CN114662781A - Cloud-based intelligent carbon footprint evaluation management system and evaluation method thereof - Google Patents
Cloud-based intelligent carbon footprint evaluation management system and evaluation method thereof Download PDFInfo
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Abstract
The invention discloses an intelligent carbon footprint evaluation management system based on a cloud and an evaluation method thereof, wherein the system comprises a front-end display unit and a rear-end processing unit, the front-end display unit comprises an intelligent visualization module based on a DataV data visualization design, and the intelligent visualization module is used for performing visualization presentation and analysis according to different dimensions; the back-end processing unit comprises a data and application supporting module based on cloud computing and big data technology and a basic resource supporting module based on cloud database and network security. According to the method, technical means such as cloud computing, mobile internet and RFID are combined, and carbon footprint evaluation and analysis prediction of carbon emission units are achieved, so that energy efficiency is improved, energy consumption is reduced, operation cost is reduced, and benign and sustainable development of enterprises is promoted.
Description
Technical Field
The invention belongs to the technical field of carbon emission management, and particularly relates to an intelligent carbon footprint evaluation management system based on a cloud and an evaluation method thereof.
Background
With the progress of scientific technology and the rapid development of modern artificial intelligence technology, new vitality is injected into the traditional carbon footprint accounting mode. So to say, the modern carbon footprint accounting and evaluating method has been advanced to the brand-new stage of automation, intellectualization and individuation, and is developing towards the accurate and pre-estimated effect. Meanwhile, the cloud technology is based on the technologies of 5G, the Internet of things and the like, so that the computing resources and the management cost are effectively saved, the big data resources and the cloud computing technology are fully utilized, and a more convenient way is provided for the realization of high efficiency, accuracy and intelligence of carbon footprint evaluation management.
The industry currently recognizes that the carbon footprint can be defined as the amount of greenhouse gas (GHG) emissions that are directly and indirectly caused during the life cycle for a given product or activity. The carbon footprint is generally measured in units of carbon dioxide equivalents (CO2e), which is the equivalent of converting other greenhouse gases into carbon dioxide via the greenhouse effect coefficient (GWP). Internationally, it is required that the carbon footprint disclose or evaluate all information intended to cover the various stages of the life cycle, including the raw materials (including shipping), production manufacturing, distribution retail, consumer use and disposal or reuse stages. The greenhouse gases are mainly 6 greenhouse gases specified in the kyoto protocol, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs) and sulfur hexafluoride (SF 6).
The current carbon footprint accounting methods differ from each other: the analysis result of the life cycle evaluation method is targeted and suitable for a microscopic system, but the method has the problem of system boundary and has larger requirements on manpower and material resources; the investment and production analysis method takes the whole system as a boundary, requires less manpower and material resources during accounting, and is suitable for carbon footprint accounting of a macroscopic system; the mixed period carbon footprint evaluation method integrates the advantages of the former two methods, and is a hotspot of the current carbon footprint accounting research.
Problems in the current carbon footprint accounting and methods of practice:
(1) in the aspect of the method, a unified carbon footprint method accounting framework system is not established, and results of different computing methods are not highly comparable. In addition, existing methods remain controversial in addressing the carbon footprint of capital goods and land use variations.
(2) The current application research mostly expands around accounting the amount of the carbon footprint, and the research on the space-time distribution trend and the driving force of the carbon footprint is less.
(3) Uncertainty, due to both methodology and data source, few studies currently conduct uncertainty analysis of accounting results, and are inexperienced in uncertainty analysis of estimated and reduced carbon footprint results.
(4) The current carbon footprint accounting software has no strict unified standard for a visual report, and the workload of a manager is very large.
In order to solve the problems, innovative design is urgently needed on the basis of the original carbon footprint accounting system based on the internet and the internet of things.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the cloud-based intelligent carbon footprint evaluation management system and the evaluation method thereof are provided, and the carbon footprint evaluation and analysis prediction of carbon emission units are realized by combining the technical means of cloud computing, mobile internet, RFID and the like, so that the energy efficiency is improved, the energy consumption is reduced, the operation cost is reduced, and the benign and sustainable development of enterprises is promoted.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a cloud-based intelligent carbon footprint evaluation method, which includes the following steps:
s1: collecting and importing carbon emission factor types, carbon emission amounts and carbon emission factor data sources;
s2: checking and quantifying the carbon emission factor through a carbon emission factor database;
s3: according to the carbon footprint accounting formula, accounting is carried out on the carbon footprint of the product;
s4: performing uncertainty analysis on the product carbon footprint accounting result according to the carbon footprint accounting result of the step S3 and the carbon emission factor data source of the step S1;
s5: performing potential analysis and contribution analysis on the current product scheme according to results obtained in the steps S1-S4;
s6: generating an alternative solution according to the results obtained in the steps S1-S5;
s7: evaluating the alternatives;
s8: generating a product carbon footprint report according to results obtained in the steps S1-S6;
s9: and intelligently and visually presenting the product carbon footprint report.
