CN106096260B - A method of obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation - Google Patents
A method of obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation Download PDFInfo
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Abstract
The invention discloses a kind of methods for obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, combined data reliability evaluation and the reliability of the adjustment model evaluation, it is analyzed by Monte Carlo, obtains the probability density function and statistical parameter of each maintenance plan energy consumption carbon emission；It defines environment influence and compares parameter, obtain energy consumption ratio confidence interval of each asphalt pavement conserving scheme in 95% confidence level；Wherein, the data reliability evaluation is for establishing probability density function to material production energy consumption；The reliability of the adjustment model evaluation establishes probability density function for road pavement maintenance stage energy consumption, and the statistical parameter includes corresponding mean value, standard deviation, quantile and the coefficient of variation obtained from corresponding probability density function.The present invention solves the deficiency of existing asphalt pavement conserving energy consumption carbon emission calculated result reliability evaluation missing.
Description
Technical field
The invention belongs to asphalt pavement conserving energy consumption carbon emission field, in particular to a kind of acquisition asphalt pavement conserving energy consumption
Carbon emission method for evaluating reliability.
Background technique
Road is the important component in infrastructure construction system, to the economic development of countries and regions, social progress and
People's living standard raising etc. plays an important role, however the fast-developing of road construction brings severe ring simultaneously
Border problem, such as energy consumption, greenhouse gas emission, land seizure etc..It is influenced to reduce the environment of road traffic, " traffic
Transport " 12th Five-Year Plan " development plan " in propose green traffic concept, widely popularize highway construction and operation energy-conserving and emission-cutting technology.
Current highway industry conserves behavior and brings huge environment influence gradually from " build to support and take into account " to " based on maintenance ".Therefore
It is necessary to further investigate the energy consumption carbon emission of asphalt pavement conserving engineering.
Lack reliability demonstration for the research that asphalt pavement conserving energy consumption carbon emission calculates at present.Asphalt pavement conserving row
For energy consumption carbon emission calculated result be highly dependent on the quality of environmental data used in calculating process and the generation of model parameter
Table.Currently, the energy consumption strength range of manufacture of cement is 4.6-7.3MJ/kg, the energy consumption strength range 0.7- of asphalt production
6.0MJ/kg, difference are huge.This is because the difference of system boundary, the difference of production technology, the production dependent on regional area
The factors such as process and science and technology result in the fluctuation of production energy consumption intensity.Therefore it is strong to choose different energy consumptions by different researchers
Degree value can generate tremendous influence to calculated result.On the other hand, existing research focuses mostly in the analysis of case, calculating process
Attribute of the model parameter with corresponding case, such as gradation design, transportation range, mixed material heating mixing equipment efficiency,
This makes the research conclusion of different researchers lack identical Research foundation.Asphalt pavement conserving environment influences computation model at present
Above-mentioned uncertain factor is not fully considered.
Summary of the invention
Goal of the invention: the present invention lacks reliability evaluation for existing asphalt pavement conserving energy consumption carbon emission calculated result
Status considers data reliability and the reliability of the adjustment model, discloses a kind of asphalt pavement conserving energy consumption carbon emission reliability evaluation side
Method, the discharge of energy consumption carbon caused by can be in all directions the considerations of asphalt pavement conserving, so as to effectively be controlled.
Technical solution: the present invention provides it is a kind of obtain asphalt pavement conserving energy consumption carbon emission reliability evaluation method,
Combined data reliability evaluation and the reliability of the adjustment model evaluation, are analyzed by Monte Carlo, obtain each maintenance plan energy consumption carbon row
The probability density function and statistical parameter put；It defines environment influence and compares parameter, obtain each asphalt pavement conserving scheme 95%
Energy consumption ratio confidence interval in confidence level；Wherein, the data reliability evaluation is general for establishing to material production energy consumption
Rate density function；The reliability of the adjustment model evaluation establishes probability density function for road pavement maintenance stage energy consumption；The statistics
Parameter includes corresponding mean value, standard deviation, quantile and the coefficient of variation obtained from corresponding probability density function.
