Oil refining device carbon emission influence factor identification and emission reduction measure analysis method and system

Abstract

The invention discloses an oil refining device carbon emission influence factor identification and emission reduction measure analysis method and system, the oil refining device carbon emission influence factor identification and emission reduction measure analysis method comprises the following steps: collecting oil refining device process parameter ledger data; based on the machine account data, identifying carbon emission influence factors of the oil refining device; analyzing the contribution degree of the carbon emission influence factors of the oil refining device, and identifying key influence factors of the carbon emission; a carbon emission reduction measure is put forward based on the key influence factors; building an oil refining device carbon emission reduction measure quantitative evaluation model; and based on the carbon emission reduction measure quantitative evaluation model, performing quantitative evaluation on a carbon emission reduction measure implementation effect, and determining an optimal carbon emission reduction measure. The invention provides an equipment-grade carbon emission influence factor identification and emission reduction method. The resolution and operability of the carbon emission influence factors and the emission reduction method are improved; the method is wide in principle application range and can be applied to all types of oil refining enterprise production devices.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of greenhouse gas emission reduction, and particularly relates to a method and system for identifying influencing factors of carbon emission of a refinery device and analyzing emission reduction measures. BACKGROUND

[0002] At present, the factor decomposition method is the most widely used analysis method for studying influencing factors of greenhouse gas emission such as carbon dioxide. The factor decomposition method mainly includes structural decomposition analysis (SDA) and index decomposition analysis (IDA). The SDA method is a method for decomposing a complex problem or project into multiple simple components to better understand and solve the problem, which needs to be used on the basis of an established input-output model. In contrast, the IDA method is easier to collect data and more convenient and simple to operate in the application process, which makes the IDA method have wider application potential. However, the IDA method also has some problems, such as that the explained variable is represented as the product of several factor indicators, and the dependency relationship between the multiplied factors is ignored, and in addition, the subjective selection of the influencing factors entering the model introduces a certain influence on the analysis results. SDA or IDA has been widely applied at the macro level, but rarely applied at the more micro level of enterprises and production devices.

[0003] The carbon emission reduction technical approaches of the petroleum refining industry include: reducing energy consumption, optimizing reaction conditions, controlling scale, and improving catalysts. Reducing energy consumption mainly includes: waste heat recovery and improving the thermal efficiency of heating furnaces, energy-saving technology of circulating water systems; optimizing reaction conditions mainly includes: selecting new equipment, optimizing heating furnace operating parameters, optimizing process parameters, selecting appropriate air coefficient, pressure and temperature, and other reaction conditions; using intelligent system scheduling optimization technology to control the scale of process device import and export materials and energy; improving the properties of catalysts based on new materials. At present, the systematization of the application of emission reduction technology is not strong, and qualitative research is mainly used, and quantitative research and measure effect prediction are not enough.

[0004] In summary, the current research basis has made valuable contributions to the understanding of carbon emission and emission reduction of petroleum refining enterprises from a macro perspective. However, few studies have focused on exploring the influencing factors of carbon emission at the process unit level of the refinery. Due to the complexity and diversity of influencing process parameters, it is not yet clear how process parameters affect carbon emission. In addition, the carbon emission reduction technologies used by process units are generally selected based on expert experience, which usually involves general knowledge and principles. Due to the lack of specific analysis methods and data support, the suggestions for carbon emission reduction are more macro and fuzzy, and lack of pertinence and operability. SUMMARY

[0005] In view of the above problems, the present application discloses a method for identifying influencing factors of carbon emission of a refinery device and analyzing emission reduction measures, comprising:

[0006] collecting refinery process parameter account data;

[0007] identifying refinery carbon emission influencing factors based on the account data;

[0008] analyzing the contribution of the refinery carbon emission influencing factors to identify key influencing factors of carbon emission;

[0009] proposing carbon emission reduction measures based on the key influencing factors;

[0010] building a quantitative evaluation model for the carbon emission reduction measures of the refinery;

[0011] quantitatively evaluating the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model for the carbon emission reduction measures, and determining the optimal carbon emission reduction measures.

