CN106096260A - A kind of method obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation - Google Patents

Abstract

The invention discloses a kind of method obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, in conjunction with data reliability evaluation and the reliability of the adjustment model evaluation, analyzed by Monte Carlo, obtain probability density function and the statistical parameter of each maintenance plan energy consumption carbon emission；Definition ambient influnence compares parameter, obtains energy consumption ratio confidential interval in 95% confidence level for each asphalt pavement conserving scheme；Wherein, described data reliability evaluation is for setting up probability density function to manufacture of materials energy consumption；Described the reliability of the adjustment model evaluation sets up probability density function for road pavement maintenance stage energy consumption, and described statistical parameter includes corresponding average, standard deviation, quantile and the coefficient of variation obtaining from corresponding probability density function.The present invention solves the deficiency of existing asphalt pavement conserving energy consumption carbon emission result of calculation reliability evaluation disappearance.

Description

A kind of method obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation

Technical field

The invention belongs to asphalt pavement conserving energy consumption carbon emission field, obtain asphalt pavement conserving energy consumption particularly to one Carbon emission method for evaluating reliability.

Background technology

Road is the important component in infrastructure construction system, to the economic development of countries and regions, social progress and The aspects such as people's living standard raising play an important role, but the fast development of road construction brings the ring of sternness simultaneously Border problem, such as energy resource consumption, greenhouse gas emission, land seizure etc..In order to reduce the ambient influnence of road traffic, " traffic Transport " 12 " development plan " in propose green traffic concept, widely popularize highway construction and operation energy-conserving and emission-cutting technology. Current highway industry gradually by " build to support and take into account " to " based on maintenance ", and maintenance behavior brings huge ambient influnence.Therefore It is necessary to further investigate the energy consumption carbon emission of asphalt pavement conserving engineering.

Lack reliability demonstration currently for the research that asphalt pavement conserving energy consumption carbon emission calculates.Asphalt pavement conserving row By energy consumption carbon emission result of calculation be highly dependent on the quality of the environmental data that calculating process is used and the generation of model parameter Table.At present, the energy consumption strength range of manufacture of cement is 4.6-7.3MJ/kg, energy consumption strength range 0.7-of asphalt production 6.0MJ/kg, difference is huge.This is because the difference of the difference of system boundary, production technology, the production depending on regional area The factors such as flow process and science and technology result in the fluctuation of energy consumption intensity.Therefore it is strong that different researchers chooses different energy consumptions Number of degrees value can produce tremendous influence to result of calculation.On the other hand, existing research focuses mostly in the analysis of case, calculates process Model parameter also with the attribute of corresponding case, such as gradation design, transportation range, mixed material heating mixing equipment efficiency etc., This makes the research conclusion of different researcher lack identical Research foundation.Current asphalt pavement conserving ambient influnence computation model Do not take into full account above-mentioned uncertain factor.

Content of the invention

Goal of the invention: the present invention is directed to existing asphalt pavement conserving energy consumption carbon emission result of calculation and lack reliability evaluation Present situation, it is considered to data reliability and the reliability of the adjustment model, discloses a kind of asphalt pavement conserving energy consumption carbon emission reliability evaluation side Method, can consider the discharge of energy consumption carbon that asphalt pavement conserving is caused such that it is able to be effectively controlled in all directions.

Technical scheme: the invention provides a kind of method obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, In conjunction with data reliability evaluation and the reliability of the adjustment model evaluation, analyzed by Monte Carlo, obtain each maintenance plan energy consumption carbon row The probability density function put and statistical parameter；Definition ambient influnence compares parameter, obtains each asphalt pavement conserving scheme 95% Energy consumption ratio confidential interval in confidence level；Wherein, described data reliability evaluation is general for setting up manufacture of materials energy consumption Rate density function；Described the reliability of the adjustment model evaluation sets up probability density function for road pavement maintenance stage energy consumption；Described statistics Parameter includes corresponding average, standard deviation, quantile and the coefficient of variation obtaining from corresponding probability density function.