Further, the data reliability evaluation method are as follows: first by the quality testing matrix established, obtain number
According to performance figure (Data Quality Index, hereinafter referred DQI)；Secondly, be distributed by Beta, according to formula:
By quality of data index value be converted into probability density function (Probability Density Function, hereafter
Abbreviation PDF), the input parameter as subsequent Monte Carlo analysis, wherein α, β are profile shape parameter, to determine energy
Consume the dispersion degree of carbon emission probability density distribution；A, b are interval endpoint, to determine energy consumption carbon emission probability density distribution
Fluctuation range；Finally, passing through DQI value qualitative evaluation data reliability.DQI value is higher, and the quality of data is higher, and reliability is higher,
DQI value is lower, and the quality of data is lower, and reliability is lower.The PDF converted by DQI, can be with quantitative assessment data reliability.PDF
It is distributed narrower (width), the quality of data is higher (low), and reliability is higher (low), and PDF distribution is wider, and the quality of data is lower, reliability
It is lower.
Further, the method for the reliability of the adjustment model evaluation are as follows: setting, which is mixed and stirred, is obeying logarithm just with the parameter of construction stage
State distribution, the parameter Normal Distribution in other stages；According to formula:
Definition input parameter MxMinimum, maximum；M in formulax,min, Mx,max, Mx,avgRespectively input parameter MxIt is minimum
Value, maximum and mean value；Input parameter MxFluctuation range and UFxMeet:
p(Mx,min< Mx,avg< Mx,max)=0.95；
Wherein, Mx,min, Mx,max, Mx,avgRespectively input parameter MxMinimum, maximum and mean value, input parameter MxTable
Show the energy consumption in x maintenance of surface stage, UFxIndicate the uncertain index in x maintenance of surface stage.Because of the ring of mix and construction stage
It is smaller that border influences magnitude, and for same maintenance technology, differs greatly.To avoid the probability of negative value appearance excessive, i.e., negative value goes out
Existing probability > 5%, finally definition, which is mixed and stirred, obeys logarithm normal distribution with the parameter of construction stage, and the parameter in other stages is obeyed
Normal distribution.Model of the present invention is extensive concept, has extensive extension.Either specific regression model,
Such as bituminous mixture energy consumption prediction model, the input parameter of hypothesis can also be, such as asphalt content, construction platform in gradation design
Class, transportation range etc..
It further, further include establishing standardization haul distance；By counting a large amount of maintenance project sample, setting old material to mix
The standard haul distance in building is 0km；Old material to stock ground haul distance and mixture to construction site standard haul distance is set to 30km；Virgin material is extremely
The quasi- haul distance of mix emblem mark is 60km；Haulage stage uses the standard haul distance of setting to calculate transport energy consumption carbon emission to establish maintenance row
Database is influenced for standardized environment.The environment for being able to reflect maintenance technology itself in this way influences attribute, eliminates because of different fortune
Away from and generate difference, while make environment influence calculated result it is representative.
Further, described to be analyzed by Monte Carlo, obtain the probability density function of each maintenance plan energy consumption carbon emission
With the method for statistical parameter are as follows: using the result of data reliability evaluation and the reliability of the adjustment model evaluation as input parameter, carry out
Monte Carlo analysis, the number of iterations are 10000~50000 times, and establishing the final environment of maintenance plan influences probability density letter
Number extracts and calculates pertinent statistical parameters, and evaluation maintenance plan environment influences result reliability.
