United station process parameter optimization method and system based on nomogram technology

Abstract

The invention provides a united station process parameter optimization method and system based on a Nomogram technology, and the method comprises the steps: determining to-be-analyzed equipment related to energy consumption, and carrying out the thermodynamic calculation to obtain a single-equipment sub-energy-flow model; splicing the sub-energy flow models based on the process flow of the united station to obtain an overall energy flow model of the united station system; performing sensitivity analysis based on the overall energy flow model to determine target analysis parameters; performing variable-working-condition energy consumption simulation operation by taking the target analysis parameter as an adjustment parameter, and drawing a corresponding Nomoh map based on the operation parameter and an energy consumption result; and obtaining process parameter curve distribution of actual operation of the united station, and comparing and analyzing the process parameter curve distribution with the curve distribution of the nomogram to realize abnormal condition identification, abnormal energy consumption prediction and improvement measure formulation. By adopting the scheme, the defects of complex calculation process and insufficient intuition in the prior art can be overcome, and rapid and accurate judgment and adjustment of parameters can be conveniently realized on site.

Description

Technical Field

[0001] The present invention relates to the field of energy and power optimization technology, and in particular to a method and system for optimizing process parameters of a joint station based on nomogram technology. Background Art

[0002] Currently, energy consumption analysis for joint power plants mostly uses a three-link approach. This "three-link" energy structure method is a theoretical analysis method suitable for energy flow analysis in complex energy-using systems. This method considers energy conversion, utilization, and recovery, and combines the joint power plant's process flow and process node parameters to calculate evaluation indicators for each link. In addition, commonly used methods for energy consumption analysis and evaluation of joint power plants include system energy flow diagrams and DEA methods. However, these techniques not only require a large computational workload, involve large formulas, and are prone to errors, but also fail to dynamically evaluate and predict the performance of joint power plants.

[0003] The energy consumption of a joint station has a direct impact on its operational status and investment costs. The factors influencing a joint station's energy consumption per ton of oil are complex and interrelated. Simulation methods are unintuitive and cannot quickly and accurately quantify and evaluate the impact on product improvement and design optimization.

[0004] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method for optimizing process parameters of a joint station based on nomogram technology. After the method identifies the equipment to be analyzed that is related to energy consumption, thermal calculations are performed separately to obtain a sub-energy flow model of a single device; based on the process flow of the joint station, each sub-energy flow model is spliced ​​to obtain an overall energy flow model of the joint station system; sensitivity analysis is performed based on the overall energy flow model to determine the target analysis parameters; variable working condition energy consumption simulation calculations are performed using the target analysis parameters as adjustment parameters, and the corresponding nomogram is drawn based on the calculation parameters and energy consumption results; the curve distribution of the process parameters of the actual operation of the joint station is obtained and compared with the curve distribution of the nomogram to realize abnormal situation identification, abnormal energy consumption prediction and improvement measures formulation. The adoption of this solution can overcome the defects of the existing technology of complex calculation process and lack of intuitiveness, and facilitate the rapid and accurate judgment and adjustment of parameters on site; preferably, in one embodiment, the method includes:

[0006] Step S10: Identify the equipment related to the overall energy consumption in the joint station to be studied as the equipment to be analyzed;

[0007] Step S20: Perform thermal calculations on each device to be analyzed to obtain a sub-energy flow model of a single device object;

[0008] Step S30: splicing the sub-energy flow models of the single equipment objects based on the process flow of the joint station to obtain the overall energy flow model of the joint station system;

[0009] Step S40: performing sensitivity analysis based on the overall energy flow model to determine equipment-related parameters that meet the set requirements as target analysis parameters;

[0010] Step S50: using the target analysis parameters as adjustment parameters, performing variable working condition energy consumption simulation calculation based on the overall energy flow model, recording different levels of operation parameters and energy consumption results, drawing and saving the corresponding nomogram;

[0011] Step S60: Obtain the process parameter curve distribution of the actual operation of the joint station and compare it with the curve distribution of the nomogram to achieve abnormal situation identification, abnormal energy consumption prediction and improvement measures formulation.

[0012] Optionally, in one embodiment, in step S10, the main energy-consuming equipment to be analyzed in the joint station includes one or more of the following equipment: a three-phase separator, a heat exchanger, a heating furnace, a settling tank, a stabilization tower, and a stabilization pump.

[0013] Furthermore, in one embodiment, in step S20, the process of performing thermal calculation for each device to be analyzed includes:

[0014] The energy consumption data of each device is calculated, and the energy flow model between the energy consumption and the device inlet parameters is fitted to obtain the sub-energy flow model of a single device.

[0015] In one embodiment, in step S20, the process of calculating the energy consumption data of each device includes:

[0016] Based on the thermal power, type and parameters of the heated medium, fuel information, furnace structure and burner type data, the area of ​​each heat transfer element, main structural dimensions, fuel consumption, air volume and flue gas volume data are calculated and determined.

[0017] Preferably, in one embodiment, step S20 further includes: calculating the thermal efficiency, fuel consumption, air volume, flue gas volume, and temperature data of the inlet and outlet media of each heat transfer element of the heating furnace based on the existing structural dimensions, thermal power, type and parameters of the heated medium, and fuel information data, so as to verify that the heating furnace achieves the required thermal power and characteristics of the heated medium parameters.

[0018] Furthermore, in one embodiment, step S20 also includes: after completing a single round of thermal calculation process, changing the operating conditions of the participating computing equipment, performing dynamic thermal simulation on different operating states, reflecting the impact of changes in the main input parameters on the output parameters of interest, and analyzing the relationship between them to construct a corresponding sub-energy flow model.

[0019] , used to perform fitting calculations on the energy flow parameters of device objects.

