CN112485399A - Sewage toxin concentration estimation method - Google Patents
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Abstract
本发明公开了一种污水毒素浓度估计方法，将具有一定细胞指数CI的培养细胞暴露在污水样本中，连续采集污水样本内培养细胞的细胞数量测量值；根据细胞对毒素的摄取机理，以及毒素所引起的细胞数量变化创建细胞毒素动态模型；使用粒子滤波算法对细胞毒素动态模型和细胞数量测量值进行处理，估计出污水样本中的毒素浓度。本发明采用粒子滤波算法根据暴露在污水样本中的培养细胞的细胞数量测量值和细胞毒素动态模型对污水中的毒素浓度做连续在线估计，并准确估计出污水中毒素浓度，而采用的粒子滤波算法对于存在大的过程噪声、观测噪声或建模不确定的非线性动态系统可以取得良好的估计效果，保证毒素浓度估计的准确性。
The invention discloses a method for estimating the concentration of toxins in sewage. The cultured cells with a certain cell index CI are exposed to sewage samples, and the measured value of the cell number of the cultured cells in the sewage samples is continuously collected; The resulting change in cell number creates a cytotoxin dynamic model; the cytotoxin dynamic model and cell number measurements are processed using a particle filter algorithm to estimate the toxin concentration in the sewage sample. The invention adopts the particle filter algorithm to continuously estimate the toxin concentration in the sewage according to the cell number measurement value of the cultured cells exposed in the sewage sample and the cytotoxin dynamic model, and accurately estimates the toxin concentration in the sewage. The algorithm can achieve good estimation results for nonlinear dynamic systems with large process noise, observation noise or modeling uncertainty, and ensure the accuracy of toxin concentration estimation.
Description
Technical Field
The invention relates to a toxin concentration determination method, in particular to a sewage toxin concentration estimation method.
Background
Sewage treatment is a process of separating pollutants contained in sewage or converting the pollutants into harmless substances by using various different methods to purify the sewage. Generally, urban and industrial water treatment units combine primary, secondary and tertiary treatment processes to efficiently treat various pollutants in water, i.e., inorganic pollutants, organic pollutants and biological pollutants. The first-stage treatment mainly adopts a physical method to remove suspended matters and partial organic matters; the secondary treatment is to remove colloid and soluble substances in the sewage by adopting a biological method, a chemical method or a physical and chemical method on the basis of the primary treatment; and the third-stage treatment further removes refractory substances, nitrogen, phosphorus, heavy metals and the like. Due to the complexity of wastewater, it is often difficult to determine the specific poison that causes the performance degradation of a wastewater treatment plant, and therefore, timely measurement of toxicity is critical to the smooth operation of a wastewater treatment plant.
The basic premise of a successful early warning system is to continuously collect accurate data describing the risk of toxic contamination, but most of the key quality indicators need to be obtained by off-line sample analysis, which is usually expensive and requires frequent and costly maintenance, and the lack of appropriate key variable information can seriously affect the monitoring of the toxin concentration in the sewage treatment process. It is therefore important to use real-time measurements and established models to achieve continuous online estimation of key variables.
In general, the response of cells to small doses of toxicants is limited (stable), while the response of large doses of toxicants is unlimited (unstable), a phenomenon that clearly indicates the non-linearity of cytotoxicity. Common estimation methods for nonlinear systems, such as Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF), ensemble kalman filtering (EnKF), rolling time domain estimation (MHE), are suitable for nonlinear dynamic systems with small uncertainty, but are difficult to achieve good effects in nonlinear dynamic systems with large process noise, observation noise, or modeling uncertainty.
Disclosure of Invention
The invention aims to provide a sewage toxin concentration estimation method, which can accurately estimate the concentration of toxins in sewage.
In order to solve the above technical problems, the present invention provides a method for estimating the concentration of toxins in wastewater, comprising the steps of,
exposing cultured cells with a certain cell index CI in a sewage sample, and continuously collecting cell number measured values of the cultured cells in the sewage sample;
creating a cytotoxin dynamic model according to the uptake mechanism of the toxin by the cells and the change of the cell number caused by the toxin;
and processing the cytotoxin dynamic model and the cell number measured value by using a particle filtering algorithm to estimate the toxin concentration in the sewage sample.
