CN115809730A - Large crude oil storage tank heat loss prediction method - Google Patents
Large crude oil storage tank heat loss prediction method Download PDFInfo
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Abstract
The invention relates to a large crude oil storage tank heat loss prediction method, which comprises the following steps: the real-time change values of heat flux density, atmospheric temperature, soil temperature and solar radiation heat of different positions of the large crude oil storage tank are obtained 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 temperature fields of the crude oil 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 property of the internal crude oil; carrying out correlation test on each influence factor and the heat flux density of the boundary of the storage tank independently; the method comprises the steps of establishing a mathematical model of the boundary heat flux density loss of the wax-containing crude oil storage tank, and obtaining a functional relation between the boundary heat flux density of the storage tank and the external dynamic thermal environment and the internal crude oil variable physical property parameters to determine the overall heat loss of the storage tank.
Description
The technical field is as follows:
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.
Background art:
in recent years, the total oil storage capacity of an oil depot in China is nearly 8500 ten thousand tons, the gas consumption and the oil consumption of a heating furnace for maintaining the safe storage and the outward transportation of oil products account for more than 85% of the heat consumption of a ground production system every year, and a large amount of carbon dioxide is discharged, so that the reduction of the energy consumption of crude oil storage on the premise of ensuring the safe production of the oil depot plays an important role in promoting the energy conservation and consumption reduction of oil field enterprises and the implementation of low-carbon development. In the process of crude oil storage, the boundary of the crude oil storage tank is directly contacted with the external environment, so that the temperature difference between the inside and the outside of the storage tank is overlarge, the heat loss is serious, a large amount of energy is wasted, the fuel consumption is high, and the carbon emission is increased. Therefore, the overall heat loss condition of the crude oil storage tank needs to be mastered, so that theoretical support is provided for oil field enterprises to reduce the carbon emission of crude oil reserves and improve the utilization efficiency of reserve 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 the heat flow density value through the product of the temperature difference inside and outside the storage tank and the heat transfer coefficient, and determine the heat loss condition of the boundary of the storage tank through the product of the surface area and the time of the boundary of the storage tank on the basis. The method ideally simplifies the thermal environment of the storage tank into definite 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 the physical properties of the internal crude oil, the heat transfer process of the crude oil from inside to outside is in oscillation fluctuation type change, so that the heat transfer coefficient is in dynamic change, the loss condition of the boundary heat of the storage tank cannot be accurately measured, and the theoretical calculation method has certain limitation on the measurement of the boundary heat loss of the storage tank, so that a new prediction method for the heat loss of the large crude oil storage tank needs to be established, and the accurate prediction of the whole heat loss of the storage tank is realized.
In summary, the current determination of the heat loss of the crude oil storage tank has certain limitations, and the heat loss condition of a large crude oil storage tank cannot be scientifically and accurately predicted.
The invention content is as follows:
the invention aims to provide a large crude oil storage tank heat loss prediction method which is used for solving the problem that the prior art cannot accurately determine the loss condition of the storage tank boundary heat.