[0020] Optionally, in one embodiment, if the participating computing device is a heating furnace, the corresponding sub-energy flow model is constructed according to the following strategy:

[0021] The outlet temperature calculation model is:

[0022]

[0023] in:

[0024]

[0025]

[0026] α o =0.13λ w (Gr w Pr w ) 0.33 / ro

[0027] Where, t2 is the outlet temperature, °C; t l is the water bath temperature, °C; t1 is the inlet temperature, °C; subscripts l and g represent the mixed liquid and gas, respectively; λ is the thermal conductivity, W / m °C; r i , r o Re is the Reynolds number; Pr is the Prandtl number; μ is the viscosity in Pa·s; x is the water content; ρ is the density in kg / m 3 ;

[0028] The outlet pressure calculation model is:

[0029] P2=P1-(987.034λ+9.58)ρu 2 nN / 1000000

[0030] Re≤2300,λ=64 / Re;

[0031] Among them, 2,300 <Re<2×10 6 ,λ=0.186×Re -0.2

[0032] (120 / ε) 1.125<Re,λ=[1.74+2lg(1 / 2ε)] -2

[0033] Where, P1 and P2 represent the inlet and outlet pressures, respectively, in MPa; u represents the flow rate, in m / s; n represents the number of coils; N represents the length of the coil; ε represents the relative roughness of the tube wall;

[0034] The exhaust gas temperature calculation model is:

[0035]

[0036] t p =(H-1672.4798) / 1702.057*100+100

[0037] Where Q i Indicates the lower calorific value of the fuel, kJ / m 3 ;q m represents the mass flow rate of the working fluid, kg / s; C represents the mass heat capacity of the working fluid, kJ / (kg·K); S represents the fuel consumption, m 3 / s;

[0038] The thermal efficiency calculation model is:

[0039]

[0040] Where, represents boiler thermal efficiency, %; g represents excess air coefficient; t p Indicates exhaust gas temperature, ℃; Q i Indicates the lower calorific value of the fuel, kJ / m 3 ; B w Indicates the thickness of the insulation layer, in m; C indicates the operation correction coefficient.

[0041] Furthermore, in one embodiment, in step S30, the overall energy flow model of the joint station system is determined according to the following formula:

[0042] Joint station outbound temperature energy flow model:

[0043]

[0044] Energy flow model of heat dissipation loss of joint station system:

[0045]

[0046] Where Q s Indicates the heat loss of the system, W; Q in Indicates the amount of liquid entering the station, m 3 / s;t in Indicates the inlet temperature, ℃; H indicates the liquid level in the tank, m; fw_in Indicates the moisture content of the incoming station; f g_in Indicates the gas content at the station; δ f Indicates the thickness of the separator insulation layer, m; δg indicates the thickness of the sedimentation tank insulation layer, m.

[0047] Optionally, in one embodiment, step S60 includes: when the actual operating parameters of the joint station site deviate significantly, determining the corresponding energy consumption per ton of oil based on the change in key parameters, and estimating the efficiency of related equipment;

[0048] When the degree of deviation reaches the set conditions, the optimization plan for improving energy consumption is determined by combining the nomogram and the actual situation on site;

[0049] When the amount of crude oil to be processed and the water content at the inlet of the joint station system change to a set level, a new nomogram is established based on the energy flow model.

[0050] Based on other aspects of the method described in any one or more of the above embodiments, the present invention further provides a storage medium storing program codes that can implement the method described in any one or more of the above embodiments.

[0051] Based on the application aspects of the method described in any one or more of the above embodiments, the present invention also provides a joint station process parameter optimization system based on nomogram technology, which executes the method described in any one or more of the above embodiments.

[0052] Compared with the closest prior art, the present invention also has the following beneficial effects:

[0053] The present invention provides a method and system for optimizing process parameters of a joint station based on nomogram technology. The method identifies the equipment to be analyzed that is related to energy consumption, and then performs thermal calculations on each of the equipment to obtain a sub-energy flow model of the single equipment. Based on the process flow of the joint station, each sub-energy flow model is spliced ​​together to obtain an overall energy flow model of the joint station system. Thermal calculations of equipment within the joint station and energy flow analysis of the joint station within the oil field are implemented, and the energy flow model is used to reflect the relationship between process factors and energy consumption of the joint station, thereby facilitating variable operating condition simulation operations and obtaining energy flow simulation results for different operating conditions that meet requirements.

[0054] A sensitivity analysis is performed based on the overall energy flow model to determine the target analysis parameters; the target analysis parameters are used as adjustment parameters to simulate the energy consumption under variable operating conditions, and the corresponding nomogram is drawn based on the calculation parameters and energy consumption results; the curve distribution of the process parameters of the actual operation of the joint station is obtained and compared with the curve distribution of the nomogram to realize abnormal situation identification, abnormal energy consumption prediction and formulation of improvement measures.

[0055] Before the simulation operation, sensitivity analysis is performed to determine the parameters with high correlation, ensuring the parameter basis for the simulation of variable working conditions while avoiding parameter omissions. This is reliable and comprehensive. The nomogram drawn based on this simulation operation can ensure accuracy and versatility, and is convenient for users to intuitively realize the rapid and accurate judgment and adjustment of on-site parameters.

[0056] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 1 is a flow chart of a method for optimizing process parameters of a joint station based on nomogram technology provided by one embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of a process flow for performing thermal calculations on a single device in a method for optimizing process parameters of a joint station based on nomogram technology according to an embodiment of the present invention;

[0060] Figure 3 1. It is a flow chart of determining the overall energy flow model of a joint station in the joint station process parameter optimization method based on the nomogram technology provided in an embodiment of the present invention;

[0061] Figure 4 It is an energy flow diagram of the Northwest Tahe No. 1 Joint Station system in the joint station process parameter optimization method based on the nomogram technology provided in an embodiment of the present invention;

[0062] FIG5( a ) is an example of an energy flow Nomogram between heat exchanger efficiency and energy consumption per ton of oil in the combined station process parameter optimization method based on the Nomogram technology provided in an embodiment of the present invention;

[0063] FIG5( b ) is an example of an energy flow nomogram between heating furnace efficiency and energy consumption per ton of oil in the combined station process parameter optimization method based on the nomogram technology provided in an embodiment of the present invention;

