CN115809730B - Large crude oil storage tank heat loss prediction method - Google Patents
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Abstract
The invention relates to a method for predicting heat loss of a large crude oil storage tank, which comprises the following steps: the method comprises the steps of obtaining real-time change values of heat flux density, atmospheric temperature, soil temperature and solar radiation heat of different positions of a large crude oil storage tank through field test; taking out a small amount of crude oil from a large crude oil storage tank, testing the change rule of the density, viscosity and specific heat capacity of the crude oil at different temperatures in the tank, establishing a variable property model of the crude oil, measuring the distribution condition of the crude oil temperature field at different moments, and determining the density, viscosity and specific heat capacity of the crude oil; the heat loss of the boundary of the storage tank is comprehensively influenced by the external dynamic thermal environment and the physical properties of crude oil in the storage tank; performing correlation test on each influence factor and the boundary heat flux density of the storage tank independently; the method and the device establish a mathematical model of boundary heat flux density loss of the waxy crude oil storage tank to obtain a functional relation between the boundary heat flux density of the storage tank, an external dynamic heat environment and internal crude oil variable physical parameters to determine the whole heat loss of the storage tank.
Description
Technical field:
the invention belongs to the technical field of oil and gas storage and transportation, and particularly relates to a method for predicting heat loss of a large crude oil storage tank.
The background technology is as follows:
in recent years, the total oil storage capacity of the oil depot in China is approximately 8500 ten thousand tons, the gas consumption and the oil consumption of the heating furnace for maintaining the safe storage and the outward transportation of the oil products each year account for more than 85% of the heat consumption of a ground production system, a large amount of carbon dioxide emission is generated, and the important promotion effects on the energy conservation and consumption reduction of oil field enterprises and the realization of low-carbon development are realized on the premise of ensuring the safe production of the oil depot. In the crude oil storage process, the boundary of the crude oil storage tank is directly contacted with the external environment, so that the temperature difference inside and outside the storage tank is overlarge, the heat loss is serious, and a large amount of energy sources are wasted, the fuel consumption is large and the carbon emission is increased. Therefore, the integral heat loss condition of the crude oil storage tank is necessary to be mastered, so that theoretical support is provided for oil field enterprises to reduce carbon emission of crude oil storage and improve the utilization efficiency of storage energy.
The boundary heat loss of the crude oil storage tank is usually obtained by adopting a theoretical calculation method, wherein the calculation method is to determine a heat flow density value through the product of the temperature difference between the inside and outside of the storage tank and the heat transfer coefficient, and on the basis, the heat flow density value is multiplied by the boundary surface area of the storage tank and the time to determine the boundary heat loss condition of the storage tank. The method is characterized in that the thermal environment of the storage tank is ideally simplified into a solution condition treatment, so that the heat transfer coefficient is used as a fixed value to calculate the heat loss, but in practice, the storage tank is influenced by boundary factors such as atmospheric temperature, solar radiation, soil temperature and the like and physical properties of crude oil in the interior, the heat transfer process of the crude oil from inside to outside is changed in an oscillation fluctuation mode, so that the heat transfer coefficient is changed dynamically, the heat loss condition of the boundary of the storage tank cannot be accurately measured, and the theoretical calculation method has a certain limitation on the measurement of the heat loss of the boundary of the storage tank, so that a novel prediction method of the heat loss of the storage tank for large crude oil needs to be established, and the whole heat loss of the storage tank is accurately predicted.
In summary, the measurement of the heat loss of the crude oil storage tank at present has a certain limitation, and the heat loss condition of the large crude oil storage tank cannot be predicted scientifically and accurately.
The invention comprises the following steps:
the invention aims to provide a large crude oil storage tank heat loss prediction method which is used for solving the problem that the loss condition of the boundary heat of a storage tank cannot be accurately measured in the prior art.
