Crude oil heating and conveying process path selection method

Abstract

The invention discloses a crude oil heating and conveying process path selection method. The method comprises the following steps that crude oil physical property parameters, storage tank and heating equipment parameters, oil receiving and dispatching plans and environment temperature data are collected; establishing a storage tank temperature drop model to dynamically evaluate an oil temperature change trend, if the predicted oil temperature is lower than a condensation point, directly adopting a pre-tank heating oil receiving and dispatching process, otherwise, respectively constructing energy consumption cost optimization models of the pre-tank heating oil receiving and dispatching process and the post-tank heating oil receiving and dispatching process; dynamically adjusting algorithm parameters based on an adaptive differential evolution algorithm, and solving a comprehensive cost optimal solution of the two processes; and comparing an optimization result to determine a heating conveying path with better economical efficiency. According to the method, the temperature drop model and a multi-parameter optimization mechanism are combined, the limitation of a traditional empirical method is broken through, the safety boundary is quantified, the energy consumption is accurately evaluated, the risk of'tank coagulation 'is avoided, the global optimization of the process path is realized, a decision basis with safety and economy is provided for an oil depot, and the operation efficiency and adaptability of a crude oil storage and transportation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crude oil storage and transportation, and particularly to a method for selecting a process path for heating and transporting crude oil. Background Art

[0002] During the production operation of crude oil storage and transportation, in order to ensure the safe and stable flow of crude oil in pipelines and storage tanks and prevent the occurrence of "pipe blockage due to coagulation" and "tank blockage due to coagulation" phenomena, it is necessary to heat the crude oil. Currently, there are two methods. One is that the crude oil is heated by a heating furnace and then enters the storage tank for storage, and is dispatched when there is a dispatching task. The other is that the crude oil directly enters the storage tank, and the crude oil leaving the tank is heated again when the crude oil is transported out. Each of these two processes has its own advantages and disadvantages. The "pre-tank heating" receiving and dispatching process can ensure the storage safety of crude oil in the storage tank, but most of the energy is dissipated in the air in the form of heat energy, and the energy is not effectively utilized. The "post-tank heating" receiving and dispatching process can adjust the setting of the furnace outlet temperature according to the downstream boundary conditions, but it is necessary to ensure the storage safety of crude oil in the storage tank. Currently, there is a lack of scientific and effective optimization methods for the selection of these two processes, and it often relies on empirical judgment, resulting in the economy and efficiency of crude oil storage and transportation being difficult to reach the best state. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for selecting a process path for heating and transporting crude oil, which selects a suitable process path for heating and transporting crude oil according to different transportation conditions by determining key variables, establishing an optimization model and comparing the results, so as to improve the economy and efficiency of crude oil storage and transportation.

[0004] The present invention is realized by the following technical solutions:

[0005] The above-mentioned method for selecting a process path for heating and transporting crude oil includes the following steps:

[0006] Step 1: Determine the key parameters of the crude oil heating and transportation process, including the basic physical property parameters of the crude oil, the key equipment parameters, and the crude oil receiving and dispatching plan parameters;

[0007] Step 2: Calculate the temperature drop under the maximum storage period in the tank when the oil product directly enters the tank, and determine whether the temperature in the storage tank meets the pour point;

[0008] If the oil temperature in the tank is higher than the pour point after the maximum storage period of the crude oil, go to Step 3; otherwise, directly adopt the "pre-tank heating" receiving and dispatching process to avoid the occurrence of "tank blockage due to coagulation" when the crude oil is stored in the tank;

[0009] Step 3: Taking the lowest comprehensive cost as the goal, establish an optimization model for the "pre-tank heating" receiving and dispatching process and an optimization model for the "post-tank heating" receiving and dispatching process respectively, and determine the constraint conditions and decision variables of the optimization model;

[0010] Step 4: Combine the characteristics of the constructed model and select the adaptive differential evolution method for model solution;

[0011] Step 5: Compare the comprehensive costs of the "pre-tank heating" and "post-tank heating" oil receiving and sending process optimization models under the same working conditions, and select the appropriate crude oil heating and transportation process path.