The technical scheme adopted by the invention 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:
the method comprises the following steps: carrying out field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of the 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 establishing a variable physical property model of the crude oil by using a petroleum density determinator, a crude oil rheological property determinator and a differential scanning calorimeter to test the change rules of the density, viscosity and specific heat capacity of the crude oil at different temperatures in the tank; measuring the distribution conditions of crude oil temperature fields at different moments by a temperature sensing probe arranged on a guide post in a large crude oil storage tank, and determining the density, viscosity and specific heat capacity of the crude oil;
the crude oil metamorphic model is as follows:
crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
in the formula, ρ oil Is crude oil density, kg.m -3 ；ρ 20 Crude oil density at 20 ℃ kg. M -3 ；t oil Crude oil temperature, deg.C; xi is a regression coefficient;
crude oil specific heat capacity:
in the formula, c oil Is the specific heat capacity of crude oil, J (kg. Degree. C.) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Each regression coefficient is taken as a reference value;
viscosity of crude oil:
in the formula, mu oil The dynamic viscosity of the crude oil is shown, and Pa.s, K, m are regression coefficients;
step three: the heat loss of the boundary of the storage tank is comprehensively influenced by the physical properties of the external dynamic thermal environment and the internal crude oil, the data analysis result is influenced by considering the different dimensions of the external dynamic thermal environment and the internal crude oil variable physical property parameter influence factors of the storage tank, 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 have comparability;
step four: performing correlation test on each influence factor and the boundary heat flux density of the storage tank independently, taking a correlation coefficient as a correlation judgment basis, when the absolute value of the correlation coefficient is more than 0.6, considering that the correlation between the two is obvious, and eliminating the influence factors with the absolute value below 0.6 to determine main influence factors;
in the formula (I), the compound is shown in the specification,
a correlation coefficient representing the jth influencing factor and the heat flow density of the boundary of the storage tank, wherein when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, and otherwise, the correlation between the two variables is weak;
shows the jth influence factorCovariance of element and tank boundary heat flux density;
standard deviation, σ, representing the jth influencing factor q Representing the standard deviation of the heat flux density of the boundary of the storage tank;
represents the average value of the jth influencing factor;
an average value representing the heat flux density at the boundary of the tank;
step five: establishing a mathematical model of the boundary heat flux density loss of the wax-containing crude oil storage tank on the basis of a multivariate nonlinear regression mathematical method to obtain a functional relation between the boundary heat flux density of the storage tank and the variable physical property parameters of the external dynamic thermal environment and the internal crude oil, and determining the integral heat loss of the storage tank by multiplying the product of the area and the time of the top of the tank, the wall of the tank and the bottom of the tank on the basis;
the regression model of the heat flux density loss of the boundary of the crude oil storage tank is shown as the following formula:
wherein q is i As a measure of the heat flux density at the i-th tank boundary, W/m 2 ；
The value of the heat flux density at the ith boundary after the jth influencing factor is normalized is represented as follows: a is 0 、a 1 …a j And k is a coefficient of the power of an influencing factor in the regression model.
The third step of the scheme is as follows:
taking the heat flux density of the boundary of the storage tank as a reference sequence and taking external and internal influence factors as a comparison sequence, wherein the reference sequence is formed by the heat flux density of the boundary of the storage tank directly measured by a measuring instrument and is recorded as { q } i I =1,2,3 …, n; the comparison sequence is composed of the dynamic thermal environment outside the storage tank and the internal crude oil variable physical property parameters and is recorded as { X } j,i Where j =1,2,3,4,5,6, i =1,2,3 …, n; x j,i Showing the value of the jth influencing factor at the ith boundary heat flow density, and recording the atmospheric temperature as { X } 1,i Recording solar radiation heat as { X } 2,i And recording the density of the crude oil as { X } 3,i And the viscosity of the crude oil is recorded as { X } 4,i Taking specific heat capacity of crude oil as { X } 5,i Recording the soil temperature as { X } 6,i }；
In order to eliminate the influence of different dimensions and magnitude among the influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to enable the data change interval to be [0,1], and the following formula is shown as follows:
in the formula (I), the compound is shown in the specification,
expressing the value of the heat flux density at the ith boundary after the jth influence factor is subjected to normalization processing; x j,i Representing the value of the jth influencing factor at the ith boundary heat flow density; max (X) j,i ) The maximum value, min (X), among the comparison series of the respective influencing factors j,i ) The minimum value among the comparison series of the influence factors is represented;
the new number series obtained by normalization processing of the comparative number series of the influence factors comprises the atmospheric temperature recorded as
Solar radiation heat is recorded as
Crude oil density is recorded as
Crude oil viscosity is recorded as
The specific heat capacity of crude oil is recorded as
The soil temperature is recorded as
The invention has the following beneficial effects:
the method comprehensively considers factors such as dynamic change of the environment outside the storage tank, variable physical property characteristics of the internal crude oil and the like, and based on a multivariate nonlinear regression mathematical method, the method for predicting the heat loss of the large crude oil 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 prediction of the heat loss of the storage tank, and provides theoretical support for production management of the 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 external radial and axial measurement points of a large crude oil storage tank.