Further, environment influence compares parameter definition and isCompare to evaluate the influence of maintenance plan environment
As a result conspicuousness；Monte Carlo analysis is carried out to R, establishes the probability density distribution of R, obtains pertinent statistical parameters,
The confidence interval of R value is established under 95% confidence level, wherein Env1Indicate that the environment of the first maintenance plan to be compared influences,
Env2Indicate that the environment of the first maintenance plan to be compared influences, wherein the environment of maintenance plan influences the energy including maintenance plan
Consumption and carbon emission amount, main here is exactly to compare the energy consumption of two schemes and carbon emission amount.Then P (R < 1)=P (Env1
< Env2) the environment influence of maintenance of surface scheme 1 is characterized less than the probability that the environment of maintenance of surface scheme 2 influences.In this way can
The more intuitive energy consumption carbon emission amount for knowing which maintenance plan is small, more environmentally-friendly.
Working principle: energy consumption carbon emission calculated result reliability evaluation object is divided into two classes by the present invention, i.e. data are reliable
Property and the reliability of the adjustment model.The former evaluates according to the quality testing matrix qualitative evaluation quality of data, is distributed using Beta, will be defeated
Enter parameter and is converted into probability density function；The latter's evaluation then by defining uncertain index, determines sample distribution form and mould
Shape parameter.The present invention can effectively be caught according to the standardization energy consumption carbon emission database established, the method for evaluating reliability of proposition
The variability for catching calculated result, the energy consumption carbon emission ratio of more different maintenance plans from 95% confidence level.
The utility model has the advantages that compared with prior art, the present invention solves existing asphalt pavement conserving energy consumption carbon emission and calculates knot
The deficiency of fruit reliability evaluation missing, while method disclosed by the invention is simple, convenient, the result of acquisition is more accurate, from
And optimal asphalt pavement conserving scheme can be used according to the result of calculating, it is more energy-saving and environmentally friendly.
Detailed description of the invention
Fig. 1 is method flow diagram provided by the invention；
Fig. 2 is the matrix pitch production energy consumption probability density distribution when DQI is 3.85；
Fig. 3 is different data quality matrix pitch production energy consumption probability density distribution；
Fig. 4 is that modified heat mixes maintenance plan energy consumption probability density function, wherein Fig. 4 (a) is in 50,000 iteration
Lower modification heat mixes the energy consumption and its frequency of scheme, the accumulative density function distribution map of Fig. 4 (b) energy consumption；
Fig. 5 is that matrix heat mixes and stirs the energy consumption comparison schematic diagram that modified heat mixes maintenance plan；Fig. 5 (a) is that heat mixes matrix maintenance
Scheme consumes energy comparison diagram with modified maintenance plan is mixed；Fig. 5 (b) is that heat mixes matrix maintenance plan and mixes modified maintenance plan ring
Border influences to compare the accumulative density function distribution map of parameter.
Specific embodiment
Further explanation is done to the present invention with reference to the accompanying drawing.
As shown in Figure 1, the method that a kind of asphalt pavement conserving energy consumption carbon emission of the present invention calculates reliability evaluation, main to wrap
Include following steps:
(1) data reliability is evaluated:
Each phase data of different maintenance projects, such as raw materials consumption, mixing building energy consumption, transportation range are acquired first.
The energy consumption carbon emission of raw material production is calculated by table 1, other phase datas pass through collection in worksite.As shown in table 2, different maintenance sides
Case environment influences inventory.
Table 1:
Wherein, China provides that the calorific value of every kilogram of standard coal (kgce) is 29.27MJ.
Table 2:
Wherein, standard haul distance is all made of about transport section in each maintenance plan to be calculated.It is set in the present embodiment old
The standard haul distance for expecting mixing building is 0km；Old material to stock ground haul distance and mixture to construction site standard haul distance is set to
30km；Virgin material to the quasi- haul distance of mix emblem mark is 60km.
The quality testing method of foundation, the quality of evaluation path material production environment data.With matrix pitch material
For, the energy consumption of production is 170.34kgce.As shown in table 3, respectively refer in the quality testing matrix of matrix pitch material
Marking score value is respectively { 3.0,5.0,4.0,4.0,3.0,5.0,3.0 }, then DQI is 3.85.DQI is mainly by seeking each index
The average value of score value obtains.