[0012] A method for selecting a crude oil heating and transportation process path. In Step 1, the basic physical property parameters of the crude oil that need to be determined include the initial temperature T of the crude oil before entering the heating furnace in , the specific heat capacity c of the crude oil, and the pour point T of the crude oil c ; the parameters of the key equipment that need to be determined include the heat transfer coefficient K of the crude oil storage tank and the thermal efficiency η of the heating furnace; clarify the crude oil receiving and sending plan, including the throughput and the requirements for the oil sending cycle, and determine the maximum cycle t for the crude oil to be stored in the tank max .

[0013] In Step 2, it is necessary to calculate the temperature drop of the crude oil during the maximum storage period in the tank according to the storage tank temperature drop formula. The storage tank temperature drop calculation formula is as follows:

[0014] T ed = T h +(T st - T h )e -KFt / (Gc) (1)

[0015] In the formula, t is the storage time of the crude oil in the storage tank, s; T st is the initial temperature of the crude oil in the storage tank, °C; T ed is the temperature of the crude oil after a certain storage period, °C; T h is the ambient temperature around the storage tank, °C; G is the mass of the crude oil stored in the storage tank, kg; c is the specific heat capacity of the stored crude oil, J / (kg·°C); F is the surface area of the storage tank, m 2 ; K is the total heat transfer coefficient of the storage tank, W / (m 2 ·°C);

[0016] If the oil temperature in the tank drops below the pour point, directly adopt the "pre-tank heating" oil receiving and sending process; otherwise, go to Step 3.

[0017] The specific process of Step 3 is as follows: Taking the minimum comprehensive cost as the goal, establish the optimization models of the two processes respectively;

[0018] S31. Establish the optimization model of the "pre-tank heating" oil receiving and sending process: The objective function is minF = F1 + F2 + F3, where F1 is the fuel cost, F2 is the equipment maintenance cost, and F3 is the equipment operation cost; The decision variables include the outlet temperature of the heating furnace and the heating furnace heat load; The fuel cost is In the formula, SP is the fuel price, Yuan / m 3 ; G is the flow rate into the heating furnace, t / d; c0 is the specific heat capacity of crude oil, KJ / (kg·℃); T o and T i are the outlet temperature and inlet temperature of the heating furnace respectively, ℃; Q r is the calorific value of fuel combustion, KJ / kg; η is the thermal efficiency of the heating furnace;

[0019] S32, determine the constraint conditions of the "pre-tank heating" oil receiving and sending process optimization model:

[0020] a) Storage tank pour point constraint: Considering the safe storage of crude oil, the temperature of the oil stored in the storage tank needs to be higher than the pour point of the crude oil; at the same time, due to the need to reduce heat dissipation loss and evaporation loss, the storage temperature of the crude oil should not be too high; in summary, the crude oil temperature in the storage tank needs to be controlled within a certain range; the constraint condition is T w,min ≤T w ≤T w,max , where T w,min and T w,max are the lower limit and upper limit of the storage temperature of the crude oil in the storage tank respectively; T w is the storage temperature of the crude oil in the storage tank;

[0021] b) Heating furnace heat load constraint: During the production operation process, considering safety, the heat load of the heating furnace cannot exceed a certain limit; and there is an efficient operating range for the heating furnace. Generally speaking, the heat load of the heating furnace needs to be controlled within a certain reasonable range; the constraint condition is Q min ≤Q≤Q max , where Q min and Q max are the lower limit and upper limit of the heating furnace heat load respectively, KW; Q is the actual working heat load of the heating furnace, KW;

