CN116151134A - Carbon dioxide emission metering method - Google Patents

Carbon dioxide emission metering method Download PDF

Info

Publication number
CN116151134A
CN116151134A CN202310436633.6A CN202310436633A CN116151134A CN 116151134 A CN116151134 A CN 116151134A CN 202310436633 A CN202310436633 A CN 202310436633A CN 116151134 A CN116151134 A CN 116151134A
Authority
CN
China
Prior art keywords
industrial analysis
carbon dioxide
carbon content
carbon
sample
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310436633.6A
Other languages
Chinese (zh)
Other versions
CN116151134B (en
Inventor
李泽瑞
康宇
吕文君
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Artificial Intelligence of Hefei Comprehensive National Science Center
Original Assignee
Institute of Artificial Intelligence of Hefei Comprehensive National Science Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Artificial Intelligence of Hefei Comprehensive National Science Center filed Critical Institute of Artificial Intelligence of Hefei Comprehensive National Science Center
Priority to CN202310436633.6A priority Critical patent/CN116151134B/en
Publication of CN116151134A publication Critical patent/CN116151134A/en
Application granted granted Critical
Publication of CN116151134B publication Critical patent/CN116151134B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Abstract

The invention relates to the technical field of carbon dioxide emission metering, and discloses a carbon dioxide emission metering method, which comprises the following steps: collecting an industrial analysis sample; generating a multi-element single-element characteristic according to the industrial analysis sample to obtain a multi-element single-element characteristic matrix; defining a carbon content estimation model and an optimization objective function; solving a carbon content estimation model optimization objective function; obtaining a carbon content estimated value of an industrial analysis sample of the carbon content to be detected; calculating the carbon dioxide emission in the fixed combustion process of the coal; according to the invention, by establishing the relation model between the industrial analysis sample and the element analysis parameters, the carbon content estimation considering the actual coal types can be realized on the basis of not increasing the cost and the additional analysis process.