Further, described data reliability evaluation method is: first pass through the quality testing matrix of foundation, obtains number According to performance figure (Data Quality Index, hereinafter referred DQI)；Secondly, it is distributed by Beta, according to formula:

$\begin{array}{c}f(x;\mathrm{\α},\mathrm{\β},a,b)=\[1/(b-a)\]\×\{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})/\mathrm{\Γ}\left(\mathrm{\α}\right)\×\mathrm{\Γ}\left(\mathrm{\β}\right)\}\×{\[(x-a)/(b-a)\]}^{\mathrm{\α}-1}\×{\[(b-x)/(b-a)\]}^{\mathrm{\β}-1}\\ (a\≤x\≤b)\end{array};$

Quality of data exponential quantity is converted into probability density function (Probability Density Function, hereafter It is called for short PDF), the input parameter analyzed as follow-up Monte Carlo, wherein, α, β are profile shape parameter, in order to determine energy The dispersion degree of consumption carbon emission probability density distribution；A, b are interval endpoint, in order to determine energy consumption carbon emission probability density distribution Fluctuation range；Finally, by 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 being converted by DQI, can be with quantitative assessment data reliability.PDF Being distributed narrower (wide), the quality of data higher (low), reliability higher (low), PDF distribution is wider, and the quality of data is lower, reliability Lower.

Further, the method for described the reliability of the adjustment model evaluation is: sets the parameter mixed and stirred with the construction stage and is just obeying logarithm State is distributed, the parameter Normal Distribution in other stages；According to formula:

$\left\{\begin{array}{c}{M}_{x,min}=\frac{M_{x,avg}}{{\mathrm{UF}}_x}\\ M_{x,max}={\mathrm{UF}}_x\×M_{x,avg}\end{array}\right.;$

Definition input parameter M_xMinimum, maximum；M in formula_x,min, M_x,max, M_x,avgIt is respectively input parameter M_xMinimum Value, maximum and average；Input parameter M_xFluctuation range and UF_xMeet:

p(M_x,min＜ M_x,avg＜ M_x,max)=0.95；

Wherein, M_x,min, M_x,max, M_x,avgIt is respectively input parameter M_xMinimum, maximum and average, input parameter M_xTable Show the energy consumption in x maintenance of surface stage, UF_xRepresent the uncertain index in x maintenance of surface stage.Ring because of mix and construction stage It is less that border affects value, and for same maintenance technology, differs greatly.Probability for avoiding negative value to occur is excessive, i.e. negative value goes out Existing probability > 5%, finally definition is mixed and stirred the parameter with the construction stage and is obeyed logarithm normal distribution, and the parameter in other stages is obeyed Normal distribution.Model of the present invention is extensive concept, has extensive extension.Both can be specific regression model, Such as bituminous mixture energy consumption forecast model, it is possible to the input parameter assuming that, such as gradation design medium pitch consumption, construction platform Class, transportation range etc..

Further, Criterion haul distance is also included；By the substantial amounts of maintenance project sample of statistics, set and old expect mix The standard haul distance in building is 0km；Old material is set to 30km to stock ground haul distance and compound to job site standard haul distance；Virgin material is extremely The quasi-haul distance of mix emblem mark is 60km；Haulage stage uses the standard haul distance setting to calculate transport energy consumption carbon emission to set up maintenance row Affect database for standardized environment.So can reflect the ambient influnence attribute of maintenance technology itself, eliminate because different transport Away from and the difference that produces, the result of calculation simultaneously making ambient influnence is representative.

Further, described analyzed by Monte Carlo, obtain the probability density function of each maintenance plan energy consumption carbon emission Method with statistical parameter is: the result evaluating data reliability evaluation and the reliability of the adjustment model, as input parameter, is carried out Monte Carlo analyzes, and iterations is 10000～50000 times, sets up the final ambient influnence probability density letter of maintenance plan Number, extraction calculates pertinent statistical parameters, evaluates maintenance plan ambient influnence result reliability.

Further, described ambient influnence compares parameter and is defined asCompare in order to evaluate maintenance plan ambient influnence The conspicuousness of result；Carry out Monte Carlo analysis to R, set up the probability density distribution of R, obtain pertinent statistical parameters, The confidential interval of R value, wherein, Env is set up under 95% confidence level₁Represent the ambient influnence of the first maintenance plan to be compared, Env₂Representing the ambient influnence of the first maintenance plan to be compared, wherein the ambient influnence of maintenance plan includes the energy of maintenance plan Consumption and carbon emission amount, main here is exactly to contrast energy consumption and the carbon emission amount of two schemes.Then P (R ＜ 1)=P (Env₁ ＜ Env₂) characterize the probability of ambient influnence less than maintenance of surface scheme 2 for the ambient influnence of maintenance of surface scheme 1.So can Know that the energy consumption carbon emission amount of which maintenance plan is little more intuitively, more environmentally-friendly.

