Multi-gas sensing system
阅读说明：本技术 多气体感测系统 (Multi-gas sensing system ) 是由 A·克赖姆斯 K·比瑞安 N·哈 K·卡兰塔尔-扎德 于 2018-05-04 设计创作，主要内容包括：本文披露了一种用于确定多气体混合物中的至少一种气体的类型和相应浓度的方法,该方法包括：将气体传感器的气敏元件暴露于该多气体混合物；调制供应给该气体传感器的温度控制元件的驱动信号,以使该气敏元件的温度从初始温度变化；在该气敏元件的温度发生变化时记录该气敏元件的瞬态阻抗响应,以获得作为该多气体混合物的特性的瞬态阻抗响应；使用该瞬态阻抗响应以便根据数据库来确定该多气体样本中的至少一种气体的类型和相应浓度,该数据库包括与该至少一种气体相对应的校准数据。本文还披露了一种对多气体感测系统进行校准的方法、一种多气体感测系统、以及用于确定多气体混合物中的至少一种气体的类型和相应浓度的相关方法。(Disclosed herein is a method for determining the type and corresponding concentration of at least one gas in a multi-gas mixture, the method comprising: exposing a gas sensing element of a gas sensor to the multi-gas mixture; modulating a drive signal supplied to a temperature control element of the gas sensor to vary a temperature of the gas sensor from an initial temperature; recording a transient impedance response of the gas sensor when the temperature of the gas sensor changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; the transient impedance response is used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas. Also disclosed herein are a method of calibrating a multi-gas sensing system, and related methods for determining the type and corresponding concentration of at least one gas in a multi-gas mixture.)
1. A method for determining a type and corresponding concentration of at least one gas in a multi-gas mixture, the method comprising:
exposing a gas sensing element of a gas sensor to the multi-gas mixture;
modulating a drive signal supplied to a temperature control element of the gas sensor to vary a temperature of the gas sensor from an initial temperature;
recording a transient impedance response of the gas sensor when the temperature of the gas sensor changes to obtain a transient impedance response that is characteristic of the multi-gas mixture;
the transient impedance response is used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas.
2. The method of claim 1, further comprising: a fractional value is derived from the transient impedance response and used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas.
3. The method of claim 2, wherein the fraction value is determined by comparing the transient impedance response to a database of calibration data having corresponding calibration fraction values and interpolating the fraction value using the calibration fraction values.
4. The method of claim 3, wherein the method further comprises performing a regression analysis on the fractional value to identify a type of multi-gas mixture comprising the at least one gas corresponding to the fractional value.
5. The method of claim 4, wherein after the type of the multi-gas mixture has been identified, the method further comprises: a multi-spline function corresponding to the multi-gas mixture is identified, and the type and concentration of the at least one gas is interpolated using the score values according to the multi-spline function.
6. The method of any of claims 2 to 5, wherein the fractional value is derived from the transient impedance response using principal component analysis.
7. A method according to any of the preceding claims, wherein modulating the drive signal comprises providing the drive signal as pulses, wherein the pulses are applied for a time of 50ms or less.
8. The method of any of the preceding claims, wherein measuring the transient impedance response of the gas sensor occurs before the gas sensor returns to the initial temperature.
9. The method of any one of claims 1 to 8, wherein measuring the transient impedance response of the gas sensor continues after the application of the drive signal has ceased for 150ms or less.
10. A method according to any preceding claim, wherein the method is used to determine the type and respective concentration of two or more gases in a multi-gas mixture.
11. A method of calibrating a multi-gas sensing system, the method comprising:
(a) exposing the gas sensing element to a multi-gas mixture comprising at least two known gases at known concentrations;
(b) applying the modulated drive signal to a temperature control element of the gas sensor to change the temperature of the gas sensing element from an initial temperature;
(c) recording the transient impedance response of the gas sensor when the temperature of the gas sensor changes to obtain a calibration curve of the transient impedance response as a characteristic of the multi-gas mixture; and
(d) the calibration curve is stored in a database.
12. The method of claim 11, wherein the method further comprises: a fractional value is derived from the transient impedance response and stored in the database.
13. The method of claim 12, wherein the score value is derived using principal component analysis.
14. The method of any one of claims 11 to 13, wherein the method further comprises: repeating steps (a) through (c) to obtain a plurality of different relative concentrations of the at least two known gases, and storing calibration data corresponding to each of the plurality of different relative concentrations of the at least two known gases.