Description

Carbon dioxide emission metering method
Technical Field
The invention relates to the technical field of carbon dioxide emission metering, in particular to a carbon dioxide emission metering method.
Background
Global climate change is a significant challenge facing current humans, and to address this challenge, the core is to reduce greenhouse gas emissions, particularly carbon dioxide emissions generated during energy consumption. In some countries and regions, coal is the most important energy source, primary energy is mainly coal, and a power supply structure is mainly coal electricity. Therefore, the invention provides a carbon dioxide emission metering method for a coal-fired power plant, which is helpful for enterprises to master the carbon emission condition of the enterprises and provides scientific data support for the enterprises to participate in carbon transaction and carbon emission reduction.
The existing method for calculating the carbon dioxide emission of a large number of coal-fired power plants is mostly designed according to the coal statistical data of respective countries, the running conditions of power equipment and the like. Because the coals in different areas have heterogeneity, the combustion emission characteristics of different coals have large difference, so that the quality condition of the coals needs to be analyzed, and the influence of the different coals on the operation of the power equipment is required, thereby improving the accuracy of carbon emission metering.
Although the power generation efficiency can be improved by using high-quality coal, in view of the current state of coal quality in certain countries and regions and the consideration of power generation cost, enterprises usually do not burn according to designed coal types completely, but adopt modes of blending combustion, mixed combustion and the like, so that large mass difference can exist between actual coal types and designed coal types, and coal quality change has an important influence on carbon dioxide emission, and in order to obtain an accurate carbon dioxide emission measurement result, coal quality parameters such as heating value, volatile matters, ash, sulfur content, moisture and the like need to be fused, and emission amount calculation is performed.
The carbon content of the coal is required to be obtained through element analysis, and the element analysis process is complex and high in cost, so that the carbon dioxide emission metering is realized by establishing a relation model between industrial analysis parameters (total moisture, volatile matters, fixed carbon, ash, high and low heat productivity, total sulfur content, coal dust fineness, ash combustible content and the like) and the element analysis parameters.
Disclosure of Invention
In order to solve the technical problems, the invention provides a carbon dioxide emission metering method.
In order to solve the technical problems, the invention adopts the following technical scheme:
a carbon dioxide emissions metering method comprising the steps of:
step one: collecting industrial analysis samples
Figure SMS_2
Forming a training sample set; wherein->
Figure SMS_5
Representing an industrial analysis sample->
Figure SMS_9
Is>
Figure SMS_3
Personal input (s)/(s)>
Figure SMS_7
Sample for industrial analysis->
Figure SMS_8
Dimension total of (A) industrial analysis sample->
Figure SMS_10
Including various coal quality parameters; training sample set, partial industrial analysis sample with tag y +.>
Figure SMS_1
The sample is called as marked sample, and the rest industrial analysis samples are unmarked samples; wherein the label y is an industrial analysis sample->
Figure SMS_4
Corresponding carbon content; />
Figure SMS_6
Representing real space;
step two: from industrial analysis of samples
Figure SMS_11
Generating a multi-element single-element characteristic, and expanding an original characteristic space in which the multi-element single-element characteristic is positioned to a multi-element single-element characteristic space with high dimension; arranging marked samples before and unmarked samples after to obtain a multi-element single-item feature matrix ++>
Figure SMS_12
, wherein />
Figure SMS_13
For the total number of industrial analysis samples in the training sample set, d is the dimension of the multi-element single-element feature space,/->
Figure SMS_14
Is a line vector representing a multiple element single feature ++>
Figure SMS_15
；/>
Step three: carbon content estimation model
Figure SMS_16
, wherein />
Figure SMS_17
For outputting a weight vector; defining a carbon content estimation model optimization objective function +.>
Figure SMS_18
：
Figure SMS_19
；
wherein ,
Figure SMS_20
for ensuring->
Figure SMS_21
Sparsity model complexity measure, +.>
Figure SMS_22
As a term of experience loss,
Figure SMS_23
is a smoothness metric term +.>
Figure SMS_24
Is a coefficient for weighing each item and is a positive number;
step four: solving a carbon content estimation model optimization objective function by a near-end gradient descent method to obtain an optimal output weight vector
Figure SMS_25
；
Step five: industrial analysis sample of carbon content to be measured
Figure SMS_26
Input to the carbon quantity estimation model->
Figure SMS_27
Obtaining the corresponding estimated value of the carbon content +.>
Figure SMS_28
；
Step six: based on the estimated value of the carbon content
Figure SMS_29
Calculating the carbon dioxide emission amount in the coal-fired fixed combustion process>
Figure SMS_30
。
Specifically, industrial analysis samples
Figure SMS_31
The dimensions of (2) comprise full moisture, volatile matters, fixed carbon, ash, high and low heat productivity, full sulfur content, coal dust fineness and ash residue combustible content.
Specifically, in step three, the model complexity measure
Figure SMS_32
。
Specifically, in step three, experience loss term
Figure SMS_34
, wherein />
Figure SMS_38
For the tag vector +.>
Figure SMS_41
The number of marked samples and the number of unmarked samples, respectively,>
Figure SMS_33
is->
Figure SMS_37
Dimension all zero line vector,>
Figure SMS_40
representing a transpose; intermediate variable->
Figure SMS_42
，/>
Figure SMS_35
Is->
Figure SMS_36
Dimension full line vector, ">
Figure SMS_39
As a function for constructing a diagonal matrix.
Specifically, in step three, the smoothness metric term
Figure SMS_45
；/>
Figure SMS_49
Representing transpose, laplace matrix
Figure SMS_52
，/>
Figure SMS_43
Is a similarity matrix, +.>
Figure SMS_48
The element of (2) is->
Figure SMS_51
，/>
Figure SMS_55
Description of the ith Industrial analysis sample->
Figure SMS_44
And j industrial analysis sample->
Figure SMS_47
Similarity between->
Figure SMS_50
Is the bandwidth; />
Figure SMS_53
Is a diagonal matrix->
Figure SMS_46
Element->
Figure SMS_54
。