The technical scheme adopted for solving the technical problems is as follows: the method for predicting the heat loss of the large crude oil storage tank comprises the following steps:
step one: the method comprises the steps of performing field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of a large crude oil storage tank by adopting a heat flux meter, a surface thermometer and a solar radiation measuring instrument to obtain real-time change values of the heat flux density, the atmospheric temperature, the soil temperature and the solar radiation heat;
step two: taking out a small amount of crude oil from a large crude oil storage tank, and using a petroleum density tester, a crude oil rheological tester and a differential scanning calorimeter to test the change rules of density, viscosity and specific heat capacity of the crude oil at different temperatures in the tank to establish a variable physical property model of the crude oil; the distribution condition of the crude oil temperature field at different moments is measured through a temperature sensing probe arranged on a guide column in a large crude oil storage tank, and the density, viscosity and specific heat capacity of the crude oil are determined;
the crude oil becomes the physical property model as follows:
crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
wherein ρ is oil Is the density of crude oil, kg.m -3 ；ρ 20 The density of crude oil at 20 ℃ is kg.m -3 ；t oil Is the temperature of crude oil, DEG C; ζ is a regression coefficient;
specific heat capacity of crude oil:
wherein, c oil Is the specific heat capacity of crude oil, J (kg · DEG C) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Is the regression coefficient of each item;
viscosity of crude oil:
wherein mu is oil The dynamic viscosity of crude oil is Pa.s, K, m is a regression coefficient;
step three: the boundary heat loss of the storage tank is comprehensively influenced by the external dynamic thermal environment and the physical properties of the internal crude oil, the data analysis result is influenced by considering the difference of dimension between the external dynamic thermal environment of the storage tank and the physical property parameter influence factors of the internal crude oil, the data is normalized on the basis of not changing the original distribution rule of the data, the dimension influence among indexes is eliminated, and the data indexes are comparable;
step four: performing relevance test on each influence factor and boundary heat flux density of the storage tank independently, taking a relevance coefficient as a relevance judgment basis, considering that the relevance between the two is obvious when the absolute value of the relevance coefficient is more than 0.6, and eliminating influence factors with absolute values below 0.6 to determine main influence factors;
in the method, in the process of the invention,
the correlation coefficient of the j-th influencing factor and the boundary heat flux density of the storage tank is represented, when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, otherwise, the correlation between the two variables is weaker;
representing covariance of j-th influencing factors and boundary heat flux density of the storage tank;
Represents the jthStandard deviation of influencing factor, sigma q Representing the standard deviation of the boundary heat flux density of the storage tank;
Mean value of j-th influencing factors;
Representing an average value of the boundary heat flux density of the storage tank;
step five: establishing a boundary heat flux density loss mathematical model of the waxy crude oil storage tank based on a multiple nonlinear regression mathematical method to obtain a functional relation between the boundary heat flux density of the storage tank, an external dynamic heat environment and internal crude oil variable physical parameters, and determining the whole heat loss of the storage tank based on the functional relation and the product of the boundary heat flux density of the storage tank, the tank top, the tank wall and the tank bottom area and time;
the crude oil storage tank boundary heat flux density loss regression model is shown as follows:
wherein q i W/m is a measure of the boundary heat flux density of the ith tank 2 ；
The value of the heat flux density at the ith boundary after normalization treatment is represented by the jth influencing factor: a, a 0 、a 1 …a j And (3) representing regression coefficients of the model, wherein k is the power coefficient of the influencing factors in the regression model.
The scheme comprises the following steps:
taking the boundary heat flux density of the storage tank as a reference sequence and external and internal influence factors as a comparison sequence, wherein the reference sequence consists of the boundary heat flux density of the storage tank directly measured by a measuring instrument and is recorded as { q } i I=1, 2,3 …, n; the comparative series is composed of dynamic thermal environment outside the storage tank and variable physical property parameters of crude oil inside the storage tank and is marked as { X } j,i J=1, 2,3,4,5,6，i＝1,2,3…,n；X j,i the value of the heat flux density of the jth influencing factor at the ith boundary is represented, and the atmospheric temperature is recorded as { X ] 1,i Solar radiation heat is recorded as { X } 2,i Density of crude oil is recorded as { X } 3,i Viscosity of crude oil is recorded as { X } 4,i Specific heat capacity of crude oil as { X } 5,i The temperature of the soil is recorded as { X } 6,i }；
In order to eliminate the influence of different scales and magnitude orders among influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to ensure that the data change interval is in [0,1], and the following formula is shown:
in the method, in the process of the invention,
representing the value of the heat flux density of the ith boundary after normalization treatment of the jth influencing factor; x is X j,i Representing the value of the heat flux density of the jth influencing factor at the ith boundary; max (X) j,i ) Represents the maximum value, min (X j,i ) Representing the minimum value among the comparative series of each influencing factor;
the new series obtained by normalizing the influence factor comparison series comprises atmospheric temperature record as
Solar radiation heat mark as->
Crude oil density is recorded as->
The viscosity of crude oil was recorded as->
Specific heat capacity of crude oil as
Soil temperature is recorded as->
The invention has the following beneficial effects:
the prediction method for the heat loss of the large crude oil storage tank is established based on a multi-element nonlinear regression mathematical method by comprehensively considering the factors such as dynamic change of the environment where the storage tank is positioned and the variable property characteristics of the crude oil in the storage tank, breaks through the limitation that the heat transfer coefficient is influenced by internal and external factors to change in real time in the theoretical calculation process, realizes the prediction of the heat loss of the storage tank, and provides theoretical support for the production management of an oil depot to realize the aims of reducing energy consumption and carbon emission.
Description of the drawings:
FIG. 1 is a schematic view of the outside radial and axial survey points of a large crude oil storage tank.
The specific embodiment is as follows:
the invention is further described below with reference to the accompanying drawings:
the prediction method for the heat loss of the large crude oil storage tank comprises the following steps:
step one: the method comprises the steps of taking a large crude oil storage tank as a research object, and carrying out field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of the storage tank by adopting a heat flux meter, a surface thermometer and a solar radiation measuring instrument to obtain various real-time change data.