15. The method of claim 14, wherein the method further comprises: score values are derived from a plurality of the calibration data and stored in the database.
16. The method of claim 15, wherein the method further comprises forming a spline model based on the fractional values.
17. A method as claimed in any one of claims 11 to 16, wherein modulating the drive signal comprises providing the drive signal as pulses, and wherein the pulses are applied for a time of 50ms or less.
18. A database having calibration model values obtained via a method of calibrating a multi-gas sensor according to any one of claims 11 to 17.
19. A multi-gas sensing system comprising:
gas sensor device, comprising at least:
a gas sensor for sensing gas in a multi-gas sample; and
a temperature control element for varying the temperature of the gas sensing element, the temperature control element being controllable by modulating a drive signal supplied to the temperature control element, wherein the system further comprises:
a data acquisition system configured to record a transient impedance response of the gas sensor as a temperature of the gas sensor changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; and
one or more processors configured to use the transient impedance response to determine a type and a respective concentration of at least one gas in the multiple gas sample from a database comprising calibration data corresponding to the at least one gas.
20. The system of claim 19, wherein the data acquisition system is configured to digitally sample the transient impedance response to obtain the transient impedance response.
21. The system of claim 19 or 20, wherein the one or more processors are configured to: a fractional value is derived from the transient impedance response and used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas.
22. A method for determining a type and corresponding concentration of at least one gas in a multi-gas mixture, the method comprising:
receiving data representing or derived from a transient impedance response from a gas sensing element of a gas sensor, wherein the data is obtained by:
exposing a gas sensing element of a gas sensor to the multi-gas mixture;
modulating a drive signal supplied to a temperature control element of the gas sensor to vary a temperature of the gas sensor from an initial temperature; and
recording a transient impedance response when a temperature of the gas sensor changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; the method further comprises the following steps:
the data is used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas.
Technical Field
The present invention relates to methods and systems for determining the type and concentration of one or more gases in a multi-gas mixture.
Background
Disclosure of Invention
In one aspect of the invention, there is provided a method for determining the type and corresponding concentration of at least one gas in a multi-gas mixture, the method comprising:
exposing a gas sensing element of a gas sensor to the multi-gas mixture;
modulating a drive signal supplied to a temperature control element of the gas sensor to vary a temperature of the gas sensor from an initial temperature;
recording a transient impedance response of the gas sensor when the temperature of the gas sensor changes to obtain a transient impedance response that is characteristic of the multi-gas mixture;
the transient impedance response is used to determine a type and a corresponding concentration of at least one gas in the multi-gas sample from a database that includes calibration data corresponding to the at least one gas.
Drawings
FIG. 1 is a flow chart illustrating a process of sensor calibration and sensor use, showing interrelated components and information flow.
FIG. 2 is a schematic diagram of a typical gas sensor system showing the elements of the gas sensor, the heater voltage power supply, the data acquisition system, the computer processing system, and the user application.
FIG. 3 shows O at 1.7% of (A)2Environment and (B) 0% of O2Voltages measured across the sensor element during the 15ms heater pulse in the environment for the following different gases: (i) h2(1% of N)2)、(ii)CH4(100%) and (iii) H2S(56ppm)。
Fig. 4(a) is a graph showing principal component analysis coefficient vectors (PCA vectors) of the first three dominant principal components for model gas testing in oxygen.
Fig. 4(B) is a graph showing principal component coefficient analysis vectors (PCA vectors) of the first three dominant principal components for conducting model gas tests in the absence of oxygen.
Fig. 5(a) is a graph showing the main component (PC) fraction observed for each gas concentration in the presence of oxygen.
Fig. 5(B) is a graph showing the Principal Component (PC) fractions observed for each gas concentration in the absence of oxygen.
FIG. 6 shows the system in aerobic (1.7% O)2) Ambient and anaerobic (0% O)2) Energy of separating gas in environmentForce graph: (A) sensor output voltage data for several gas mixtures tested in the presence of oxygen and (B) based on the gas concentrations calculated in response thereto. (C) Sensor output voltage data for several gas mixtures tested in the absence of oxygen and (D) gas concentration based on the corresponding calculations in response.
Detailed Description
The present invention relates broadly to a multi-gas sensing system, a method of calibrating the multi-gas sensing system, and a method of determining the type and corresponding concentration of at least one gas in a multi-gas sample. The system and method are adapted for sensing (i.e., determining) the type and concentration of a large number of different gases. Such gases may include, but are not limited to: NOX；SOX；CO2；CO；H2；H2S；NH3；O2(ii) a An inert gas; halogen; a hydrogen halide; volatile hydrocarbons (such as alkanes, alkenes, alkynes), alcohols, organic acids (in particular volatile fatty acids), wherein the volatile hydrocarbons may be halogenated.