The specific implementation mode is as follows:
the invention will be further described with reference to the accompanying drawings in which:
the method for predicting the heat loss of the large crude oil storage tank comprises the following steps:
the method comprises the following steps: a large crude oil storage tank is taken as a research object, and heat flow meters, surface thermometers and solar radiation measuring instruments are adopted to carry out field tests 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.
The out-of-tank environment test adopts a heat flow meter, a surface thermometer and a solar radiation measuring instrument to carry out field test on heat flow density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the outer surface of the storage tank. The storage tank to be tested is divided into a plurality of testing areas, the accuracy of a measuring result is guaranteed in each area, meanwhile, the testing efficiency is considered, when a testing point is selected, a representative position is selected, the testing point is arranged as many as possible, and when a local testing result has a large change gradient or abnormal change, the testing position is considered to be subjected to encrypted point arrangement.
The heat flow density change data at the top of the storage tank can be directly measured by a heat flow meter, but the heat flow meter cannot measure the heat flow density of the steel tank wall due to the influence of the heat insulation layer and external reflection strips on the tank wall, so that measuring points need to be arranged on the tank wall along the direction of a spiral staircase, the reflection strips and the heat insulation material at the measuring points are lifted to ensure the accuracy of the measured heat flow density data of the tank wall, the measured heat flow density data are reduced after the measurement is finished, the heat flow density of the tank bottom cannot be measured due to the direct contact of the bottom of the storage tank and the ground, the data of the measuring points of the tank wall close to the ground are used for replacing the data, and the values of the atmospheric temperature, the soil temperature, the solar radiation heat and the heat flow density per hour are determined by averaging 10 times of test results, as shown in figure 1.
Step two: taking out a small amount of crude oil from the tank, testing the change rules of the density, viscosity, specific heat capacity and other related physical properties of the crude oil at different temperatures in the tank by using indoor testing instruments such as a petroleum density tester, a crude oil rheological property tester, a differential scanning calorimeter and the like, establishing a crude oil variable property model, obtaining the distribution conditions of the temperature fields of the crude oil at different moments by using temperature sensing probes arranged on guide columns in the tank, and determining the density, viscosity, specific heat capacity and other related physical property parameters of the crude oil.
Because the density, viscosity and specific heat capacity of the wax-containing crude oil can show different change rules along with the change of temperature, a small amount of crude oil in the storage tank needs to be extracted, the physical property parameters of the crude oil at different temperatures are measured by indoor experiment instruments such as a petroleum density measuring instrument, a crude oil rheological property measuring instrument, a differential scanning calorimeter and the like, and the measured data are fitted to obtain the change curve of the density, viscosity and specific heat capacity of the crude oil along with the temperature.
Along 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 and is greatly influenced by the change of the temperature of the crude oil, the specific heat capacity shows the trend of increasing firstly and then decreasing, and the measured data are subjected to numerical fitting to obtain a relevant variable property model.
Crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
in the formula, ρ oil Is crude oil density, kg.m -3 ；ρ 20 Crude oil density at 20 ℃ kg. M -3 ；t oil Crude oil temperature, deg.C; ξ is the regression coefficient.
Crude oil specific heat capacity:
in the formula, c oil Is the specific heat capacity of crude oil, J (kg. Degree. C.) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Each regression coefficient is taken as a reference value;
viscosity of crude oil:
in the formula, mu oil Pa · s, K, m is the regression coefficient for crude oil dynamic viscosity.
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 post in the tank, the distribution condition of the temperature field of the crude oil in the storage tank is obtained, the average value of the temperature field is taken as the temperature of the crude oil in the tank, the measured value is determined by taking the average value of 10 times of test results, and relevant physical property parameters such as the density, the viscosity, the specific heat capacity and the like of the crude oil are determined through a built variable physical property model.
Step three: the heat loss of the boundary of the storage tank is comprehensively influenced by the physical properties of the external dynamic thermal environment and the internal crude oil, and the data analysis result is influenced by considering the dimensional difference among the influence 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 are 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 have comparability.