The external environment test adopts a heat flux meter, a surface thermometer and a solar radiation measuring instrument to carry out field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions on the outer surface of the storage tank. Dividing the tested storage tank into a plurality of test areas, ensuring the accuracy of the measurement results in each area, simultaneously considering the test efficiency, selecting representative positions and arranging as many as possible when selecting the measurement points, and considering the encryption distribution of the test positions when the local test results have larger gradient or abnormal change.
The heat flux density change data at the top of the storage tank can be directly measured by the heat flux meter, but the heat flux meter cannot measure the heat flux density of the steel tank wall due to the influence of the heat insulation layer and the external reflection strips, so that the measurement points are required to be arranged on the tank wall along the direction of a spiral ladder, the reflection strips and the heat insulation materials at the measurement point positions are lifted to ensure the accuracy of the heat flux density data of the measured tank wall, the heat flux density data at the bottom of the storage tank cannot be measured due to direct contact with the ground after the measurement is finished, the measurement point data of the tank wall close to the ground are used for replacing the heat flux density data, and the values of the atmospheric temperature, the soil temperature, the solar radiation heat and the heat flux density are averaged for 10 times per hour to be determined according to the measurement results, as shown in fig. 1.
Step two: taking out a small amount of crude oil in the tank, testing the change rules of the relevant physical properties such as density, viscosity, specific heat capacity and the like of the crude oil at different temperatures in the tank by using an oil density tester, a crude oil rheological property tester, a differential scanning calorimeter and other indoor experimental instruments, establishing a variable physical property model of the crude oil, obtaining the distribution condition of the temperature field of the crude oil at different moments by using a temperature sensing probe arranged on a guide column in the tank, and determining the relevant physical property parameters such as the density, the viscosity, the specific heat capacity and the like of the crude oil.
Because the density, viscosity and specific heat capacity of the waxy crude oil change with temperature, a small amount of crude oil in the storage tank needs to be extracted, physical parameters of the crude oil at different temperatures are measured through indoor experimental instruments such as a petroleum density measuring instrument, a crude oil rheological property measuring instrument, a differential scanning calorimeter and the like, and measured data are fitted to obtain a change curve of the density, viscosity and specific heat capacity of the crude oil with temperature.
With the rise of the temperature of the crude oil, the density gradually decreases to form a linear change rule, the viscosity is in an exponential change rule, the viscosity is greatly influenced by the change of the temperature of the crude oil, the specific heat capacity is in a trend of rising first and then decreasing, and the numerical fitting is carried out on all measured data to obtain a related variable property model.
Crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
wherein ρ is oil Is the density of crude oil, kg.m -3 ；ρ 20 The density of crude oil at 20 ℃ is kg.m -3 ；t oil Is the temperature of crude oil, DEG C; and ζ is a regression coefficient.
Specific heat capacity of crude oil:
wherein, c oil Is the specific heat capacity of crude oil, J (kg · DEG C) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Is the regression coefficient of each item;
viscosity of crude oil:
wherein mu is oil The dynamic viscosity of crude oil is Pa.s, K, m, and the regression coefficient.
The temperature of crude oil from the bottom of the tank to the top of the tank is monitored in real time through a temperature sensing probe arranged on a guide column in the tank to obtain the distribution condition of a crude oil temperature field in the storage tank, the average value is taken as the temperature of crude oil in the tank, the measured value is determined by taking the average value from 10 test results, and the relevant physical parameters such as the density, viscosity, specific heat capacity and the like of the crude oil are determined through the built physical model.
Step three: the boundary heat loss of the storage tank is comprehensively influenced by the external dynamic thermal environment, the internal crude oil physical properties, and the data analysis results are influenced by considering the difference of dimensions among influencing factors such as the external dynamic thermal environment, the internal crude oil physical property parameters and the like of the storage tank, so that the data is normalized on the basis of not changing the original distribution rule of the data, the dimensional influence among indexes is eliminated, and the data indexes are comparable.
Taking the boundary heat flux density of the storage tank as a reference sequence, and taking external and internal influence factors as a comparison sequence, wherein the reference sequence is directly measured by a measuring instrumentThe boundary heat flux density of the storage tank is denoted as { q } i I=1, 2,3 …, n; the comparative series is composed of dynamic thermal environment outside the storage tank and variable physical property parameters of crude oil inside the storage tank and is marked as { X } j,i -j = 1,2,3,4,5,6, i = 1,2,3 …, n; x is X j,i The value of the heat flux density of the jth influencing factor at the ith boundary is represented, wherein the atmospheric temperature, solar radiation heat, crude oil density, crude oil viscosity, crude oil specific heat capacity and soil temperature are respectively recorded as { X } 1,i }、{X 2,i }、{X 3,i }、{X 4,i }、{X 5,i }、{X 6,i }。
In order to eliminate the influence of different scales and magnitude orders among influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to ensure that the data change interval is in [0,1], and the following formula is shown:
wherein,,
representing the value of the heat flux density of the ith boundary after normalization treatment of the jth influencing factor; x is X j,i Representing the value of the heat flux density of the jth influencing factor at the ith boundary; max (X) j,i )、min(X j,i ) The maximum value and the minimum value in each influence factor comparison number sequence are respectively shown.