Operation principle: energy consumption carbon emission result of calculation 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, utilizes Beta to be distributed, by defeated Enter parameter and be converted into probability density function；The latter evaluates then by the uncertain index of definition, determines sample distribution form and mould Shape parameter.The present invention is according to the standardization energy consumption carbon emission database set up, and the method for evaluating reliability of proposition can effectively be caught Catch the variability of result of calculation, the energy consumption carbon emission ratio of relatively more different maintenance plan from 95% confidence level.

Beneficial effect: compared with prior art, the present invention solves existing asphalt pavement conserving energy consumption carbon emission and calculates knot The really deficiency of reliability evaluation disappearance, method simultaneously disclosed by the invention is simple, convenient, it is thus achieved that result more accurate, from And optimum asphalt pavement conserving scheme, more energy-conserving and environment-protective can be used according to the result calculating.

Brief description

The method flow diagram that Fig. 1 provides for the present invention；

Fig. 2 is the matrix pitch energy consumption probability density distribution when DQI is 3.85；

Fig. 3 is different pieces of information quality matrix pitch energy consumption probability density distribution；

Fig. 4 mixes maintenance plan energy consumption probability density function for modified heat, and wherein, Fig. 4 (a) is 50,000 iteration Lower modified heat mixes energy consumption and the frequency thereof of scheme, the accumulative density function distribution map of Fig. 4 (b) energy consumption；

Fig. 5 is that matrix heat mixes and stirs the observable index relatively schematic diagram that modified heat mixes maintenance plan；Fig. 5 (a) is that heat mixes matrix maintenance Scheme consumes energy comparison diagram with mixing modified maintenance plan；Fig. 5 (b) is that heat is mixed matrix maintenance plan and mixes modified maintenance plan ring Border impact is compared parameter and is added up density function distribution map.

Detailed description of the invention

Below in conjunction with the accompanying drawings the present invention is done and further explain.

As it is shown in figure 1, the present invention a kind of asphalt pavement conserving energy consumption carbon emission calculates the method for reliability evaluation, mainly wrap Include following steps:

(1) data reliability evaluation:

First each phase data of different maintenance project is gathered, such as raw materials consumption, mixing building energy consumption, transportation range etc.. Calculating, by table 1, the energy consumption carbon emission that raw material produce, other phase data pass through collection in worksite.As shown in table 2, different maintenance sides Case ambient influnence inventory.

Table 1:

Wherein, China specifies that the calorific value of every kilogram of standard coal (kgce) is 29.27MJ.

Table 2:

Wherein, each maintenance plan all use standard haul distance calculate with regard to transport section.The present embodiment set old The standard haul distance expecting mixing building is 0km；Old material is set to job site standard haul distance to stock ground haul distance with compound 30km；Virgin material is 60km to the quasi-haul distance of mix emblem mark.

The quality testing method set up, the quality of evaluation path manufacture of materials environmental data.With matrix pitch material As a example by, its energy consumption producing is 170.34kgce.As shown in table 3, the quality testing matrix of matrix pitch material respectively refers to Respectively { 3.0,5.0,4.0,4.0,3.0,5.0,3.0}, then DQI is 3.85 to mark score value.DQI is mainly by asking for each index The mean 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)

Obtain matrix pitch manufacture of materials energy consumption PDF in conjunction with Beta function parameter, as shown in table 4, it is thus achieved that shape function α, β For (3,3), interval endpoint is (-20% ,+20%).Matrix pitch manufacture of materials energy consumption PDF obtaining is as shown in Figure 2.

Table 4:

Different DQI values represents the different qualities of data.As a example by matrix pitch, different DQI values, corresponding matrix pitch The PDF of energy consumption is also different.As it is shown on figure 3, DQI is higher, the quality of data is higher, then PDF distribution is narrower, corresponding reliability Higher.

The PDF setting up according to Fig. 2, extraction calculates pertinent statistical parameters, and wherein statistical parameter includes average, standard deviation, divides Digit, the coefficient of variation etc., can evaluate matrix pitch manufacture of materials energy consumption numerical value and reliability thereof according to statistical parameter, such as all Value represents average energy consumption, and the coefficient of variation represents reliability.Being similar to, the energy consumption carbon emission that can produce other road-making materials is set up Corresponding PDF, analyzing for follow-up Monte Carlo provides data source.