Taking the heat flux density of the boundary of the storage tank as a reference array and the external and internal influence factors as a comparison array, wherein the reference arrayThe heat flux density at the boundary of the tank, measured directly by the measuring instrument, is recorded as q i Where i =1,2,3 …, n; the comparison sequence is composed of the dynamic thermal environment outside the storage tank and the internal crude oil variable physical property parameters and is recorded as { X } j,i Where j =1,2,3,4,5,6, i =1,2,3 …, n; x j,i Showing the value of the jth influencing factor at the ith boundary heat flow density, wherein the atmospheric temperature, the solar radiation heat, the crude oil density, the crude oil viscosity, the crude oil specific heat capacity and the 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 dimensions and magnitude among the influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to enable the data change interval to be [0,1], which is shown as the following formula:
wherein, the first and the second end of the pipe are connected with each other,
expressing the value of the heat flux density of the ith boundary after the jth influencing factor is subjected to normalization processing; x j,i Representing the value of the jth influencing factor at the ith boundary heat flow density; max (X) j,i )、min(X j,i ) The maximum value and the minimum value in the comparison sequence of the influencing factors are respectively shown.
The new number series obtained by normalization processing of the comparative number series of the influence factors 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 correlation test on each influence factor and the heat flow density of the boundary of the storage tank, taking a correlation coefficient as a correlation judgment basis, when the absolute value of the correlation coefficient is more than 0.6, considering that the correlation between the two is obvious, and eliminating the influence factors with the absolute value of less than 0.6 to determine the main influence factors.
Wherein the content of the first and second substances,
a correlation coefficient representing the jth influencing factor and the heat flow density of the boundary of the storage tank, wherein when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, and otherwise, the correlation between the two variables is weak;
representing the covariance of the jth influencing factor and the heat flux density at the boundary of the storage tank;
σ q respectively representing the standard deviation of the jth influencing factor and the standard deviation of the heat flux density of the boundary of the storage tank;
represents the average value of the jth influencing factor;
represents the average of the heat flux density at the boundary of the tank.
Step five: based on a multivariate nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the wax-containing crude oil storage tank is established to obtain a functional relation between the boundary heat flux density of the storage tank and the variable physical property parameters of the external dynamic thermal environment and the internal crude oil, and on the basis, the integral heat loss of the storage tank is determined by the product of the area and the time of the tank top, the tank wall and the tank bottom.
On the premise of ensuring that the heat flux density of the boundary of the storage tank has obvious correlation with the influence factors, establishing a regression model of the heat flux density loss of the boundary of the crude oil storage tank, as shown in the following formula:
wherein q is i Is a measure of the heat flux density at the i-th tank boundary, W/m 2 ；
The value of the heat flux density at the ith boundary after the jth influencing factor is normalized is represented as follows: a is 0 、a 1 …a j And k is a coefficient of the power of an influencing factor in the regression model.
After the initial value of the regression model is given, iterative solution is carried out to obtain a regression coefficient a 0 、a 1 …a j And the second power coefficient k of the influence factors, and on the basis, the external and internal influence factor values are introduced into the established regression model of the heat flux density of the boundary of the crude oil storage tank to obtain the calculated value of the heat flux density of the boundary of the storage tank, and the calculated value is recorded as the calculated value
Will fit goodness of fit R 2 When R is used as the basis for judging whether the regression model is good or not 2 When the temperature approaches 1, the model is proved to be highly consistent with the actual data, and can be used for representing the functional relation between the heat flux density of the storage tank boundary and the external dynamic thermal environment and crude oil variable physical property parameters, as shown in the following formula:
wherein q is i Is a measure of the heat flux density at the i-th tank boundary, W/m 2 ；
Calculated as the heat flux density at the ith tank boundary, W/m 2 ；SS tot For total error, for totalThe sources of error are mainly two: a. multiple influencing factors, resulting in q i A variety of (2); b. random error SS res . Obviously, random error SS res The smaller R 2 The closer to 1.