Further, the calculation formula of the carbon footprint in step S3 is specifically as follows:
aiming at the power grid enterprise:
wherein the RECCapacity, iRefers to the sulfur hexafluoride capacity, REC, of decommissioned equipment iRecovery of iRefers to the actual recovery quantity, REP, of sulfur hexafluoride of the decommissioned equipment iCapacity, jRefers to the sulfur hexafluoride capacity, REP, of the repair facility jRecovery of jRefers to the actual recovery, GWP, of sulfur hexafluoride repairing the equipment jSF6Refers to the greenhouse gas potential, EL, of sulfur hexafluorideInternet accessRefers to the power on-line quantity of the power plant, ELOutput ofMeans inputting electric quantity from outside, ELSelling electricityIs to output electric quantity, EFElectric networkThe method refers to annual average power supply emission factors of a regional power grid;
aiming at power generation enterprises:
wherein, ADElectric powerMeans that the enterprise purchases electric quantity, EFElectric powerIs the annual average power supply emission factor, FC, of the regional power gridiRefers to fossil fuel consumption, EFiIs referred to as combustion emission factor, BkMeans the amount of desulfurizing agent consumed, IkRefers to the carbonate content, EF, in the desulfurizing agentkCarbonate emission factor, TR conversion rate;
aiming at electronic equipment manufacturing enterprises:
wherein, ADiRefers to the net consumption of fossil fuel, EFiIs referred to as the discharge factor, h is the gas residual proportion of the feed gas vessel, IBiMeans initial inventory of raw material gas i, OiRefers to the amount of feed gas i purchased, IEiMeans the initial inventory of the raw material gas i, SiOutward sale or output of raw material gas i, UiMeans the utilization ratio of the raw material gas i, aiMeans the collection efficiency for the feed gas i, diMeans the removal efficiency, GWP, of the feed gas iiRefers to the global warming potential of the feed gas i, BijRefers to the conversion factor, a, of the by-product j produced from the raw material gas iiRefers to the collection efficiency of the by-product j, djMeans the removal efficiency, GWP, of the by-product jiRefers to the global warming tendency, AD, of the by-product jElectric powerMeans that the enterprise purchases electric quantity, EFElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor;
aiming at the steel industry:
therein, FCiRefers to the net consumption of fossil fuels, EFiIs referred to as the fuel emission factor, PSolvent(s)Means the net consumption of solvent, EFSolvent(s)Is referred to as the emission factor, PElectrode for electrochemical cellIs the amount of electrode, EF, consumed by electric furnace steelmaking, refining, etcElectrode for electrochemical cellRefers to the fuel emission factor, MRaw materialsRefers to the purchased amount of the carbonaceous material, EFRaw materialsRefers to the emission factor, ADCarbon sequestrationIs the yield of carbon fixation product, EFCarbon sequestrationRefers to the emission factor, ADElectric powerMeans that the net amount of power purchased, EFElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to a heat supply emission factor;
aiming at the chemical industry:
wherein, ADElectric powerMeans that the electric quantity, EF, is purchasedElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerThe power of net purchased heat, EF heat power heat supply emission factor, AD fossil fuel consumption, EF emission factor and ADrIs the raw material input amount, CC is the carbon content, ADpRefers to the yield of carbonaceous product, ADwRefers to carbon-containing waste output, ADiMeans the consumption of carbonate used as raw material, flux and desulfurizing agent, EFiRefers to N of production technology type j2O production factor, ADjRefers to the adipic acid yield of process j, akN of different tail gas treatment types k in adipic acid production2O removal efficiency, Q means Co recovered and supplied to the outside2Gas volume, PURCO2Refers to CO2Purity of externally supplied gas.
Further, in step S4, a monte carlo method is used to realize uncertainty analysis of carbon footprint, and the specific analysis method is as follows:
a1: constructing or describing a probabilistic process:
generating random variable factors with known probability distribution through the constructed product carbon footprint probability prediction model, and converting the product carbon footprint variable factors without random properties into the product carbon footprint variable factors with random properties;
a2: the sampling from the known probability distribution is achieved:
various variable factors of the carbon footprint of the product are collected, and various variable factors of the product aiming at different scenes are randomly generated through a computer, so that the random variable factors of the carbon footprint of the product under different scenes are generated and sampled;
a3: establishing various estimators, and generating an uncertainty analysis report:
determining a random variable factor as a solution to the required problem, called product carbon footprint unbiased estimation; and establishing various estimators, investigating and registering the results of the simulation experiment, obtaining the required product carbon footprint information from the results, and generating a product carbon footprint uncertainty analysis report.