[0022] c) Thermal constraint of the external pipeline: The crude oil using the "pre-tank heating" oil receiving and sending process is stored in the storage tank. When there is a downstream oil sending demand, it needs to flow out of the storage tank for external transportation; the lower limit of the external transportation temperature is determined by the temperature requirements downstream of the external pipeline. At the same time, there is also an upper limit for the external transportation temperature to avoid changing the properties of the crude oil and affecting the anti-corrosion and heat insulation performance of the external pipeline due to too high an external transportation temperature; therefore, there is a certain constraint range for the external transportation temperature of the crude oil; the constraint condition is T R,min ≤T R ≤T R,max , where T R,min and T R,max are the lower limit and upper limit of the external transportation temperature respectively; T R is the external transportation temperature;

[0023] d) Constraint on the throughput of the external pipeline: The total throughput of the crude oil pipeline is equal to the crude oil flow rate entering each heating furnace;

[0024] S33. Establish the "post-tank heating" oil receiving and dispatching process model: The objective function is minF' = F1' + F2' + F3', where F1' is the raw material cost consumed by the heating furnace, F2' is the equipment maintenance cost, and F3' is the equipment operation cost; The decision variables include the outlet temperature of the heating furnace and the heat load of the heating furnace;

[0025] S34. Determine the constraint conditions of the "post-tank heating" oil receiving and dispatching process optimization model: including the freezing point constraint of the storage tank, the heat load constraint of the heating furnace, the thermal constraint of the external pipeline, and the throughput constraint of the external pipeline.

[0026] In the above step 4, the established optimization model has the characteristics of multiple constraints and strong non-linearity. The adaptive differential evolution method is used to solve the model, and the specific steps are as follows:

[0027] S41. Determine the decision variables, set the population parameters, and generate the initial population. Suppose there are n decision variables X = (x1, x2,... x n ) in the optimization problem, and the value range of each variable is [x i,min , x i,max , i = 1, 2... n; Set the population size NP, the range of the mutation factor F [F min , F max , the range of the crossover probability [Cr min , Cr max , the maximum number of evolutions G max and the adaptive adjustment parameters α, β, etc. Randomly generate NP individuals within the value range of each variable according to the uniform distribution to form the initial population;

[0028] S42. Fitness evaluation: Suppose the objective function is f(x), for each individual in the population (g represents the current iteration number), calculate its fitness Check the constraint conditions. If the constraint conditions are not satisfied, the penalty function method can be used to correct the fitness to obtain the penalized fitness value

[0029] S43. Mutation operation: For each individual in the population Adaptively adjust the mutation factor F according to its fitness value j , and at the same time randomly select three different individual indices r1, r2, r3 to generate the mutation vector To ensure that the variables after mutation are within the feasible domain, boundary processing is performed on the variables that exceed the variable value range [x i,min , x i,max ; If then let If then let

[0030] S44, Crossover operation: For each individual in the population Adaptively adjust the crossover probability Cr according to the current iteration generation g p The calculation formula is: For each individual and its mutation vector Generate a trial vector

[0031] S45, Selection operation: Compare the penalty-adjusted fitness values of each individual and its corresponding trial vector If then let Otherwise, let

[0032] S46, Termination condition judgment: When the maximum number of evolutions G max is reached, terminate the algorithm operation, and output the individual with the minimum fitness value in the current population as the approximate optimal solution.

[0033] In the fifth step, after using the adaptive differential evolution method to solve the model, compare the comprehensive costs obtained by solving the two process models under the same working conditions, and select the crude oil heating and transportation process with a smaller cost, which can better achieve the energy-saving effect.

[0034] Due to the adoption of the above technical solutions, the present invention can achieve the following beneficial effects:

[0035] (1) A method for selecting a process path for crude oil heating and transportation provided by the method of the present invention, by establishing an optimization model for the "pre-tank heating" and "post-tank heating" receiving and dispatching oil processes, and combining with the adaptive differential evolution algorithm, dynamically adjusting the mutation factor and crossover probability parameters, realizing the global optimization of the heating temperature, heat load and comprehensive cost; this method introduces a parameter adaptive mechanism within the algorithm framework, solves the problems of traditional empirical methods relying on manual trial calculations and slow convergence speed, improves the solution efficiency of the optimization model, and at the same time automatically avoids "condensing tanks" through constraint conditions, improving the stability and economy of the process path selection.

