CN109886339B - GWP regression prediction method and device for chemical substances - Google Patents
GWP regression prediction method and device for chemical substances Download PDFInfo
- Publication number
- CN109886339B CN109886339B CN201910138797.4A CN201910138797A CN109886339B CN 109886339 B CN109886339 B CN 109886339B CN 201910138797 A CN201910138797 A CN 201910138797A CN 109886339 B CN109886339 B CN 109886339B
- Authority
- CN
- China
- Prior art keywords
- lumo
- set compound
- energy
- prediction
- homo
- 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.)
- Active
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application provides a GWP regression prediction method and device for a chemical substance, the previous orbits HOMO and LUMO energy and GWP value of a training set compound are utilized, a random forest regression method is adopted to train a regression model, and therefore the GWP value of the prediction set compound can be obtained through the trained classification model according to the previous orbits HOMO and LUMO energy of the prediction set compound.
Description
Technical Field
The application relates to the technical field of chemical detection, in particular to a GWP regression prediction method and device for chemical substances.
Background
The inter-government board on Climate Change (IPCC) is an international organization that evaluates information on the scientific, technical, social, and economic aspects of Climate Change and its influence, Climate Change alleviation, and adaptation measures on a global scale, and provides scientific and technical advice for the implementation of the united nations Climate Change framework convention on demand.
IPCC defines GWP (Global Warming potential) as the instantaneous pulse discharge of 1kg of chemical x, the integral of the radiation forcing induced over a certain time frame being relative to the discharge of an equivalent amount of reference gas (CO) under the same conditions2) The ratio of the radiation forced integrals over the same time frame, i.e.:
x(t)＝e-t/τ；
where TH is the time frame (e.g., 20, 100 and 500 years), we took 100 years in this study; t represents time; RF (radio frequency)xAnd RFrRespectively representing compound x and reference gas CO2(ii) radiation forcing; a isxAnd arRespectively representing the corresponding radiation efficiency; x (t) and r (t) represent the time corresponding functions of compound x and the reference gas, respectively; τ is atmospheric lifetime in units of a; reference compound CO2The atmospheric response function r (t) is the newly published formula by IPCC at 2007, parameter a0，ai，τiIs constant published by IPCC.
Atmospheric lifetime (tau) of chemical substance ii) Depending on its reaction rate k with hydroxyl radicalsiAnd is expressed as the relative atmospheric lifetime with methyl chloroform (CH3CCl3)Service:
wherein
And k isiRespectively represent the reaction rate constants of CH3CCl3 with chemical i and hydroxyl radicals at 277K. Thus:
thus, the hydroxyl radical reaction rate k of the compound in the atmosphereiIs a key parameter for the calculation of GWP.
To address global warming, a framework agreement has been promulgated between the governments of many national governments that mandates future emissions of chemicals with a GWP of 200 or less. In view of this, it is very important to use a material having a GWP of 200 or less. However, testing GWP values for thousands of compounds is very time consuming and costly, and therefore, the industry (e.g., refrigeration, power industry) needs a fast, inexpensive method to increase the efficiency of finding low GWP compounds and to reduce costs.
In the prior art, combined radiation efficiency prediction and hydroxyl radical reaction rate prediction are adopted, and then GWP of a compound is predicted, and the prediction method is complex and needs three steps of calculation. Therefore, it is urgently needed by those skilled in the art to propose a simpler and accurate GWP prediction method.
Disclosure of Invention
The embodiment of the application provides a GWP regression prediction method and device for chemical substances, so that the calculation steps of the GWP prediction value of a compound are simpler.
In view of the above, the first aspect of the present application provides a method for predicting GWP regression of a chemical substance, the method comprising:
obtaining a training set compound with known GWP value;
calculating the front line orbital HOMO and LUMO energies of the training set compounds;
taking the front-line orbits HOMO and LUMO energy of the training set compound as independent variables, taking the GWP value of the training set compound as a dependent variable, and training a regression model by adopting a random forest regression method to obtain a trained regression model;
obtaining a prediction set compound;
calculating the energy of the front line orbitals HOMO and LUMO of the prediction set compound;
and predicting GWP of the prediction set compound through a random forest predictor by using the front-line orbital HOMO and LUMO energy of the prediction set compound and the trained regression model, so as to obtain the GWP value of the prediction set compound output by the trained regression model.
Optionally, the calculating of the energy of the front-line orbitals HOMO and LUMO of the training set compound is specifically:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by adopting a semi-empirical quantum mechanics method;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
and calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by adopting a semi-empirical quantum mechanics method.