Further, the method for constructing the product carbon footprint probability prediction model in the step a1 is as follows:
b1: performing principal component analysis on the carbon emission factor to obtain variance percentage;
b2: performing multiple linear regression analysis by using the extracted principal components as new variables instead of original variables to obtain a regression equation, and substituting the principal components into the regression equation according to the loads of the respective variables to obtain the regression equation about each explanatory variable;
b3: drawing a histogram by using a regression residual of a regression equation, and drawing a standardized residual and a standardized predicted value into a scatter diagram to check the homogeneity of the variances; and finishing the construction of the carbon footprint probability prediction model of the product.
Further, the alternative of step S6 is generated, and the specific method is as follows:
and generating a product operation mode alternative scheme according to the existing operation mode of the product and by referring to China EDP and ISO standards based on the obtained results of the steps S1-S5.
Further, the alternative evaluation of step S7 includes the following steps:
c1: realizing virtual operation and carbon emission analysis of the alternative scheme by depending on a basic resource service module;
c2: and analyzing and comparing the carbon reduction indexes of the alternative scheme and the original scheme, and giving an alternative scheme evaluation.
Further, the existing product existing operation mode carbon emission reduction potential analysis of the step S5 specifically includes, for the chemical industry:
d1: under the operation mode, the absolute carbon dioxide emission reduction potential of the product is as follows:
wherein the content of the first and second substances,
refers to the total carbon dioxide emission amount of a specific scene of the industrial industry in the y time period,
total carbon dioxide emission under the condition of internalization industrial scene l in the y time period;
d2: the carbon dioxide emission intensity of the chemical industry is as follows:
wherein V is an industrial added value in the chemical industry;
d3: it can be seen that the relative carbon dioxide emission potential of the chemical industry in the y time period is:
wherein the content of the first and second substances,
in the reference time period, the carbon dioxide emission intensity in the chemical industry,
the carbon dioxide emission intensity of the chemical industry under the situation l in the time period y is shown.
Further, the influence element contribution analysis of the existing product in step S5 includes the following specific analysis method:
wherein, CsFor the cumulative emission of carbon dioxide in this time period, i.e. the degree of contribution cejRepresents the cumulative emission reduction intensity, ce, over a period of j0As intensity of carbon emission in a reference period of time, EjIs the total amount of energy consumed in the period j.
The invention also provides an intelligent carbon footprint evaluation management system based on the cloud, which comprises a front-end display unit and a back-end processing unit, wherein the front-end display unit is used for presenting a front-end interface to a user to realize user interface interaction of an internet product; the back-end processing unit is connected with the cloud server to complete the liberation of the resource consumption management of the user by using a database technology, the experience and the capability of an external API interface of a cross-platform are designed and developed, the external API is called to carry out autonomous design, and the system modularization is realized by a packing function.
The front-end display unit comprises an intelligent visualization module based on the visual design of the DataV data, and the intelligent visualization module is used for performing visual presentation and analysis according to different dimensions;
the back-end processing unit comprises a data and application supporting module based on cloud computing and big data technology and a basic resource supporting module based on a cloud database and network security;
the data and application support module is used for carbon footprint calculation, uncertainty analysis and generation of product carbon footprint reports;
the basic resource support module is used for providing platform foundation and data resources for functions of carbon footprint calculation, uncertainty analysis, generation of product carbon footprint reports and the like, and meanwhile, the network security of the platform is guaranteed. The cloud server provides computer resources such as a database and the like, and the computer resources are remotely called to realize computer control and configuration and serve as the basis of the cloud technology. The storage space of the cloud server in resource aspect, the functions and management of the database in various aspects and provide a foundation for storage and calculation of the collected data. Updating the database, the association and data transmission between the databases and the network security management guarantee platform security.
And the connection and transmission among data and the analysis, evaluation and display of reports are all completed by an intelligent carbon footprint evaluation management system based on a cloud end.
The intelligent visualization module is based on the visual design of the DataV data, supports the display of various chart components and data types, and supports the access of various data sources; the system can be accessed to an Aricloud analysis type database, a relational database, local CSV uploading and online API access, and supports dynamic requests; the method supports screen display with various resolutions, and can be optimized for special screens; and a graphical building tool is supported, and a non-professional programmer can operate the tool.
The data and application support module takes bottom support of big data calculation as a high-performance real-time memory database: the system is constructed based on technologies such as distributed storage, distributed computation, interactive query, full text retrieval, data encryption, system disaster tolerance and the like, has functions of big data acquisition, cleaning, conversion, storage, analysis, display and the like, provides a standardized access interface for the outside, and realizes acquisition, auditing and unified management and distribution of carbon emission factor data. All data storage is uploaded to the cloud end through unified quantification by virtue of virtual services, and standardized storage and management of carbon emission factors are realized; the carbon footprint accounting depends on the data and application support module to realize multi-source data access, the data quantization is realized through the middle platform processing, then the data is extracted through the carbon footprint calculator, and the carbon footprint of the product is accounted according to the carbon footprint accounting formula.