[0064] FIG5( c ) is an example of an energy flow nomogram between water content and energy consumption per ton of oil in a joint station in the method for optimizing process parameters of a joint station based on the nomogram technology according to an embodiment of the present invention;

[0065] Figure 6 It is a structural diagram of a joint station process parameter optimization system based on nomogram technology provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will describe in detail the implementation methods of the present invention in conjunction with the accompanying drawings and embodiments, so that practitioners of the present invention can fully understand how the present invention applies technical means to solve technical problems and achieve the implementation process of technical effects, and can implement the present invention in accordance with the above implementation process. It should be noted that as long as no conflict exists, the various embodiments and various features of each embodiment in the present invention can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0067] Although the flowcharts depict the operations as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. The order of the operations can be rearranged. A process can be terminated when its operations are completed, but can also have additional steps not included in the figures. A process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0068] Computer devices include user devices and network devices. User devices or clients include, but are not limited to, computers, smartphones, and PDAs (Personal Digital Assistants). Network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud computing-based cloud consisting of a large number of computers or network servers. Computer devices can operate independently to implement the present invention, or they can connect to a network and interact with other computer devices in the network to implement the present invention. The network in which the computer device resides includes, but is not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and VPN networks.

[0069] The terms used herein are intended only to describe specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "a", "an", "an item" used herein are also intended to include the plural. It should also be understood that the terms "comprise" and / or "include" used herein specify the presence of stated features, integers, steps, operations, units and / or components, and do not preclude the presence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0070] Currently, energy consumption analysis for joint power plants often uses a three-link approach. This "three-link" energy structure method is a theoretical analysis method suitable for energy flow analysis in complex energy-using systems. This method considers energy conversion, utilization, and recovery, and combines the joint power plant's process flow and process node parameters to calculate evaluation indicators for each link. In addition, commonly used methods for energy consumption analysis and evaluation in joint power plants include system energy flow diagrams and DEA methods.

[0071] However, the above-mentioned technologies not only have huge computational workloads and large formulas, but are also prone to errors, and are unable to dynamically evaluate and predict joint stations.

[0072] The primary evaluation metric for energy consumption in a joint station is energy consumption per ton of oil, which has a direct impact on the station's operational status and investment costs. The factors influencing this consumption are complex and interrelated. Simulation methods are often unintuitive for easier on-site monitoring, analysis, evaluation, and adjustment of node parameters. However, targeted graphs allow for intuitive, accurate, and rapid determination of geothermal efficiency under current operating parameters.

[0073] In order to solve the above problems, the present invention provides a method and system for optimizing the process parameters of a joint station based on the nomogram technology, and proposes a nomogram of the energy flow of the joint station system for the first time, which involves thermal calculation of the equipment in the joint station and energy flow analysis of the joint station in the oil field. Thermal calculation is performed on the equipment in the joint station, and through dynamic simulation of variable working conditions, a family of energy consumption curves per ton of oil of the joint station under different heat exchanger efficiencies, heating furnace efficiencies and water contents is constructed to obtain the nomogram of the energy flow of the joint station, which can dynamically analyze and predict the energy consumption of the joint station; realize fast and accurate adjustment of on-site parameters, avoid blind parameter adjustment by on-site staff, and improve the reliability and stability of the process flow of the joint station.

[0074] Next, the detailed process of the method according to the embodiment of the present invention is described in detail based on the accompanying drawings. The steps shown in the flowcharts of the accompanying drawings can be executed in a computer system including, for example, a set of computer-executable instructions. Although the logical order of the steps is shown in the flowcharts, in some cases, the steps shown or described can be executed in a different order than here.

[0075] Example 1

[0076] Figure 1 The flow chart of the joint station process parameter optimization method based on the nomogram technology provided in the first embodiment of the present invention is shown. Figure 1 It can be seen that the method includes the following steps.

[0077] Step S10: Identify the equipment related to the overall energy consumption in the joint station to be studied as the equipment to be analyzed;

[0078] Step S20: Perform thermal calculations on each device to be analyzed to obtain a sub-energy flow model of a single device object;

[0079] Step S30: splicing the sub-energy flow models of the single equipment objects based on the process flow of the joint station to obtain the overall energy flow model of the joint station system;

[0080] Step S40: performing sensitivity analysis based on the overall energy flow model to determine equipment-related parameters that meet the set requirements as target analysis parameters;

[0081] Step S50: using the target analysis parameters as adjustment parameters, performing variable working condition energy consumption simulation calculation based on the overall energy flow model, recording different levels of operation parameters and energy consumption results, drawing and saving the corresponding nomogram;

[0082] Step S60: Obtain the process parameter curve distribution of the actual operation of the joint station and compare it with the curve distribution of the nomogram to achieve abnormal situation identification, abnormal energy consumption prediction and improvement measures formulation.

[0083] First, in step S10, the equipment related to the overall energy consumption in the joint station to be studied is identified as the equipment to be analyzed;

[0084] In actual operation, all the electricity, gas and heat consuming equipment in the joint station should be considered as energy consuming equipment to be analyzed. In a preferred embodiment, the main energy consuming equipment to be analyzed in the joint station include three-phase separators, heat exchangers, heating furnaces, sedimentation tanks and other equipment. For the Northwest Tahe No. 1 Joint Station, Figure 4 As shown, the heat exchanger, three-phase separator, heating furnace, settling tank, stabilization tower and pump are used as the equipment to be analyzed.

[0085] Furthermore, step S20 is executed to perform thermal calculations on each device to be analyzed to obtain a sub-energy flow model of a single device object;

[0086] In this step, thermal calculations are performed on different equipment to fit the sub-energy flow model of a single equipment. Taking the heating furnace as an example, according to Figure 2 The process shown here implements its thermal calculation. The thermal calculation process is strictly in accordance with the thermal and resistance calculation method of the fire tube heating furnace published by the petroleum and natural gas industry standard of the People's Republic of China (SY / T 0535-94). According to the given conditions and calculation purposes, it is divided into two types: design thermal calculation and verification thermal calculation:

[0087] Among them, the design thermal calculation is the calculation used in the design of the fire tube type heating furnace; its purpose is to calculate and determine the area and main structural dimensions of each heat transfer element as well as fuel consumption, air volume, flue gas volume and other data based on the given thermal power, type and parameters of the heated medium, fuel information and selected furnace structure, burner type and other conditions. The design thermal calculation also provides data for fluid resistance calculation and strength calculation.

