A method and system for data volatility assessment for life cycle assessment

Abstract

The present application relates to a kind of data fluctuation evaluation method and system for life cycle assessment, the method comprises the following steps: according to the corresponding field data of life cycle assessment requirement acquisition;According to the simulation analysis object determined in advance, each simulation analysis object is simulated based on the field data for life cycle assessment, and the simulation is realized based on Monte Carlo method;The simulation result of the data analysis module is shown, and the data accuracy discrimination result is generated based on the simulation result and is shown.Compared with prior art, the present application can accurately evaluate the fluctuation of data, and finally can calculate the fluctuation of LCA result, thereby improving the reliability of life cycle assessment, can reduce the time length of data verification, and using the method of the present application can help enterprises reduce cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of life cycle assessment, and in particular to a data fluctuation evaluation method and system for life cycle assessment. BACKGROUND

[0002] With the continuous development of green manufacturing, green design product evaluation and other work, and the continuous deepening of the concept of sustainable development, product environmental performance has become the focus of attention. Life cycle assessment (LCA) is a method for quantitatively calculating product life cycle environmental load and environmental impact. LCA can investigate the environmental performance of products throughout the production process, identify the direction and approach to reducing product environmental impact, and develop decision-making schemes to improve product environmental performance, thereby achieving the goals of circular economy and clean production. In recent years, LCA, a general method for evaluating product environmental load, has become increasingly important.

[0003] In LCA research, life cycle assessment requires the collection of data, which is divided into field data and background data. Field data refers to data collected from factories performing specific manufacturing processes, and background data comes from commonly used data sources, including commercial databases and free databases.

[0004] There are many requirements for the quality of field data, including the representativeness of time, region, and technology, the completeness of unit process data, the accuracy of field data, and the consistency of data sources. For the verification of the accuracy of field data collection, industry experts are usually used for identification and sorting at the present stage. This verification work is related to human experience, time-consuming, and cannot guarantee the accuracy of the results, nor does it provide specific evaluation criteria for accuracy.

[0005] Therefore, it is necessary to propose a data fluctuation evaluation technology for life cycle assessment to analyze the accuracy of the data and improve the reliability of life cycle assessment. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art and provide a data fluctuation evaluation method and system for life cycle assessment with improved evaluation accuracy and high efficiency.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A data fluctuation evaluation method for life cycle assessment, comprising the following steps:

[0009] According to the requirements of life cycle assessment, the corresponding field data is obtained;

[0010] According to a predetermined simulation analysis object, a life cycle assessment simulation is performed on each simulation analysis object based on the field data, and the life cycle assessment simulation on each simulation analysis object is implemented based on a Monte Carlo method, and specifically includes:

[0011] The simulation analysis object is taken as an uncertain factor, one of the uncertain factors is assumed to be a freely valued factor, values of other related factors are assigned based on dependencies between the uncertain factors, one life cycle assessment simulation is implemented, all the uncertain factors are cycled to obtain a final simulation result, the dependencies between the uncertain factors include dependency relationships and dependency degrees, value intervals of the other related factors are controlled based on the dependencies, the dependency relationships include positive correlation, negative correlation and no correlation, the dependency degrees include high, medium and low, and a relationship between the value intervals of the other related factors and the freely valued factor is represented by the following formula:

[0012] For the positive correlation, the value interval of the other related factors is:

[0013]

[0014] For the negative correlation, the value interval of the other related factors is:

[0015]

[0016] For the no correlation, the value interval of the other related factors is:

[0017]

[0018] wherein, is a random value of the freely valued factor, n represents the dependency degree;

[0019] The life cycle assessment simulation result is displayed, and a data accuracy discrimination result is generated based on the life cycle assessment simulation result and is displayed.

[0020] The simulation analysis object is a preconfigured object or a plurality of objects with the highest contribution rate determined based on a life cycle assessment calculation result obtained from the field data.

[0021] A data volatility evaluation system for life cycle assessment includes:

[0022] A data acquisition module is configured to receive a life cycle assessment requirement and acquire corresponding field data based on the life cycle assessment requirement.

[0023] A data analysis module is configured to perform life cycle assessment simulation on each simulation analysis object based on the field data according to predetermined simulation analysis objects, and the simulation is realized based on a Monte Carlo method;

[0024] A data display module is configured to display the simulation results of the data analysis module, generate data accuracy discrimination results based on the simulation results, and display the data accuracy discrimination results.

