CN111650339A - Reverse identification method for release intensity of multiple indoor pollution sources of building - Google Patents
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Abstract
The invention discloses a reverse identification method for release intensity of a plurality of indoor pollution sources of a building, which comprises the following steps: the method comprises the following steps: adopting a CFD numerical simulation technology to obtain a stable heat flow field for the indoor environment parameters of the building; step two: acquiring a transmission matrix A between indoor concentrations released by a plurality of pollution sources and concentrations of monitoring points based on a pollution source Contribution Rate (CRPS) method, and establishing an inverse model by using the transmission matrix A; step three: enhancing the stability of the inverse model solution by adopting a Tikhonov regularization method; step four: and selecting a regularization matrix L, calculating a regularization parameter lambda, and solving the concentration of the pollution source based on the concentration of the monitoring points. The method for identifying the release intensity of the pollution source solves the problems of singleness and complexity of the traditional identification method, and provides a new scheme for quickly and effectively identifying the release intensity of a plurality of pollution sources in a building room.
Description
Technical Field
The invention relates to the technical field of building indoor air quality identification, in particular to a reverse identification method for the release intensity of a plurality of indoor pollution sources of a building.
Background
Studies have shown that humans spend most of their life indoors, and thus, human health is closely related to the Quality of Indoor Air Quality (IAQ). The gaseous pollutant is one of the main indoor air pollutants, is colorless, tasteless and practically invisible, and often produces great harm to human health. Common gaseous pollutants such as CO, CO2, nitrides, volatile organic compounds of building materials and the like can enter blood through a respiratory system to reach all parts of the whole body, and threaten the respiratory system, the heart, tissue organs and the like. The existing detection and alarm of the concentration of indoor pollutants works after the pollutants are released, and the technology for searching the sources of the pollutants and determining the release intensity is not perfect. Therefore, how to give timely early warning and quantification of pollutant concentration when gaseous pollutants appear in the air and adopt emergency measures to eliminate pollution sources is very important.
The problem of reverse identification of the release strength of the indoor gaseous pollution source of the building is solved, and the indoor air is diversified, so that the indoor diffusion mode of the gaseous pollution source is very complicated and is influenced by a plurality of physical factors such as air flow rate and wall surface temperature. The existing QR method, PR method, adjoint method, neural network and other methods can determine the release intensity of the pollution source, but the methods can eliminate the influence of some physical factors, can identify the intensity of the indoor pollutants under specific conditions, and lack universality; and the workload is complex in the identification process, and the pollution source release strength cannot be quickly identified.
The above background disclosure is only for the purpose of assisting understanding of the concept and technical solution of the present invention and does not necessarily belong to the prior art of the present patent application, and should not be used for evaluating the novelty and inventive step of the present application in the case that there is no clear evidence that the above content is disclosed at the filing date of the present patent application.
Disclosure of Invention
The invention provides a reverse identification method for the release intensity of a plurality of indoor pollution sources of a building aiming at the technical problems, so as to solve the technical problem that the traditional reverse identification method is difficult to quickly capture the pollution sources, and facilitate better identification and control of the pollution sources.
In order to achieve the purpose, the invention adopts the following technical scheme:
a reverse identification method for release strength of a plurality of indoor pollution sources of a building mainly comprises the steps of obtaining stable heat flow fields released by the plurality of indoor pollution sources of the building by using a CFD numerical simulation technology in the early stage, obtaining a transmission matrix A between indoor concentrations released by the plurality of indoor pollution sources and concentrations of monitoring points by using a pollution source Contribution Rate (CRPS) method, and establishing an inverse model; regularizing the inverse model based on a Tikhonov regularization method at a later stage, enhancing the stability of the inverse model, and finally calculating the release strength of a pollution source; the method specifically comprises the following steps:
the method comprises the following steps: adopting a CFD numerical simulation technology to obtain a stable heat flow field for the indoor environment parameters of the building;
step two: acquiring a transmission matrix A between indoor concentrations released by a plurality of pollution sources and concentrations of monitoring points based on a pollution source Contribution Rate (CRPS) method, and establishing an inverse model by using the transmission matrix A;
step three: enhancing the stability of the inverse model solution by adopting a Tikhonov regularization method;
step four: and selecting a regularization matrix L, calculating regularization parameters, and solving the concentration of the pollution source based on the concentration of the monitoring points.