In a preferred embodiment of the present invention, further comprising, creating the cytotoxin kinetic model comprises,
creating a kinetic model of the toxin uptake process by the cells:
creating an apoptotic mechanism for intracellular endotoxin concentration:
cicharacterizing the intracellular endotoxin concentration; c. CeCharacterizing the extracellular toxin concentration; n represents the cell number, and N is approximately equal to CI; k is a radical of1，k2，k3，KiIs a relevant parameter; kciCharacterizing the relative cell killing rate in the presence of the toxin; k is a radical ofsThe relative cell proliferation rate was characterized in the absence of toxin.
In a preferred embodiment of the present invention, further comprising, creating the cytotoxin kinetic model comprises,
exposing the cultured cells with a certain cell index CI to toxins with different concentrations, and continuously collecting cell number measured values of the cultured cells under the action of the toxins;
substituting the cell number measured value of the cultured cells under the action of the toxin into the (formula 1) and the (formula 2), identifying the unknown parameters in the kinetic model and the cell endotoxin concentration apoptosis mechanism by adopting a nonlinear regression method, and calculating the related parameter k1，k2，k3，Ki。
In a preferred embodiment of the present invention, the cultured cells further comprise a cell index CI of 1.0 to 1.2 for cells exposed to different concentrations of the toxin.
In a preferred embodiment of the present invention, the cultured cells exposed to the wastewater sample have a cell index CI of 1.0 to 1.2.
In a preferred embodiment of the present invention, the cultured cell is further cultured in the cell line NIH 3T 3.
In a preferred embodiment of the present invention, the initial cell number of cultured cells exposed to the contaminated water sample and cultured cells exposed to different concentrations of toxin each comprises 10000 cells per well.
In a preferred embodiment of the present invention, the method for estimating the concentration of the toxin in wastewater further comprises discretizing the dynamic model of the toxin in wastewater to obtain a discretized dynamic model of the toxin in wastewater
xk＝f(xk-1)+wk；
yk＝g(xk)+vk；
And the particle filtering algorithm is combined with the discretized cytotoxin dynamic model to process the measured value of the cell number in the sewage sample.
In a preferred embodiment of the present invention, the particle filter algorithm further comprises a function of defining a state variable x ═ c for processing the measured cell number in the wastewater samplei，ce，N]Defining a measurement vector y ═ N, based on a discretized dynamic model of the cytotoxin, and estimating the true intracellular endotoxin concentration c during the cytotoxic process from the noisy measurements of the number of cellsiExtracellular toxin concentration ceAnd the number of cells N.
In a preferred embodiment of the present invention, the discretization method further comprises one of an euler method, a longge-kutta method, and a linear multi-step method.
The invention has the beneficial effects that:
the method for estimating the concentration of the toxin in the sewage adopts a particle filter algorithm to continuously estimate the concentration of the toxin in the sewage on line according to the cell number measured value of the cultured cells exposed in the sewage sample and a cytotoxin dynamic model, and accurately estimates the concentration of the toxin in the sewage. The adopted particle filtering algorithm can obtain a good estimation effect on a nonlinear dynamic system with large process noise, observation noise or uncertain modeling, and the accuracy of toxin concentration estimation is ensured.
Drawings
FIG. 1 is a flow chart of a method for estimating the concentration of toxins in wastewater according to a preferred embodiment of the present invention;
FIG. 2 shows the estimation of toxin concentration in wastewater using the method of the present invention at ceA plot of estimated toxin chromium (VI) concentration versus true at 0.62;
FIG. 3 shows the estimation of toxin concentration in wastewater using the method of the present invention at ceAn error plot of estimated toxin chromium (VI) concentration at 0.62;
FIG. 4 shows the estimation of toxin concentration in wastewater using the method of the present invention at ceA plot of estimated toxin mercury (II) chloride concentration versus true at 22.35;
FIG. 5 shows the estimation of toxin concentration in wastewater using the method of the present invention at ceError plot of estimated toxin mercuric (II) chloride concentration at 48.3.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Examples
It should be noted that (1) CI is a cell index value, which is an indirect indicator of toxic pollutants and biological consequences in the aquatic environment, and a dynamic model of the cellular toxicity response established based on the CI value can be used for online estimation of cytotoxicity.
(2) NIH 3T3 is a mouse embryo fibroblast cell line established by the national institutes of health (NIH for short), and is characterized in that the cell line is passaged once every 3 days and inoculated with 3 x 10^5cells/ml each time, and the cell line is commonly used for the research of transfection and gene expression in laboratories.