The present invention will use a monte carlo simulation (monte carlo simulation) based calculation of uncertainty, simulating 100,000 times with a-0.05, 95% confidence interval. The monte carlo method (monte carlo method) is characterized in that an approximate result can be obtained by calculation on random sampling, and the probability that the obtained result is a correct result is gradually increased along with the increase of sampling. Due to the multi-dimensionality and complexity of carbon footprint influence factors, other methods are used for uncertainty analysis of the carbon footprint, the difficulty is high, the result is inaccurate, and the Monte Carlo method is simpler to calculate the carbon footprint. But also has the following advantages that other using methods do not have: the carbon footprint of the product is directly tracked, the physical thought is clear, and the product is easy to understand; by adopting a random sampling method, the track of the carbon footprint of the product is simulated more truly, and the fluctuation rule is reflected; the method is not limited by the complexity of multiple dimensions, multiple factors and the like of the system, and is a good method for solving the carbon footprint of the complex system.
The invention adopts MC method to write program to solve the problem of uncertainty analysis of carbon footprint and obtain the intermediate result wanted by the user.
In the aspect of result analysis, a database and a quantity model of production and consumption activities are established, various environmental quantities are calculated, and carbon emission of the whole process of a product is calculated. Secondly, after the total amount is calculated, the system can identify the product with the largest energy consumption in a certain link as the key point of improvement. Moreover, in these advanced points, various alternatives are possible. The system finally displays the scheme of the optimal solution of the user through comparative analysis of various schemes, and provides a method basis for continuous improvement. In fact, the improvement scheme mainly aims at energy conservation and energy source substitution, and material conservation and raw material substitution, and provides accurate data after emission reduction. LCA provides an optimal solution for carbon reduction in all industries by measuring energy consumption and total emissions from the production process upstream of the feedstock.
The model of the invention can predict future changes at the same time. In a power grid application scenario, the carbon emission per degree of electricity is decreasing with the change of the power grid itself, and the system is predicting the future change trend of 1.7 tons for producing high density polyethylene. The carbon footprint of the product is not constant and the acquisition of data is dependent on the data acquisition of the technical route.
According to the invention, a product carbon footprint report is generated according to the product carbon footprint type by combining ChinaEDP and ISO international standards, and in the report, contribution analysis and potential analysis are carried out on the product by integrating the carbon emission, uncertainty analysis and the whole process of the product life cycle, and comparison and evaluation are carried out. Based on the carbon footprint of the current product, the aspects of raw material acquisition, research and development design, production flow, product flow direction, product recovery and the like of the product are checked, the advantages and disadvantages of the life cycle of the current product are analyzed, various alternative schemes are generated, and the carbon footprint of an enterprise is subjected to one-stop accounting service.
The invention provides intelligent visual display of the product carbon footprint in the whole period, and the final report generation is presented to enterprises in a dynamic visual manner, so that the enterprises can understand the advantages and disadvantages of the product carbon footprint more easily, and the global low-carbon advocate is better adapted.
Has the advantages that: compared with the prior art, the method and the device have the advantages that technical means such as cloud computing, mobile internet, RFID and the like are combined, and carbon footprint evaluation and analysis prediction of carbon emission units are realized, so that the energy efficiency is improved, the energy consumption is reduced, the operation cost is reduced, and the benign and sustainable development of enterprises can be promoted.
Drawings
FIG. 1 is an architectural diagram of the system of the present invention;
FIG. 2 is a flow chart of the intelligent carbon footprint evaluation of the system of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The invention provides an intelligent carbon footprint evaluation management system based on a cloud, which comprises a front-end display unit and a back-end processing unit, wherein the front-end display unit is used for presenting a front-end interface to a user to realize user interface interaction of an internet product; the back-end processing unit uses a database technology, is simultaneously connected with the cloud server to complete the liberation of the resource consumption management of a user, designs and develops the experience and the capability of an external API (application program interface) interface of a cross-platform, simultaneously calls an external API to carry out autonomous design, and realizes the system modularization by a packing function.
The front-end display unit comprises an intelligent visualization module based on the visual design of the DataV data, and the intelligent visualization module is used for performing visual presentation and analysis according to different dimensions;
the back-end processing unit comprises a data and application supporting module based on cloud computing and big data technology and a basic resource supporting module based on a cloud database and network security;
the data and application support module is used for carbon footprint calculation, uncertainty analysis and generation of a product carbon footprint report;
the basic resource support module is used for providing platform basis and data resources for functions such as carbon footprint calculation, uncertainty analysis and generation of product carbon footprint reports, and meanwhile guaranteeing the network security of the platform. The cloud server provides computer resources such as a database and the like, and the computer resources are remotely called to realize computer control and configuration and serve as the basis of the cloud technology. The storage space of the cloud server in resource aspect, the functions and management of the database in various aspects and provide a foundation for storage and calculation of the collected data. Updating the database, the association and data transmission between the databases and the network security management guarantee platform security.