[0025] A data statistics module is configured to store each data accuracy discrimination result, obtain data characteristics through statistics, and feed back the data characteristics to the data analysis module.

[0026] The life cycle assessment simulation on each simulation analysis object specifically includes:

[0027] The simulation analysis object is taken as an uncertain factor, one of the uncertain factors is taken as a freely valued factor, values of other related factors are assigned based on the dependency between the uncertain factors, one cycle of life cycle assessment simulation is realized, and all the uncertain factors are cycled to obtain the final simulation results.

[0028] The dependency between the uncertain factors includes a dependency relationship and a dependency degree, and the value range of the other related factors is controlled based on the dependency.

[0029] The relationship between the value range of the other related factors and the freely valued factor is represented by the following formula:

[0030] For a positive correlation, the value range of the other related factors is:

[0031]

[0032] For a negative correlation, the value range of the other related factors is:

[0033]

[0034] wherein, is a random value of the freely valued factor, n represents the dependency degree.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] 1. The present application can reduce the time length of data verification, and the method can help enterprises reduce costs.

[0037] 2. The present application can accurately evaluate the volatility of data and ultimately calculate the volatility of LCA results, thereby improving the reliability of life cycle assessment.

[0038] 3、The fluctuation analysis of the application perfects the life cycle assessment method of environmental impact, which not only helps to improve the reliability of the environmental impact index, but also helps to understand the fluctuation of the main input factors in the life cycle assessment, so as to put forward various measures and improve the reliability of the environmental impact index.

[0039] 4、The improved implementation of the application based on Monte Carlo simulation considers the interdependence between uncertainty factors, controls the extraction range of random numbers of uncertainty factors with dependent relationship through the dependent type and dependent degree between uncertainty factors, overcomes the defect that the classical Monte Carlo simulation independently extracts random values of each uncertainty factor and ignores the correlation between them, so that the random combination of uncertainty factors is more in line with the actual situation, the simulation analysis result is closer to the real situation, and the reliability of data evaluation is improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The flowchart of the application is shown in the figure;

[0041] Figure 2 The architecture diagram of the system of the application is shown in the figure. DETAILED DESCRIPTION

[0042] The application will be described in detail below in combination with the drawings and specific embodiments. The embodiments are implemented on the premise of the technical scheme of the application, and detailed implementation modes and specific operation processes are given, but the protection scope of the application is not limited to the following embodiments.

[0043] The Monte Carlo method is a method for solving problems by generating random numbers. The problem to be solved is first analyzed comprehensively to form a certain algorithm, then the result is simulated by generating random numbers, and the simulation result is analyzed from the aspects of rationality, etc. The Monte Carlo simulation can randomly extract all the fluctuation of the life cycle assessment method, and study its influence on the environmental impact index through a large number of simulation. For a specific process, accurate data can be represented as a range or standard deviation. In theory, statistical methods such as Monte Carlo technique can be used to handle these types of fluctuation, and finally the fluctuation of LCA result can be calculated. The application is based on improved Monte Carlo implementation.

[0044] Embodiment 1

[0045] The embodiment provides a data fluctuation evaluation method for life cycle assessment, comprising the following steps: obtaining corresponding field data according to the requirements of life cycle assessment; performing life cycle assessment simulation on each simulation analysis object based on the field data according to the pre-determined simulation analysis object, and the simulation is realized based on the Monte Carlo method; displaying the simulation result of the data analysis module, and generating a data accuracy discrimination result based on the simulation result and displaying it.

[0046] In the above method, the simulation analysis objects are pre-configured objects or objects with the highest contribution rate determined based on the life cycle assessment calculation results obtained from the field data. Specifically, the life cycle assessment simulation on each simulation analysis object specifically includes: taking the simulation analysis object as an uncertain factor (an environmental impact factor), assuming that one of the uncertain factors is a freely valued factor, based on the dependency between the uncertain factors, assigning values to other related factors, implementing one life cycle assessment simulation, and cycling through all the uncertain factors to obtain the final simulation results.