Optionally, the semi-empirical quantum mechanical method is specifically any one of AM1, PM3, PM6, or PM 7.
Optionally, the calculating of the energy of the front-line orbitals HOMO and LUMO of the training set compound is specifically:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by adopting a density functional theory;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
and calculating the front line orbital HOMO and LUMO energies of the prediction set compound by adopting a density functional theory.
Optionally, the density functional theory is specifically B2LYP or APFD.
Optionally, the calculating of the energy of the front-line orbitals HOMO and LUMO of the training set compound is specifically:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by using a de novo calculation method;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
the energy of the HOMO and LUMO of the front-line orbitals of the prediction set compounds was calculated using a de novo calculation.
A second aspect of the present application provides a GWP regression prediction apparatus for a chemical substance, the apparatus comprising:
a first obtaining unit, configured to obtain a training set compound with a known GWP value;
a first calculation unit for calculating the front-line orbital HOMO and LUMO energies of the training set compounds;
a training unit, configured to use the front-line orbits HOMO and LUMO energy of the training set compound as independent variables, use the GWP values of the training set compound as dependent variables, and perform training of a regression model by using a random forest regression method to obtain a trained regression model;
a second acquisition unit configured to acquire a prediction set compound;
a second calculation unit for calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound;
and the prediction unit is used for predicting the GWP of the prediction set compound through a random forest predictor by using the front line orbital HOMO and LUMO energy of the prediction set compound and the trained regression model, so as to obtain the GWP value of the prediction set compound output by the trained regression model.
Optionally, the first calculation unit is further configured to calculate the front-line orbital HOMO and LUMO energies of the training set compound by using a semi-empirical quantum mechanical method;
correspondingly, the second calculation unit is also used for calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by adopting a semi-empirical quantum mechanical method.
Optionally, the first calculation unit is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of the training set compound by using density functional theory;
correspondingly, the second calculating unit is also used for calculating the energy of the front line orbitals HOMO and LUMO of the prediction set compound by adopting a density functional theory.
Optionally, the first calculation unit is further configured to calculate the front-line orbital HOMO and LUMO energies of the training set compounds using a de novo calculation method;
correspondingly, the second calculation unit is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by using a de novo calculation method.
According to the technical scheme, the embodiment of the application has the following advantages:
in the embodiment of the application, the GWP regression prediction method for the chemical substances is provided, the previous orbits HOMO and LUMO energy and GWP value of the training set compound are utilized, and a random forest regression method is adopted to train a regression model, so that the GWP value of the prediction set compound can be obtained through the trained classification model according to the previous orbits HOMO and LUMO energy of the prediction set compound.
Drawings
Figure 1 is a flow chart of a method for GWP regression prediction of a chemical substance in an embodiment of the present application;
fig. 2 is a block diagram of an apparatus for predicting GWP regression of a chemical substance in an embodiment of the present application;
fig. 3 is a graph comparing the literature value and the predicted value of GWP in the example of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application designs a GWP regression prediction method and device for chemical substances, so that the calculation steps of the GWP prediction value of the compound are simpler.
For easy understanding, please refer to fig. 2, fig. 2 is a flowchart of a method of XX in the embodiment of the present application, and as shown in fig. 2, the method specifically includes:
for convenience of understanding, please refer to fig. 1, in which fig. 1 is a flowchart of a method for predicting GWP regression of a chemical substance in an embodiment of the present application, and as shown in fig. 1, the method specifically includes:
101. obtaining a training set compound with known GWP value;
it is noted that the training set compounds were the compounds and their GWP values collected from the climate change report of IPCC, wherein the compound structure is expressed in SMILES format, and the GWP of the compounds is collected wherein the value is 100 years, as shown in table 1:
table 1 Compound Structure, numbering, GWP literature values and their HOMO, LUMO energies, Classification and regression predictions
Note: the data, except NOVEC4710, was from IPCC reports.
102. Calculating the energy of the front line orbitals HOMO and LUMO of the training set compound;
103. taking the front-line orbital HOMO and LUMO energy of a training set compound as independent variables, taking the GWP value of the training set compound as a dependent variable, and training a regression model by adopting a random forest regression method to obtain the trained regression model;
the front-line orbital HOMO and LUMO energies of the training set compound are used as independent variables, the GWP value of the training set compound is used as a dependent variable, and the input training set compound is trained by using a random forest regression module of the Weka 3.7 software package to obtain a trained regression model.