The new series obtained by normalizing the influence factor comparison series comprises atmospheric temperature, solar radiation heat, crude oil density, crude oil viscosity, crude oil specific heat capacity and soil temperature which are respectively recorded as
Step four: and (3) performing relevance test on each influence factor and the boundary heat flux density of the storage tank independently, taking the relevance coefficient as a relevance judgment basis, considering that the relevance between the two is obvious when the absolute value of the relevance coefficient is more than 0.6, and eliminating the influence factors with the absolute value below 0.6 to determine main influence factors.
Wherein,,
the correlation coefficient of the j-th influencing factor and the boundary heat flux density of the storage tank is represented, when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, otherwise, the correlation between the two variables is weaker;
representing covariance of j-th influencing factors and boundary heat flux density of the storage tank;
σ q The standard deviation of the jth influencing factor and the standard deviation of the boundary heat flow density of the storage tank are respectively represented;
Mean value of j-th influencing factors;
The average value of the boundary heat flux density of the storage tank is shown.
Step five: based on a multiple nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the waxy crude oil storage tank is established, and the functional relation between the boundary heat flux density of the storage tank, the external dynamic heat environment and the internal crude oil variable physical parameters is obtained.
On the premise of ensuring that the boundary heat flux density of the storage tank has obvious correlation with the influencing factors, a regression model of the boundary heat flux density loss of the crude oil storage tank is established, and the regression model is shown in the following formula:
wherein q i W/m is a measure of the boundary heat flux density of the ith tank 2 ；
The value of the heat flux density at the ith boundary after normalization treatment is represented by the jth influencing factor: a, a 0 、a 1 …a j And representing regression coefficients of the built model, wherein k is the power coefficient of the influence factors in the regression model.
After giving the initial value of the regression model, carrying out iterative solution to obtain a regression coefficient a 0 、a 1 …a j And the power coefficient k of the influence factors, on the basis, bringing the values of the external and internal influence factors into a built crude oil storage tank boundary heat flux density loss regression model to obtain a calculated value of the storage tank boundary heat flux density, and recording the calculated value as
Will fit goodness of fit R 2 As the basis for judging whether the regression model is good or not, when R 2 When approaching 1, the built model is highly consistent with actual data, and can be used for representing the functional relation between the boundary heat flow density of the storage tank and external dynamic heat environment and crude oil variable parameters, wherein the functional relation is shown in the following formula:
wherein q i W/m is a measure of the boundary heat flux density of the ith tank 2 ；
W/m is the calculated value of the boundary heat flux density of the ith storage tank 2 ；SS tot As total errors, there are mainly two sources of total errors: a. the diversity of influencing factors leads to q i Is a variety of (3); b. random error SS res . Obviously, random error SS res The smaller R 2 The closer to 1.
Through the fitting goodness R 2 After the superiority of the built regression model is checked, a final mathematical model of the boundary heat flux density loss of the crude oil storage tank is determined, and the mathematical model is shown as the following formula:
after a functional relation between the boundary heat flow density of the storage tank and the external dynamic heat environment and the internal crude oil variable property parameters is established, the heat loss of the whole storage tank can be determined, and the heat loss is shown in the following formula:
wherein Q is tot Indicating the heat loss of the whole storage tank, J; q (Q) roof 、Q wall 、Q bottom The heat loss, J, at the top, wall and bottom positions of the storage tank are respectively represented; s is S roof 、S wall 、S bottom Respectively represent the surface area of the top, the wall and the bottom of the storage tank, m 2 ，S roof ＝S bottom ＝πr 2 、S wall =2pi rh, where r is the tank radius, m, h is the tank height, m;
calculated values, W/m, representing heat flux density of the tank roof, wall and bottom, respectively 2 The method comprises the steps of carrying out a first treatment on the surface of the t is time, s.
In order to make the above-mentioned contents of the invention more comprehensible, a certain storage tank of Daqing oilfield is taken as a secret study object to predict the heat loss condition of the storage tank in the heating process, and the following detailed description is given:
daqing oil field certain oil10-kilocubic meter floating roof storage tank with bottom diameter of 80m, wall height of 20m and density of 860kg/m of oil product at 20 DEG C 3 Viscosity of 4.94 Pa.s, specific heat capacity of 2986.53J (kg. Degree. C.) -1 . The invention establishes a mathematical model of the boundary heat flux density loss of a crude oil storage tank based on a multi-element nonlinear regression mathematical method to obtain a functional relation between the boundary heat flux density of the storage tank and external dynamic heat environment and internal crude oil variable physical parameters, thereby realizing the prediction of the whole heat loss of the storage tank, and the specific method comprises the following steps:
step one: the method is characterized in that a large crude oil storage tank is used as a research object, a heat flow meter, a surface thermometer and a solar radiation measuring instrument are adopted to perform field test on heat flow density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of the storage tank, so that various real-time change data are obtained, and specific changes are shown in a table.