The embodiment of the invention discloses a sewage toxin concentration estimation method, which is used for estimating the concentration of toxins in sewage on line in the sewage treatment process, and is shown by referring to FIG. 1, and comprises the following steps,
in the first step, a dynamic model of the cytotoxin is created based on the mechanism of uptake of the toxin by the cells, and the change in the number of cells caused by the toxin.
(1) And obtaining the cultured cells with a certain cell index CI.
Selecting cultured cell with cell line NIH 3T3, placing the cultured cell in CO2Culturing in an incubator until the cell index value CI reaches 1.0-1.2, wherein the purpose of culturing is to ensure that the initial cell number of cultured cells put into use reaches 10000 cells per hole.
(2) Exposing cultured cells with a cell index CI of 1.0-1.2 in toxins with different concentrations, continuously and automatically monitoring the reaction of the cells to the toxic substances by using an RT-CES system every hour, continuously recording a CI value every hour for 24 hours, and continuously collecting cell number measurement values of the cultured cells in the toxins with different concentrations.
(3) Creating a kinetic model of the toxin uptake process by the cells:
and creating an apoptotic mechanism for intracellular endotoxin concentration:
cicharacterizing the intracellular endotoxin concentration; c. CeCharacterizing the extracellular toxin concentration; n represents the cell number, and N is approximately equal to CI; k is a radical of1，k2，k3，KiIs a relevant parameter; kciCharacterizing the relative cell killing rate in the presence of the toxin; k is a radical ofsThe relative cell proliferation rate was characterized in the absence of toxin.
k1ceRepresents a linear diffusion component;
is representative of fullAnd a carrier-mediated component.
Substituting the cell number measured value of the cultured cells under the action of the toxic elements obtained in the step (2) into the (formula 1) and the (formula 2), identifying unknown parameters in the kinetic model and the cell endotoxin concentration apoptosis mechanism by adopting a nonlinear regression method, and calculating the related parameter k1，k2，k3，KiAnd determining the dynamic model of the cytotoxin.
A second step of discretizing the obtained dynamic model of the cytotoxin, wherein the discretization method comprises one of an Euler method, a Runge-Kutta method and a linear multi-step method; obtaining a discretized cytotoxin dynamic model,
xk＝f(xk-1)+wk；
yk＝g(xk)+vk；
And step three, collecting a water body sample (hereinafter referred to as a sewage sample) after sewage treatment, exposing the same cultured cells obtained in the step (1) in the sewage sample, continuously and automatically monitoring the reaction of the cells to the toxic substances every hour by using an RT-CES system, continuously recording a CI value every 24 hours, and continuously collecting the cell number measured value of the cultured cells in the sewage sample.
And step four, processing the discretized cytotoxin dynamic model and the cell number measured value obtained in the step three by using a particle filtering algorithm, and estimating the toxin concentration in the sewage sample, wherein the specific method comprises the following steps:
define state variable x ═ ci，ce，N]A measurement vector y is defined as N.
S01, initializing: from the prior distribution p (x)0) Generating initial particles
S02, importance sampling: sampling distributions from importance
To generate a priori particles
S03, calculating the weight value: (3) calculating the weight of each particle
Normalizing the weights according to the newly obtained measurements
S04, resampling: if it is not
From discrete distributions
In the random sampling of N samples
And all weights are set to a constant value
Obtaining an approximate posterior probability density function
Wherein N isthrFor the set threshold, δ (·) is a dirac δ function;
s05, outputting: obtaining a state estimate
That is, the true intracellular endotoxin concentration c during cytotoxicity was estimated from the measurement of the number of cells containing noiseiExtracellular toxin concentration ceAnd the number of cells N.
Referring to FIG. 2, the method for estimating the toxin concentration in wastewater using the present invention is shown in step cePlot of estimated toxin chromium (VI) concentration versus true at 0.62. Wherein the first plot of FIG. 2 is a comparison of the estimated intracellular endotoxin concentration value with the actual value; the second graph is a comparison graph of the estimated value of the concentration of the extracellular toxin and the real value; the third graph is a comparison of the estimated cell number value and the actual value.
Referring to FIG. 3, the method for estimating the toxin concentration in wastewater using the present invention is shown in step ceError plot of estimated toxin chromium (VI) concentration at 0.62. Wherein the first graph of figure 3 is the intracellular endotoxin concentration estimation error; the second graph is the extracellular toxin concentration estimation error; the third plot is the cell number estimation error.