Specifically, in the sixth step, the estimated value based on the carbon content is obtained
Figure SMS_56
Calculating the carbon dioxide emission amount in the coal-fired fixed combustion process>
Figure SMS_57
When (1):
Figure SMS_58
；
Figure SMS_59
indicating the amount of fire coal>
Figure SMS_60
Indicating the total slag discharge amount of the boiler>
Figure SMS_61
Representing the carbon content of the ash.
Compared with the prior art, the invention has the beneficial technical effects that:
according to the invention, by establishing a relation model between the industrial analysis sample and the element analysis parameters, the carbon content estimation considering the actual coal types can be realized on the basis of not increasing the cost and the additional analysis process; meanwhile, in the establishment of the relation model, besides the information of the marked sample, the structural information contained in the unmarked sample is fully fused, and the estimation accuracy of the model can be improved under the condition of limited labels; in addition, the carbon content estimation model obtained by solving is sparse, and extraction of key features in the model is realized, so that the interpretability of the model is improved to a certain extent.
Drawings
FIG. 1 is a schematic flow chart of the carbon dioxide emission metering method of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
The carbon dioxide emission metering method comprises the following steps:
s1, collecting industrial analysis samples to form a training sample set:
industrial analysis sample
Figure SMS_69
, wherein />
Figure SMS_70
Representation->
Figure SMS_71
Is>
Figure SMS_72
Personal input (s)/(s)>
Figure SMS_73
Representing real space, +.>
Figure SMS_74
Sample for industrial analysis->
Figure SMS_75
Is a sum of dimensions of (a) and (b). In this example, the industrial analysis sample +.>
Figure SMS_62
The dimensions of (2) include the information of total moisture, volatile matter, fixed carbon, ash, high and low heat productivity, total sulfur content, fineness of pulverized coal, and combustible content of ash, i.e., & lt- & gt in this embodiment>
Figure SMS_63
. For industrial analysis samples with corresponding carbon content +.>
Figure SMS_64
The carbon content of which is used as a label->
Figure SMS_65
And->
Figure SMS_66
Form sample->
Figure SMS_67
. Industrial analysis sample with tag in training sample set +.>
Figure SMS_68
Referred to as marked samples, the remainder as unmarked samples.
S2: from industrial analysis of samples
Figure SMS_78
Generating a multiple single feature->
Figure SMS_79
, wherein />
Figure SMS_80
And then, the form and the quantity of the multi-element single-item type features are properly determined according to background knowledge, so that the original feature space where the multi-element single-item type features are positioned can be expanded to a high-dimensional multi-element single-item type feature space. Industrial analysis sample
Figure SMS_81
Is>
Figure SMS_82
，/>
Figure SMS_83
Representing dimensions of a multi-element single-item feature space; arranging marked samples before and unmarked samples after to obtain a multi-element single-item feature matrix ++>
Figure SMS_84
, wherein />
Figure SMS_76
For the total number of industrial analysis samples in the training sample set, +.>
Figure SMS_77
Is representative of a polynomial feature.
S3: the carbon content estimation model can be written as
Figure SMS_85
, wherein />
Figure SMS_86
To output a weight vector.
Defining a carbon content estimation model optimization objective function:
Figure SMS_87
；
wherein the coefficient is
Figure SMS_88
For weighing the items.
Experience loss term
Figure SMS_93
, wherein />
Figure SMS_94
For the tag vector +.>
Figure SMS_95
The number of marked samples and the number of unmarked samples, respectively,>
Figure SMS_96
is->
Figure SMS_97
Personal tag (S)>
Figure SMS_98
Is->
Figure SMS_99
Dimension all zero line vector,>
Figure SMS_89
representing a transpose;
Figure SMS_90
，/>
Figure SMS_91
is->
Figure SMS_92
A full row of vectors is maintained.
At the same time, because the space dimension of the constructional feature is higher, a model complexity measure term is introduced
Figure SMS_100
To ensure->
Figure SMS_101
The sparsity of the model is adopted, so that the key characteristics of the structural characteristics are selected and reserved, and a multi-element polynomial model with a simpler form can be obtained.
Employing manifold regularization to exploit implications within dataThe distribution information improves the performance of the model, and two industrial analysis samples with similar distances in the characteristic space are assumed to have similar labels, namely the smoothness assumption is satisfied, and the smoothness assumption conformity degree of the model is measured and introduced
Figure SMS_109
, wherein />
Figure SMS_110
Description of two Industrial analysis samples->
Figure SMS_111
And->
Figure SMS_112
Similarity between->
Figure SMS_113
For bandwidth, & gt>
Figure SMS_114
Representing the carbon content estimation model with respect to the industrial analysis sample +.>
Figure SMS_115
Is provided. Thus, a smoothness metric term can be derived: />
Figure SMS_102
；/>
Figure SMS_103
Representing transpose, laplace matrix +.>
Figure SMS_104
；/>
Figure SMS_105
Is a similarity matrix, the elements of which are +.>
Figure SMS_106
；/>
Figure SMS_107
Is a pair ofCorner matrix, its elements->
Figure SMS_108
。
S4: solving the carbon content estimation model optimization objective function to obtain the optimal carbon content estimation model optimization objective function
Figure SMS_116
The solution method can adopt a proximal gradient descent method. />
S5: industrial analysis sample for carbon content to be measured
Figure SMS_117
Output of carbon content estimation model->
Figure SMS_118
Namely, industrial analysis sample->
Figure SMS_119
Corresponding carbon content estimation values.
S6: model estimation
Figure SMS_120
The method comprises the steps of collecting relevant parameters of a coal-fired power plant, including the coal amount, the total slag discharge amount of a boiler and the carbon element content in ash, and calculating the carbon dioxide emission amount in the coal-fired fixed combustion process by adopting the following formula:
Figure SMS_121
；
wherein ,
Figure SMS_122
represents the carbon dioxide emission (unit: ton) of the coal-fired fixed combustion process; />
Figure SMS_123
Represents the amount of coal (unit: ton); />
Figure SMS_124
Represents the total slag discharge amount (unit: ton) of the boiler and is +.>
Figure SMS_125
Represents the carbon element content (unit:%) in the ash.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a single embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to specific embodiments, and that the embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.