Through goodness of fit R 2 After the goodness of the built regression model is checked, a final mathematical model of the boundary heat flow density loss of the crude oil storage tank is determined, and the mathematical model is shown as the following formula:
after establishing a functional relationship between the heat flux density of the boundary of the storage tank and the external dynamic thermal environment and the internal crude oil variable physical property parameters, determining the overall heat loss of the storage tank, as shown in the following formula:
wherein Q tot Represents the overall heat loss of the tank, J; q roof 、Q wall 、Q bottom Respectively representing the heat loss of the top, the wall and the bottom of the storage tank; s roof 、S wall 、S bottom Respectively representing the surface areas of the top, wall and bottom of the storage tank 2 ，S roof ＝S bottom ＝πr 2 、S wall =2 pi rh, where r is the radius of the storage tank, m, h is the height of the storage tank, m;
respectively representing the calculated values of the heat flux density of the top, wall and bottom of the storage tank, W/m 2 (ii) a t is time, s.
In order to make the above-mentioned contents of the present invention more obvious and understandable, a storage tank in Daqing oil field is used as a secret research object, and the heat loss condition of the storage tank in the temperature rising process is predicted, and the following is detailed:
a10-million cubic meter floating roof storage tank of a certain oil depot in Daqing oil field has the diameter of the bottom of the tank being 80m and the height of the tank wall being 20m,the density of the oil product at 20 ℃ is 860kg/m 3 Viscosity of 4.94 pas and specific heat capacity of 2986.53J (kg. DEG C) -1 . The invention establishes a mathematical model of the boundary heat flux density loss of the crude oil storage tank on the basis of a multivariate nonlinear regression mathematical method to obtain a functional relation between the boundary heat flux density of the storage tank and the variable physical property parameters of an external dynamic thermal environment and internal crude oil, thereby realizing the prediction of the integral heat loss of the storage tank, and the specific method comprises the following steps:
the method comprises the following steps: a large crude oil storage tank is taken as a research object, and heat flow density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of the storage tank are tested on site by adopting a heat flow meter, a surface thermometer and a solar radiation measuring instrument to obtain various real-time change data, wherein the specific change is shown in a table.
Step two: taking out a small amount of crude oil in the tank, testing the change rules of the related physical properties such as density, viscosity, specific heat capacity and the like of the crude oil at different temperatures in the tank by using indoor testing instruments such as a petroleum density tester, a crude oil rheological property tester, a differential scanning calorimeter and the like, establishing a crude oil variable physical property model, obtaining the distribution conditions of crude oil temperature fields at different moments by using a temperature sensing probe arranged on a guide column in the tank, and determining the related physical property parameters such as the density, the viscosity, the specific heat capacity and the like of the crude oil.
The physical property parameters of the crude oil at different temperatures are measured by indoor experiment 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 crude oil variable physical property model, which is as follows:
the density of the crude oil is in negative linear correlation with the temperature, and the density is reduced along with the increase of the temperature of the crude oil; the viscosity of crude oil is changed exponentially and greatly influenced by temperature change, and the temperature of the crude oil is 20 ℃ as a demarcation pointThe viscosity is reduced rapidly at 2-20 ℃ from 36.46 Pa.s to 4.94 Pa.s; the viscosity decreases slowly at 20-50 ℃ and only changes by 4.58Pa s; the specific heat capacity of the crude oil shows the change of ascending before descending along with the temperature rising, and the specific heat capacity of the crude oil reaches the maximum value 2986.53J (kg-DEG C) at the temperature of 20 DEG C -1 Then the specific heat capacity of the crude oil begins to decrease along with the temperature rise, and the specific heat capacity of the crude oil at 44 ℃ is taken to be the minimum value 2228.12J (kg-DEG C) -1 The constructed crude oil variable physical property model comprises the following steps:
crude oil density:
ρ oil ＝870×[1-0.00061(t oil -20)]
crude oil specific heat capacity:
viscosity of crude oil:
the real-time change of the temperature of the crude oil in the tank is obtained according to the temperature sensing probe on the guide post in the tank, and the numerical value of the real-time change of the physical property of the crude oil in the tank at different temperatures is obtained by applying the variable physical property model of the built crude oil, and is shown in the following table:
step three: the heat loss of the storage tank boundary is comprehensively influenced by the external dynamic thermal environment and the physical property of the internal crude oil, and the data analysis result is influenced by considering the dimensional difference between the external dynamic thermal environment of the storage tank and the variable physical property parameters of the internal crude oil, so that the normalization processing is carried out on the data on the basis of not changing the original distribution rule of the data to eliminate the dimensional influence between indexes, so that the data indexes have comparability, and the measured data of the heat flux density and the influence factors of the top of the storage tank are taken as an example.