[0047] The dependency between the above-mentioned uncertain factors includes a dependency relationship and a dependency degree, the value range of the other related factors is controlled based on the dependency, the dependency relationship includes positive correlation, negative correlation and no correlation, and the dependency degree includes high, medium and low. The relationship between the value range of the other related factors and the freely valued factor is represented by the following formula (1):

[0048] For positive correlation, the value range of the other related factors is:

[0049]

[0050] For negative correlation, the value range of the other related factors is:

[0051]

[0052] For no correlation, the value range of the other related factors is: ;

[0053] wherein, is a random value of the freely valued factor, n represents the dependency degree, in the specific embodiment, the value of the high dependency degree n is 4, the value of the medium dependency degree n is 2, and the value of the low dependency degree n is 1.

[0054] As shown in Figure 1 , the life cycle assessment simulation process based on the improved Monte Carlo in the embodiment includes the following steps:

[0055] S101, determining the dependency relationship of each pair of uncertain environmental impact factors;

[0056] S102, determining the dependency degree of the environmental impact factors;

[0057] S103, determining the distribution function of each uncertain environmental impact factor, which can be determined according to expert suggestions and historical records;

[0058] S104. Select free uncertain environmental influencing factors from all uncertain environmental influencing factors using a cyclical method, and randomly select values ​​based on the distribution function;

[0059] S105. Use formula (1) to determine the controlled intervals for all uncertain environmental impact factors with dependencies;

[0060] S106. Draw random numbers and calculate the utility function value;

[0061] S107. Determine if the number of iterations has been reached. If yes, proceed to step S108. If no, return to step S104.

[0062] S108. Draw frequency charts and analyze the simulation results.

[0063] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] Example 2

[0065] like Figure 2 As shown, this embodiment provides a data volatility assessment system for life cycle assessment, including a data acquisition module 1, a data analysis module 2, a data display module 3, and a data statistics module 4. The data acquisition module receives life cycle assessment requirements and acquires corresponding field data based on these requirements. The data analysis module performs life cycle assessment simulations on predetermined simulation objects based on the field data; the simulations are implemented using the Monte Carlo method. The data display module displays the simulation results from the data analysis module and generates and displays data accuracy judgment results based on these results. The data statistics module stores the data accuracy judgment results for each iteration, statistically analyzes the obtained data characteristics, and feeds them back to the data analysis module. Specific descriptions of each module are as follows.

[0066] 1. Data Acquisition Module

[0067] According to the requirements of life cycle assessment method, obtain the complete on-site data of the customer, select appropriate background data, complete the input into the system, the data will be stored in the background database, and enter the next module.

[0068] 2. Data analysis module

[0069] 2.1 Select simulation analysis objects

[0070] The data analysis module is the core module of the system. According to the data obtained in the previous step, the life cycle assessment calculation is carried out, and according to the obtained result information, a number of simulation analysis objects with the highest contribution rate are obtained (which can also be selected according to the demand). Assuming that one of the uncertain factors is freely valued, then the other related factors are controlled. Through the use of a heuristic loop algorithm, the freely assigned uncertain factor is selected in each round of simulation process, and the heuristic loop algorithm changes the freely assigned uncertain factor in each round of operation. Through this method, not only can the appearance of infeasible mode be avoided, but also the simulation process can search in a more accurate solution space.

[0071] 2.2 Dependency analysis (i.e. correlation)

[0072] According to the possible dependency types and degrees, the values assigned to the uncertain factors with dependency relationship are controlled. First, the corresponding types include: positive correlation, negative correlation (counteraction) and no correlation. And the positive and negative correlation is divided into three degrees: high, medium and low. The corresponding coefficient values can be considered as 4, 2 and 1 respectively. When the dependency type and degree are determined, the loop algorithm can be used to select the free uncertain factor, which ensures that the algorithm loop selects the free uncertain factor in different operation processes. When the last uncertain factor is selected, it returns to the first uncertain factor in the next round. The use of this loop algorithm makes each uncertain factor affect other related factors, so that more accurate search results can be obtained. The free uncertain factor can take any random value in the range of [1-100], while the other related uncertain factors can only take any value within the control range, which is determined by the dependency degree and type of the related uncertain factor and the free uncertain factor. The length of the controlled uncertain factor interval is determined by the dependency degree. The general expression of the controlled interval is described in formula (1) of embodiment 1.