[0036] (2) A method for selecting a process path for heating and transporting crude oil provided by the method of the present invention comprehensively considers the collaborative effects of multiple factors such as the physical properties of crude oil (such as pour point, specific heat capacity), equipment parameters (heating furnace efficiency, storage tank heat insulation performance), ambient temperature, and oil receiving and sending plans, and constructs a dynamic correlation mechanism between the temperature drop model of the crude oil storage tank and the heat load; this method can predict the temperature drop trend of the storage tank, the energy consumption of the heating furnace, and the comprehensive cost under different process paths, solves the defect that the prior art cannot quantitatively analyze energy loss and safety boundaries, provides a decision-making basis that takes into account both economy and safety for the crude oil receiving and sending site, reduces unnecessary heating frequencies, and extends the service life of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of a method for selecting a process path for heating and transporting crude oil provided by an embodiment of the present invention.

[0038] Figure 2 It is a flowchart of an optimization model solving algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0040] As Figure 1 shown, the present invention provides a method for selecting a process path for heating and transporting crude oil, including the following steps:

[0041] Step 1: Determine the key parameters for selecting the process path for heating and transporting crude oil.

[0042] In a specific embodiment, the key parameters include the density of crude oil, the specific heat capacity of crude oil, the pour point of crude oil, the total heat transfer coefficient of the storage tank, the maximum storage period of crude oil in the storage tank, and the thermal efficiency of the heating furnace. The determination method of the key parameters is as follows:

[0043] (1) Calculation of the density of crude oil

[0044]

[0045] In the formula is the relative density of the oil product to water at temperature t; is the relative density of crude oil at 20°C; ζ is the temperature coefficient;

[0046] (2) Calculation of the specific heat capacity of crude oil

[0047] According to the wax precipitation point temperature T of the oil product SL and the maximum specific heat capacity temperature T cmax , the c-T relationship of the crude oil can be divided into three intervals.

[0048] ① T > T SL . Within this range, as the temperature increases, the specific heat capacity of the crude oil rises slowly, and the specific heat capacity is calculated by the following formula:

[0049]

[0050] In the formula, c y is the specific heat capacity of the oil product, KJ / (kg·℃); is the relative density of the oil product at 15℃; T is the temperature of the oil product, ℃;

[0051] ② T SL > T > T cmax . When the temperature of the crude oil decreases, the specific heat capacity increases sharply, and the required specific heat capacity is calculated by the following formula:

[0052] c Ly = 4.186 - Ae nT (4)

[0053] In the formula, c Ly is the specific heat capacity of the waxy crude oil below the wax precipitation temperature and above T cmax , kJ / (kg·℃); A and n are constants, which vary with different crude oils;

[0054] ③ T cmax > T > 0℃. When the temperature of the crude oil decreases, the specific heat capacity gradually decreases again. The specific heat capacity calculation formula is:

[0055] c Ly = 4.186 - Be -mT (5)

[0056] In the formula, c Ly is the specific heat capacity of the waxy crude oil below T cmax , kJ / (kg·℃); B and m are constants, which vary with different crude oils; (3) Pour point of the crude oil: The pour points of different types of oil products vary greatly, and the pour points of different oil products need to be determined through experiments; In a specific embodiment, the pour points of different oil products measured through experiments are shown in Table 1;

[0057] Table 1 Crude oil pour point test results

[0058]

[0059] (4) Calculation of the overall heat transfer coefficient of the storage tank