Claims (6)

1. A carbon dioxide emissions metering method comprising the steps of:
step one: collecting industrial analysis samples
Figure QLYQS_2
Forming a training sample set; wherein->
Figure QLYQS_5
Representing an industrial analysis sample->
Figure QLYQS_7
Is>
Figure QLYQS_3
Personal input (s)/(s)>
Figure QLYQS_6
Sample for industrial analysis->
Figure QLYQS_9
Dimension total of (A) industrial analysis sample->
Figure QLYQS_10
Including various coal quality parameters; training sample set, partial industrial analysis sample with tag y +.>
Figure QLYQS_1
The sample is called as marked sample, and the rest industrial analysis samples are unmarked samples; wherein the label y is an industrial analysis sample->
Figure QLYQS_4
Corresponding carbon content; />
Figure QLYQS_8
Representing real space;
step two: from industrial analysis of samples
Figure QLYQS_11
Generating a multi-element single-element characteristic, and expanding an original characteristic space in which the multi-element single-element characteristic is positioned to a multi-element single-element characteristic space with high dimension; arranging marked samples before and unmarked samples after to obtain a multi-element single-item feature matrix ++>
Figure QLYQS_12
, wherein />
Figure QLYQS_13
For the total number of industrial analysis samples in the training sample set, d is the dimension of the multi-element single-element feature space,/->
Figure QLYQS_14
Is a line vector representing a multiple element single feature ++>
Figure QLYQS_15
；
Step three: carbon content estimation model
Figure QLYQS_16
, wherein />
Figure QLYQS_17
For outputting a weight vector; defining a carbon content estimation model optimization objective function +.>
Figure QLYQS_18
：
Figure QLYQS_19
；
wherein ,
Figure QLYQS_20
for ensuring->
Figure QLYQS_21
Sparsity model complexity measure, +.>
Figure QLYQS_22
For experience loss term->
Figure QLYQS_23
Is a smoothness metric term +.>
Figure QLYQS_24
Is a coefficient for weighing each item and is a positive number;
step four: solving a carbon content estimation model optimization objective function by a near-end gradient descent method to obtain an optimal output weight vector
Figure QLYQS_25
；
Step five: industrial analysis sample of carbon content to be measured
Figure QLYQS_26
Input to the carbon quantity estimation model->
Figure QLYQS_27
Obtaining the corresponding estimated value of the carbon content +.>
Figure QLYQS_28
；
Step six: based on the estimated value of the carbon content
Figure QLYQS_29
Calculating the carbon dioxide emission amount in the coal-fired fixed combustion process>
Figure QLYQS_30
。
2. The carbon dioxide emissions metering method of claim 1, wherein: industrial analysis sample
Figure QLYQS_31
The dimensions of (2) comprise full moisture, volatile matters, fixed carbon, ash, high and low heat productivity, full sulfur content, coal dust fineness and ash residue combustible content.
3. The carbon dioxide emissions metering method of claim 1, wherein: in step three, model complexity metrics
Figure QLYQS_32
。
4. The carbon dioxide emissions metering method of claim 1, wherein: in step three, experience loss term
Figure QLYQS_34
, wherein />
Figure QLYQS_39
For the tag vector +.>
Figure QLYQS_40
The number of marked samples and the number of unmarked samples, respectively,>
Figure QLYQS_36
is->
Figure QLYQS_38
Personal tag (S)>
Figure QLYQS_42
Is->
Figure QLYQS_44
Dimension all zero line vector,>
Figure QLYQS_33
representing a transpose; intermediate variable->
Figure QLYQS_37
，
Figure QLYQS_41
Is->
Figure QLYQS_43
Dimension full line vector, ">
Figure QLYQS_35
As a function for constructing a diagonal matrix.
5. The carbon dioxide emissions metering method of claim 1, wherein: in step three, the smoothness metric term
Figure QLYQS_48
；/>
Figure QLYQS_50
Representing transpose, laplace matrix +.>
Figure QLYQS_54
，/>
Figure QLYQS_47
Is a similarity matrix, +.>
Figure QLYQS_51
The element of (2) is->
Figure QLYQS_53
，
Figure QLYQS_56
Description of the ith Industrial analysis sample->
Figure QLYQS_45
And j industrial analysis sample->
Figure QLYQS_52
Similarity between->
Figure QLYQS_55
Is the bandwidth; />
Figure QLYQS_57
Is a diagonal matrix->
Figure QLYQS_46
Element->
Figure QLYQS_49
。
6. The carbon dioxide emissions metering method of claim 1, wherein in step six, the carbon content estimation value is based on
Figure QLYQS_58
Calculating the carbon dioxide emission amount in the coal-fired fixed combustion process>
Figure QLYQS_59
When (1): />
Figure QLYQS_60
；
Figure QLYQS_61
Indicating the amount of fire coal>
Figure QLYQS_62
Indicating the total slag discharge amount of the boiler>
Figure QLYQS_63
Representing the carbon content of the ash. />
CN202310436633.6A 2023-04-23 2023-04-23 Carbon dioxide emission metering method Active CN116151134B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310436633.6A CN116151134B (en) 2023-04-23 2023-04-23 Carbon dioxide emission metering method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310436633.6A CN116151134B (en) 2023-04-23 2023-04-23 Carbon dioxide emission metering method