Table 3:
According to formula:
f(x；α, β, a, b)=[1/ (b-a)] × { Γ (alpha+beta)/Γ (α) × Γ (β) } × [(x-a)/(b-a)]α-1×
[(b-x)/(b-a)]β-1
Wherein, (a≤x≤b)
Matrix pitch material production energy consumption PDF is obtained in conjunction with Beta function parameter, as shown in table 4, obtains shape function α, β
For (3,3), interval endpoint is (- 20% ,+20%).The matrix pitch material production energy consumption PDF of acquisition is as shown in Figure 2.
Table 4:
Different DQI values represent the different qualities of data.By taking matrix pitch as an example, different DQI values, corresponding matrix pitch
The PDF of production energy consumption is also different.As shown in figure 3, DQI is higher, the quality of data is higher, then PDF is distributed narrower, corresponding reliability
It is higher.
The PDF established according to fig. 2 is extracted and is calculated pertinent statistical parameters, and wherein statistical parameter includes mean value, standard deviation, divides
Digit, coefficient of variation etc. can evaluate matrix pitch material production energy consumption numerical value and its reliability according to statistical parameter, such as
Value represents average energy consumption, and the coefficient of variation represents reliability.Similar, the energy consumption carbon emission that other road-making materials produce can be established
Corresponding PDF provides data source for subsequent Monte Carlo analysis.
(2) the reliability of the adjustment model is evaluated:
Because the environment numerical value of mix and construction stage are smaller, and for same maintenance technology, differ greatly, to avoid negative value
The probability of appearance is excessive, i.e. probability > 5%, it is therefore assumed that construction and mix stage obey logarithm normal distribution；Other stages are then false
Determine Normal Distribution.According to formula
p(Mx,min< Mx,avg< Mx,max)=0.95；
Wherein, UF value defines the fluctuation range between minimum value and maximum value.UF value is bigger, then fluctuation range is bigger,
Antisense.Then UF value corresponds to the range that fluctuation range is 95% confidence interval, and corresponding UF value is as shown in table 5.In table
NA indicates that the numerical value is not present.According to UF value and distribution form, M is calculatedx,avgAverage value standard deviation, then can determine corresponding PDF.
Wherein, Mx,min, Mx,max, Mx,avgRespectively input parameter MxMinimum, maximum and mean value, input parameter MxIndicate the road surface x
The energy consumption in maintenance stage, x expression are heated, are mixed and stirred, transporting, Mx,avgAverage acquisition is taken according to multiple samples of different schemes.
Table 5:
(3) maintenance plan environment influences reliability evaluation: by data reliability evaluate in the PDF and mould of each data that obtain
The PDF of each parameter obtained in type reliability evaluation carries out Monte Carlo points as input parameter, to different maintenance plans
Analysis, the number of iterations 50,000 time.The analog result that modified heat mixes maintenance plan energy consumption is as shown in Figure 4.Such as Fig. 4 (a) institute
Show, modified heat mixes the energy consumption and its frequency of scheme under 50,000 iteration.It makes a variation in the numerical value quality and model parameter of setting
Under, the average energy consumption that modified heat mixes scheme (1 ton of quality) is 35.58kgce, standard deviation 7.08kgce.Such as Fig. 4 (b) institute
Show, the accumulative density function (Cumulative Density Function, CDF) of energy consumption can be used for determining the energy of maintenance plan
Consume intensity.Schemed according to CDF, modified heat mixes scheme 25thWith 75thPercentage is respectively 30.15kgce and 40.29kgce.
For other maintenance plans and CO2Calculating, it is same to carry out Monte Carlo analysis, obtain pertinent statistical parameters,
As shown in table 6.