[0088] The thermal calculation is used when the heating furnace already exists or the main structural dimensions are given. Its purpose is to calculate and determine the heating furnace's thermal efficiency, fuel consumption, air volume, flue gas volume, and the temperature of the inlet and outlet media of each heat transfer element based on the existing structural dimensions and given thermal power, type and parameters of the heated medium, fuel information and other conditions, so as to verify the feasibility, economy and reliability of the heating furnace in achieving the required thermal power and heated medium parameters.

[0089] Taking a 230kW heating furnace as an example, the thermal calculation process is as follows.

[0090] 1. Fuel property calculation

[0091] Table 1

[0092] Element percentage <![CDATA[Low calorific value (KJ / m 3 )]]> Mass specific heat density Volumetric specific heat <![CDATA[CH4]]> 96.51 35906 2.17 0.717 1.55589 <![CDATA[C2H6]]> 1.97 64397 1.65 1.357 2.23905 <![CDATA[C3H8]]> 0.34 93244 1.55 2.02 3.131 <![CDATA[C4H 10 ]]> 0.15 123649 1.59 2.673 4.25007 <![CDATA[N2]]> 1.03 0 1.302 1.251 1.628802

[0093] 2. Fuel combustion calculation

[0094] Table 2

[0095] Low calorific value of gas <![CDATA[Q fg ]]> <![CDATA[Q fg =∑(C m H n *Q CmHn ) / 100]]> 36424.0 <![CDATA[KJ / m 3 ]]> Gas inlet pressure <![CDATA[P fg ]]> Given 32 Kpa Water vapor saturation pressure Ps Approximate calculation using the formula 4.2406 Kpa Base water content for gas applications <![CDATA[d ufg ]]> <![CDATA[d ufg =804·P s / (P fg -P s )]]> 122.821 <![CDATA[g / m 3 ]]> Gas dry basis density <![CDATA[ρ dfg ]]> <![CDATA[ρ dfg =0.01(∑C m H n ·r CmHn +N2·p N2 )]]> 0.74247 <![CDATA[kg / m 3 ]]> Application base density <![CDATA[ρ ufg ]]> <![CDATA[ρ ufg =(ρ dfg +d ufg / 1000)*804 / (804+d ufg )]]> 0.75063 <![CDATA[kg / m 3 ]]> Theoretical air volume <![CDATA[V ath ]]> <![CDATA[V ath =0.000268*Q fg ]]> 9.76163 <![CDATA[Nm 3 / Nm 3 ]]> Excess air coefficient α set up 1.05 Actual air volume <![CDATA[V a ]]> <![CDATA[V a =V ath *a]]> 10.2497 <![CDATA[Nm 3 / Nm 3 ]]> Excess air volume <![CDATA[V a' ]]> <![CDATA[Va'=V a -V ath ]]> 0.48808 <![CDATA[Nm 3 / Nm3]]> Gas temperature <![CDATA[t' fg ]]> Generally speaking, it is a death sentence 30 ℃

[0096] Among them, C m H n Fuel components CH4, C2H6, C3H8, C4H 10 The abbreviation expression of subscript m and n is the corresponding atomic number; Q CmHn Indicates the lower calorific value of the fuel component, KJ / m 3 ρ CmHn Indicates the density of the fuel, kg / m 3 ; N2 represents the percentage of fuel component N2, %; ρ N2 Indicates the density of the fuel component N2, kg / m 3 ;

[0097] 3. Fuel flue gas calculation

[0098] Table 3

[0099]

[0100] Among them, CO2 d 、H2S d Respectively represent the percentage of fuel composition; V a th Indicates the theoretical air volume, Nm 3 / Nm 3 ρ fg u Indicates application base density, kg / m 3 ;d fg uIndicates the basic water content of gas, g / m 3 ;d a Indicates the water content of air, g / m 3 ;m sm Indicates flue gas mass, kg / m 3 ; The superscript d indicates the fuel dry basis; the superscript o indicates the theoretical value.

[0101] 4. Input thermal calculation

[0102] After the fuel parameters are calculated, the heat supply available to the water jacket furnace is further calculated.

[0103] Table 4

[0104]

[0105]

[0106] Among them, C' fg Indicates the volumetric specific heat of gas, KJ / (m 3 · η a ,η m ,η e Represents total voltage efficiency, mechanical efficiency and motor efficiency, %; Q pfd Indicates the heat converted by the blower, KJ / m 3 .

[0107] 5. Calculation of heat loss rate

[0108] Here we need to consider all the heat loss items of the water jacket furnace

[0109] Table 5

[0110]

[0111] 6. Fire tube thermal calculation

[0112] Table 6

[0113]

[0114]

[0115] Among them, T ft ' represents the smoke temperature at the fire tube outlet, ℃; B represents the actual fuel consumption, m 3 / s; is the heat retention coefficient.

[0116] 7. Thermal calculation of smoke pipe

[0117] Table 7

[0118]

[0119]

[0120] 8. Coil thermal calculation

[0121] Table 8

[0122]

[0123]

[0124]

[0125] 9. Coil pressure drop calculation

[0126] Table 9

[0127]

[0128] The thermal calculation of the fire tube type heating furnace is composed of several links such as the fire tube, smoke tube, coil and other heat transfer elements. The calculation results of each link should be error checked, and the calculation error ε shall not exceed the corresponding regulations. After completing the thermal calculation process of the water jacket furnace, it is necessary to change the operating conditions of the water jacket furnace several times, and perform dynamic thermal simulation of the operating state of the water jacket furnace to fully reflect the impact of the changes in the main input parameters on the output parameters of concern, and find the dependence between the two, to provide a basis for further fitting of the energy flow parameters, and for Figure 3 This provides data support for modeling individual devices within the system, generating a sub-energy flow model for each device object. This also provides a basis for verifying the correctness of thermal calculations. The energy flow model for a 230kW heating furnace is used as an example; energy flow models for other heating furnace models can be developed based on this process.