[0012] Further, the account data includes yield data, production parameter data, energy consumption data, assay analysis data, and environmental protection monitoring data.

[0013] Further, the specific steps of identifying the refinery carbon emission influencing factors based on the account data are as follows:

[0014] Taking carbon dioxide and methane in the environmental protection monitoring data as response variables, and taking the yield data, production parameter data, energy consumption data, and assay analysis data as explanatory variables, a redundancy analysis is carried out to obtain the relationship between the explanatory variables and the response variables, and then the refinery carbon emission influencing factors are identified.

[0015] Further, the specific steps of analyzing the contribution of the refinery carbon emission influencing factors to identify the key influencing factors of carbon emission are as follows:

[0016] determining the contribution rate of each explanatory variable to the response variable;

[0017] According to the contribution rate, the key influencing factors of carbon emission are identified.

[0018] Further, the contribution rate is determined by the following formula:

[0019]

[0020] wherein, Δλ i is the contribution rate of the explanatory variable factor i to the total variation of carbon emission; λ i is the eigenvalue of the i th principal component in the fitting value matrix; λ T is the cumulative value of all eigenvalues in the fitting value matrix.

[0021] Further, the carbon emission reduction measures include reducing energy consumption, optimizing reaction conditions, controlling scale, and improving catalysts.

[0022] Further, the specific steps of the quantitative evaluation model of the carbon emission reduction measures of the oil refining device are as follows:

[0023] Based on the key influencing factors of carbon emission and the account data, the stepwise regression method of multiple regression analysis is used to build the quantitative evaluation model of the carbon emission reduction measures between the response variable and the key influencing factors.

[0024] Further, the quantitative evaluation model of the carbon emission reduction measures is as follows:

[0025] y i = β0+ β1x i + ε

[0026] Wherein, y i is the response variable; β0 is the parameter; β1 is the parameter; x i is the key influencing factor; and ε is the random error.

[0027] Further, based on the quantitative evaluation model of the carbon emission reduction measures, the specific steps of quantitatively evaluating the implementation effect of the carbon emission reduction measures and determining the optimal carbon emission reduction measures are as follows:

[0028] After the carbon emission reduction measures are schematized, parameterized and quantified, the implementation effect of the carbon emission reduction measures is quantitatively evaluated by substituting the carbon emission reduction measures into the quantitative evaluation model of the carbon emission reduction measures;

[0029] Based on the implementation effect, the optimal carbon emission reduction measures are determined.

[0030] The application also discloses an oil refining device carbon emission influencing factor identification and emission reduction measure analysis system, which comprises:

[0031] A collection unit is configured to collect process parameter account data of the oil refining device.

[0032] An identification unit is configured to identify the carbon emission influencing factors of the oil refining device based on the account data.

[0033] An analysis unit is configured to analyze the contribution degrees of the carbon emission influencing factors of the oil refining device and identify the key influencing factors of carbon emission.

[0034] A carbon emission reduction measure unit is configured to propose carbon emission reduction measures based on the key influencing factors.

[0035] A model unit is configured to build a quantitative evaluation model of the carbon emission reduction measures of the oil refining device.

[0036] A determination unit is configured to quantitatively evaluate the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model of the carbon emission reduction measures and determine the optimal carbon emission reduction measures.

[0037] Compared with the prior art, the embodiment of the present application has at least the following advantages: the present application provides an equipment-level carbon emission impact factor identification and emission reduction method; the resolution and operability of the carbon emission impact factor and emission reduction method are improved; the method principle is widely applicable and can be applied to all types of refinery production devices.