In various forms of the invention, the multi-gas system operates by: modulating the temperature of the gas sensor in the presence of the multiple gas sample; sampling a transient output signal from the gas sensor when the temperature of the gas sensor changes over time; and extracting selectivity and sensitivity data by applying a mathematical algorithm to the digitally sampled data. This data can be obtained from a single gas element, but can also be applied to an array of different elements, each element providing its own unique information based on its particular gas sensitivity. However, in a preferred form, the gas sensing device comprises at least a single gas sensitive element capable of sensing multiple gases (such as more than one different type of gas).
The invention applies to a range of different gas sensing systems, such as: micro-component sensors, CMOS sensors, multi-gas sensing, neural networks, electronic noses, process monitoring, environmental monitoring, wastewater treatment monitoring, chemical process monitoring, biological system monitoring, ingestible sensors, and personal monitoring. The systems and methods of the present invention can be used in a wide variety of applications, particularly applications that benefit from a low power portable system for measuring and identifying multiple gases in a multiple gas environment. Non-limiting disclosures of such applications include:
industrial applications: monitoring a factory; exhausting; a power plant; and (5) volatile gas monitoring.
Defense applications: personal or personal safety; and monitoring body data.
Home applications: monitoring toxic gases (such as carbon monoxide and NO) in premises2) Is accumulated.
Mobile phone: personal or personnel safety and monitoring; a portable breath analysis system; and (5) pollution monitoring.
Environmental monitoring: the movement and concentration of gases from livestock/cattle, power production facilities, and many other heavy industries (mining, oil, gas, etc.) around cities are monitored.
The automotive industry: monitoring cabin air quality, monitoring vehicle performance, etc.
The aeronautical industry: monitoring cabin air quality, monitoring vehicle performance, etc.
Chemical and processing industries: monitoring active chemical processes; the safety of personnel; community and environmental monitoring and security.
The mining industry: the safety of personnel; community and environmental monitoring and security.
In one particular form, the gas sensor is contained within an ingestible gas-sensing capsule. This is useful for monitoring gases in humans and animals. This application requires a low power, but highly sensitive system. In this case, the gas sensor is contained within the ingestible capsule. The ingestible capsule is formed from an insoluble material that contains a gas permeable but fluid selective membrane to protect the sensor from gastric acid, bile or other digestive fluids within the digestive tract of a human or non-human animal (such as sheep, cattle, goat, chicken, dog, cat, pig, etc.). Permeation of gas components through the membrane exposes the sensor to the gut environment, allowing the sensor to report the gas detected in the gut. In such instances, the multi-gas sensor includes a wireless communication device (such as a wireless transmitter) for transmitting information from the multi-gas sensor to a user interface at a remote location (such as outside of an animal, for example).
The process of measuring unknown gases first requires calibrating a multi-gas sensing system using known gases and gas mixtures and numerically modeling the calibration data. The process generates a unique model for each gas species for a particular gas sensor. The basic steps of the modeling process (which is also shown below heading 1 in fig. 1) are as follows:
1.1. applying a known gas to a sensor
1.2. Operating a temperature control element of a gas sensor and recording transient impedance response of the gas sensor in real time
1.3. Generating Principal Component (PC) models and generating PC score values for all recorded calibration data
1.4. Repeating steps 1.1-1.3 until the PC model converges (i.e., the effect of adding new observations on the model is below the variance threshold)
1.5. For each gas species, a spline curve is fitted to the PC score values to generate a gas concentration vector.
Once the appropriate model has been generated, the sensor can be used to measure the unknown gas. The process (shown under heading 2 in fig. 1) is as follows:
2.1. applying unknown gas to a sensor
2.2. Operating a temperature control element of a gas sensor and recording transient impedance response of the gas sensor in real time
2.3. Determining PC fraction of unknown gas using calibration PC model
2.4. Assigning unknown gases to spline curves in a model using regression fitting
2.5. The calibrated absolute concentration of the unknown gas is calculated by correlating the location of the unknown gas along the model curve using information from the curve in step 2.4.
This process, which is directly related to the steps presented in fig. 1 above, will now be explained in more detail.