Publications (2)

Publication Number Publication Date
CN116151134A true CN116151134A (en) 2023-05-23
CN116151134B CN116151134B (en) 2023-07-18

Family

ID=86354724

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310436633.6A Active CN116151134B (en) 2023-04-23 2023-04-23 Carbon dioxide emission metering method

Country Status (1)

Country Link
CN (1) CN116151134B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101276455A (en) * 2007-03-29 2008-10-01 株式会社日立制作所 Evaluation system for amount of emission gases through fuel supply chain
CN106485051A (en) * 2016-09-13 2017-03-08 广东电网有限责任公司电力科学研究院 A kind of boiler CO2 Emission amount calculation method and its system
CN106650825A (en) * 2016-12-31 2017-05-10 中国科学技术大学 Automotive exhaust emission data fusion system
CN109918702A (en) * 2019-01-03 2019-06-21 上海交通大学 A kind of blast-furnace burden cooperates with Multipurpose Optimal Method with operation
US20200325408A1 (en) * 2019-04-15 2020-10-15 L'air Liquide, Societe Anonyme Pour L'etude Et L'exploitation Des Procedes Georges Claude Method of online control of a slag forming gasification process and plant for a gasification process
CN115238597A (en) * 2022-09-23 2022-10-25 国网浙江省电力有限公司宁波供电公司 Construction method of source network carbon-loaded emission model of park level comprehensive energy system
CN115656461A (en) * 2022-08-23 2023-01-31 上海明华电力科技有限公司 Coal electric unit real-time carbon emission monitoring method based on coal quality soft measurement