Table 6:
Mean value represents the average energy consumption of maintenance plan in table 6, and standard deviation then reflects the energy consumption fluctuation range of maintenance plan；
25thWith 75thPercentage then can be used for defining the energy consumption intensity of different maintenance plans.
(4) environment affecting parameters R is established: although table 6 is capable of providing statistical parameter and describes what each maintenance plan environment influenced
Reliability, however can not determine whether the energy consumption carbon emission between different maintenance plans has significant statistical discrepancy.The present invention
It defines environment influence and compares parameterThe conspicuousness compared is influenced to describe maintenance plan environment.
Wherein, P (R < 1)=P (Env1< Env2) the environment influence of maintenance of surface scheme 1 is characterized less than maintenance of surface scheme
The probability that 2 environment influences.It chooses matrix heat and mixes maintenance plan as benchmark, using energy consumption as index, carry out 50,000 Monte
Carlo analysis, as a result as shown in Figure 5.
As shown in Fig. 5 (a), in 95% confidence level, matrix maintenance plan is mixed compared to heat, heat mixes modified maintenance plan
The energy of consumption is more.As shown in Fig. 5 (b), two kinds of maintenance plan energy consumption ratios can be further extracted in 95% confidence level
On confidence interval.
According to environment affecting parameters R, the PDF that different maintenance plans compare is established, associated statistical information is extracted, is summarized in
Table 7.
Table 7:
| Reference scheme | Compare scheme | P(R<1) | P(R>1) | Conspicuousness | Intermediate value | Standard deviation | 95% confidence interval |
| Matrix heat is mixed | Modified heat is mixed | 99.9% | NA | Significantly | 0.70 | 0.08 | (0.56,0.87) |
| Matrix heat is mixed | It is hot in-plant reclaimed | NA | 99.7% | Significantly | 1.15 | 0.08 | (1.00,1.32) |
| Matrix heat is mixed | Cold in-plant recycling | NA | 99.9% | Significantly | 2.27 | 0.18 | (1.92,2.63) |
| Matrix heat is mixed | Cold in place recycling | NA | 99.9% | Significantly | 3.46 | 0.31 | (2.82,4.04) |
| Cold in-plant recycling | Cold in place recycling | NA | 99.9% | Significantly | 1.52 | 0.15 | (1.24,1.81) |
| Cold in-plant recycling | It is hot in-plant reclaimed | 99.9% | NA | Significantly | 0.51 | 0.06 | (0.41,0.65) |
The mean value and waving interval that the different schemes that table 7 provide compare, can be evaluated from statistics level different maintenance plans it
Between environment influence disparity range and its reliability.For example, the average energy consumption of cold in-plant recycling scheme supports side than cold in place recycling
The average energy consumption of case more 52%, the energy consumption ratio waving interval in 95% confidence level are (1.24,1.81), i.e. the former energy consumption
It is the 124%-181% of the latter's energy consumption.It is consistent with energy consumption analysis for the analysis process of carbon emission.
In conclusion the present invention solves the problems, such as asphalt pavement conserving energy consumption carbon emission calculated result reliability evaluation.
When specific utilization, if the reliability of the adjustment model evaluation sample is insufficient (n=1), following processing mode can be used: regulation UF=1 divides
Cloth form is equal using mean value, standard deviation, establishes PDF.