Further, in the first step, the concentration contribution of each part of pollutants released in the building room to the fixed monitoring point is analyzed one by using a CFD numerical simulation technology, and all parts of sub-concentration fields are superposed to form a total concentration field in the building room.
Further, the indoor environment parameters of the building comprise temperature, humidity and air flow rate.
Further, the pollution source Contribution Rate (CRPS) method in step two is to release each pollution source individually to obtain the concentration contribution to the monitoring point, and then release a plurality of pollution sources simultaneously to obtain the concentration contribution to the monitoring point.
Further, the establishing of the inverse model in the step two is to invert the transmission matrix A, and the obtained monitoring point concentration is used for reversely solving the release intensity of the pollution source.
Further, in the third step, when the stability of the solution of the inverse model is enhanced by adopting a Tikhonov regularization method, a preset limit value is added to the range and the size of the solution of the inverse model.
Further, the pollution source release strength is solved in the fourth step, and the specific steps are as follows:
s1: selecting a proper regularization matrix L according to a matrix regularization principle;
s2: when an MATLAB self-compiling program is used for solving the regularization coefficient, firstly determining;
s3: and solving the release strength of the pollution source by using the obtained regularization parameters, the regularization matrix L and the concentration of the monitoring points measured in advance.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method comprises the steps of firstly obtaining a transmission matrix A between the concentrations of a plurality of pollution sources and monitoring point concentrations by utilizing a CFD technology, then regularizing the transmission matrix A by utilizing an MATLAB self-compiling program by taking the monitoring point concentrations as known conditions, finally calculating the concentrations of the pollution sources, and outputting a calculation result. The identification method solves the problems of singleness and complexity of the traditional identification method, provides a new scheme for quickly and effectively identifying the pollution source in the building room, and further improves and maintains the indoor air quality.
(2) In the invention, the CFD technology is utilized to simulate an indoor fluid field, and an abstract indoor environment is visualized; the complex gas diffusion is simplified using a pollution source concentration Contribution Rate (CRPS) method; constructing a transmission matrix between the concentration of the pollution source and the concentration of the monitoring point to obtain a release rule of the concentration of the pollution source; and (3) perfecting instability in the solution process of the inverse model by using a regularization method.
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FIG. 1 is a flow chart of a method for reverse identification of the release intensity of a plurality of indoor pollution sources in a building according to the present invention;
FIG. 2 is a schematic diagram of a pollution source Contribution (CRPS) method.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the embodiment. It should be understood that the directions "up", "down", "left" and "right" mentioned in the following embodiments of the present invention are based on the positions of the corresponding drawings. These directional terms are used for convenience of description only and do not represent limitations on the particular embodiments of the present invention. Unless otherwise specified, like reference numerals in the reference numerals refer to like structures.
The invention relates to a reverse identification method of release strength of a plurality of indoor pollution sources of a building, which mainly comprises the steps of obtaining stable heat flow fields released by the plurality of indoor pollution sources of the building by using a CFD numerical simulation technology in the early stage, obtaining a transmission matrix A between the concentrations of the plurality of indoor pollution sources and the concentrations of monitoring points by using a pollution source Contribution Rate (CRPS) method, and establishing an inverse model; and regularizing the inverse model based on a Tikhonov regularization method in the later stage, enhancing the stability of the inverse model, and finally calculating the release strength of the pollution source. The specific implementation steps are shown in the work flow chart of fig. 1.
The method comprises the following steps: adopting a CFD numerical simulation technology to obtain a stable heat flow field for the indoor environment parameters of the building;
step two: acquiring a transmission matrix A between indoor concentrations released by a plurality of pollution sources and concentrations of monitoring points based on a pollution source Contribution Rate (CRPS) method, and establishing an inverse model by using the transmission matrix A;
step three: enhancing the stability of the inverse model solution by adopting a Tikhonov regularization method;
step four: and selecting a regularization matrix L, calculating a regularization parameter lambda, and solving the concentration q of the pollution source based on the concentration C of the monitoring point.