Calibration and modeling of sensors
1.1: applying known gas types and concentrations to a sensor
Fig. 2 illustrates a gas sensor 200 that includes a resistive gas sensing element 202 and a heating element in the form of a microheater 204. The micro-heater 204 and the gas sensor 202 are in thermal contact with each other. The gas sensor 202 is made of a conductive electrode coated on a gas sensing film. The sensing element changes its impedance when exposed to different gases at various applied temperatures. The various applied temperatures are modulated using a function generator 205 that applies a voltage to heat the heating element.
Examples of materials that can be used for the gas sensor 202 are semiconductive metal oxides such as tin oxide, zinc oxide, and tungsten oxide; but may also comprise many other metal oxides. Other resistive or semiconducting elements may be used for the sensing element, such as polymeric materials and graphite elements; however, these materials may limit the range of thermal modulation. The gas sensing element 202 can also be modified by surface functionalization to improve gas sensitivity and selectivity.
The gas sensing element 202 can be thicker or thinner depending on the required modulation time and response time as well as the desired concentration range and gas sensitivity. The thicker gas sensor material can improve the sensitivity of the material; however, these thicker materials will have slower response times than thinner materials. With respect to gas sensitivity, the thickness of the material should be chosen to optimize the dynamic response.
The parameters of the gas sensing element 202 are measured using a
1.2: pulsing a sensor heating element and collecting responses
The gas sensing element 202 provides different sensitivities and responses to the various gases, which are directly measured as changes in the impedance of the sensing element. For example, if the gas sensing element 202 comprises tin oxide, the impedance of the sensing element changes dramatically as the sensing element heats up from room temperature to as high as 400 ℃. The different gases affect the impedance distribution of the gas sensor as it is heated and cooled. In general, the invention is described with respect to the transient response behavior of the sensor 200 when it is heated and cooled by applying a pulse modulated signal to the heating element. However, other signals, such as triangular, square, and sine waves, may also be applied to the heating element to provide the transient response. This approach is in contrast to current commercial systems that aim to measure the steady state response of the sensor after thermal equilibrium has been reached or when a constant voltage or current is applied to the heater.
The micro-heating elements 204 of the sensor 200 may be modulated using a voltage pulse, which may take the form of a sine wave, a ramp, or a series of voltage pulses, which may take the form of a sine wave or pseudo-random noise. The type, amplitude, and frequency of the voltage pulses are adjustable (such as with function generator 205), and each combination may provide unique information about the gas present around the sensor. Therefore, the selection of the heater voltage for the sensor 200 is important for the desired application, sensor material, and target gas.
As an example, for three different gases (H)2(1% of N)2)、CH4(100%) and H2S (56ppm)), the micro-heating element 204 operates with a few voltage pulses applied for 15 milliseconds. Before the gas sensor 202 has returned to preheat equilibrium, the change in resistance of the gas sensor 202 at the time each gas was measured while the heater was on and off is recorded. FIG. 3 shows O at 1.7% of (A)2Environment and (B) 0% of O2Environmental needleResults of the voltage change measured across the sensor element during the 15ms heater pulse for the following different gases: (i) h2(1% of N)2)、(ii)CH4(100%) and (iii) H2S (56 ppm). In this example, monitoring of the transient response occurs before the temperature returns to the preheat equilibrium temperature, which typically takes about 100 ms.
The voltage change is measured as an analog signal that is digitized by sampling the analog signal at an appropriate sampling rate. In this particular example, the sampling rate is 6kHz and the digital resolution from a 1.255V reference voltage is 15 bits. Thus, the number of samples in a 100ms monitoring period is 600 samples. The digitized results are then processed using a Principal Component Analysis (PCA) algorithm.
1.3: data were processed using PCA: record the principal component fraction of each test
In this example, the transient response of the gas sensors and the use of Principal Component Analysis (PCA) and polynomial curve fitting and related post-processing allows the identification of the type and concentration of each gas in the multi-gas sample. However, other mathematical algorithms may be employed to extract specific gas information. To study correlations in gas distribution, including predictive interactions, factor analysis, Independent Component Analysis (ICA), and other methods and corresponding R functions are available. For this reason, PCA is the preferred method because it provides a simplified model of the data; however, a problem with PCA is that it does not perform well in the presence of outlier data points. This can be overcome using additional algorithms to pre-filter the data to remove these outlier data points.