[0073] In the specific embodiment, for high dependence degree, 10% tolerance should be specified, so when the random value of the free uncertainty factor is A, the random value of the related uncertainty factor should be controlled in [A-5, A+5], if A-5 is less than 1, the lower boundary is 1; if A+5 is greater than 100, the upper boundary is 100. If the dependence degree is medium, the tolerance is 20%, and if the dependence degree is low, the tolerance is 40%. For example, for a negative correlation with medium dependence degree, if the random value of the free factor is 82, the corresponding control interval can be determined as follows: the midpoint of the controlled interval is 100-82=18, so the controlled interval range is [8, 28]. If the dependence degree is low, because (18-10)>1, the lower boundary is 1, and the corresponding controlled range is [1, 38].

[0074] 2.3 Simulation results

[0075] In the initial stage of the system, expert intervention is needed, and in the later stage, with the accumulation of data, the intervention of experts will be gradually replaced. Through the collected data and the opinions of experts, the distribution function of each uncertainty factor is given, and the random value of the factor is obtained according to the approximate probability distribution of each uncertainty factor. With the value of the uncertainty factor, the result of the life cycle evaluation method also changes accordingly. Finally, according to the simulation results, the frequency diagram of the life cycle evaluation method in different value segments can be drawn, and the characteristic values such as expected value and standard deviation can be calculated.

[0076] Generally, the number of simulations to be performed is determined according to the size and contribution of the data. The simulation can be performed 1000 times, 2000 times, 5000 times, etc. The more the simulation times, the more random patterns can be found in the solution space. When the number of simulations is sufficient, the characteristic values of the life cycle evaluation method will tend to be stable, and the shape of the frequency distribution will also tend to be stable. The characteristic values and frequency distribution of the indicators directly reflect the stability of the results of the life cycle evaluation method.

[0077] 3. Data display module

[0078] The results obtained from the data analysis module are displayed on the data front end, and based on the suggestions of experts, a conclusion is given on whether the data is accurate or not. In the initial stage of the system, expert intervention is needed, and in the later stage, with the accumulation of data, the intervention of experts will be gradually replaced.

[0079] 4. Data statistics module

[0080] The data display results and the feedback opinions of experts are stored in the database, the data collection results of the same module are counted, and the data analysis characteristics are analyzed to provide data information for the data analysis module, and to provide data support for the data analysis module.

[0081] At present, green and low-carbon development has affected all industries in China. As an emission-intensive industry, the steel industry has great pressure on emission reduction, heavy responsibility and difficult task. At the same time, as a basic material for industrial development, steel has a great influence on the environmental performance of downstream industries. At present, in addition to a few steel plants with advanced production concepts, small and medium-sized steel plants have various problems such as difficulty in calculating, evaluating and collecting data on environmental performance. At the same time, downstream users are increasingly strict in evaluating the environmental impact of raw materials, and the market urgently needs a service that can help steel plants complete product environmental evaluation and output downstream user-recognized environmental performance reports.

[0082] Life Cycle Assessment (LCA) originated from the tracking and quantitative analysis of the whole process of beverage containers from raw material mining to waste final disposal commissioned by Coca-Cola in the Midwest Institute in 1969. LCA has been included in the ISO 14000 environmental management series standards and has become an important supporting tool for international environmental management and product design. According to the definition of ISO 14040:1999, LCA refers to "the compilation and evaluation of inputs, outputs and potential environmental impacts of a product system throughout its life cycle, which includes four steps: determination of purpose and scope, inventory analysis, impact assessment and result interpretation. Life cycle assessment is a technology and method for assessing the environmental impact of a product throughout its entire life cycle, i.e. from the acquisition of raw materials, production of the product to disposal after use.

[0083] There are many requirements for the quality of field data, including the representativeness of time, region and technology of field data, the completeness of unit process data, the accuracy of field data, the consistency of data sources, etc. For the verification of the accuracy of field data collection, industry experts are usually identified and sorted at this stage. This verification work is related to human experience, time-consuming and cannot guarantee the accuracy of the results. There is no specific accuracy evaluation standard. For example, in the coking process of the steelmaking process, the input data includes coking coal, compressed air, etc., and the output includes tar, crude benzene, etc. Then collect the corresponding data from the beginning of coking coal to the end of tar and crude benzene. The main production processes of the coking plant include: coal preparation, coking, screening and storage of coke, cold drum, electric capture, desulfurization and sulfur recovery, ammonia evaporation, ammonium sulfate, benzene washing and other processes. Then the data is from the beginning of coal preparation to the end of the final product.