[0129] Outlet temperature:

[0130]

[0131] in:

[0132]

[0133]

[0134] α o =0.13λ w (Gr w Pr w ) 0.33 / ro

[0135] Where: t2 is the outlet temperature, °C; t lis the water bath temperature, °C; t1 is the inlet temperature, °C; subscripts m, l, and g represent the three-phase mixture, liquid phase, and gas phase, respectively; λ represents the thermal conductivity of the corresponding medium, W / (m·K); k represents the total heat transfer coefficient, W / (m 2 ·K); r1 and r2 represent the internal and external fouling thermal resistance of the coil, respectively. (m 2 ·K) / W;r i 、r o are the inner and outer diameters of the coil, in meters; Re is the Reynolds number; Pr is the Prandtl number; μ is the viscosity in Pa·s; x is the water content; ρ is the density in kg / m 3 ; S represents fuel consumption, m 3 / s; α i , α o Respectively represent the convection heat transfer coefficient inside and outside the tube, W / (m 2 ·K); x wg Indicates mass air content.

[0136] Outlet pressure:

[0137] P2=P1-(987.034ζ+9.58)ρu 2 nN / 1000000

[0138] Re≤2300,ζ=64 / Re;

[0139] The parameter ζ is 2300 <Re<2×10 6 ,ζ=0.186×Re -0.2

[0140] (120 / ε) 1.125 <Re,ζ=[1.74+2lg(1 / 2ε)] -2

[0141] Where: P1 and P2 are the inlet and outlet pressures, respectively, in MPa; u is the flow rate, in m / s; n is the number of coils; N is the length of the coil; ε is the relative roughness of the tube wall;

[0142] Exhaust temperature:

[0143]

[0144] t p =(H-1672.4798) / 1702.057*100+100

[0145] Where: t p Indicates exhaust gas temperature, ℃; Q i Indicates the lower calorific value of the fuel, kJ / m 3 ;q mrepresents the mass flow rate of the working fluid, kg / s; C represents the mass heat capacity of the working fluid, kJ / (kg·K); S represents the fuel consumption, m 3 / s;

[0146] Thermal efficiency:

[0147]

[0148] in: represents boiler thermal efficiency, %; g represents excess air coefficient; t p Indicates exhaust gas temperature, ℃; Q i Indicates the lower calorific value of the fuel, kJ / m 3 ; B w Indicates the thickness of the insulation layer, in m; C' indicates the operating correction coefficient, which can generally be taken as 0.93-0.96 for older, smaller-power boilers, and 0.97-1 for newer, larger-power boilers.

[0149] After the energy flow model for the heating furnace is established, an error analysis is performed between the simulated and actual values ​​to ensure a good fit between the model and the actual values. Simultaneously, the thermal calculations for other equipment are completed.

[0150] This embodiment of the present invention performs thermal calculations on each device within the joint station (such as the three-phase separator, heating furnace, heat exchanger, and settling tank), calculates the energy consumption of each device, and fits an energy flow model between the energy consumption and the device's inlet parameters to obtain a sub-energy flow model for each device. The energy flow model is then verified and corrected using field data. In actual application, energy flow modeling is required for a variety of devices, not just the heating furnace used as an example. Sensitivity analysis of each device yields different sensitivity parameters, meaning that different input parameters significantly influence the output parameters of interest. This results in differences in the independent variables within the subsystem energy flow models for each device.

[0151] Step S30: splicing the sub-energy flow models of the single equipment objects based on the process flow of the joint station to obtain the overall energy flow model of the joint station system;

[0152] The equipment in the joint station system is formed into a new integrated structure through complex pipelines or manifolds. For the system itself, it is also in a relatively stable operating state. Therefore, analogous to the energy flow model of a single device, the energy flow model of the entire system can be modeled and the operating status of the system can be studied.

[0153] like Figure 4As shown in the figure, for the Northwest Tahe No. 1 Joint Station system, following the crude oil processing flow, the individual sub-energy flow models for the three-phase separator, heat exchanger, three-phase separator, raw stabilization heating furnace, negative pressure stabilization desulfurization tower, tower bottom pump, and settling tank were sequentially spliced. The splicing process was supplemented with calculation models for equipment such as the manifold (including intersections and variable diameters). Based on this, an energy flow model for the joint station system was established using MATLAB software. This model was used to conduct sensitivity analysis of the parameters within the joint station and identify sensitive factors that are strongly correlated with the station's energy consumption.

[0154] Taking temperature and heat loss, which are closely related to system energy consumption, as output parameters, the temperature energy flow model and heat dissipation loss energy flow model of the Northwest Oilfield Tahe No. 1 Joint Station system are obtained.

[0155] Joint station outbound temperature energy flow model:

[0156]

[0157] Where, t out Indicates the outgoing station temperature, ℃; Q in Indicates the amount of liquid entering the station, m 3 / s;t in is the inlet temperature, ℃; H is the liquid level in the tank, m; f w_in Indicates the moisture content of the incoming station; f g_in Indicates the gas content at the station; δ f Indicates the thickness of the separator insulation layer, m; δ g Indicates the thickness of the insulation layer of the sedimentation tank, m.

[0158] Energy flow model of heat dissipation loss of joint station system:

[0159]

[0160] Where Q s Indicates the heat loss of the system, W; Q in Indicates the amount of liquid entering the station, m 3 / s;t in Indicates the inlet temperature, ℃; H indicates the liquid level in the tank, m; f w_in Indicates the moisture content of the incoming station; f g_in Indicates the gas content at the station; δ f Indicates the thickness of the separator insulation layer, m; δg indicates the thickness of the sedimentation tank insulation layer, m.