To determine the type and concentration of the detected gas, the PCA algorithm must be trained by measuring known gases and mixtures. In this example, several H's are made2、CH4And H2S and used as sensor training data. The PCA algorithm can reduce the 100ms raw data to a series of fractional values. These fractional values can be conveniently visualized as coordinates in three-dimensional (3D) space, which are then used to compute spline curves to "connect dots" and make observations of the absence in the gas sensing modelAnd (6) interpolation. Fig. 4(a) and 4(B) show three examples of sensor training data (PC observation) in which sensors detect gas H, respectively2、CH4And H2And S. FIG. 4(A) is a graph showing the first three dominant principal components (H2, CH) for model gas testing in the presence of oxygen4And H2S), and fig. 4(B) is a graph showing the first three dominant principal components (H) for model gas testing in the absence of oxygen2、CH4And H2S) principal component coefficient analysis vector (PCA vector).
1.4: repeating the steps 1-3 until the PC model converges
The calibration model of the gas sensor must be made more robust by repeated measurements using a wide variety of gas types and concentrations. More results included in the model will reduce the error in gas correlation when measuring unknown gases. For this example, each gas mixture was measured at five (5) different concentration values. The fractions given for each gas test are shown as dots in fig. 5(a) and 5 (B).
1.5: for each gas species, a spline curve is fitted to the PC score values to generate a gas concentration vector
The process for generating the model for each gas concentration and gas type/mixture must be done independently. Shown in FIGS. 5(A) and 5(B) at H2、CH4And H2Example cubic spline vectors of sensor model data at different concentrations of S. For CH is shown in each plot4And H2Mixture of (A) and (B), CH4And H2S mixtures and H2And H2Three sets of data for mixtures of S. The curve here is for 'connecting dots' (dots between known measurement points) (from the previous step) and is used to give an estimate of any gas found between the known measurement points. The spline helps to give a direct relationship between the PC score and the gas concentration value and is used to make measurements of unknown gases.
2: use of sensors
Using the information obtained from (i) PCA analysis, (ii) subsequent gas mixture PCA model, and (iii) gas concentration vectors, the type and concentration of each gas in the unknown multi-gas mixture can be obtained (calibration of its type and concentration has been previously completed).
2.1: applying unknown gas type and concentration to a sensor
This step is similar to step 1.1 except that the sensing element is exposed to a multi-gas mixture comprising one or more gases of unknown type and concentration.
2.2: pulsing a heating element of a sensor and collecting a response
This step is similar to step 1.2. The voltage applied to the heater element is preferably the same as that used in the calibration phase. FIGS. 6(A) and 6(C) show the presence of 1.7% and 0% O, respectively2The response of the sensor to various gas mixtures.
2.3: determining PC fraction of unknown gas using calibration PC model
This step relies on the developed PCA model in the calibration phase (step 1.3). For PCA-based algorithms, the PCA model is a series of principal component curves. Example principal component curves are shown in fig. 4(a) and 4 (B). The response from the unknown gas is compared to these curves and a fractional value of the unknown gas is generated.
2.4: assigning unknown gases to spline curves in a model using regression fitting
Regression fitting is then used on the fractional values of the unknown gas to determine which gas mixture type it belongs to. This step only reveals the type of gas measured.
2.5: the calibrated absolute concentration of the unknown gas is calculated by correlating the location of the unknown gas along the model curve.
This last step is used to calculate the concentration of the unknown gas. A spline curve generated from the model is used, wherein the fractional value of the unknown gas is compared to the spline curve and the concentration value of the gas is determined. Fig. 6(B) shows the gas concentration calculated accordingly based on the sensor response illustrated in fig. 6(a), and fig. 6(D) shows the gas concentration calculated accordingly based on the sensor response illustrated in fig. 6 (C).
In this example, the test was repeated 40 times, and error bars are shown (see fig. 6(B) and 6 (D)). The errors include sensor errors, PCA algorithm errors, and vector calculation and correlation errors. The errors are all less than 20% — the highest error is CH4And H2And (4) separating S. By training the gas sensor model more comprehensively, errors can be improved, so that gas can be separated very well in both aerobic and anaerobic environments.
It should be noted that even the exemplary tin oxide sensor is at 0% O2Perform poorly in the environment, but still allow the identification and measurement of gases. Exceptions occur when measuring pure H2Or pure H2S, the error bars are larger in this exceptional case. This can be improved, for example, by selecting different materials for the gas sensors or by operating an array of gas sensors.
It will be understood that the invention disclosed and defined in this specification extends to all alternative combinations of two or more of the individual features mentioned or evident from the text or drawings. All of these different combinations constitute various alternative aspects of the present invention.
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