(1) In the first step:
the measuring personnel measure environmental parameters such as temperature, humidity, air flow rate and the like inside the building as known environmental conditions. And (4) utilizing CFD simulation to obtain a thermal flow field of the indoor environment of the building.
In the invention, two indoor pollution sources in a building are released simultaneously as an example: two pollution sources are released at a position 0.5m away from the air inlet and at a position 0.5m close to the opposite wall surface at the same height as the air inlet, and are marked as pollution sources q1Pollution source q2The release concentration was 100 ppm. Because the air in the stable heat flow field flows slowly, the monitoring points are arranged at the air inlet and the air outlet, and the coordinates of the monitoring points are 1 (0.5, 1.6) and 2 (1.7, 0.1). Activating UDF in CFD simulation to set a pollution source release mode to obtain a stable heat flow field of the indoor environment of the building, wherein the concentration of monitoring points is as shown in the following table 1:
TABLE 1 concentration at monitoring points
Because the pollution source release concentration of each monitoring point before the pollution source is released is 0, the pollution source release concentration change quantity monitored by the monitoring point is the pollution source release concentration monitored by the point. Namely, the actual release intensity of the pollution source monitored by the monitoring points is as follows: q' ═ 17.08433.489T。
(2) In the second step:
the pollution source Contribution Rate (CRPS) method is based on the principle that the airflow is in a stable state, the pollutant release concentrations of different sources can be superposed, and the contribution of each pollution source in a room can be analyzed. In the release of pollutants such as air inlets, ceilings, floors and walls in the building room, the concentration contribution of each part to a fixed monitoring point is analyzed one by CFD, and the sub-concentration fields of all parts are superposed to form a total concentration field in the building room, which is shown in a schematic diagram of fig. 2.
At the point of measurement X, the source of contamination qiThe contribution factor to the amount of change in spatial concentration is defined as follows:
in the formula,. DELTA.Ci(X) is the ith contamination source qiChange in concentration, Δ C, at the site XiPThe concentration change quantity generated by the ith pollution source to the spatial concentration distribution under the uniform mixing action of all the pollution sources is V, and the air flow quantity of the air outlet is V.
The contribution rate of a single pollution source to the pollution source generated by the monitoring point by a plurality of pollution sources is as follows:
CRPS11＝ΔC11/ΔC1＝99.99％；CRPS21＝ΔC12/ΔC2＝86.29％
CRPS12＝ΔC21/ΔC1＝0.01％；CRPS22＝ΔC22/ΔC2＝10.75％
in conclusion, a transmission matrix A between the release concentration of the indoor concentration of the plurality of pollution sources and the concentration of the monitoring point is obtained:
in the step, the transmission matrix A of the pollution sources obtained by the CRPS method is only related to fluid and is not related to the release of the pollution sources in a building room, and the transmission matrix A can be constructed by the CFD technology as long as a fixed flow field is unchanged.
(3) In the third step:
and enhancing the stability of the solution of the inverse model by adopting a Tikhonov regularization method.
According to an energy equation, under the condition of a stable heat flow field, concentration fields released by various pollution sources in a building room form a superposition system. The superposition of the sub-concentration fields formed by each pollution source in the space forms a total concentration field formed under the combined action of all the pollution sources in the space. According to the superposition of the concentration field under the stable heat flow field and the CRPS method, the concentration of the monitoring point in the space and the release intensity of the pollution source satisfy the following linear relationship:
C＝A·q
in the formula, C is a vector formed by the concentrations of monitoring points under the combined action of a plurality of pollution sources in the building room, and q is a vector formed by the release concentrations of the plurality of pollution sources in the building room. In the method, the release intensity of a plurality of pollution sources in the building is reversely solved by the known concentration of the monitoring points, so that the release intensity of the pollution sources can be calculated by using the concentration of the monitoring points, and the following relation is satisfied:
q＝A-1·C
the method for reversely identifying the release intensity of a plurality of indoor pollution sources in a building is characterized in that the idea of solving the release intensity problem of the plurality of indoor pollution sources in the building by using the known pollution source release concentration information is just opposite to the idea of solving the concentration of a monitoring point by using the traditional known pollution source release intensity. At this time, solving the energy equation in reverse becomes a ill-conditioned problem without numerical stability. In order to ensure the numerical stability of the inverse model solution, the ill-conditioned problem is treated by a Tikhonov regularization method in step three.