Step two: taking out a small amount of crude oil in the tank, testing the change rules of the relevant physical properties such as density, viscosity, specific heat capacity and the like of the crude oil at different temperatures in the tank by using an oil density tester, a crude oil rheological property tester, a differential scanning calorimeter and other indoor experimental instruments, establishing a variable physical property model of the crude oil, obtaining the distribution condition of the temperature field of the crude oil at different moments by using a temperature sensing probe arranged on a guide column in the tank, and determining the relevant physical property parameters such as the density, the viscosity, the specific heat capacity and the like of the crude oil.
Measuring physical property parameters of crude oil at different temperatures by using an oil density measuring instrument, a crude oil rheological property measuring instrument, a differential scanning calorimeter and other indoor experimental instruments, and fitting measured data to obtain a crude oil rheological property model, wherein the physical property model is as follows:
the density of crude oil is related to the temperature in a negative linear manner, and the density is opposite to the temperature of crude oil as the temperature of crude oil increasesLowering; the viscosity of crude oil changes exponentially and is greatly influenced by temperature change, the viscosity of crude oil drops rapidly from 36.46 Pa.s to 4.94 Pa.s at the temperature of 2-20 ℃ by taking 20 ℃ as a demarcation point; the viscosity drop is slowed down at 20-50 ℃ and only changed by 4.58 Pa.s; the specific heat capacity of the crude oil shows a change of rising and falling with the rising of the temperature, and reaches the maximum value 2986.53J (kg · DEG C) at the temperature of 20 DEG C -1 The specific heat capacity of the crude oil starts to decrease with increasing temperature, and the specific heat capacity of the crude oil at 44 ℃ reaches a minimum value of 2228.12J (kg ℃ C.) -1 The crude oil variant property model is built as follows:
crude oil density:
ρ oil ＝870×[1-0.00061(t oil -20)]
specific heat capacity of crude oil:
viscosity of crude oil:
according to the temperature sensing probe on the guide post in the tank, the real-time change of the temperature of the crude oil in the tank is obtained, and the real-time change numerical value of the physical property of the crude oil in the tank at different temperatures is obtained by using the built variable physical property model of the crude oil, as shown in the following table:
step three: the heat loss of the boundary of the storage tank is comprehensively influenced by the external dynamic thermal environment, the physical properties of the internal crude oil, and the like, and the data analysis result is influenced by considering the difference of dimensions among influencing factors such as the external dynamic thermal environment of the storage tank, the variable physical property parameters of the internal crude oil, and the like, so that the data is normalized on the basis of not changing the original distribution rule of the data, the dimensional influences among indexes are eliminated, the data indexes are comparable, and the heat flow density measured at the top of the storage tank and the measuring data of influencing factors are taken as examples.
The heat flux density of the top of the storage tank measured by the experimental instrument is recorded as { q } i Atmospheric temperature, tank top solar radiant heat, crude oil density, crude oil viscosity, crude oil specific heat capacity as the influence factors of tank top heat flow density are respectively recorded as { X } 1,i }、{X 2,i }、{X 3,i }、{X 4,i }、{X 5,i -as follows:
{q i }＝{82.34,81.79,81.13,80.04,78.86,77.60,73.20,69.39,65.92,63.30,61.74,61.33,61.86,63.58,66.46,69.95,74.21,78.50,80.10,81.45,82.70,83.59,84.23,84.45}
{X 1,i }＝{-30.01,-29.61,-28.98,-28.15,-27.19,-26.15,-25.12,-24.15,-23.32,-22.69,-22.29,-22.15,-22.28,-22.68,-23.32,-24.14,-25.11,-26.14,-27.18,-28.14,-28.97,-29.61,-30.01,-30.15}
{X 2,i }＝{0,0,0,0,0,0,0,48.27,91.82,126.38,148.57,156.21,148.57,126.38,91.82,48.27,0,0,0,0,0,0,0,0}
{X 3,i }＝{853.72,853.70,853.68,853.66,853.64,853.61,853.55,853.51,853.47,853.44,853.41,853.40,853.39,853.41,853.41,853.43,853.47,853.51,853.52,853.53,853.53,853.55,853.54,853.53}