[0161] Comparing the fitted values ​​of the system's energy flow model with simulated and measured values ​​revealed a basic error range of ≤10% compared to field operation data, confirming that the system meets applicable requirements. Error analysis revealed that the main discrepancies between the energy flow model and measured values ​​are: For outlet temperatures, the calculated values ​​are generally higher than the measured values. This is due to significant heat losses in actual conditions, while theoretical calculations consider ideal conditions, such as insufficient insulation performance from numerous insulation layers and heat exchanger fouling.

[0162] Step S40: performing sensitivity analysis based on the overall energy flow model to determine equipment-related parameters that meet the set requirements as target analysis parameters;

[0163] Sensitivity analysis is one of the commonly used methods for analyzing influencing factors. From multiple uncertain factors, find out the sensitive factors that have a significant impact on the variables one by one, and analyze and measure their degree of influence and sensitivity on the variables. If a small change in a parameter can lead to a large change in the economic benefit index, then this parameter is called a sensitive factor, otherwise it is called a non-sensitive factor. The object of sensitivity analysis is a specific technical solution and the economic benefits it reflects. Therefore, equipment benefit evaluation indicators, such as pressure loss, temperature loss, heat loss, COP, etc., can be used as sensitivity analysis indicators. Based on the Northwest Tahe No. 1 Joint Station, other energy consumption such as electricity consumption, gas consumption and heat consumption in the joint station system are converted into energy consumption per ton of oil, and the impact of uncertain factors in the system on energy consumption per ton of oil is analyzed.

[0164] Using the energy flow model of the joint station system, a dynamic simulation of the joint station was conducted to determine the relationship between strongly correlated sensitive factors and the energy consumption per ton of oil in the joint station system, allowing for the creation of an energy flow diagram for the joint station system. Analysis revealed that the strongly correlated factors within the Northwest Oilfield Tahe No. 1 Joint Station were heat exchanger efficiency, heater efficiency, and moisture content.

[0165] Step S50: using the target analysis parameters as adjustment parameters, performing variable working condition energy consumption simulation calculation based on the overall energy flow model, recording different levels of operation parameters and energy consumption results, drawing and saving the corresponding nomogram;

[0166] The Nomograph of the Northwest Tahe No. 1 combined system is shown in Figure 5. When the Nomograph was drawn, the heat exchanger efficiency, heater efficiency, and separator outlet moisture content versus system energy consumption per ton of oil were plotted.

[0167] Step S60: Obtain the process parameter curve distribution of the actual operation of the joint station and compare it with the curve distribution of the nomogram to achieve abnormal situation identification, abnormal energy consumption prediction and improvement measures formulation.

[0168] The method for optimizing process parameters of a joint station based on nomogram technology provided by an embodiment of the present invention first performs thermal calculations on each device in the joint station and creates an energy flow model so as to conduct a sensitivity analysis on the joint station system and obtain factors that are strongly correlated with energy consumption per ton of oil in the joint station. Then, a nomogram is drawn between these factors and energy consumption per ton of oil, which ultimately facilitates rapid and accurate judgment and adjustment of parameters on the actual site. The nomogram is used for analysis and improvement, which is intuitive, fast, easy to use and accurate. In actual application, it can not only flexibly determine the energy consumption calculation results of the joint station based on forward calculations of ambient temperature, incoming liquid parameters and heating equipment efficiency, but also predict the actual efficiency of the heating furnace based on actual energy consumption, ambient temperature and incoming liquid parameters as needed, which can effectively save detection costs.

[0169] After obtaining the combined station energy flow nomogram, when actual operating parameters deviate significantly from the combined station's on-site operations, operators can quickly determine the approximate energy consumption per ton of oil based on key parameter changes and roughly estimate the efficiency of heat exchangers and heaters. When the degree of deviation is high, operators can combine different nomograms with actual on-site conditions to determine whether energy consumption can be improved by examining the dependence of energy consumption on highly sensitive parameters or adjusting a range of other parameters.

[0170] For example, when the system's energy consumption per ton of oil changes, the nomogram can be used to determine the efficiency of individual equipment or the water content at the separator outlet, thereby determining the effectiveness of each individual equipment and enabling repair and maintenance. It should be noted that when the volume of crude oil to be processed and the water content at the inlet of the combined station system change significantly, a new nomogram must be created based on the energy flow model to ensure its accuracy.

[0171] For simplicity of description, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, as certain steps can be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for the present invention.

[0172] It should be pointed out that in other embodiments of the present invention, the method can also obtain a new joint station process parameter optimization method based on nomogram technology by combining one or several of the above embodiments to achieve optimized control of the joint station process flow.

[0173] It should be noted that, based on the method in any one or more of the above-mentioned embodiments of the present invention, the present invention also provides a storage medium, which stores program code that can implement the method as described in any one or more of the above-mentioned embodiments, and when the code is executed by the operating system, it can implement the joint station process parameter optimization method based on the nomogram technology as described above.

[0174] Example 2

[0175] The methods are described in detail in the embodiments disclosed above. The methods of the present invention can be implemented using various devices or systems. Therefore, based on other aspects of the methods described in any one or more of the above embodiments, the present invention further provides a system for optimizing process parameters of a joint station based on nomogram technology. This system is used to execute the method for optimizing process parameters of a joint station based on nomogram technology described in any one or more of the above embodiments. Specific embodiments are given below for detailed description.