Based on the above system, the embodiment applies the system to an example, and provides a cloud-based intelligent carbon footprint evaluation method, as shown in fig. 2, which includes the following steps:
s1: acquiring and importing carbon emission factor types, carbon emission amounts and carbon emission factor data sources through a carbon emission source of the system and source data acquisition equipment of the carbon emission source;
s2: checking and quantifying the carbon emission factor through a carbon emission factor database;
s3: according to the carbon footprint accounting formula, accounting is carried out on the carbon footprint of the product;
s4: performing uncertainty analysis on the product carbon footprint accounting result according to the carbon footprint accounting result of the step S3 and the carbon emission factor data source of the step S1;
s5: performing potential analysis and contribution analysis on the current product scheme according to results obtained in the steps S1-S4;
s6: generating an alternative solution according to the results obtained in the steps S1-S5;
s7: evaluating the alternatives;
s8: generating a product carbon footprint report according to results obtained in the steps S1-S6;
s9: and intelligently and visually presenting the product carbon footprint report.
The steps S2 to S8 are implemented by the data and application support module and the basic resource support module, wherein the data and application support module is used for carbon footprint calculation, uncertainty analysis and generation of a product carbon footprint report, and the basic resource support module is used for providing a platform basis and data resources for the data and application support module.
Further, the calculation formula of the carbon footprint in step S3 is specifically as follows:
aiming at a power grid enterprise:
wherein the RECCapacity, iRefers to the sulfur hexafluoride capacity, REC, of decommissioned equipment iRecovery of iRefers to the actual recovery quantity, REP, of sulfur hexafluoride of the decommissioned equipment iCapacity, jRefers to the sulfur hexafluoride capacity, REP, of the repair facility jRecovery of jRefers to the actual recovery, GWP, of sulfur hexafluoride repairing the equipment jSF6Refers to the greenhouse gas potential, EL, of sulfur hexafluorideInternet accessRefers to the power on-line quantity of the power plant, ELOutput the outputMeans inputting electric quantity from outside, ELSelling electricityIs to output electric quantity, EFElectric networkThe method refers to annual average power supply emission factors of a regional power grid;
aiming at power generation enterprises:
wherein, ADElectric powerMeans that the enterprise purchases electric quantity, EFElectric powerIs the annual average power supply emission factor, FC, of the regional power gridiRefers to fossil fuel consumption, EFiIs referred to as combustion emission factor, BkMeans the amount of desulfurizing agent consumed, IkIs the carbonate content, EF, in the desulfurizing agentkCarbonate emission factor, TR conversion rate;
aiming at electronic equipment manufacturing enterprises:
wherein, ADiRefers to the net consumption of fossil fuel, EFiIs referred to as the discharge factor, h is the gas residual proportion of the feed gas vessel, IBiMeans initial inventory, P, of raw gas iiRefers to the amount of feed gas i purchased, IEiMeans the initial inventory of the raw material gas i, SiOutward sale or output of raw material gas i, UiMeans the utilization ratio of the raw material gas i, aiRefers to the collection efficiency of the raw material gas i, di refers to the removal efficiency and GWP of the raw material gas iiRefers to the global warming potential of the feed gas i, BijRefers to the conversion factor, a, of the by-product j produced from the raw material gas iiRefers to the collection efficiency of the by-product j, djMeans the removal efficiency, GWP, of the by-product jiThe global warming tendency of a byproduct j is shown, the AD electric power is net electric quantity purchased by an enterprise, and the EF electric powerElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor;
aiming at the steel industry:
therein, FCiRefers to the net consumption of fossil fuels, EFiIs referred to as the fuel emission factor, PSolvent(s)Means the net consumption of solvent, EF solutionAgent for treating cancerIs referred to as the emission factor, PElectrode for electrochemical cellIs the amount of electrode, EF, consumed by electric furnace steelmaking, refining, etcElectrode for electrochemical cellRefers to the fuel emission factor, MRaw materialsRefers to the purchased amount of the carbonaceous material, EFRaw materialsRefers to the emission factor, ADCarbon sequestrationIs the yield of carbon fixation product, EFCarbon sequestrationRefers to the emission factor, ADElectric powerMeans that the net amount of power purchased, EFElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor;
aiming at the chemical industry:
wherein, ADElectric powerMeans that the electric quantity, EF, is purchasedElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor, AD refers to fossil fuel consumption, EF refers to emission factor, ADrIs the raw material input amount, CC is the carbon content, ADpMeans the yield of carbonaceous product, ADwRefers to carbon-containing waste output, ADiMeans the consumption of carbonate used as raw material, flux and desulfurizing agent, EFiRefers to N of production technology type j2O production factor, ADjRefers to the adipic acid yield of process j, akN of different tail gas treatment types k in adipic acid production2O removal efficiency, Q means CO recovered and supplied externally2Gas volume, PURCO2Refers to CO2Purity of externally supplied gas.