(2) the reliability of the adjustment model evaluation:

Because the environment numerical value of mix and construction stage is less, and for same maintenance technology, differ greatly, for avoiding negative value The probability occurring is excessive, i.e. probability > 5%, it is therefore assumed that construction and mix stage obedience logarithm normal distribution；Other stages are then false Determine Normal Distribution.According to formula

$\left\{\begin{array}{c}{M}_{x,min}=\frac{M_{x,avg}}{{\mathrm{UF}}_x}\\ M_{x,max}={\mathrm{UF}}_x\×M_{x,avg}\end{array}\right.;$

p(M_x,min＜ M_x,avg＜ M_x,max)=0.95；

Wherein, UF value defines the fluctuation range between minimum of a value and maximum.UF value is bigger, then fluctuation range is bigger, Antisense is as the same.Then UF value is corresponding to the scope that fluctuation range is 95% confidential interval, and corresponding UF value is as shown in table 5.In form NA represents that this numerical value does not exists.According to UF value and distribution form, calculate M_x,avgAverage value standard deviation, then can determine that corresponding PDF. Wherein, M_x,min, M_x,max, M_x,avgIt is respectively input parameter M_xMinimum, maximum and average, input parameter M_xRepresent x road surface The energy consumption in maintenance stage, x represent heating, mix and stir, transport etc., M_x,avgIt is averaged acquisition according to multiple samples of different schemes.

Table 5:

(3) maintenance plan ambient influnence reliability evaluation: the PDF of each data obtaining during data reliability is evaluated and mould The PDF of each parameter obtaining in type reliability evaluation, as input parameter, carries out Monte Carlo to different maintenance plan and divides Analysis, iterations is 50,000 time.Modified heat mixes the analog result of maintenance plan energy resource consumption as shown in Figure 4.Such as Fig. 4 (a) institute Showing, 50, under 000 iteration, modified heat mixes energy consumption and the frequency thereof of scheme.In the numerical value quality setting and model parameter variation Under, the average energy consumption that modified heat mixes scheme (1 ton of quality) is 35.58kgce, and its standard deviation is 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 Consumption intensity.Scheming according to CDF, modified heat mixes scheme 25^thWith 75^thPercentage is respectively 30.15kgce and 40.29kgce.

For other maintenance plan and CO₂Calculating, carry out Monte Carlo analysis equally, obtain pertinent statistical parameters, As shown in table 6.

Table 6:

In table 6, average represents the average energy consumption of maintenance plan, and standard deviation then reflects the energy consumption fluctuation range of maintenance plan； 25^thWith 75^thPercentage then can be used for defining the energy consumption intensity of different maintenance plan.

(4) ambient influnence parameter R is set up: although table 6 can provide statistical parameter to describe each maintenance plan ambient influnence Reliability, but cannot determine whether the energy consumption carbon emission between different maintenance plan has significant statistical discrepancy.The present invention Definition ambient influnence compares parameterIn order to describe the conspicuousness that maintenance plan ambient influnence compares.

Wherein, P (R ＜ 1)=P (Env₁＜ Env₂) characterize the ambient influnence of maintenance of surface scheme 1 less than maintenance of surface scheme The probability of the ambient influnence of 2.Choose matrix heat and mix maintenance plan as benchmark, with energy consumption as index, carry out 50,000 Monte Carlo analyzes, and result is as shown in Figure 5.

As shown in Fig. 5 (a), in 95% confidence level, mixing matrix maintenance plan compared to heat, heat mixes modified maintenance plan The energy consuming is more.As shown in Fig. 5 (b), two kinds of maintenance plan energy consumption ratios can be extracted further in 95% confidence level On confidential interval.

According to ambient influnence parameter R, set up the PDF that different maintenance plan compares, extract associated statistical information, be summarized in Table 7.

Table 7:

Reference scheme	Relatively scheme	P(R<1)	P(R>1)	Conspicuousness	Intermediate value	Standard deviation	95% confidential 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	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	Hot in-plant reclaimed	99.9%	NA	Significantly	0.51	0.06	(0.41,0.65)

Average that the different schemes that provides table 7 compares and waving interval, can from statistics aspect evaluate different maintenance plan it Between the disparity range of ambient influnence and reliability thereof.For example, the average energy loss-rate cold in place recycling of cold in-plant recycling scheme supports side The average energy consumption many 52% of case, the energy consumption ratio waving interval in 95% confidence level is (1.24,1.81), i.e. the former energy consumption It is the 124%-181% of the latter's energy consumption.Analysis process for carbon emission is consistent with energy consumption analysis.