[0038] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0040] Figure 1 A flowchart of a refinery device carbon emission impact factor identification and emission reduction measure analysis method according to an embodiment of the present application is shown;

[0041] Figure 2 A redundancy analysis schematic diagram between carbon emission and process parameters of a catalytic cracking device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0042] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] In view of the above key problems, the present application takes a refining unit as a research object, determines key influencing factors of carbon emission of the refining unit based on production account data of the refining unit by using redundancy analysis (RDA). In view of the identified influencing factors, the influence contribution of different factors to carbon emission of the unit is quantitatively analyzed, and carbon emission reduction measures of the target unit are proposed in a targeted manner, and the carbon emission reduction measures are quantified and parameterized by scenario analysis; further, a carbon emission reduction measure quantitative evaluation model between the key influencing factors and the carbon emission is established by using multiple regression analysis (MRA), the emission reduction effect of the carbon emission reduction measures is quantified, and the best reduction scheme is optimized. The present application enriches and perfects the method for identifying and quantifying carbon emission influencing factors and carbon emission reduction measures at a micro level.

[0044] Figure 1 A flow chart of a refining unit carbon emission influencing factor identification and emission reduction measure analysis method according to an embodiment of the present application is shown. As shown in Figure 1 , the refining unit carbon emission influencing factor identification and emission reduction measure analysis method proposed by the present application comprises:

[0045] Step 1, collecting process parameter account data of the refining unit;

[0046] The present application is driven by account data of the refining unit, and does not need to exclude certain data in advance, and can be seamlessly connected with the account data of the refining unit. The account data of the refining unit mainly includes production technology monthly reports, and the main data in the production technology monthly reports include yield data, production parameter data, energy consumption data, assay analysis data and environmental protection monitoring data.

[0047] Step 2, identifying carbon emission influencing factors of the refining unit based on the account data;

[0048] Taking carbon dioxide and methane in the environmental protection monitoring data as response variables, and taking the account data such as yield data, production parameter data, energy consumption data and assay analysis data as explanatory variables, redundancy analysis is carried out to obtain a significant relationship ranking between the explanatory variables and the response variables, and then the carbon emission influencing factors of the refining unit are identified.

[0049] The principle of the redundancy analysis method is as follows:

[0050] (1) first, each response variable in the matrix Y is subjected to multiple regression with all explanatory variables in the matrix X, the fitting value and the residual of each response variable are obtained through the regression model, the fitting value and the residual of each response variable are collected, and finally the fitting value matrix and the residual matrix (Y res ) are obtained. The matrix Y is a standardized response variable matrix, and the matrix X is a standardized explanatory variable matrix.

[0051] (2) the fitting value matrix Run principal component analysis (PCA) to obtain the canonical eigenvector matrix U. Use matrix U to calculate two sets of quadrat ordination scores (coordinates): one set using matrix Y to obtain quadrat ordination coordinates in the original variable y (response variable) space (i.e. calculate Y*U, the obtained coordinates are called "quadrat scores", i.e. the weighted sum of response variable scores); the other set using the fitted value matrix to obtain quadrat ordination coordinates in the explanatory variable x space (i.e. calculate X*U, the obtained coordinates are called "quadrat constraints", i.e. the linear combination of constraint variables).

[0052] (3) In the ordination plot of RDA explanatory variables and response variables, the length of the projection of the explanatory variable on the response variable axis represents the degree of correlation between the distribution of the response variable and the explanatory variable, and the longer the projection length, the greater the influence of the explanatory variable on the response variable; the angle between the explanatory variables represents the correlation between them, and the smaller the angle, the closer the relationship, and if it is orthogonal, it is not related.

[0053] (4) The variance explanation in the RDA result can be divided into variance decomposition of the response variable and the explanatory variable. The response variable variance explanation reflects the proportion of the variance in the response variable that can be explained by the model; the variance contribution of the explanatory variable reflects the significance of the influence of each explanatory variable on the response variable.

[0054] Step 3, analyze the contribution of the influencing factors of carbon emissions of the oil refining device, and identify the key influencing factors of carbon emissions;

[0055] Through the obtained significant relationship ranking between the explanatory variables and the response variables, the contribution rate and specific value of each explanatory variable to the response variable are given, and the calculation formula is shown as formula (1), and according to Δλ i The contribution rate of the factor i is greater, and the influence degree on carbon emissions is greater. i

[0056]

[0057] In the formula, Δλ i is the contribution rate (%) of the explanatory variable factor i to the total variation of carbon emissions; λ i is the eigenvalue of the i-th principal component in the fitted value matrix λ T is the sum of all eigenvalues of the fitted value matrix .