Referring to FIG. 4Using the method for estimating the toxin concentration in sewage of the present invention in cePlot of estimated toxin mercury (II) chloride concentration versus true at 22.35. Wherein the first plot of FIG. 4 is a comparison of the estimated intracellular endotoxin concentration value with the actual value; the second graph is a comparison graph of the estimated value of the concentration of the extracellular toxin and the real value; the third graph is a comparison of the estimated cell number value and the actual value.
Referring to FIG. 5, the method for estimating the toxin concentration in wastewater using the present invention is shown in step ceError plot of estimated toxin mercuric (II) chloride concentration at 48.3. Wherein the first graph of figure 5 is the intracellular endotoxin concentration estimation error; the second graph is the extracellular toxin concentration estimation error; the third plot is the cell number estimation error.
According to the graph 2, the real value can be tracked by the sewage toxin concentration estimation method, and the error map of the graph 3 is combined to show that the estimation errors of the sewage toxin concentration estimation method on the cell endotoxin concentration, the cell exotoxin concentration and the cell number are small, so that the accurate estimation on the cell endotoxin concentration, the cell exotoxin concentration and the cell number can be realized.
Similarly, it can be seen from fig. 4 that the estimation and prevention of the concentration of the toxins in wastewater of the present invention can track the true value, and the error map of fig. 5 is combined to determine that the estimation error of the method for estimating the concentration of the toxins in the cells to the concentration of the toxins in the cells, and the number of the cells is very small, so that the accurate estimation of the concentration of the toxins in the cells, and the number of the cells can be realized.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. A sewage toxin concentration estimation method is characterized in that: comprises the following steps of (a) carrying out,
exposing cultured cells with a certain cell index CI in a sewage sample, and continuously collecting cell number measured values of the cultured cells in the sewage sample;
creating a cytotoxin dynamic model according to the uptake mechanism of the toxin by the cells and the change of the cell number caused by the toxin;
and processing the cytotoxin dynamic model and the cell number measured value by using a particle filtering algorithm to estimate the toxin concentration in the sewage sample.
2. The method of estimating a wastewater toxin concentration according to claim 1, wherein: creating the cytotoxin dynamic model includes creating a dynamic model of the cytotoxin including,
creating a kinetic model of the toxin uptake process by the cells:
creating an apoptotic mechanism for intracellular endotoxin concentration:
cicharacterizing the intracellular endotoxin concentration; c. CeCharacterizing the extracellular toxin concentration; n represents the cell number, and N is approximately equal to CI; k is a radical of1，k2，k3，KiIs a relevant parameter; kciCharacterizing the relative cell killing rate in the presence of the toxin; k is a radical ofsThe relative cell proliferation rate was characterized in the absence of toxin.
3. The method of estimating a wastewater toxin concentration according to claim 2, characterized in that: creating the cytotoxin dynamic model includes creating a dynamic model of the cytotoxin including,
exposing the cultured cells with a certain cell index CI to toxins with different concentrations, and continuously collecting cell number measured values of the cultured cells under the action of the toxins;
substituting the cell number measurement value of the cultured cells under the action of the toxin into the formula1) And (2) identifying unknown parameters in the kinetic model and the apoptosis mechanism of the intracellular endotoxin concentration by adopting a nonlinear regression method, and calculating the related parameter k1，k2，k3，Ki。
4. The method of estimating a wastewater toxin concentration according to claim 3, wherein: the cell index CI of cultured cells exposed to different concentrations of toxin is 1.0-1.2.
5. The method of estimating a wastewater toxin concentration according to claim 1, wherein: the cell index CI of the cultured cells exposed in the sewage sample is 1.0-1.2.
6. The method of estimating a wastewater toxin concentration according to claim 1, wherein: the cell line of the cultured cells is NIH 3T 3.
7. The method of estimating a wastewater toxin concentration according to claim 6, wherein: the initial cell numbers of cultured cells exposed to the wastewater sample, cultured cells exposed to different concentrations of toxin, each included 10000 cells per well.
8. The method according to any one of claims 1 to 7, wherein the method comprises: the method for estimating the toxin concentration in the sewage further comprises,
discretizing the cytotoxin dynamic model to obtain a discretized cytotoxin dynamic model
xk＝f(xk-1)+wk；
yk＝g(xk)+vk；
And the particle filtering algorithm is combined with the discretized cytotoxin dynamic model to process the measured value of the cell number in the sewage sample.