Claims (4)
1. a kind of method for obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, it is characterised in that: combined data is reliable
Property evaluation and the reliability of the adjustment model evaluation, analyzed by Monte Carlo, obtain the probability density of each maintenance plan energy consumption carbon emission
Function and statistical parameter；It defines environment influence and compares parameter, obtain each asphalt pavement conserving scheme in 95% confidence level
Energy consumption ratio confidence interval；Wherein, the data reliability evaluation is for establishing probability density function to material production energy consumption；Institute
It states the reliability of the adjustment model evaluation and establishes probability density function for road pavement maintenance stage energy consumption, the statistical parameter includes from corresponding
Corresponding mean value, standard deviation, quantile and the coefficient of variation obtained in probability density function；
Wherein, the method for the reliability of the adjustment model evaluation are as follows: setting, which is mixed and stirred, obeys logarithm normal distribution with the parameter of construction stage,
The parameter Normal Distribution in other stages；According to formula:
Definition input parameter MxMinimum, maximum；M in formulax,min, Mx,max, Mx,avgRespectively input parameter MxMinimum, pole
Big value and mean value；Input parameter MxFluctuation range and UFxMeet:
p(Mx,min< Mx,avg< Mx,max)=0.95；
Wherein, Mx,min, Mx,max, Mx,avgRespectively input parameter MxMinimum, maximum and mean value, input parameter MxIndicate x
The energy consumption in maintenance of surface stage, UFxIndicate the uncertain index in x maintenance of surface stage；
The environment influence compares parameter definition and isThe significant of comparison result is influenced to evaluate maintenance plan environment
Property；Monte Carlo analysis is carried out to R, establishes the probability density distribution of R, intermediate value, standard deviation and quantile are obtained, 95%
The confidence interval of R value is established under confidence level, wherein Env1Indicate that the environment of the first maintenance plan to be compared influences, Env2Table
Show that the environment of the first maintenance plan to be compared influences.
2. the method according to claim 1 for obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, feature exist
In: the data reliability evaluation method are as follows: first by the quality testing matrix established, obtain quality of data index；
Secondly, be distributed by Beta, according to formula:
Probability density function is converted by quality of data index value, the input parameter as subsequent Monte Carlo analysis；Its
In, α, β are profile shape parameter, to determine the dispersion degree of energy consumption carbon emission probability density distribution；A, b are interval endpoint,
To determine the fluctuation range of energy consumption carbon emission probability density distribution；Finally, passing through quality of data index value qualitative evaluation data
Reliability, quality of data index value is higher, and the quality of data is higher, and reliability is higher；The probability converted by quality of data index
Density function quantitative assessment data reliability；Probability density function profiles are narrower, and the quality of data is higher, and reliability is higher.
3. the method according to claim 1 for obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, feature exist
In: it further include establishing standardization haul distance；By counting a large amount of maintenance project sample, the standard haul distance of setting old material to mixing building
For 0km；Old material to stock ground haul distance and mixture to construction site standard haul distance is set to 30km；Virgin material to mix emblem mark standard is transported
Away from for 60km；Haulage stage uses the standard haul distance of setting to calculate transport energy consumption carbon emission to establish maintenance behavioral standard environment
Influence database.
4. the method according to claim 1 for obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, feature exist
In: it is described to be analyzed by Monte Carlo, obtain the probability density function and statistical parameter of each maintenance plan energy consumption carbon emission
Method are as follows: using the result of data reliability evaluation and the reliability of the adjustment model evaluation as input parameter, carry out Monte Carlo points
Analysis, the number of iterations are 10000~50000 times, and establishing the final environment of maintenance plan influences probability density function, and it is equal to extract calculating
Value, standard deviation, quantile and the coefficient of variation, evaluation maintenance plan environment influence result reliability.
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| CN107122591B (en) * | 2017-03-29 | 2019-10-15 | 长安大学 | A kind of Construction of Asphalt Pavement carbon emission evaluation method |
| CN107122530A (en) * | 2017-04-13 | 2017-09-01 | 东南大学 | The method of simulation asphalt pavement construction effect on environment based on discrete event |
| CN109614575B (en) * | 2018-11-30 | 2023-06-06 | 中国电建集团贵阳勘测设计研究院有限公司 | Measuring and calculating self-emission CO of asphalt mixture in construction site 2 Is calculated by the method of (a) |
| CN112801452A (en) * | 2020-12-29 | 2021-05-14 | 中公高科养护科技股份有限公司 | Method and device for acquiring energy consumption data |
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