{X 4,i }＝{1.47,1.46,1.46,1.45,1.44,1.44,1.42,1.41,1.40,1.39,1.39,1.38,1.38,1.39,1.39,1.39,1.40,1.41,1.41,1.42,1.42,1.42,1.42,1.42}
{X 5,i }＝{2626.83,2624.87,2623.60,2621.46,2619.54,2617.33,2612.19,2608.08,2604.73,2602.29,2599.47,2598.37,2597.89,2599.70,2599.46,2601.58,2604.88,2608.55,2609.40,2610.30,2610.36,2611.50,2611.39,2610.01}
similarly, the heat flux density of the tank wall of the storage tank is also affected by five factors, namely, the atmospheric temperature, solar radiation heat of the tank wall, crude oil density, crude oil viscosity and crude oil specific heat capacity, and is recorded as:
{q i }＝{21.70,21.62,21.43,21.19,20.69,20.43,18.93,19.10,19.00,18.94,19.00,19.01,19.01,18.98,19.05,18.99,18.96,20.45,20.78,21.12,21.58,21.77,21.80,21.88}
{X 1,i }＝{-30.01,-29.61,-28.98,-28.15,-27.19,-26.15,-25.12,-24.15,-23.32,-22.69,-22.29,-22.15,-22.28,-22.68,-23.32,-24.14,-25.11,-26.14,-27.18,-28.14,-28.97,-29.61,-30.01,-30.15}
{X 2,i }＝{0,0,0,0,0,0,128.69,125.60,122.82,120.60,119.19,118.70,119.19,120.60,122.82,
125.60,128.69,0,0,0,0,0,0,0}
{X 3,i }＝{853.72,853.70,853.68,853.66,853.64,853.61,853.55,853.51,853.47,853.44,
853.41,853.40,853.39,853.41,853.41,853.43,853.47,853.51,853.52,853.53,853.53,
853.55,853.54,853.53}
{X 4,i }＝{1.47,1.46,1.46,1.45,1.44,1.44,1.42,1.41,1.40,1.39,1.39,1.38,1.38,1.39,1.39,
1.39,1.40,1.41,1.41,1.42,1.42,1.42,1.42,1.42}
{X 5,i }＝{2626.83,2624.87,2623.60,2621.46,2619.54,2617.33,2612.19,2608.08,2604.73,
2602.29,2599.47,2598.37,2597.89,2599.70,2599.46,2601.58,2604.88,2608.55,2609.40,
2610.30,2610.36,2611.50,2611.39,2610.01}
because the bottom of the storage tank is directly contacted with the soil, the heat flux density change is not influenced by the atmospheric temperature and solar radiation heat, and is only related to the density of crude oil, the viscosity of crude oil, the specific heat capacity of crude oil and the temperature of the soil. The heat flux density of the tank bottom, the density of crude oil, the viscosity of crude oil, the specific heat capacity of crude oil and the soil temperature can be recorded as follows:
{q i }＝{50.08,49.76,49.28,48.63,47.87,47.03,46.23,45.46,44.84,44.33,44.04,43.98,44.12,
44.50,45.08,45.78,46.62,47.53,48.41,49.27,49.96,50.55,50.91,50.97}
{X 3,i }＝{853.72,853.70,853.68,853.66,853.64,853.61,853.55,853.51,853.47,853.44,
853.41,853.40,853.39,853.41,853.41,853.43,853.47,853.51,853.52,853.53,853.53,
853.55,853.54,853.53}
{X 4,i }＝{1.47,1.46,1.46,1.45,1.44,1.44,1.42,1.41,1.40,1.39,1.39,1.38,1.38,1.39,1.39,
1.39,1.40,1.41,1.41,1.42,1.42,1.42,1.42,1.42}
{X 5,i }＝{2626.83,2624.87,2623.60,2621.46,2619.54,2617.33,2612.19,2608.08,2604.73,
2602.29,2599.47,2598.37,2597.89,2599.70,2599.46,2601.58,2604.88,2608.55,2609.40,
2610.30,2610.36,2611.50,2611.39,2610.01}
{X 6,i }＝{-26.51,-26.11,-25.48,-24.65,-23.69,-22.65,-21.62,-20.65,-19.82,-19.19,-18.79,-18.65,
-18.78,-19.18,-19.82,-20.64,-21.61,-22.64,-23.68,-24.64,-25.47,-26.11,-26.51,-26.65}
taking the atmospheric temperature in the heat flux density influence factor of the tank top as an example, in a plurality of rows { X ] 1,i Finding the maximum max (X) 1,i ) Is-22.15, minimum value max (X 1,i ) -30.15, whereby the values in the series are normalized:
similarly, the normalized values of the various items of the number are shown in the following table:
thus, a new array of normalized atmospheric temperature can be obtained:
likewise, other influencing factors influencing the heat flux density of the tank top can be obtained, and the normalized number series can be obtained:
similarly, a new array of normalized heat flux density influencing factors of the tank wall and the tank bottom is obtained;
the normalized number of the heat flux density influencing factors of the tank wall is as follows:
the number columns of the tank bottom heat flow density influence factors after normalization:
step four: and (3) performing relevance test on each influence factor and the boundary heat flux density of the storage tank independently, taking the relevance coefficient as a relevance judgment basis, considering that the relevance between the two factors is obvious when the absolute value of the relevance coefficient is more than 0.6, and removing the influence factors with the absolute value below 0.6 to determine main influence factors.