The heat flux density of the top of the storage tank measured by the experimental instrument is recorded as q i The atmospheric temperature, the solar radiant heat of the tank top, the density of crude oil, the viscosity of crude oil and the specific heat capacity of crude oil are taken as the influence factors of the heat flow density of the tank top and 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 influenced by five factors of atmospheric temperature, solar radiant 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 change of the heat flux density is not influenced by the atmospheric temperature and solar radiant heat and is only related to the density of the crude oil, the viscosity of the crude oil, the specific heat capacity of the crude oil and the soil temperature. The heat flux density at the bottom of the tank, the density of crude oil, the viscosity of crude oil, the specific heat capacity of crude oil and the temperature of soil can be recorded as follows in sequence:
{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 as an example of the influence factor of the heat flux density of the tank top, in the series { X } 1,i Find the maximum value max (X) 1,i ) At-22.15, minimum value max (X) 1,i ) To-30.15, whereby the values in the log are normalized:
similarly, the normalized values for the various terms of the series are shown in the following table:
from this, a new array of normalized atmospheric temperatures can be obtained:
similarly, a normalized array of other factors affecting the top heat flux density can be obtained:
in the same way, a new array of normalized influence factors of the heat flux density of the tank wall and the tank bottom is obtained;
normalized sequence of the pot wall heat flux density influencing factors:
the normalized number of the influence factors of the heat flow density of the tank bottom is as follows:
step four: and (3) performing correlation test on each influence factor and the heat flow density of the boundary of the storage tank, taking a correlation coefficient as a correlation judgment basis, when the absolute value of the correlation coefficient is more than 0.6, considering that the correlation between the two is obvious, and removing the influence factors with the absolute value of less than 0.6 to determine the main influence factors.
Taking the heat flux density of the top of the storage tank and the atmospheric temperature as an example to carry out correlation test, the specific calculation process is as follows:
mean value of
And (3) calculating:
standard deviation of
σ Q And (3) calculating:
covariance
And (3) calculating:
correlation coefficient of heat flux density and atmospheric temperature of storage tank top boundary
Through calculation, the correlation coefficient between the atmospheric temperature and the tank top heat flow density is-0.975, and the correlation between the atmospheric temperature and the tank top heat flow density is obvious. Similarly, the correlation coefficients of the tank top heat flow density, 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 are respectively as follows:
similarly, the correlation coefficients of the heat flux density of the tank wall and the tank bottom and respective influence factors are obtained through calculation:
the correlation coefficients of the heat flux density of the tank wall and the solar radiant heat, the density of crude oil, the viscosity of crude oil and the specific heat capacity of crude oil of the tank wall are respectively as follows:
the heat flow 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 temperatureThe correlation coefficients of the degrees are respectively:
usually, the absolute value of the correlation coefficient is more than 0.6, the two variables have strong correlation, and the fact that the variation of the heat flow density of the tank top, the tank wall and the tank bottom has obvious correlation with each corresponding influence factor can be seen, so that the variation condition of the heat flow density of the boundary of the storage tank can be represented by establishing a mathematical relationship among an external dynamic thermal environment, a crude oil variation parameter and the heat flow density of the boundary of the storage tank.
Step five: based on a multivariate nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the wax-containing crude oil storage tank is established to obtain a functional relation between the boundary heat flux density of the storage tank and the variable physical property parameters of the external dynamic thermal environment and the internal crude oil, and on the basis, the integral heat loss of the storage tank is determined by the product of the area and the time of the tank top, the tank wall and the tank bottom. Taking the establishment of the mathematical model of the heat flux density loss of the top of the storage tank as an example, the heat flux density of the top of the storage tank is taken as a dependent variable q i The atmospheric temperature, the solar radiation heat of the tank top, the density, the viscosity and the specific heat capacity of the crude oil are respectively used as independent variables
Tank top heat flux density loss regression model:
after multivariate nonlinear regression, the obtained coefficients and the coefficients of the power are respectively:
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 values of the external and internal influence factors are brought into the established regression model of the heat flux density loss of the top of the crude oil storage tank to obtain the calculated value of the heat flux density of the top of the storage tank, and the calculated value is recorded as
To verify the accuracy of the model created, the goodness of fit R to the model is required 2 And calculating, and when the goodness of fit is close to 1, indicating that the built tank top heat flow density 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 Comprises the following steps:
through calculation, the goodness of fit R 2 Close to 1, the established tank top heat flow density loss regression model is proved to be capable of completely reflecting the actual loss condition of the tank top heat flow density, so that the mathematical model of the tank top heat flow density loss can be determined as follows:
the mathematical models of the heat flux density loss of the wall and the bottom of the storage tank can be obtained by the same calculation steps
The tank wall heat flux density loss mathematical model has the goodness of fit of R 2 ＝0.988：
A tank bottom heat flux density loss mathematical model with a goodness of fit of R 2 ＝0.999：
After the heat flux density loss value of the storage tank boundary is calculated, a formula is used
The total heat Q dissipated to the outside from the boundary of the storage tank in one day in the heating 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:
heat loss at the bottom of the tank:
overall heat loss of the storage tank:
Q tot ＝Q roof +Q wall +Q bottom ＝32333419.01+8775623.93+20532223.87＝61641266.81(KJ)
the overall heat loss of the storage tank in one day is 61641266.81KJ through calculation, wherein the most serious heat loss at the top position of the storage tank is 32333419.01KJ; the heat loss at the tank wall position is 8775623.93KJ; the heat loss at the bottom of the tank was 20532223.87KJ.
In a comprehensive view, the large crude oil storage tank heat loss prediction method directly converts the problem that the heat transfer coefficient is influenced by internal and external factors and is dynamically changed in a theoretical calculation method into the functional relation between the storage tank boundary heat flow density, the external dynamic heat boundary condition and the crude oil variable property parameters, realizes the accurate prediction of the storage tank heat loss condition, and can provide a theoretical basis for the energy saving and consumption reduction work of oil field enterprises in the oil depot production management.
Claims (4)
1. A large crude oil storage tank heat loss prediction method is characterized by comprising the following steps:
the method comprises the following steps: carrying out field test on heat flux density, atmospheric temperature, soil temperature and solar radiation heat at different positions of the boundary of the 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 establishing a variable physical property model of the crude oil by using a petroleum density determinator, a crude oil rheological property determinator and a differential scanning calorimeter to test the change rules of the density, viscosity and specific heat capacity of the crude oil at different temperatures in the tank; measuring the distribution conditions of crude oil temperature fields at different moments by a temperature sensing probe arranged on a guide post in a large crude oil storage tank, and determining the density, viscosity and specific heat capacity of the crude oil;
step three: the heat loss of the boundary of the storage tank is comprehensively influenced by the physical properties of the external dynamic thermal environment and the internal crude oil, the data analysis result is influenced by considering the different dimensions of the external dynamic thermal environment and the internal crude oil variable physical property parameter influence factors of the storage tank, 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 have comparability;
step four: performing correlation test on each influence factor and the boundary heat flux density of the storage tank independently, taking a correlation coefficient as a correlation judgment basis, and eliminating the influence factors with the absolute values below 0.6 when the absolute values of the correlation coefficients are above 0.6 and the correlation between the correlation coefficients and the storage tank is obvious;
in the formula (I), the compound is shown in the specification,
a correlation coefficient representing the jth influencing factor and the heat flow density of the boundary of the storage tank, wherein when the absolute value of the coefficient is more than 0.6, the correlation between the two variables is obvious, and otherwise, the correlation between the two variables is weak;
representing the covariance of the jth influencing factor and the heat flux density at the boundary of the storage tank;
standard deviation, σ, representing the jth influencing factor q Representing the standard deviation of the heat flux density of the boundary of the storage tank;
represents the average value of the jth influencing factor;
an average value representing the heat flux density at the boundary of the tank;
step five: based on a multivariate nonlinear regression mathematical method, a mathematical model of the boundary heat flux density loss of the wax-containing crude oil storage tank is established, and the functional relation between the boundary heat flux density of the storage tank and the external dynamic thermal environment and the internal crude oil variable physical property parameters is obtained, and the product of the area and the time of the tank top, the tank wall and the tank bottom is used for determining the integral heat loss of the storage tank.
2. The method of predicting heat loss of a large crude oil storage tank according to claim 1, wherein: the crude oil metamorphism model is as follows:
crude oil density:
ρ oil ＝ρ 20 [1-ξ(t oil -20)]
in the formula, ρ oil Is crude oil density, kg.m -3 ；ρ 20 Crude oil density at 20 ℃ kg. M -3 ；t oil Crude oil temperature, deg.C; xi is a regression coefficient;
crude oil specific heat capacity:
in the formula, c oil Is the specific heat capacity of crude oil, J (kg. Degree. C.) -1 ；b 0 、b 1 、b 2 、b 3 、b 4 Each regression coefficient is taken as a reference value;
viscosity of crude oil:
in the formula, mu oil Pa · s, K, m is the regression coefficient for crude oil dynamic viscosity.
3. The method of predicting heat loss of a large crude oil storage tank according to claim 2, wherein: the third step is specifically as follows:
taking the heat flux density of the boundary of the storage tank as a reference sequence and taking external and internal influence factors as a comparison sequence, wherein the reference sequence is formed by the heat flux density of the boundary of the storage tank directly measured by a measuring instrument and is recorded as { q } i I =1,2,3 …, n; the comparison sequence is composed of the dynamic thermal environment outside the storage tank and the internal crude oil variable physical property parameters and is recorded as { X } j,i J =1,2,3,4,5,6, i =1,2,3 …, n; x j,i Showing the value of the jth influencing factor at the ith boundary heat flow density, and recording the atmospheric temperature as { X } 1,i Solar radiation heat meterAs { X 2,i Recording the density of crude oil as { X } 3,i Recording the viscosity of crude oil as { X } 4,i Taking specific heat capacity of crude oil as { X } 5,i Recording the soil temperature as { X } 6,i }；
In order to eliminate the influence of different dimensions and magnitude among the influence factors on the data analysis result, on the basis of the original data distribution rule, normalization processing is adopted to enable the data change interval to be [0,1], which is shown as the following formula:
in the formula (I), the compound is shown in the specification,
expressing the value of the heat flux density of the ith boundary after the jth influencing factor is subjected to normalization processing; x j,i Representing the value of the jth influencing factor at the ith boundary heat flow density; max (X) j,i ) The maximum value, min (X), among the comparison series of the respective influencing factors j,i ) The minimum value among the comparison series of the influence factors is represented;
the new number series obtained by normalization processing of the comparative number series of the influence factors comprises the atmospheric temperature recorded as
Solar radiation heat is recorded as
Crude oil density is recorded as
Crude oil viscosity is recorded as
The specific heat capacity of crude oil is recorded as
The soil temperature is recorded as
4. The large crude oil storage tank heat loss prediction method of claim 3, wherein: the regression model of the heat flux density loss of the boundary of the crude oil storage tank is shown as the following formula:
wherein q is i Is a measure of the heat flux density at the i-th tank boundary, W/m 2 ；
The value of the heat flux density at the ith boundary after the jth influencing factor is normalized is represented as follows: a is 0 、a 1 …a j And k is a coefficient of the power of an influencing factor in the regression model.
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