Further, in step S4, a monte carlo method is used to realize uncertainty analysis of carbon footprint, and the specific analysis method is as follows:
a1: constructing or describing a probabilistic process:
generating random variable factors with known probability distribution through the constructed product carbon footprint probability prediction model, and converting the product carbon footprint variable factors without random properties into the product carbon footprint variable factors with random properties;
a2: the sampling from the known probability distribution is achieved:
various variable factors of the carbon footprint of the product are collected, and various variable factors of the product aiming at different scenes are randomly generated through a computer, so that the random variable factors of the carbon footprint of the product under different scenes are generated and sampled;
a3: establishing various estimators, and generating an uncertainty analysis report:
determining a random variable factor as a solution to the required problem, called product carbon footprint unbiased estimation; and establishing various estimators, investigating and registering the results of the simulation experiment, obtaining the required product carbon footprint information from the results, and generating a product carbon footprint uncertainty analysis report.
Further, the method for constructing the product carbon footprint probability prediction model in the step a1 is as follows:
b1: performing principal component analysis on the carbon emission factor to obtain variance percentage;
b2: performing multiple linear regression analysis by using the extracted principal components as new variables instead of original variables to obtain a regression equation, and substituting the principal components into the regression equation according to the loads of the respective variables to obtain the regression equation about each explanatory variable;
b3: drawing a histogram by using a regression residual of a regression equation, and drawing a standardized residual and a standardized predicted value into a scatter diagram to check the homogeneity of the variances; and finishing the construction of the carbon footprint probability prediction model of the product.
In this embodiment, the alternative of step S6 is generated, and the specific method is as follows:
and generating a product operation mode alternative scheme by referring to China EDP and ISO standards according to the existing operation mode of the product based on the obtained results of the steps S1-S5.
In this embodiment, the alternative evaluation of step S7 is specifically performed as follows:
c1: realizing virtual operation and carbon emission analysis of the alternative scheme by depending on a basic resource service module;
c2: and analyzing and comparing the carbon reduction indexes of the alternative scheme and the original scheme, and giving an alternative scheme evaluation.
In the embodiment of the present invention, for the analysis of the carbon emission reduction potential of the existing product in the existing operation mode in step S5, the specific analysis method is as follows:
d1: under the operation mode, the absolute carbon dioxide emission reduction potential of the product is as follows:
wherein the content of the first and second substances,
refers to the total carbon dioxide emission amount of a specific scene of the chemical industry in the y time period,
the total carbon dioxide emission amount is the total carbon dioxide emission amount under the situation 1 of the chemical industry in the time period y;
d2: the carbon dioxide emission intensity of the chemical industry is as follows:
wherein V is an industrial added value in the chemical industry;
d3: it can be obtained that the relative carbon dioxide emission potential of the chemical industry in the y time period is:
wherein the content of the first and second substances,
for reference time period chemical engineeringThe carbon dioxide emission intensity of the industry is high,
the carbon dioxide emission intensity of the chemical industry under the situation l in the time period y.
In this embodiment, the influence element contribution analysis of the existing product in step S5 includes the following specific analysis methods:
wherein, CsFor the accumulated emission reduction of carbon dioxide in the time period, i.e. the contribution degree cejRepresents the cumulative emission reduction intensity, ce, over a period of j0Intensity of carbon emission in a reference period of time, EjIs the total amount of energy consumed in the period j.
In order to verify the effect of the intelligent carbon footprint evaluation management system provided by the present invention, in this embodiment, the intelligent carbon footprint evaluation management system provided by the present invention is compared with the existing carbon footprint accounting system, and the system of the present invention has the following advantages compared with the existing carbon footprint accounting system and method:
1. the invention establishes a complete set of complete evaluation system, and compared with the existing system, the evaluation system is more complete, the evaluation method is more advanced, and the evaluation efficiency is more efficient.
2. According to the invention, a unified carbon footprint method accounting framework system is established according to ChinaEDP and ISO international standards, and comparability between results of different calculation methods is increased. The problem of controversy in addressing the carbon footprint of capital goods and land use variations is solved.
3. The invention innovatively increases the time-space distribution trend and the driving force analysis of the carbon footprint of the product on the carbon footprint evaluation method, and solves the problem that the existing evaluation system has incomplete carbon footprint evaluation links.