The invention adopts a Tikhonov regularization method to enhance the stability of the operation of a transmission matrix A obtained by a CRPS method, and the principle is as follows: and adding a preset limit value to the range and the size of the inverse model solution to ensure that the numerical value solution is finally kept in a certain range, thereby reducing and avoiding the oscillation or divergence of the numerical value solution.
(4) In step four:
and then selecting a regularization matrix L, and calculating a regularization parameter lambda to obtain the release strengths q of a plurality of indoor pollution sources of the building, wherein the method specifically comprises the following steps:
1) using least square method to obtain the target function q ═ A-1C, optimizing to minimize the target residual function Z,
2) a penalty function is introduced, the Tikhonov regularization method converts the residual error function Z into a minimum value objective function problem,
in the formula | · | non-conducting phosphor2Is a matrix two norm, λ is a regularization parameter, which can directly affect the pollution source release strength q of the solution, and L is a regularization matrix.
3) Selecting a regularization matrix L, wherein the regularization matrix L is a calculation item combined with the pollution source release strength q, and the most common expression is as follows:
when M is 2, the effective balance between the data correctness and the smoothness can be met. At this time, the expression of L is:
4) when solving for the minimum value of C ═ a · q, it is necessary to calculate the first derivative of z (q) to q, and to obtain the extreme point of z (q) where the first derivative is zero, and further determine the minimum value of z (q) by other means. The mathematical derivation process for determining the extreme points is as follows:
since the expression of z (q) involves a matrix of two norms, which can be represented by the product of itself and the transpose of the matrix, the derivation of the four arithmetic operations of the matrix or vector is as follows:
it is possible to obtain,
in the derivation process of the objective function, the derivation of the product of the matrix a and q to q is involved, and can be obtained by a matrix derivation formula:
the first derivative of z (q) to q can be found:
when the above formula equals zero, Z (q) takes an extreme value, at which time (A)TA+λ2LTL)q＝ATC。
Further, the release intensity of a plurality of pollution sources in the building room is obtained:
q＝(ATA+λ2LTL)-1×(ATC)。
in summary, assuming that the concentration C of the monitoring points, the regularization matrix L, the regularization parameter λ and the transmission matrix a are known, the release strengths q of the plurality of pollution sources in the building room can be obtained.
In step two, the transmission matrix a is obtained by using CFD numerical simulation technique, and the self-compiler calculates log λ 1.0352231, and further λ 0.1871, which is known as λ 0.1871
C＝(47.08 26.28)TBy q ═ aTA+λ2LTL)-1×(ATC) And obtaining the calculated release strength q ═ of the indoor pollution source of the building (16.2332.01)T。
It is known that in step one, the actual emission intensity q' of a plurality of pollution sources in the building room is obtained (17.0833.49)TQ was compared to q' and the results are shown in table 2:
TABLE 2 calculation of pollution Source Release Strength and actual pollution Source Release Strength (Unit: ppm)
As can be seen from the comparison result of the release intensities, the inverse model has good precision performance on the identification of the release intensities of the plurality of indoor pollution sources of the building within the error allowable range, and the identification of the release intensities of the plurality of indoor pollution sources of the building is basically consistent with the actual release intensity of the pollution sources.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention may be made by those skilled in the art without departing from the spirit of the present invention, which is defined by the claims.
Claims (7)
1. A reverse identification method for release intensity of a plurality of indoor pollution sources of a building is characterized by comprising the following steps:
the method comprises the following steps: adopting a CFD numerical simulation technology to obtain a stable heat flow field for the indoor environment parameters of the building;
step two: obtaining a transmission matrix A between indoor concentrations released by a plurality of pollution sources and concentrations of monitoring points based on a pollution source contribution rate method, and establishing an inverse model by using the transmission matrix A;
step three: enhancing the stability of the inverse model solution by adopting a Tikhonov regularization method;
step four: and selecting a regularization matrix L, calculating a regularization parameter lambda, and solving the concentration of the pollution source based on the concentration of the monitoring points.