Taking the heat flux density of the top of the storage tank and the atmospheric temperature as examples for carrying out correlation test, the specific calculation process is as follows:
average value of
And (3) calculating:
standard deviation of
σ Q And (3) calculating:
covariance (covariance)
And (3) calculating:
correlation coefficient of boundary heat flux density of storage tank top and atmospheric temperature
The correlation coefficient between the atmospheric temperature and the heat flux density of the tank top is-0.975 after calculation, and the correlation of the atmospheric temperature and the heat flux density is obvious. Likewise, the correlation coefficients of the tank top heat flux density and the tank top solar radiant heat, the crude oil density, the crude oil viscosity and the crude oil specific heat capacity can be obtained according to the same calculation steps, and the correlation coefficients are respectively as follows:
and similarly, calculating to obtain the correlation coefficients of the heat flow density of the tank wall and the tank bottom and the respective influence factors:
tank wall heat flux density and tank wall solar radiant heat, crude oil density, crude oil viscosity and crude oil specific heat capacityThe correlation coefficients of (a) are respectively:
the correlation coefficients of the tank bottom heat flux density and the crude oil density, the crude oil viscosity, the crude oil specific heat capacity and the soil temperature are respectively as follows:
the absolute value of the correlation coefficient is generally more than 0.6, two variables are considered to have strong correlation, and it can be seen that the heat flow density changes of the tank top, the tank wall and the tank bottom have obvious correlation with each corresponding influence factor, so that mathematical relations can be established between the external dynamic heat environment, the crude oil variable physical property parameters and the boundary heat flow density of the storage tank to represent the change condition of the boundary heat flow density of the storage tank.
Step five: based on a multiple nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the waxy crude oil storage tank is established, and the functional relation between the boundary heat flux density of the storage tank, the external dynamic heat environment and the internal crude oil variable physical parameters is obtained. Taking the establishment of a mathematical model of heat flux density loss of a storage tank top as an example, the heat flux density of the storage tank top is taken as a dependent variable q i Atmospheric temperature, tank top solar radiant heat, crude oil density, viscosity and specific heat capacity are taken as independent variables respectively
Tank top heat flux density loss regression model:
after multiple nonlinear regression, each coefficient and the power coefficient are respectively obtained:
a 0 ＝84.3451、a 1 ＝-25.9386、a 2 ＝2.5873、a 3 ＝-28.0169、a 4 ＝3.8357、a 5 ＝21.4503、k＝2。
the external and internal influencing factor values are brought into a built crude oil storage tank top heat flux density loss regression model to obtain a calculated value of the storage tank top heat flux density, and the calculated value is recorded as
To verify the accuracy of the model, the goodness of fit R to the model is required 2 And (3) calculating, and when the goodness of fit tends to 1, describing that the built tank top heat flux loss regression model accords with the actual situation.
Random error SS res And (3) calculating:
total error SS tot And (3) calculating:
thus, goodness of fit R 2 The method comprises the following steps:
calculated and fitted with goodness R 2 Close to 1, the established tank top heat flux density loss regression model can fully reflect the actual loss condition of the tank top heat flux density, so that the mathematical model of the tank top heat flux density loss of the storage tank can be determined as follows:
the mathematical model of the heat flux density loss of the wall and the bottom of the storage tank can be obtained respectively by the same calculation steps
Tank wall heat flux density loss mathematical model with goodness of fit of R 2 ＝0.988：
Mathematical model of heat flux density loss of tank bottom, and goodness of fit is R 2 ＝0.999：
After calculating the heat flux density loss value of the boundary of the storage tank, the formula is utilized
The total heat Q dissipated to the outside from the boundary of the storage tank in one day in the temperature rising process of the storage tank can be obtained tot 。
S roof ＝S bottom ＝πr 2 ＝3.14×40 2 ＝5024(m 2 )
S wall ＝2πrh＝2×3.14×40×20＝5024(m 2 )
Tank top position heat loss:
tank wall position heat loss:
tank bottom heat loss:
overall heat loss from the tank:
Q tot ＝Q roof +Q wall +Q bottom ＝32333419.01+8775623.93+20532223.87＝61641266.81(KJ)
the calculation shows that the integral heat loss of the storage tank in one day is 61641266.81KJ, wherein the heat loss at the top of the tank is 32333419.01KJ most seriously; the heat loss of the tank wall position is 8775623.93KJ; the heat loss at the tank bottom is 20532223.87KJ.
In a comprehensive view, the heat loss prediction method of the large crude oil storage tank directly converts the problem that the heat transfer coefficient in the theoretical calculation method is affected by internal and external factors and is dynamically changed into the functional relation between the boundary heat flow density of the storage tank, the external dynamic heat boundary condition and the crude oil variable physical property parameters, so that the accurate prediction of the heat loss condition of the storage tank is realized, and the prediction method can provide theoretical basis for energy saving and consumption reduction work of oil field enterprises in oil depot production management.