[0060] In the process of calculating the temperature drop of crude oil storage tanks, the heat transfer coefficient of the storage tank is a key parameter for the calculation, and its calculation formula is as follows:

[0061]

[0062] In the formula, F ding is the surface area of the tank top, m 2 ; F bi is the surface area of the tank wall, m 2 ; F di is the bottom area of the tank, m 2 ; K ding is the heat transfer coefficient of the tank top, W / (m 2 ·℃); K bi is the heat transfer coefficient of the tank wall, W / (m 2 ·℃); K di is the heat transfer coefficient of the tank bottom, W / (m 2 ·℃);

[0063] (5) The maximum storage period of crude oil in the storage tank: When the crude oil directly enters the storage tank, if the oil temperature in the storage tank drops below the freezing point during the maximum storage period of the crude oil, the phenomenon of "tank freezing" will occur, seriously affecting the safe operation on site. Therefore, it is necessary to determine the maximum storage period of the crude oil in the storage tank by counting the receiving and sending cycles of the on-site crude oil storage tanks;

[0064] In a specific embodiment, according to the statistics and sorting of the receiving and sending cycles of the on-site crude oil, it is determined that the maximum storage days of the crude oil in the tank is 10 days;

[0065] (6) The actual thermal efficiency of the heating furnace

[0066] There is an efficient interval for the thermal efficiency of the heating furnace. In order to clarify the relationship between the thermal efficiency of the heating furnace and the heat load, the characteristic equation of the heating furnace is fitted, and the fitting relationship of the thermal efficiency of the heating furnace is shown by the following formula:

[0067]

[0068] In the formula, η is the thermal efficiency of the heating furnace; Q i is the heat load of the heating furnace, KW; x, y, and z are the fitting coefficients of the characteristic equation;

[0069] In a specific embodiment, the fitting result of the characteristic equation of the heating furnace is:

[0070] η = -3×10 -4 Q 2 +0.098Q + 9.2 (8)

[0071] In a specific embodiment, the key parameters include ambient temperature, crude oil throughput, specific heat capacity, density, pour point, heat transfer coefficient of the storage tank, and maximum storage period of the crude oil in the storage tank. The parameters are shown in Table 2.

[0072] Table 2 Key Parameters for Selecting the Process Route of Crude Oil Heating and Transportation

[0073]

[0074] Step 2: Calculate the temperature drop of the crude oil directly entering the tank during the maximum storage period in the tank: Determine whether the oil temperature in the tank will drop below the pour point of the crude oil during the maximum storage period; if it drops below the pour point, directly adopt the "pre-tank heating" receiving and shipping process and calculate the required energy consumption cost; otherwise, go to Step 3 to compare the total costs of the "pre-tank heating" and "post-tank heating" processes and select the appropriate receiving and shipping process. The formula for calculating the temperature drop of the storage tank is:

[0075] T ed = T h +(T st - T h )e -KFt / (Gc) (9)

[0076] In the formula, t is the storage time of the crude oil in the storage tank, s; T st is the initial temperature of the crude oil in the storage tank, °C; T ed is the temperature of the crude oil after a certain storage period, °C; T h is the ambient temperature around the storage tank, °C; G is the mass of the crude oil stored in the storage tank, kg; c is the specific heat capacity of the stored crude oil, J / (kg·°C); F is the surface area of the storage tank, m 2 ; K is the total heat transfer coefficient of the storage tank, W / (m 2 ·°C).

[0077] Step 3: Establish an optimization model for the "pre-tank heating" and "post-tank heating" receiving and shipping processes, and determine the decision variables and constraints of the optimization model.