[0176] Specifically, Figure 6 FIG. 1 shows a schematic diagram of the structure of the joint station process parameter optimization system based on the nomogram technology provided in an embodiment of the present invention. Figure 6 As shown, the system includes:

[0177] A related equipment analysis module is configured to identify the equipment related to the overall energy consumption in the joint station to be studied as the equipment to be analyzed;

[0178] A single-device energy flow calculation module is configured to perform thermal calculations on each device to be analyzed and obtain a sub-energy flow model of the single-device object;

[0179] An overall energy flow determination module is configured to combine the sub-energy flow models of the single equipment objects based on the process flow of the joint station to obtain an overall energy flow model of the joint station system;

[0180] A target parameter analysis module is configured to perform a sensitivity analysis based on the overall energy flow model to determine equipment-related parameters that meet set requirements as target analysis parameters;

[0181] a simulation operation module configured to use the target analysis parameters as adjustment parameters, perform variable working condition energy consumption simulation operations based on the overall energy flow model, record different levels of operation parameters and energy consumption results, draw and save corresponding nomograms;

[0182] The improvement measure determination module is configured to obtain the comparative analysis of the process parameter curve distribution of the actual operation of the joint station and the curve distribution of the nomogram, so as to realize the abnormal situation identification, abnormal energy consumption prediction and improvement measure formulation.

[0183] Optionally, in one embodiment, the main energy-consuming equipment to be analyzed in the joint station includes one or more of the following equipment: a three-phase separator, a heat exchanger, a heating furnace, a settling tank, a stabilization tower, and a stabilization pump.

[0184] Furthermore, in one embodiment, the single-device energy flow calculation module performs thermal calculations for each device to be analyzed according to the following operations:

[0185] The energy consumption data of each device is calculated, and the energy flow model between the energy consumption and the device inlet parameters is fitted to obtain the sub-energy flow model of a single device.

[0186] In one embodiment, the single-device energy flow calculation module calculates the energy consumption data of each device according to the following logic:

[0187] Based on the thermal power, type and parameters of the heated medium, fuel information, furnace structure and burner type data, the area of ​​each heat transfer element, main structural dimensions, fuel consumption, air volume and flue gas volume data are calculated and determined.

[0188] Preferably, in one embodiment, the single-device energy flow calculation module is further configured to calculate the thermal efficiency, fuel consumption, air volume, flue gas volume, and temperature data of the inlet and outlet media of each heat transfer element of the heating furnace based on the existing structural dimensions, thermal power, type and parameters of the heated medium, and fuel information data, so as to verify that the heating furnace achieves the required thermal power and characteristics of the heated medium parameters.

[0189] Furthermore, in one embodiment, the single-device energy flow calculation module is further configured to, after completing a single round of thermal calculation, modify the operating conditions of the participating computing devices, perform dynamic thermal simulations for different operating states, reflect the impact of changes in key input parameters on the output parameters of interest, analyze the relationships between them, and construct corresponding sub-energy flow models for fitting the energy flow parameters of the device objects.

[0190] Optionally, in one embodiment, if the participating computing device is a heating furnace, the single-device energy flow computing module constructs a corresponding sub-energy flow model according to the following strategy:

[0191] The outlet temperature calculation model is:

[0192]

[0193] in:

[0194]

[0195]

[0196] α o =0.13λw (Gr w Pr w ) 0.33 / ro

[0197] Where, t2 is the outlet temperature, °C; t l is the water bath temperature, °C; t1 is the inlet temperature, °C; subscripts l and g represent the mixed liquid and gas, respectively; λ is the thermal conductivity, W / m °C; r i , r o Re is the Reynolds number; Pr is the Prandtl number; μ is the viscosity in Pa·s; x is the water content; ρ is the density in kg / m 3 ;

[0198] The outlet pressure calculation model is:

[0199] P2=P1-(987.034λ+9.58)ρu 2 nN / 1000000

[0200] Re≤2300,λ=64 / Re;

[0201] 2,300 of them <Re<2×10 6 ,λ=0.186×Re -0.2

[0202] (120 / ε) 1.125 <Re,λ=[1.74+2lg(1 / 2ε)] -2

[0203] Where, P1 and P2 represent the inlet and outlet pressures, respectively, in MPa; u represents the flow rate, in m / s; n represents the number of coils; N represents the length of the coil; ε represents the relative roughness of the tube wall;

[0204] The exhaust gas temperature calculation model is:

[0205]

[0206] t p =(H-1672.4798) / 1702.057*100+100

[0207] Where Q i Indicates the lower calorific value of the fuel, kJ / m 3 ;q m represents the mass flow rate of the working fluid, kg / s; C represents the mass heat capacity of the working fluid, kJ / (kg·K); S represents the fuel consumption, m 3 / s;

[0208] The thermal efficiency calculation model is:

[0209]

[0210] Where, represents boiler thermal efficiency, %; g represents excess air coefficient; t p Indicates exhaust gas temperature, ℃; Q i Indicates the lower calorific value of the fuel, kJ / m 3 ; B w Indicates the thickness of the insulation layer, in m; C indicates the operation correction coefficient.

[0211] Furthermore, in one embodiment, the overall energy flow determination module determines the overall energy flow model of the joint station system according to the following logic:

[0212] Joint station outbound temperature energy flow model:

[0213]

[0214] Energy flow model of heat dissipation loss of joint station system:

[0215]

[0216] Where Q s Indicates the heat loss of the system, W; Q in Indicates the amount of liquid entering the station, m 3 / s;t in Indicates the inlet temperature, ℃; H indicates the liquid level in the tank, m; f w_in Indicates the moisture content of the incoming station; f g_in Indicates the gas content at the station; δ f Indicates the thickness of the separator insulation layer, m; δg indicates the thickness of the sedimentation tank insulation layer, m.

[0217] Optionally, in one embodiment, the improvement measure determination module is configured to: when a large deviation occurs in the actual operating parameters of the joint station site, determine the corresponding energy consumption per ton of oil based on the change in key parameters, and estimate the efficiency of related equipment;

[0218] When the degree of deviation reaches the set conditions, the optimization plan for improving energy consumption is determined by combining the nomogram and the actual situation on site;

[0219] When the amount of crude oil to be processed and the water content at the inlet of the joint station system change to a set level, a new nomogram is established based on the energy flow model.

[0220] In the joint station process parameter optimization system based on nomogram technology provided by an embodiment of the present invention, each module or unit structure can operate independently or in combination according to the actual equipment energy flow simulation requirements and graph file application requirements to achieve corresponding technical effects.