[0058] Step 4, based on the key influencing factors, propose carbon emission reduction measures;

[0059] ​​According to the identified carbon emission key influencing factors, based on prior knowledge and field investigation, corresponding carbon emission reduction measures are proposed, which mainly include reducing energy consumption, optimizing reaction conditions, scale control and improving catalyst. Among them, the reaction condition optimization mainly involves equipment process parameters, and the scale control mainly involves the scale optimization of material flow and energy flow; and according to different carbon emission reduction measures, specific and qualitative carbon emission reduction measures are optimized.

[0060] Step 5, building a quantitative evaluation model of carbon emission reduction measures for oil refining devices;

[0061] According to the identified carbon emission key influencing factors of oil refining devices and the production account data of oil refining devices, a quantitative evaluation model of carbon emission reduction measures is built by using the stepwise regression method of multiple regression analysis between the response variable carbon emission (y i ) value and the key influencing factors (x i ), which provides a basis for quantitative evaluation of the implementation effect of carbon emission reduction measures.

[0062] Expression:

[0063] y i = β0+ β1x i + ε (2)

[0064] In the formula, y i is the response variable; β0 and β1 are the parameters to be estimated, which can be estimated by the least square method; x i is the key influencing factor; and ε is a random error.

[0065] Compared with the factor decomposition method, the redundancy analysis combined with the multiple regression method proposed in the present application can analyze the factors influencing carbon emission at a more microscopic level, and provides a high-resolution (fine to the device scale) carbon emission influencing factor identification method. The method can identify important factors influencing carbon emission from process parameters, and is more objective and practical, avoiding subjective judgment of carbon emission influencing factors.

[0066] Step 6, quantitative evaluation of the implementation effect of carbon emission reduction measures based on the quantitative evaluation model of carbon emission reduction measures, and determination of the optimal carbon emission reduction measures.

[0067] Based on the proposed qualitative carbon emission reduction measures, the qualitative carbon emission reduction measures are schematized, parameterized and quantified by scenario analysis method, and then substituted into the built quantitative evaluation model of carbon emission reduction measures to quantitatively evaluate the implementation effect of carbon emission reduction measures, and the best carbon emission reduction measures are optimized based on the implementation effect.

[0068] The application approaches of the method include: (1) the carbon emission influencing factors of each process device can be identified according to the daily account data of the enterprise; (2) more targeted quantitative carbon emission reduction measures are proposed and the emission reduction effect is predicted according to the identified carbon emission influencing factors.

[0069] The oil refining device carbon emission influencing factor identification and emission reduction measure analysis method of the present application is based on production account data related to the process of the oil refining device, uses redundancy analysis and multiple regression analysis to identify carbon emission influencing factors of the production device of the oil refining enterprise, and quantifies the contribution degree of key influencing factors to carbon emission. For the identified key influencing factors of the carbon emission of the oil refining device, specific measures for implementing carbon emission reduction of the oil refining device are screened out, the quantitative evaluation model based on the carbon emission reduction measures is used to quantitatively compare the emission reduction effects of different carbon emission reduction measures, and the optimal carbon emission reduction measure of the oil refining device is proposed, so that the identification of carbon emission influencing factors and the design of emission reduction measures at the device level of the enterprise are realized, and the resolution, accuracy and operability of carbon emission reduction of the enterprise are improved.

[0070] Based on the above-mentioned oil refining device carbon emission influencing factor identification and emission reduction measure analysis method, the present embodiment proposes an oil refining device carbon emission influencing factor identification and emission reduction measure analysis system, which comprises:

[0071] A collection unit is configured to collect process parameter account data of the oil refining device.

[0072] An identification unit is configured to identify carbon emission influencing factors of the oil refining device based on the account data.

[0073] An analysis unit is configured to analyze the contribution degree of the carbon emission influencing factors of the oil refining device and identify key influencing factors of carbon emission.

[0074] A carbon emission reduction measure unit is configured to propose carbon emission reduction measures based on the key influencing factors.