Claims (4)
1. The method for predicting the heat loss of the large crude oil storage tank is characterized by comprising the following steps of:
step one: the method comprises the steps of performing field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of a large crude oil storage tank by adopting a heat flux meter, a surface thermometer and a solar radiation measuring instrument to obtain real-time change values of the heat flux density, the atmospheric temperature, the soil temperature and the solar radiation heat;
step two: taking out a small amount of crude oil from a large crude oil storage tank, and using a petroleum density tester, a crude oil rheological tester and a differential scanning calorimeter to test the change rules of density, viscosity and specific heat capacity of the crude oil at different temperatures in the tank to establish a variable physical property model of the crude oil; the distribution condition of the crude oil temperature field at different moments is measured through a temperature sensing probe arranged on a guide column in a large crude oil storage tank, and the density, viscosity and specific heat capacity of the crude oil are determined;
step three: the boundary heat loss of the storage tank is comprehensively influenced by the external dynamic thermal environment and the physical properties of the internal crude oil, the data analysis result is influenced by considering the difference of dimension between the external dynamic thermal environment of the storage tank and the physical property parameter influence factors of the internal crude oil, the data is normalized on the basis of not changing the original distribution rule of the data, the dimension influence among indexes is eliminated, and the data indexes are comparable;
step four: performing relevance test on each influence factor and the boundary heat flux density of the storage tank independently, taking a relevance coefficient as a relevance judgment basis, when the absolute value of the relevance coefficient is more than 0.6, obviously judging the relevance between the two, and eliminating influence factors with absolute values below 0.6;
in the method, in the process of the invention,
the correlation coefficient of the j-th influencing factor and the boundary heat flux density of the storage tank is represented, when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, otherwise, the correlation between the two variables is weaker;
Representing covariance of j-th influencing factors and boundary heat flux density of the storage tank;
Standard deviation sigma representing the jth influencing factor q Representing the standard deviation of the boundary heat flux density of the storage tank;
Mean value of j-th influencing factors;
Representing an average value of the boundary heat flux density of the storage tank;
step five: based on a multiple nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the waxy crude oil storage tank is established, and the product of the functional relation between the boundary heat flux density of the storage tank, the external dynamic heat environment and the variable physical parameters of the internal crude oil, the tank top, the tank wall and the tank bottom area and the time is obtained to determine the integral heat loss of the storage tank.
2. The method for predicting heat loss of a large crude oil storage tank according to claim 1, wherein: the variable property model of the crude oil is as follows:
crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
wherein ρ is oil Is the density of crude oil, kg.m -3 ；ρ 20 The density of crude oil at 20 ℃ is kg.m -3 ；t oil Is the temperature of crude oil, DEG C; ζ is a regression coefficient;
specific heat capacity of crude oil:
wherein, c oil Is the specific heat capacity of crude oil, J (kg · DEG C) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Is the regression coefficient of each item;
viscosity of crude oil:
wherein mu is oil The dynamic viscosity of crude oil is Pa.s, K, m, and the regression coefficient.
3. The method for predicting heat loss of a large crude oil storage tank according to claim 2, wherein: the third step is specifically as follows:
taking the boundary heat flux density of the storage tank as a reference sequence and external and internal influence factors as a comparison sequence, wherein the reference sequence consists of the boundary heat flux density of the storage tank directly measured by a measuring instrument and is recorded as { q } i I=1, 2,3 …, n; the comparative series is composed of dynamic thermal environment outside the storage tank and variable physical property parameters of crude oil inside the storage tank and is marked as { X } j,i -j = 1,2,3,4,5,6, i = 1,2,3 …, n; x is X j,i The value of the heat flux density of the jth influencing factor at the ith boundary is represented, and the atmospheric temperature is recorded as { X ] 1,i Solar radiation heat is recorded as { X } 2,i Density of crude oil is recorded as { X } 3,i Viscosity of crude oil is recorded as { X } 4,i Specific heat capacity of crude oil as { X } 5,i The temperature of the soil is recorded as { X } 6,i }；
In order to eliminate the influence of different scales and magnitude orders among influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to ensure that the data change interval is in [0,1], and the following formula is shown:
in the method, in the process of the invention,
representing the value of the heat flux density of the ith boundary after normalization treatment of the jth influencing factor; x is X j,i Representing the value of the heat flux density of the jth influencing factor at the ith boundary; max (X) j,i ) Represents the maximum value, min (X j,i ) Representing the minimum value among the comparative series of each influencing factor;
the new series obtained by normalizing the influence factor comparison series comprises atmospheric temperature record as
Solar radiation heat mark as->
Crude oil density is recorded as->
The viscosity of crude oil was recorded as->
Specific heat capacity of crude oil as
Soil temperature is recorded as->
4. A method for predicting heat loss from a bulk crude oil storage tank as set forth in claim 3, wherein: the crude oil storage tank boundary heat flux density loss regression model is shown as the following formula:
wherein q i W/m is a measure of the boundary heat flux density of the ith tank 2 ；
The value of the heat flux density at the ith boundary after normalization treatment is represented by the jth influencing factor: a, a 0 、a 1 …a j And (3) representing regression coefficients of the model, wherein k is the power coefficient of the influencing factors in the regression model. />
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