104. Obtaining a prediction set compound;
105. calculating the energy of the front line orbitals HOMO and LUMO of the prediction set compound;
106. predicting GWP of the prediction set compound through a random forest predictor by using the front-line orbital HOMO and LUMO energy of the prediction set compound and the trained regression model to obtain the GWP value of the prediction set compound output by the trained regression model;
the GWP of the compound to be predicted is predicted by a random forest predictor using the calculated energy of the front-line orbits HOMO and LUMO of the prediction set compound and the trained regression model, and the reliability of prediction is evaluated by Root-mean-square deviation (RMSD) between the experimental value and the predicted value of the GWP of the compound:
wherein Xobs,iIs the GWP experimental value, X, of compound ipred,iIs the predicted value of GWP for compound i. The smaller the RMSD, the higher the accuracy of the prediction.
GWP was predicted for the prediction set and the prediction results are shown in Table 1. As shown in fig. 3, the literature value and the predicted value of GWP in the example of the present application are plotted in fig. 3, and the results show the linear regression coefficient R between the predicted GWP value and the literature value20.9398, the root mean square error RMSD of the predicted and literature values 1041. The application can obtain the prediction effect of more complex calculation only by carrying out energy calculation of HOMO and LUMO of the compound.
In the embodiment of the application, the GWP regression prediction method for the chemical substances is provided, the previous orbits HOMO and LUMO energy and GWP value of the training set compound are utilized, and a random forest regression method is adopted to train a regression model, so that the GWP value of the prediction set compound can be obtained through the trained classification model according to the previous orbits HOMO and LUMO energy of the prediction set compound.
Further, the calculation of the energy of the front-line orbitals HOMO and LUMO of the training set compounds is specified as:
calculating the energy of the front-line orbits HOMO and LUMO of the training set compound by adopting a semi-empirical quantum mechanics method;
accordingly, calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compounds is specifically:
and calculating the front line orbital HOMO and LUMO energy of the prediction set compound by adopting a semi-empirical quantum mechanical method.
Further, the semi-empirical quantum mechanical method is specifically any one of AM1, PM3, PM6, or PM 7.
Further, the calculation of the energy of the front-line orbitals HOMO and LUMO of the training set compounds is specified as:
calculating the energy of the front line orbitals HOMO and LUMO of the training set compound by adopting a density functional theory;
accordingly, calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compounds is specifically:
and calculating the front line orbital HOMO and LUMO energy of the prediction set compound by adopting a density functional theory.
Further, the density functional theory is specifically B2LYP or APFD.
Further, the calculation of the energy of the front-line orbitals HOMO and LUMO of the training set compounds is specified as:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by using a de novo calculation method;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
the energy of the HOMO and LUMO of the front-line orbitals of the prediction set compounds was calculated using a de novo calculation.
The above is a description of a method for predicting the GWP of a chemical substance provided in the present application, and the following is a description of a device for predicting the GWP of a chemical substance provided in the present application.
Referring to fig. 2, the present application provides a structure diagram of an apparatus for predicting GWP regression of a chemical substance, the apparatus comprising:
a first obtaining unit 201, configured to obtain a training set compound with a known GWP value;
a first calculation unit 202 for calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound;
a training unit 203, configured to use the front-line orbits HOMO and LUMO energy of the training set compound as independent variables, use the GWP value of the training set compound as a dependent variable, and perform training of a regression model by using a random forest regression method to obtain a trained regression model;
a second obtaining unit 204 for obtaining a prediction set compound;
a second calculation unit 205 for calculating the front-line orbital HOMO and LUMO energies of the prediction set compound;
and the prediction unit 206 is configured to perform GWP prediction on the prediction set compound through the random forest predictor by using the front-line orbital HOMO and LUMO energies of the prediction set compound and the trained regression model, so as to obtain a GWP value of the prediction set compound output by the trained regression model.
Further, the first calculating unit 202 is further configured to calculate front-line orbital HOMO and LUMO energies of the training set compound by using a semi-empirical quantum mechanical method;
accordingly, the second calculation unit 204 is also configured to calculate the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by using a semi-empirical quantum mechanical method.
Further, the first calculating unit 202 is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of the training set compound by using a density functional theory;
accordingly, the second calculation unit 204 is also configured to calculate the energy of the front-line orbitals HOMO and LUMO of the prediction set compound using the density functional theory.