[0075] A model unit is configured to build a quantitative evaluation model of carbon emission reduction measures of the oil refining device.

[0076] A determination unit is configured to quantitatively evaluate the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model of the carbon emission reduction measures, and determine the optimal carbon emission reduction measure.

[0077] The following further illustrates the technical scheme of the present application by taking the carbon emission of a catalytic cracking process device of an oil refinery as an example.

[0078] The technical monthly report of the catalytic cracking device in 2021-2022 is collected. Carbon dioxide (CO2) and methane (CH4) are selected as response variables, and 128 production and dosage parameters related to carbon emission are selected as explanatory variables, mainly including process parameters such as reaction regeneration part, PSA system (i.e. pressure swing adsorption system), fractionation part, absorption stabilization, desulfurization part, demercaptan part, flue gas desulfurization and denitrification, catalyst dosage and properties, and raw material amount. Redundancy analysis is performed, and the results are as follows: Figure 2, as shown in Table 1. It can be seen that the angle between the circulating amount (L6) and the CO2 axis is smaller, and the length is the longest, indicating that it has the highest positive correlation with CO2 emission. At the same time, the specific surface area of the catalyst (SA) and the oil slurry (P7) also have a strong positive correlation with the CO2 emission. The key influencing factors affecting CH4 emission mainly include C-5002 pressure (PR11) and bottom loose steam (VF6). Figure 2 In the formula, C3 is the amount of passivator; VF11 is the blast furnace steam emission; L9 is the oil slurry delivery flow; AC is the micro-reaction activity; T15 is the middle gas phase temperature; W1 is the primary air (large); L23 is the D-9005 boundary position; L10 is the total amount of feed; VF6 is the bottom loose steam; PR10 is the C-5001 pressure; T26 is the C-5004 top temperature; L17 is the mixed dry gas lean liquid; PR1 is the settler pressure; PR13 is the D-5010 pressure; PR9 is the reabsorption tower pressure; and PM is the flue gas outlet particulate matter.

[0079] Table 1 Redundancy analysis results

[0080] Factor name Abbreviation Contribution rate (%) Significance level P value Primary recycle amount (t / h) L6 54.8 0.002 Oil slurry (kg) P7 17.5 0.002 Catalyst specific surface area (m 2 / g) SA 6.6 0.020 C-5002 pressure (MPa) PR11 5.8 0.022 Bottom loose steam (kg / h) VF6 2.5 0.016

[0081] Based on the explanatory variable factors in Table 1, a multiple regression model of the carbon dioxide index (y) is established by using stepwise regression method:

[0082] y = 0.07x1 + 1.21x10 -6 x2 + 5.455 (3)

[0083] In the formula, y is the CO2 emission concentration, mg / m 3 ; x1 is the circulating amount in the first reactor, t / h; x2 is the oil slurry, kg; the adjusted R 2 of the model reaches 0.761, and the t test results of formula (3) are shown in Table 2. The P value values of L6 and P7 are less than 0.05, indicating that the independent variables have a high fitting effect and significance.

[0084] As can be seen from formula (3), the carbon dioxide emission can be explained or predicted by the circulating amount in the first reactor (L6) and the oil slurry (P7). The results are basically consistent with the RDA analysis results.

[0085] Further, based on the identified key influencing factors, carbon emission reduction measures are proposed, as shown in Table 3. On this basis, the carbon emission reduction measures are parameterized and quantified based on scenario analysis method, and finally the emission reduction schemes are formed, as shown in schemes A, B and C in Table 4. The carbon emission reduction effects of each scheme are shown in Table 4.

[0086] According to the comparison of carbon emission reduction effect in Table 4, the scheme C is selected as the best carbon emission reduction measure, and the specific content includes that the circulating amount (L6) and the slurry (P7) scale are reduced by 10% and 15% respectively compared with the present situation, and the carbon dioxide is reduced by 9.2% compared with the present situation according to the formula (3).