2. The method of claim 1, wherein the method comprises the following steps: in the first step, the concentration contribution of the release of each part of pollutants in the building to a fixed monitoring point is analyzed one by utilizing a CFD numerical simulation technology, and all parts of sub-concentration fields are superposed to form a total concentration field in the building.
3. The method of claim 1, wherein the method comprises the following steps: in the first step, the indoor environment parameters of the building comprise temperature, humidity and air flow rate.
4. The method of claim 1, wherein the method comprises the following steps: and step two, the pollution source contribution rate method is that each pollution source is released independently to obtain the concentration contribution to the monitoring point, and then a plurality of pollution sources are released simultaneously to obtain the concentration contribution to the monitoring point.
5. The method of claim 1, wherein the method comprises the following steps: and step two, establishing an inverse model, namely inverting the transmission matrix A, and reversely solving the release intensity of the pollution source by using the obtained concentration of the monitoring point.
6. The method of claim 1, wherein the method comprises the following steps: and in the third step, when the stability of the solution of the inverse model is enhanced by adopting a Tikhonov regularization method, a preset limit value is added to the range and the size of the solution of the inverse model.
7. The method of claim 1, wherein the method comprises the following steps: solving the release intensity of the pollution source in the fourth step, which comprises the following specific steps:
s1: selecting a proper regularization matrix L according to a matrix regularization principle;
s2: when an MATLAB self-compiling program is used for solving the regularization coefficient lambda, firstly determining log lambda;
s3: and solving the release strength of the pollution source by using the obtained regularization parameter lambda, the regularization matrix L and the concentration of the monitoring points measured in advance.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145737A (en) * | 2017-04-28 | 2017-09-08 | 南京工业大学 | Pollution source reverse recognition algorithm under unsteady flow field based on Markov chain |
| CN108826488A (en) * | 2018-05-07 | 2018-11-16 | 大连理工大学 | A kind of indoor pollution purification removal device and method sourcing formula automatically |
| CN109885804A (en) * | 2019-01-23 | 2019-06-14 | 大连理工大学 | A kind of air monitoring and source discrimination method based on monitoring car |
| CN111257519A (en) * | 2020-03-18 | 2020-06-09 | 南京工业大学 | Leakage source identification device and method in building space based on sensor array and impulse response principle |
-
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- 2020-06-13 CN CN202010539020.1A patent/CN111650339A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145737A (en) * | 2017-04-28 | 2017-09-08 | 南京工业大学 | Pollution source reverse recognition algorithm under unsteady flow field based on Markov chain |
| CN108826488A (en) * | 2018-05-07 | 2018-11-16 | 大连理工大学 | A kind of indoor pollution purification removal device and method sourcing formula automatically |
| CN109885804A (en) * | 2019-01-23 | 2019-06-14 | 大连理工大学 | A kind of air monitoring and source discrimination method based on monitoring car |
| CN111257519A (en) * | 2020-03-18 | 2020-06-09 | 南京工业大学 | Leakage source identification device and method in building space based on sensor array and impulse response principle |
Non-Patent Citations (5)
| Title |
|---|
| HANEUL CHOI等: "Long-term simulation for predicting indoor air pollutant concentration considering pollutant distribution based on concept of CRPS index", 《BUILD SIMUL》 * |
| ROSEANE A.S. ALBANI等: "Tikhonov-type regularization and the finite element method applied to point source estimation in the atmosphere", 《ATMOSPHERIC ENVIRONMENT》 * |
| TAEYEON KIM等: "New Scales for Assessing Contribution Ratio of Pollutant Sources to Indoor Air Quality", 《INDOOR BUILT ENVIRON》 * |
| TENGFEI (TIM) ZHANG等: "An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source", 《ATMOSPHERIC ENVIRONMENT》 * |
| 周鸿彪: "气态固定污染源的逆向辨识", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
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Application publication date: 20200911 |