In sum, the problem that the present invention solves asphalt pavement conserving energy consumption carbon emission result of calculation reliability evaluation. During concrete utilization, if the reliability of the adjustment model evaluates sample deficiency (n=1), then can use following processing mode: specify UF=1, i.e. divide Cloth form uses average, standard deviation equal, sets up PDF.

Claims (6)

1. the method obtaining asphalt pavement conserving energy consumption carbon emission reliability evaluation, it is characterised in that: combine data reliable Property evaluate 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；Definition ambient influnence compares parameter, obtains each asphalt pavement conserving scheme in 95% confidence level Energy consumption ratio confidential interval；Wherein, described data reliability evaluation is for setting up probability density function to manufacture of materials energy consumption；Institute Stating the reliability of the adjustment model evaluation and setting up probability density function for road pavement maintenance stage energy consumption, described statistical parameter includes from accordingly Corresponding average, standard deviation, quantile and the coefficient of variation obtaining in probability density function.

2. the method for acquisition asphalt pavement conserving energy consumption carbon emission reliability evaluation according to claim 1, its feature exists In: described data reliability evaluation method is: first pass through the quality testing matrix of foundation, obtains quality of data index； Secondly, it is distributed by Beta, 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(a≤x≤b)；

Quality of data exponential quantity is converted into probability density function, the input parameter analyzed as follow-up Monte Carlo；Its In, α, β are profile shape parameter, in order to determine the dispersion degree of energy consumption carbon emission probability density distribution；A, b are interval endpoint, In order to determine the fluctuation range of energy consumption carbon emission probability density distribution；Finally, by quality of data exponential quantity qualitative evaluation data Reliability, quality of data exponential quantity is higher, and the quality of data is higher, and reliability is higher；The probability being converted by quality of data index Density function quantitative assessment data reliability；Probability density function profiles is narrower, and the quality of data is higher, and reliability is higher.

3. the method for acquisition asphalt pavement conserving energy consumption carbon emission reliability evaluation according to claim 1, its feature exists In: the method for described the reliability of the adjustment model evaluation is: sets the parameter mixed and stirred with the construction stage and obeys logarithm normal distribution, other rank The parameter Normal Distribution of section；According to formula:

Definition input parameter M_xMinimum, maximum；M in formula_x,min, M_x,max, M_x,avgIt is respectively input parameter M_xMinimum, pole Big value and average；Input parameter M_xFluctuation range and UF_xMeet:

p(M_x,min＜ M_x,avg＜ M_x,max)=0.95；

Wherein, M_x,min, M_x,max, M_x,avgIt is respectively input parameter M_xMinimum, maximum and average, input parameter M_xRepresent x The energy consumption in maintenance of surface stage, UF_xRepresent the uncertain index in x maintenance of surface stage.

4. the method for acquisition asphalt pavement conserving energy consumption carbon emission reliability evaluation according to claim 1, its feature exists In: also include Criterion haul distance；By the substantial amounts of maintenance project sample of statistics, set the old standard haul distance expecting mixing building For 0km；Old material is set to 30km to stock ground haul distance and compound to job site standard haul distance；Virgin material is to the quasi-fortune of mix emblem mark Away from for 60km；Haulage stage uses the standard haul distance setting to calculate transport energy consumption carbon emission to set up maintenance behavioral standard environment Affect database.

5. the method for acquisition asphalt pavement conserving energy consumption carbon emission reliability evaluation according to claim 1, its feature exists In: described analyzed by Monte Carlo, obtain the probability density function of each maintenance plan energy consumption carbon emission and statistical parameter Method is: the result evaluating data reliability evaluation and the reliability of the adjustment model, as input parameter, carries out Monte Carlo and divides Analysis, iterations is 10000～50000 times, sets up the final ambient influnence probability density function of maintenance plan, and extraction calculates phase Close statistical parameter, evaluate maintenance plan ambient influnence result reliability.

6. the method for acquisition asphalt pavement conserving energy consumption carbon emission reliability evaluation according to claim 1, its feature exists In: described ambient influnence compares parameter and is defined asIn order to evaluate the conspicuousness of maintenance plan ambient influnence comparative result； Carry out Monte Carlo analysis to R, set up the probability density distribution of R, obtain pertinent statistical parameters, under 95% confidence level Set up the confidential interval of R value, wherein, Env₁Represent the ambient influnence of the first maintenance plan to be compared, Env₂Represent that the first is treated The relatively ambient influnence of maintenance plan.