Further, the first calculation unit 202 is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of the training set compound by using a de novo calculation method;
accordingly, the second calculation unit 204 is also configured to calculate the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by using a de novo calculation method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A GWP regression prediction method for a chemical substance is characterized by comprising the following steps:
obtaining a training set compound with known GWP value;
calculating the front line orbital HOMO and LUMO energies of the training set compounds;
taking the front-line orbits HOMO and LUMO energy of the training set compound as independent variables, taking the GWP value of the training set compound as a dependent variable, and training a regression model by adopting a random forest regression method to obtain a trained regression model;
obtaining a prediction set compound;
calculating the energy of the front line orbitals HOMO and LUMO of the prediction set compound;
and predicting GWP of the prediction set compound through a random forest predictor by using the front-line orbital HOMO and LUMO energy of the prediction set compound and the trained regression model, so as to obtain the GWP value of the prediction set compound output by the trained regression model.
2. The method for GWP regression prediction of chemical substances according to claim 1, wherein the calculating of the energy of the front-line orbitals HOMO and LUMO of the training set compound specifically comprises:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by adopting a semi-empirical quantum mechanics method;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
and calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by adopting a semi-empirical quantum mechanics method.
3. A method for regression prediction of GWP for chemical substances as claimed in claim 2, wherein said semi-empirical quantum mechanical method is any one of AM1, PM3, PM6 or PM 7.
4. The method for GWP regression prediction of chemical substances according to claim 1,
the calculation of the energy of the front-line orbitals HOMO and LUMO of the training set compound is specifically:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by adopting a density functional theory;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
and calculating the front line orbital HOMO and LUMO energies of the prediction set compound by adopting a density functional theory.
5. A method for regression prediction of GWP for chemical substances according to claim 4, wherein said density functional theory is B2LYP or APFD.
6. The method for GWP regression prediction of chemical substances according to claim 1,
the calculation of the energy of the front-line orbitals HOMO and LUMO of the training set compound is specifically:
calculating the energy of the front-line orbitals HOMO and LUMO of the training set compound by using a de novo calculation method;
accordingly, the calculation of the energy of the front-line orbitals HOMO and LUMO of the prediction set compound is specifically:
the energy of the HOMO and LUMO of the front-line orbitals of the prediction set compounds was calculated using a de novo calculation.
7. An apparatus for GWP regression prediction of a chemical substance, comprising:
a first obtaining unit, configured to obtain a training set compound with a known GWP value;
a first calculation unit for calculating the front-line orbital HOMO and LUMO energies of the training set compounds;
a training unit, configured to use the front-line orbits HOMO and LUMO energy of the training set compound as independent variables, use the GWP values of the training set compound as dependent variables, and perform training of a regression model by using a random forest regression method to obtain a trained regression model;
a second acquisition unit configured to acquire a prediction set compound;
a second calculation unit for calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound;
and the prediction unit is used for predicting the GWP of the prediction set compound through a random forest predictor by using the front line orbital HOMO and LUMO energy of the prediction set compound and the trained regression model, so as to obtain the GWP value of the prediction set compound output by the trained regression model.
8. The apparatus for GWP regression prediction of chemical substance of claim 7, wherein said first calculation unit is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of said training set compound by using a semi-empirical quantum mechanical method;
correspondingly, the second calculation unit is also used for calculating the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by adopting a semi-empirical quantum mechanical method.
9. The apparatus for GWP regression prediction of chemical substance of claim 7, wherein said first calculation unit is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of said training set compound using density functional theory;
correspondingly, the second calculating unit is also used for calculating the energy of the front line orbitals HOMO and LUMO of the prediction set compound by adopting a density functional theory.