9. The method of estimating a wastewater toxin concentration according to claim 8, wherein: the particle filter algorithm for processing the cell number measured value in the sewage sample comprises the step of defining a state variable x ═ ci，ce，N]Defining a measurement vector y ═ N, based on a discretized dynamic model of the cytotoxin, and estimating the true intracellular endotoxin concentration c during the cytotoxic process from the noisy measurements of the number of cellsiExtracellular toxin concentration ceAnd the number of cells N.
10. The method of estimating a wastewater toxin concentration according to claim 8, wherein: the discretization method comprises one of an Euler method, a Runge-Kutta method and a linear multi-step method.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU1482887A1 (en) * | 1987-06-10 | 1989-05-30 | Украинский Научно-Исследовательский Институт Разведения И Искусственного Осеменения Крупного Рогатого Скота | Method of assessing toxic effect of chemicals contained in aqueous medium |
| JP2001133452A (en) * | 1999-11-01 | 2001-05-18 | Nippon Steel Corp | Evaluation method for toxic substances |
| CN1664579A (en) * | 2004-12-31 | 2005-09-07 | 湖南大学 | Application of Spirogyra in Testing the Toxicity of Contaminated Wastewater and Its Test Method |
| CN104155287A (en) * | 2014-09-01 | 2014-11-19 | 湖南大学 | Method for quantitatively analyzing content of cadmium ion in environmental water body sample |
| CN108120812A (en) * | 2017-12-14 | 2018-06-05 | 常州大学 | In a kind of dyeing waste water in groups biological toxicity tests and differentiate appraisal procedure |
| CN109061091A (en) * | 2018-09-14 | 2018-12-21 | 中国科学院东北地理与农业生态研究所 | A method of estimation lake dissolved organic carbon concentration |
| CN110672804A (en) * | 2019-09-30 | 2020-01-10 | 华南师范大学 | An intelligent detection method for typical organic pollutants in urban river water bodies |
| CN110878331A (en) * | 2019-12-06 | 2020-03-13 | 西安外事学院 | A kind of microorganism-based nitroaromatic pollutant detection method |
| CN111122815A (en) * | 2019-12-23 | 2020-05-08 | 深圳大学 | A method for evaluating road rainwater toxicity |
-
2020
- 2020-12-18 CN CN202011506021.2A patent/CN112485399B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU1482887A1 (en) * | 1987-06-10 | 1989-05-30 | Украинский Научно-Исследовательский Институт Разведения И Искусственного Осеменения Крупного Рогатого Скота | Method of assessing toxic effect of chemicals contained in aqueous medium |
| JP2001133452A (en) * | 1999-11-01 | 2001-05-18 | Nippon Steel Corp | Evaluation method for toxic substances |
| CN1664579A (en) * | 2004-12-31 | 2005-09-07 | 湖南大学 | Application of Spirogyra in Testing the Toxicity of Contaminated Wastewater and Its Test Method |
| CN104155287A (en) * | 2014-09-01 | 2014-11-19 | 湖南大学 | Method for quantitatively analyzing content of cadmium ion in environmental water body sample |
| CN108120812A (en) * | 2017-12-14 | 2018-06-05 | 常州大学 | In a kind of dyeing waste water in groups biological toxicity tests and differentiate appraisal procedure |
| CN109061091A (en) * | 2018-09-14 | 2018-12-21 | 中国科学院东北地理与农业生态研究所 | A method of estimation lake dissolved organic carbon concentration |
| CN110672804A (en) * | 2019-09-30 | 2020-01-10 | 华南师范大学 | An intelligent detection method for typical organic pollutants in urban river water bodies |
| CN110878331A (en) * | 2019-12-06 | 2020-03-13 | 西安外事学院 | A kind of microorganism-based nitroaromatic pollutant detection method |
| CN111122815A (en) * | 2019-12-23 | 2020-05-08 | 深圳大学 | A method for evaluating road rainwater toxicity |
Non-Patent Citations (4)
| Title |
|---|
| JAMES ZAN XING ET AL: "Dynamic Monitoring of Cytotoxicity on Microelectronic Sensors", 《CHEM. RES. TOXICOL》 * |
| 严国俊 等: "实时细胞电子分析技术的应用研究进展", 《中国药学杂志》 * |
| 赵长胜著: "《动态测量数据处理理论》", 31 December 2016, 北京：测绘出版社 * |
| 赵顺毅 等: "模型不确定非线性Markov跳变系统的滤波算法", 《控制与决策》 * |
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