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101276455A (en) * 2007-03-29 2008-10-01 株式会社日立制作所 Evaluation system for amount of emission gases through fuel supply chain
CN106485051A (en) * 2016-09-13 2017-03-08 广东电网有限责任公司电力科学研究院 A kind of boiler CO2 Emission amount calculation method and its system
CN106650825A (en) * 2016-12-31 2017-05-10 中国科学技术大学 Automotive exhaust emission data fusion system
CN109918702A (en) * 2019-01-03 2019-06-21 上海交通大学 A kind of blast-furnace burden cooperates with Multipurpose Optimal Method with operation
US20200325408A1 (en) * 2019-04-15 2020-10-15 L'air Liquide, Societe Anonyme Pour L'etude Et L'exploitation Des Procedes Georges Claude Method of online control of a slag forming gasification process and plant for a gasification process
CN115656461A (en) * 2022-08-23 2023-01-31 上海明华电力科技有限公司 Coal electric unit real-time carbon emission monitoring method based on coal quality soft measurement
CN115238597A (en) * 2022-09-23 2022-10-25 国网浙江省电力有限公司宁波供电公司 Construction method of source network carbon-loaded emission model of park level comprehensive energy system

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
KANG Y 等: "Remote sensing and artificial neural network estimation of on-road vehicle emissions", 《2016 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS》, pages 561 - 566 *
ZHANG S 等: "Real-world fuel consumption and CO2 (carbon dioxide) emissions by driving conditions for light-duty passenger vehicles in China", 《ENERGY》, pages 247 - 257 *
卫冬丽: "中国燃煤电厂二氧化碳排放量计算方法研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》, pages 027 - 442 *
康宇 等: "移动源排放遥测主要影响因素分析及预测", 《大气与环境光学学报》, pages 220 - 229 *
白静洁 等: "京津唐区域火电厂碳排放模型的研究", 《能源环境保护》, no. 06, pages 19 - 23 *

Also Published As

Publication number Publication date
CN116151134B (en) 2023-07-18

Similar Documents

Publication Publication Date Title
JP5663510B2 (en) Coal-fired power plant control device and coal-fired power plant
Nakaishi et al. Environmental efficiency analysis of China's coal-fired power plants considering heterogeneity in power generation company groups
CN112330179B (en) Fuzzy comprehensive evaluation method for coal blending combustion based on improved entropy weight method
JP6742222B2 (en) Driving support device and program
CN104461688B (en) A kind of method that detailed chem-ical reaction mechanism skeleton simplifies
CN116151134B (en) Carbon dioxide emission metering method
Michalakakis et al. Exergy and network analysis of chemical sites
Kalina Options for using solid oxide fuel cell technology in complex integrated biomass gasification cogeneration plants
CN116304669A (en) Short-term correction distributed photovoltaic power prediction method and system
CN1771439B (en) On-line monitoring method for a fossil fuel converter apparatus
Kang et al. Energy carbon emission structure and reduction potential focused on the supply-side and demand-side
Nicolin et al. Lifetime optimization of a molten carbonate fuel cell power system coupled with hydrogen production
Cong et al. How does the carbon market impact the economy-energy-environment system in resource-based regions of China? Empirical evidence from Shanxi Province
CN112907074A (en) Energy efficiency sensitive index detection method and system for comprehensive energy system user
CN109655489B (en) Method and device for metering standard coal consumption of coal-fired unit
CN107622178B (en) Calcium balance-based circulating fluidized bed boiler performance calculation method
CN116401948A (en) Online prediction method and system for generating amount of power station boiler ash based on LSTM
CN114358601A (en) Method and device for constructing multi-dimensional evaluation index system of multi-energy system
CN112131526A (en) On-line calculation method for fly ash concentration and fly ash flow of coal-fired boiler
De Angelis et al. The next generation green data center a modified power usage effectiveness metric proposal for a CCHP natural gas or biogas fuelled architecture
CN117789850A (en) Online soft measurement method for coal quality of coal fed into grinding coal of pulverized coal boiler of power station
CN116822183B (en) Implementation method and device for bringing environmental factors into aluminum alloy material design
CN115601074A (en) Simulation analysis method and system for influence of carbon transaction mechanism on electricity consumption cost
An et al. Research on Electricity Carbon Emission Accounting Based on Classified Carbon Emission Factors
Vasilenko et al. Complex thermodynamic analysis of the heat-technological complex of sugar production: analysis method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant