature and steam flow rate inside the heating coil; the stirring direction and rotation speed of the agitator; the storage tank is a large crude oil storage tank; Step 2: By changing the outlet temperature of the tank heating furnace and the opening of the compressor valve, the steam temperature and steam flow rate inside the heating coil can be controlled and adjusted; by adjusting the electric push rod and the frequency converter, the angle and rotation speed of the agitator can be changed, thereby realizing the controllable adjustment of different parameter combinations in the orthogonal table; Step 3: By arranging the temperature sensing probe on the guide column in the storage tank, obtain the temperature change of crude oil at different liquid levels in the storage tank at different times under different parameter combinations in the orthogonal table, and use the average value of the crude oil temperature measurement on the guide column to represent the overall crude oil temperature of the storage tank, and calculate the crude oil heating rate under different parameter combinations in the orthogonal table of the storage tank; Step 4: Establish a multi-parameter combination mathematical model for 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; are regression coefficients; i1, i2, …, i w is the power coefficient; d is the degree of the polynomial; v is the heating rate of crude oil in the storage tank; Step 5: Solve the multi-parameter combination mathematical model of the tank heating process and obtain the physical parameter combination with the highest crude oil heating rate and the best heating effect in the orthogonal table.

2. The multi-parameter coordinated operation optimization method for the heating process of a large crude oil storage tank according to claim 1 is characterized in that: The step 2 is specifically as follows: By changing the outlet temperature of the electric heating furnace of the storage tank and the opening of the compressor valve, the steam temperature and steam flow rate inside the heating coil can be controlled. In the temperature control setting interface of the electric heating furnace, the temperature setting value is gradually adjusted. When the outlet temperature is close to the setting value, the control system of the heating furnace 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 is input through the compressor operation control interface. The control system will automatically calculate and adjust the valve opening according to 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 agitator can be changed by adjusting the electric push rod and the frequency converter. The stirring angle can be controlled by adjusting the length of the four inclined rods. At the same time, the motor speed can be controlled by the principle of electronic frequency conversion to change the rotation speed of the agitator.

3. The multi-parameter coordinated operation optimization method for the heating process of a large crude oil storage tank according to claim 2 is characterized in that: The fourth step is: using the mathematical method of multivariate nonlinear regression to establish a multi-parameter combination mathematical model for solving the storage tank heating process, and obtain the functional relationship between steam temperature, steam flow rate, stirring angle and rotation speed and the crude oil heating rate.

4. The multi-parameter coordinated operation optimization method for the heating process of a large crude oil storage tank according to claim 3 is characterized in that: The step five is: on the basis of conditional constraints on various physical parameters, an interior point method is used to iteratively solve a multi-parameter combination mathematical model of the storage tank heating process, so as to obtain a multi-physical synergistic mode with the highest crude oil heating rate and the best heating effect.

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