[0221] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0222] The phrase "one embodiment" mentioned in the specification means that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase "one embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0223] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A method for optimizing process parameters of a joint station based on nomogram technology, characterized in that: The method comprises: Step S10: Identify the equipment related to the overall energy consumption in the joint station to be studied as the equipment to be analyzed; Step S20: Perform thermal calculations on each device to be analyzed to obtain a sub-energy flow model of a single device object; Step S30: splicing the sub-energy flow models of the single equipment objects based on the process flow of the joint station to obtain the overall energy flow model of the joint station system; Step S40: performing sensitivity analysis based on the overall energy flow model to determine equipment-related parameters that meet the set requirements as target analysis parameters; Step S50: using the target analysis parameters as adjustment parameters, performing variable working condition energy consumption simulation calculation based on the overall energy flow model, recording different levels of operation parameters and energy consumption results, drawing and saving the corresponding nomogram; Step S60: Obtain the process parameter curve distribution of the actual operation of the joint station and compare it with the curve distribution of the nomogram to achieve abnormal situation identification, abnormal energy consumption prediction and improvement measures formulation.

2. The method according to claim 1, characterized in that In step S10, the main energy-consuming equipment to be analyzed in the joint station includes one or more of the following equipment: a three-phase separator, a heat exchanger, a heating furnace, a settling tank, a stabilization tower, and a stabilization pump.

3. The method according to claim 1, characterized in that In step S20, the process of performing thermal calculation for each device to be analyzed includes: The energy consumption data of each device is calculated, and the energy flow model between the energy consumption and the device inlet parameters is fitted to obtain the sub-energy flow model of a single device.

4. The method according to claim 3, characterized in that In step S20, the process of calculating the energy consumption data of each device includes: Based on the thermal power, type and parameters of the heated medium, fuel information, furnace structure and burner type data, the area of ​​each heat transfer element, main structural dimensions, fuel consumption, air volume and flue gas volume data are calculated and determined.

5. The method according to claim 1, wherein Step S20 also includes calculating the heating furnace's thermal efficiency, fuel consumption, air volume, flue gas volume, and the temperatures of the inlet and outlet media of each heat transfer element based on the existing structural dimensions, thermal power, type and parameters of the heated medium, and fuel data, in order to verify that the heating furnace achieves the required thermal power and heated medium parameters.

6. The method according to claim 1, characterized in that Step S20 also includes: after completing a single round of thermal calculation process, changing the operating conditions of the participating calculation equipment, performing dynamic thermal simulation on different operating states, reflecting the impact of changes in the main input parameters on the output parameters of interest, and analyzing the relationship between them, constructing a corresponding sub-energy flow model for fitting operations on the energy flow parameters of the equipment object.

7. The method according to claim 1, characterized in that If the participating computing device is a heating furnace, the corresponding sub-energy flow model is constructed according to the following strategy: The outlet temperature calculation model is: in, a o =0.13l w (Gr w Pr w ) 0.33 / ro Where, t2 is the outlet temperature, °C; t l is the water bath temperature, °C; t1 is the inlet temperature, °C; subscripts l and g represent the mixed liquid and gas, respectively; λ is the thermal conductivity, W / m °C; r i , r o Re is the Reynolds number; Pr is the Prandtl number; μ is the viscosity in Pa·s; x is the water content; ρ is the density in kg / m 3 ; The outlet pressure calculation model is: P2=P1-(987.034λ+9.58)ρu 2 nN / 1000000 Re≤2300,λ=64 / Re; Among them, 2,300 <Re<2×10 6 ,λ=0.186×Re -0.2 (120 / e) 1.125 <Re,λ=[1.74+2lg(1 / 2ε)] -2 Where, P1 and P2 represent the inlet and outlet pressures, respectively, in MPa; u represents the flow rate, in m / s; n represents the number of coils; N represents the length of the coil; ε represents the relative roughness of the tube wall; The exhaust gas temperature calculation model is: t p =(H-1672.4798) / 1702.057*100+100 Where Q i Indicates the lower calorific value of the fuel, kJ / m 3 ;q m represents the mass flow rate of the working fluid, kg / s; C represents the mass heat capacity of the working fluid, kJ / (kg·K); S represents the fuel consumption, m 3 / s; The thermal efficiency calculation model is: Where, represents boiler thermal efficiency, %; g represents excess air coefficient; t p Indicates exhaust gas temperature, ℃; Q i Indicates the lower calorific value of the fuel, kJ / m 3 ; B w Indicates the thickness of the insulation layer, in m; C indicates the operation correction coefficient.

8. The method according to claim 1, characterized in that In step S30, the overall energy flow model of the joint station system is determined according to the following formula: Joint station outbound temperature energy flow model: Energy flow model of heat dissipation loss of joint station system: Where Q s Indicates the heat loss of the system, W; Q in Indicates the amount of liquid entering the station, m 3 / s;t in Indicates the inlet temperature, ℃; H indicates the liquid level in the tank, m; f w_in Indicates the moisture content of the incoming station; f g_in Indicates the gas content at the station; δ f Indicates the thickness of the separator insulation layer, m; δg indicates the thickness of the sedimentation tank insulation layer, m.

9. The method according to claim 1, characterized in that Step S60 includes: when the actual operating parameters of the joint station site deviate significantly, determining the corresponding energy consumption per ton of oil based on the changes in key parameters, and estimating the efficiency of related equipment; When the degree of deviation reaches the set conditions, the optimization plan for improving energy consumption is determined by combining the nomogram and the actual situation on site; When the amount of crude oil to be processed and the water content at the inlet of the joint station system change to a set level, a new nomogram is established based on the energy flow model.

10. A storage medium, characterized in that: The storage medium stores program code that can implement the method according to any one of claims 1 to 9.

11. A joint station process parameter optimization system based on nomogram technology, characterized in that: The system executes the method according to any one of claims 1 to 9.

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