[0084] In data collection, due to the experience of collectors, system conditions, data collection equipment conditions and other information, the collected data may have errors and problems, so that the data of the coking process are analyzed to obtain relevant data tables, and currently, whether the collected data is reasonable is analyzed and combed by industry experts, for example, the information of the amount of tar and the amount of crude benzene used for producing one ton of coke is relatively familiar to industry experts, but it is relatively complex for general LCA analysts, according to the initial data collection and the feedback of experts, the initial data distribution is formed by being stored in a database. The correlation between different data items can be obtained according to correlation analysis, and the data distribution assumption, data processing and the like are carried out based on the above analysis method. According to the increase of the number of data collection, the automatic identification of data accuracy can be realized.

[0085] The above method and system can replace the review of experts and manual review, thereby reducing the time length of data verification, reducing the threshold for LCA analysis calculation, and effectively reducing the calculation cost of enterprises.

[0086] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment on the basis of the prior art according to the concept of the present application shall be within the protection scope determined by the claims.

Claims

1. A method for evaluating data volatility in life cycle assessment, characterized in that, Includes the following steps: Obtain the corresponding field data according to the life cycle assessment requirements; Based on the predetermined simulation analysis objects, life cycle assessment simulations are performed on each simulation analysis object using the field data. The life cycle assessment simulations are implemented using the Monte Carlo method and specifically include: The simulation analysis object is treated as an uncertain factor. Assuming one uncertain factor is a freely selectable factor, values ​​are assigned to other relevant factors based on the dependencies between uncertain factors. This completes one life cycle assessment simulation. The simulation is iterated over all uncertain factors to obtain the final simulation results. The dependencies between uncertain factors include the relationship and degree of dependency. The value ranges of other relevant factors are controlled based on these dependencies. The relationships include positive correlation, negative correlation, and no correlation. The degree of dependency includes high, medium, and low. The relationship between the value ranges of other relevant factors and the freely selectable factor is expressed by the following formula: For a positive correlation, the range of values ​​for other related factors is as follows: For a negative correlation, the range of values ​​for other related factors is as follows: For unrelated factors, the range of values ​​for other related factors is as follows: in, For random values ​​of factors that can take any value, n Represents the degree of dependence; The simulation results of the life cycle assessment are displayed, and data accuracy judgment results are generated and displayed based on the simulation results.

2. The data volatility assessment method for life cycle assessment according to claim 1, characterized in that, The simulation analysis objects are either pre-configured objects or a number of objects with the highest contribution rates determined based on the life cycle assessment calculation results obtained from the field data.

3. A data volatility assessment system for life cycle assessment, characterized in that, include: The data acquisition module is used to receive life cycle assessment requirements and acquire corresponding field data based on those requirements. The data analysis module is used to perform life cycle assessment simulations on each pre-determined simulation analysis object based on the field data, and the simulation is implemented based on the Monte Carlo method. The data display module is used to display the simulation results of the data analysis module, and generate and display data accuracy judgment results based on the simulation results; The data statistics module is used to store the accuracy judgment results of each data session, statistically obtain data characteristics, and feed them back to the data analysis module.

4. The data volatility assessment system for life cycle assessment according to claim 3, characterized in that, The life cycle assessment simulation for each simulation analysis object specifically includes: The simulation analysis object is treated as an uncertain factor. One of the uncertain factors is assumed to be a freely selectable factor. Based on the dependencies between the uncertain factors, the values ​​of other relevant factors are assigned to achieve a life cycle assessment simulation. The simulation is repeated for all uncertain factors to obtain the final simulation results.

5. The data volatility assessment system for life cycle assessment according to claim 4, characterized in that, The dependencies between the uncertain factors include the dependency relationship and the degree of dependency, and the value range of other related factors is controlled based on the dependencies.

6. The data volatility assessment method for life cycle assessment according to claim 5, characterized in that, The relationship between the value ranges of the other relevant factors and the freely selectable factors is expressed by the following formula: For a positive correlation, the range of values ​​for other related factors is as follows: For a negative correlation, the range of values ​​for other related factors is as follows: in, For random values ​​of factors that can take any value, n This represents the degree of dependence.

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