[0087] Table 2 t test results

[0088]

[0089] Table 3 carbon emission reduction measures

[0090]

[0091] Table 4 carbon emission reduction effect

[0092]

[0093] Although the present application is described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying factors affecting carbon emissions of a refinery and analyzing measures for reducing the carbon emissions, characterized by, The method comprises the following steps: Collecting refinery process parameter account data; Identifying refinery carbon emission influencing factors based on the account data; Analyzing the contribution of the refinery carbon emission influencing factors to identify key influencing factors of carbon emission; Proposing carbon emission reduction measures based on the key influencing factors; Building a quantitative evaluation model for the carbon emission reduction measures of the refinery; Quantitatively evaluating the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model, and determining the optimal carbon emission reduction measures.

2. The method of claim 1, wherein the method further comprises: The account data includes yield data, production parameter data, energy consumption data, laboratory analysis data, and environmental protection monitoring data.

3. The method of claim 2, wherein the carbon emission influencing factor identification and abatement measure analysis method of the oil refining apparatus is characterized by, The specific steps of identifying the refinery carbon emission influencing factors based on the account data are as follows: Taking carbon dioxide and methane in the environmental protection monitoring data as response variables, and taking the yield data, production parameter data, energy consumption data, and laboratory analysis data as explanatory variables, a redundancy analysis is carried out to obtain the relationship between the explanatory variables and the response variables, and then the refinery carbon emission influencing factors are identified.

4. The method of claim 1, wherein the method further comprises: The specific steps of analyzing the contribution of the refinery carbon emission influencing factors to identify key influencing factors of carbon emission are as follows: Determining the contribution rate of each explanatory variable to the response variable; According to the contribution rate, the key influencing factors of carbon emission are identified.

5. The method of claim 4, wherein the refinery carbon emission impact factor identification and abatement measure analysis method is characterized by, The contribution rate is determined by the following formula: Wherein, Δλ i is the contribution rate of the variable factor i to the total variation of carbon emissions; λ i is the eigenvalue of the i th principal component in the fitting value matrix; λ T is the cumulative value of all eigenvalues in the fitting value matrix.

6. The method of claim 1, wherein the method further comprises: The carbon emission reduction measures include reducing energy consumption, optimizing reaction conditions, controlling scale, and improving catalysts.

7. The method of claim 1, wherein the method further comprises: The specific steps of building a quantitative evaluation model for the carbon emission reduction measures of the refinery are as follows: Based on the key influencing factors of carbon emission and the account data, a quantitative evaluation model for the carbon emission reduction measures between the response variables and the key influencing factors is built by using the stepwise regression method of multiple regression analysis.

8. The method of claim 7, wherein the method further comprises: The quantitative evaluation model for the carbon emission reduction measures is as follows: y i = β0+ β1x i + ε where y i is the response variable; β0is a parameter; β1is a parameter; x i is the key influencing factor; and ε is a random error.

9. The method of claim 1, wherein the method further comprises: The specific steps of quantitatively evaluating the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model for the carbon emission reduction measures, and determining the optimal carbon emission reduction measures are as follows: After the carbon emission reduction measures are schematized, parameterized, and quantified, they are substituted into the quantitative evaluation model for the carbon emission reduction measures to quantitatively evaluate the implementation effect of the carbon emission reduction measures; Based on the implementation effect, the optimal carbon emission reduction measures are determined.

10. A system for identifying factors affecting carbon emissions of a refinery and analyzing measures for reducing the carbon emissions, the system comprising: a refinery carbon emissions database; a refinery carbon emissions analysis module; and a refinery carbon emissions reduction analysis module. The method comprises the following steps: A collection unit for collecting refinery process parameter account data; An identification unit for identifying refinery carbon emission influencing factors based on the account data; An analysis unit for analyzing the contribution of the refinery carbon emission influencing factors to identify key influencing factors of carbon emission; A carbon emission reduction measure unit for proposing carbon emission reduction measures based on the key influencing factors; A model unit for building a quantitative evaluation model for the carbon emission reduction measures of the refinery; A determination unit for quantitatively evaluating the implementation effect of the carbon emission reduction measures based on the quantitative evaluation model, and determining the optimal carbon emission reduction measures.

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