10. The apparatus for GWP regression prediction of chemical substance of claim 7, wherein said first calculation unit is further configured to calculate a front-line orbital HOMO and LUMO energy of said training set compound using a de novo calculation method;
correspondingly, the second calculation unit is further configured to calculate the energy of the front-line orbitals HOMO and LUMO of the prediction set compound by using a de novo calculation method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138797.4A CN109886339B (en) | 2019-02-25 | 2019-02-25 | GWP regression prediction method and device for chemical substances |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138797.4A CN109886339B (en) | 2019-02-25 | 2019-02-25 | GWP regression prediction method and device for chemical substances |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109886339A CN109886339A (en) | 2019-06-14 |
| CN109886339B true CN109886339B (en) | 2021-10-26 |
Family
ID=66929184
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910138797.4A Active CN109886339B (en) | 2019-02-25 | 2019-02-25 | GWP regression prediction method and device for chemical substances |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109886339B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120004113A (en) * | 2010-07-06 | 2012-01-12 | 한국지질자원연구원 | A development of prediction model for groundwater productivity potential based on probability and statistic |
| CN103631814A (en) * | 2012-08-27 | 2014-03-12 | 捷达世软件（深圳）有限公司 | Greenhouse gas examining and managing method and system |
| CN103717699A (en) * | 2011-04-21 | 2014-04-09 | 瑞典爱玛龙儿童车制造有限公司 | Working fluid for rankine cycle |
| WO2015150747A1 (en) * | 2014-03-31 | 2015-10-08 | Imperial Innovations Limted | A computer implemented method of deriving performance from a local model |
| CN108427845A (en) * | 2018-03-16 | 2018-08-21 | 中南大学 | A kind of Pb-Zn deposits mining process carbon emission short term prediction method |
-
2019
- 2019-02-25 CN CN201910138797.4A patent/CN109886339B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120004113A (en) * | 2010-07-06 | 2012-01-12 | 한국지질자원연구원 | A development of prediction model for groundwater productivity potential based on probability and statistic |
| CN103717699A (en) * | 2011-04-21 | 2014-04-09 | 瑞典爱玛龙儿童车制造有限公司 | Working fluid for rankine cycle |
| CN103631814A (en) * | 2012-08-27 | 2014-03-12 | 捷达世软件（深圳）有限公司 | Greenhouse gas examining and managing method and system |
| WO2015150747A1 (en) * | 2014-03-31 | 2015-10-08 | Imperial Innovations Limted | A computer implemented method of deriving performance from a local model |
| CN108427845A (en) * | 2018-03-16 | 2018-08-21 | 中南大学 | A kind of Pb-Zn deposits mining process carbon emission short term prediction method |
Non-Patent Citations (4)
| Title |
|---|
| Global warming potential predictions for hydrofluoroethers with two carbon atoms;Paul Blowers 等;《springer》;20081231;369-381 * |
| Prediction of Environmental Properties Using a Hybrid Group Contribution Approach;Resul Al 等;《Elsevier》;20181231;1723-1728 * |
| Radiative efficiency estimation of organic substance based on group contribution method;Xinxin Zhang 等;《Elsevier energy procedia》;20141231;492-495 * |
| 一种基于基团贡献法的气体GWP计算方法;张聪 等;《绝缘材料》;20181231;第51卷(第10期);78-82 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109886339A (en) | 2019-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tan et al. | Internet-of-things enabled real-time monitoring of energy efficiency on manufacturing shop floors | |
| US12019963B2 (en) | Operations and maintenance systems and methods employing sensor-less digital twins | |
| Ray et al. | Infectious disease prediction with kernel conditional density estimation | |
| Ye et al. | Semiparametric estimation of gamma processes for deteriorating products | |
| EP2854053A1 (en) | Defect prediction method and device | |
| Teehan et al. | Comparing embodied greenhouse gas emissions of modern computing and electronics products | |
| Fye et al. | An examination of factors affecting accuracy in technology forecasts | |
| US20190028556A1 (en) | System and method for measuring user engagement | |
| US9417981B2 (en) | Data processing system, data processing method, and program | |
| US20150233873A1 (en) | System for monitoring operating status of chromatograph | |
| CN106650433A (en) | Detecting method and system for abnormal behavior | |
| US10402736B2 (en) | Evaluation system, evaluation method, and computer-readable storage medium | |
| EP2963552A1 (en) | System analysis device and system analysis method | |
| Cai et al. | Time-varying latent effect model for longitudinal data with informative observation times | |
| Louca et al. | Detecting cyclicity in ecological time series | |
| Park et al. | Cumulative residual Kullback–Leibler information with the progressively Type-II censored data | |
| Fregonara et al. | How to model uncertain service life and durability of components in life cycle cost analysis applications? the stochastic approach to the factor method | |
| CN109886339B (en) | GWP regression prediction method and device for chemical substances | |
| Cox et al. | Cox et al. reply | |
| Fink et al. | Semi-Markov processes with semi-regenerative states for the availability analysis of chemical process plants with storage units | |
| Li et al. | On dynamically monitoring aggregate warranty claims for early detection of reliability problems | |
| CN109712679B (en) | GWP (global warming potential) classification prediction method and device for chemical substances | |
| Cheng et al. | Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels | |
| Gupta et al. | Queue Length and Server Content Distribution in an Infinite‐Buffer Batch‐Service Queue with Batch‐Size‐Dependent Service | |
| WO2023049280A1 (en) | Systems and methods to screen a predictive model for risks of the predictive model |
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 |