[0078] Establish an optimization model for the "pre-tank heating" receiving and shipping process:

[0079] minF = F1 + F2 + F3 (10)

[0080] In the formula, F1 is the fuel cost, F2 is the equipment maintenance cost, and F3 is the equipment operation cost;

[0081] The fuel cost is:

[0082] In the formula, S P is the fuel price, yuan / m 3; G is the flow rate entering the heating furnace, t / d; c0 is the specific heat capacity of crude oil, KJ / (kg·°C); T o and T i are the outlet temperature and inlet temperature of the heating furnace respectively, °C; Q r is the calorific value of fuel combustion, KJ / kg; η is the thermal efficiency of the heating furnace;

[0083] The "post-tank heating" oil receiving and shipping process model can be expressed as: minF' = F1' + F2' + F3', where F1' is the raw material cost consumed by the heating furnace, F2' is the equipment maintenance cost, and F3' is the equipment operation cost;

[0084] The decision variables for determining the optimization model are the outlet temperature of the heating furnace and the heating load of the heating furnace;

[0085] The constraint conditions of the optimization model include the freezing point constraint of the storage tank, the heating load constraint of the heating furnace, the thermal constraint of the external transmission pipeline, and the flow rate constraint of the external transmission pipeline;

[0086] In a specific embodiment, the freezing point of the crude oil is 2°C, the rated heating load of the heating furnace is 4000KW, and the lower limit of the crude oil outlet temperature from the station is 20°C.

[0087] Step Four: Use the adaptive differential evolution method to solve the optimization model.

[0088] Use adaptive differential evolution to optimize the established model, set the population size NP = 50, the range of the mutation factor F is [0.5, 1.5], the range of the crossover probability Cr is [0.3, 0.9], and the maximum number of evolutions G max = 300. Substitute the algorithm optimization results into the decision variables to obtain the optimization schemes for the "pre-tank heating" and "post-tank heating" oil receiving and shipping processes, and the results are shown in Table 3.

[0089] Step Five: Compare the solution results of the optimization models for the "pre-tank heating" and "post-tank heating" oil receiving and shipping processes, compare the total costs of the two, and select a more suitable and economical crude oil heating and transportation process path.

[0090] In a specific embodiment, use the crude oil heating and transportation process path selection method of the present invention to select it; in this example, the solution results of the optimization model are shown in Table 3:

[0091] Table 3 Comprehensive costs of "pre-tank heating" and "post-tank heating" oil receiving and shipping paths

[0092]

[0093] As can be seen from Table 3, under two different ambient temperatures, the comprehensive cost of the "post-tank heating" oil receiving and dispatching process is less than that of the "pre-tank heating" oil receiving and dispatching process. Therefore, the "post-tank heating" oil receiving and dispatching process is the best choice for the two working conditions.

[0094] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for selecting a crude oil heating and transportation process path, characterized in that: The following steps are involved: Step 1: Determine the key parameters of the crude oil heating and transportation process, including the basic physical properties of crude oil, key equipment parameters, and crude oil delivery and receipt plan parameters; Step 2: Calculate the temperature drop of the oil directly entering the tank during the maximum storage period in the tank, and determine whether the temperature in the tank meets the freezing point; If the oil temperature in the tank is higher than the freezing point after the maximum storage period, then go to step 3; otherwise, directly adopt the "pre-tank heating" oil receiving and sending process to avoid the "tank freezing" phenomenon when the crude oil is stored in the tank; Step 3: With the goal of minimizing the overall cost, establish the "pre-tank heating" oil receiving and sending process optimization model and the "post-tank heating" oil receiving and sending process optimization model respectively, and determine the constraints and decision variables of the optimization model; Step 4: Based on the characteristics of the constructed model, the adaptive differential evolution method is used to solve the model; Step 5. Compare the comprehensive costs of the "pre-tank heating" and "post-tank heating" oil receiving and sending process optimization models under the same working conditions, and select the appropriate crude oil heating and transportation process path.

2. The crude oil heating and transportation process path selection method according to claim 1 is characterized in that: In step 1, the basic physical properties of the crude oil include the initial temperature of the crude oil before entering the heating furnace, the specific heat capacity of the crude oil, and the freezing point of the crude oil; the key equipment parameters include the heat transfer coefficient of the crude oil storage tank and the thermal efficiency of the heating furnace; the crude oil receiving and sending plan parameters include the transmission volume, the oil sending cycle requirements, and the maximum storage period of the crude oil in the tank.

3. The crude oil heating and transportation process path selection method according to claim 1 is characterized in that: In step 2, the temperature drop of crude oil during the maximum storage period in the tank is calculated according to the tank temperature drop formula. The tank temperature drop calculation formula is: T ed =T h +(T st -T h )e -KFt / (Gc) (1) Where, t is the storage time of crude oil in the storage tank, s; T st is the initial temperature of the crude oil in the tank, °C; T ed T is the temperature of crude oil after a period of storage, ℃; h is the ambient temperature around the storage tank, ℃; G is the mass of crude oil stored in the storage tank, kg; c is the specific heat capacity of the stored crude oil, J / ( kg℃ ); F is the surface area of ​​the tank, m 2 ; K is the total heat transfer coefficient of the tank, W / (m 2 ·℃).

4. The crude oil heating and transportation process path selection method according to claim 1 is characterized in that: In step 3, with the goal of minimizing the comprehensive cost, the "pre-tank heating" and "post-tank heating" oil receiving and sending process optimization models are established as follows: minF=F1+F2+F3 (2) minF'=F1'+F2'+F3' (3) Where F1 is the fuel cost, F2 is the equipment maintenance cost, and F3 is the equipment operating cost; F1' is the cost of raw materials consumed by the heating furnace, F2' is the equipment maintenance cost, and F3' is the equipment operating cost; the fuel cost calculation formula is: Where S P is the fuel price, yuan / m 3 ; G is the flow rate into the heating furnace, t / d; c0 is the specific heat capacity of crude oil, KJ / (kg·℃); T o , T i are the outlet temperature and inlet temperature of the heating furnace, ℃; Q r is the combustion calorific value of the fuel, KJ / kg; η is the thermal efficiency of the heating furnace.

5. The crude oil heating and transportation process path selection method according to claim 1 is characterized in that: In step three, the constraints include storage tank freezing point constraint, heating furnace heat load constraint, external transmission pipeline thermal constraint, and external transmission pipeline thermal constraint.

6. The crude oil heating and transportation process path selection method according to claim 1 is characterized in that: The steps of using adaptive differential evolution to solve the optimization model in step 4 are as follows: S41, determine the decision variables, set the population parameters, and generate the initial population: Assume that there are n decision variables X=(x1,x2,…x n ), each variable has a corresponding value range [x i,min ,x i,max ], i = 1, 2…n; set the population size NP, the range of the variation factor F [F min ,F max ], crossover probability range [Cr min ,Cr max ], Maximum number of evolutions G max And adaptively adjust parameters α, β, etc.; within the value range of each variable, randomly generate NP individuals according to uniform distribution to form the initial population; S42, fitness evaluation: Let the objective function f(x) be (g represents the current number of iterations), calculate its fitness At the same time, check the constraints. If the constraints are not met, the penalty function method can be used to correct the fitness and obtain the fitness value after the penalty. S43, mutation operation: For each individual in the population Adaptively adjust the mutation factor F according to its fitness value j , and randomly select three different individual indexes r1, r2, r3 to generate mutation vectors To ensure that the mutated variables are within the feasible domain, the variables that exceed the value range [x i,min ,x i,max ] variables are processed by boundaries; if Then like Then S44, crossover operation: For each individual in the population Adaptively adjust the crossover probability Cr according to the current iteration number g p The calculation formula is: For each individual and its mutation vector Generate test vector S45, Select operation: Compare each individual and its corresponding test vector The fitness value after penalty, if Then On the contrary, let S46, termination condition judgment: reaching the maximum number of evolution times G max After that, the algorithm is terminated and the individual with the smallest fitness value in the current population is output as the approximate optimal solution.

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