4. The invention innovatively adds uncertainty analysis on the estimation and reduction carbon footprint results on the carbon footprint evaluation method, and solves the problems of inaccurate evaluation results and low instructive performance of the existing evaluation system.
5. The invention creatively establishes the unified presentation standard of the visual report on the visual presentation of the carbon footprint evaluation result and solves the problem of large workload of the manager of the existing evaluation system.
Claims (10)
1. A cloud-based intelligent carbon footprint evaluation method is characterized by comprising the following steps:
s1: collecting and importing carbon emission factor types, carbon emission amounts and carbon emission factor data sources;
s2: checking and quantifying the carbon emission factor through a carbon emission factor database;
s3: according to the carbon footprint accounting formula, accounting is carried out on the carbon footprint of the product;
s4: performing uncertainty analysis on the product carbon footprint accounting result according to the carbon footprint accounting result of the step S3 and the carbon emission factor data source of the step S1;
s5: performing potential analysis and contribution analysis on the current product scheme according to results obtained in the steps S1-S4;
s6: generating an alternative solution according to the results obtained in the steps S1-S5;
s7: evaluating the alternatives;
s8: generating a product carbon footprint report according to results obtained in the steps S1-S6;
s9: and intelligently and visually presenting the product carbon footprint report.
2. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the carbon footprint calculation formula in step S3 is specifically as follows:
aiming at a power grid enterprise:
wherein the RECCapacity, iRefers to the sulfur hexafluoride capacity, REC, of decommissioned equipment iRecovery of iMeans thatActual recovery of sulfur hexafluoride, REP, in decommissioned plant iCapacity, jRefers to the sulfur hexafluoride capacity, REP, of the repair facility jRecovery of jRefers to the actual recovery, GWP, of sulfur hexafluoride repairing the equipment jSF6Refers to the greenhouse gas potential, EL, of sulfur hexafluorideInternet accessRefers to the power on-line quantity of the power plant, ELOutput ofMeans inputting electric quantity from outside, ELElectricity selling deviceIs to output electric quantity, EFElectric networkThe method refers to annual average power supply emission factors of a regional power grid;
aiming at power generation enterprises:
wherein, ADElectric powerMeans that the enterprise purchases electric quantity, EFElectric powerIs the annual average power supply emission factor, FC, of the regional power gridiRefers to fossil fuel consumption, EFiIs referred to as combustion emission factor, BkMeans the amount of desulfurizing agent consumed, IkIs the carbonate content, EF, in the desulfurizing agentkCarbonate emission factor, TR conversion rate;
aiming at electronic equipment manufacturing enterprises:
wherein, ADiRefers to the net consumption of fossil fuel, EFiIs referred to as the discharge factor, h is the gas residual proportion of the feed gas vessel, IBiMeans initial inventory, P, of raw gas iiRefers to the amount of feed gas i purchased, IEiMeans the initial inventory of the raw material gas i, SiOutward sale or output of raw material gas i, UiMeans the utilization ratio of the raw material gas i, aiMeans the collection efficiency for the feed gas i, diMeans the removal efficiency, GWP, of the feed gas iiRefers to the global warming potential of the feed gas i, BijRefers to the conversion factor, a, of the by-product j produced from the raw material gas iiMeans thatEfficiency of collection of by-product j, djMeans the removal efficiency, GWP, of the by-product jiRefers to the global warming tendency, AD, of the by-product jElectric powerMeans that the enterprise purchases electric quantity, EFElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor;
aiming at the steel industry:
therein, FCiRefers to the net consumption of fossil fuels, EFiIs referred to as the fuel emission factor, PSolvent(s)Means the net consumption of solvent, EFSolvent(s)Is referred to as the emission factor, PElectrode for electrochemical cellIs the amount of electrode, EF, consumed by electric furnace steelmaking, refining, etcElectrode for electrochemical cellRefers to the fuel emission factor, MRaw materialsRefers to the purchased amount of the carbonaceous material, EFRaw materialsRefers to the emission factor, ADCarbon sequestrationIs the yield of carbon fixation product, EFCarbon sequestrationRefers to the emission factor, ADElectric powerMeans that the net amount of power purchased, EFElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor;
aiming at the chemical industry:
wherein, ADElectric powerMeans that the electric quantity, EF, is purchasedElectric powerIs the annual average power supply emission factor, AD, of the regional power gridHeating powerMeans that the heat power, EF, is purchasedHeating powerRefers to heat supply emission factor, AD refers to fossil fuel consumption, EF refers to emission factor, and AD refers torIs the raw material input amount, CC is the carbon content, ADpMeans the yield of carbonaceous product, ADwRefers to carbon-containing waste output, ADiMeans thatUse of carbonates for raw materials, fluxes and desulphurizing agents consumption, EFiRefers to N of production technology type j2O production factor, ADjRefers to the adipic acid yield of process j, akN of different tail gas treatment types k in adipic acid production2O removal efficiency, Q means CO recovered and supplied externally2Gas volume, PURCO2Refers to CO2Purity of externally supplied gas.
3. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the step S4 is implemented by using a monte carlo method to analyze uncertainty of carbon footprint, and the specific analysis method is as follows:
a1: constructing or describing a probabilistic process:
generating random variable factors with known probability distribution through the constructed product carbon footprint probability prediction model, and converting the product carbon footprint variable factors without random properties into the product carbon footprint variable factors with random properties;
a2: the sampling from the known probability distribution is achieved:
various variable factors of the carbon footprint of the product are collected, and various variable factors of the product aiming at different scenes are randomly generated through a computer, so that the random variable factors of the carbon footprint of the product under different scenes are generated and sampled;
a3: establishing various estimators, and generating an uncertainty analysis report:
determining a random variable factor as a solution to the required problem, called product carbon footprint unbiased estimation; and establishing various estimators, investigating and registering the results of the simulation experiment, obtaining the required carbon footprint information of the product, and generating an uncertainty analysis report of the carbon footprint of the product.
4. The cloud-based intelligent carbon footprint evaluation method of claim 3, wherein the product carbon footprint probability prediction model in the step A1 is constructed by the following method:
b1: performing principal component analysis on the carbon emission factor to obtain variance percentage;
b2: performing multiple linear regression analysis by using the extracted principal components as new variables instead of original variables to obtain a regression equation, and substituting the principal components into the regression equation according to the loads of the respective variables to obtain the regression equation about each explanatory variable;
b3: drawing a histogram by using a regression residual of a regression equation, and drawing a standardized residual and a standardized predicted value into a scatter diagram to check the homogeneity of the variances; and finishing the construction of the carbon footprint probability prediction model of the product.
5. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the alternative of step S6 is generated by the following specific method:
and generating a product operation mode alternative scheme according to the existing operation mode of the product and by referring to China EDP and ISO standards based on the obtained results of the steps S1-S5.
6. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the alternative evaluation of step S7 is as follows:
c1: realizing virtual operation and carbon emission analysis of the alternative scheme by depending on a basic resource service module;
c2: and analyzing and comparing the carbon reduction indexes of the alternative scheme and the original scheme, and giving an alternative scheme evaluation.
7. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the existing operation mode carbon emission reduction potential analysis of the current product of step S5 is specifically performed by the following method for chemical industry:
d1: under the operation mode, the absolute carbon dioxide emission reduction potential of the product is as follows:
wherein the content of the first and second substances,
refers to the total carbon dioxide emission amount of a specific scene of the chemical industry in the y time period,
total carbon dioxide emission under the condition of internalization industrial scene l in the y time period;
d2: the carbon dioxide emission intensity of the chemical industry is as follows:
wherein V is an industrial added value in the chemical industry;
d3: it can be obtained that the relative carbon dioxide emission potential of the chemical industry in the y time period is:
wherein the content of the first and second substances,
for the emission intensity of carbon dioxide in the chemical industry in a reference time period,
the carbon dioxide emission intensity of the chemical industry under the situation l in the time period y.
8. The cloud-based intelligent carbon footprint evaluation method of claim 1, wherein the influence element contribution analysis of the current product of step S5 is specifically as follows:
wherein, CsFor the cumulative emission of carbon dioxide, i.e. the degree of contribution, ce, during this periodjRepresents the cumulative emission reduction intensity, ce, over a period of j0Intensity of carbon emission in a reference period of time, EjIs the total amount of energy consumed in the period j.
9. The cloud-based intelligent carbon footprint evaluation management system is characterized by comprising a front-end display unit and a back-end processing unit, wherein the front-end display unit is used for presenting a front-end interface to a user to realize user interface interaction of an internet product; the back-end processing unit is connected with the cloud server to complete the liberation of the resource consumption management of the user by using a database technology, the experience and the capability of an external API interface of a cross-platform are designed and developed, the external API is called to carry out autonomous design, and the system modularization is realized by a packing function.
10. The cloud-based intelligent carbon footprint evaluation and management system of claim 9, wherein the front-end display unit comprises a smart visualization module based on DataV data visualization design, the smart visualization module being configured to perform visualization presentation and analysis according to different dimensions;
the back-end processing unit comprises a data and application supporting module based on cloud computing and big data technology and a basic resource supporting module based on a cloud database and network security;
the data and application support module is used for carbon footprint calculation, uncertainty analysis and generation of product carbon footprint reports;
the basic resource support module is used for providing platform basis and data resources for the carbon footprint calculation, uncertainty analysis and product carbon footprint report generation functions and guaranteeing the platform network security.
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