MULTI-GAS SENSING SYSTEM

Abstract

Disclosed herein is a method for determining a type and corresponding concentration of at least one gas in a multi-gas mixture, the method including: exposing a gas sensitive 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 cause a temperature of the gas sensitive element to change from an initial temperature; recording a transient impedance response of the gas sensitive element while the temperature of the gas sensitive element changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; using the transient impedance response to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including calibration data corresponding to the at least one gas. Also disclosed herein is a method of calibrating a multi-gas sensing system, a multi-gas sensing system, and related methods for determining a type and corresponding concentration of at least one gas in a multi-gas mixture.

Claims

1. A method for determining a type and corresponding concentration of at least one gas in a multi-gas mixture, the method including: exposing a gas sensitive 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 cause a temperature of the gas sensitive element to change from an initial temperature; recording a transient impedance response of the gas sensitive element while the temperature of the gas sensitive element changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; using the transient impedance response to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including calibration data corresponding to the at least one gas.

2. The method of claim 1, further including deriving a score value from the transient impedance response, and using the score value to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including calibration data corresponding to the at least one gas.

3. The method of claim 2, wherein the score value is determined by comparing the transient impedance response with a database of calibration data having corresponding calibration score values, and interpolating the score value using the calibration score values.

4. The method of claim 3, wherein, the method further includes subjecting the score value to regression analysis to identify a type of the multi-gas mixture including the at least one gas that corresponds to the score value.

5. The method of claim 4, wherein after the type of multi-gas mixture has been identified, the method further includes: identifying a multivariate spline function corresponding to the multi-gas mixture, and using the score value to interpolate the type and concentration of the at least one gas from the multivariate spline function.

6. The method of any one of claims 2 to 5, wherein the score value is derived from the transient impedance response using principal component analysis.

7. The method of any one of the preceding claims, wherein modulating the drive signal includes providing the drive signal as a pulse, wherein the pulse is applied for a time of 50 ms or less.

8. The method of any one of the preceding claims, wherein measuring the transient impedance response of the gas sensitive element occurs until the gas sensitive element 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 sensitive element continues after the drive signal has ceased being applied for a time of 150 ms or less.

10. The method of any one of the preceding claims, wherein the method is for determining a type and corresponding concentration of two or more gases in a multi-gas mixture.

11. A method of calibrating a multi-gas sensing system, the method including: (a) exposing a gas sensitive element to a multi-gas mixture including at least two known gases of known concentrations; (b) applying a modulated drive signal to a temperature control element of the gas sensor to cause a temperature of the gas sensitive element to change from an initial temperature; (c) recording a transient impedance response of the gas sensitive element while the temperature of the gas sensitive element changes to obtain a calibration curve of the transient impedance response that is characteristic of the multi-gas mixture; and (d) storing the calibration curve in a database.

12. The method of claim 11, wherein the method further includes deriving a score value from the transient impedance response, and storing the score value in the database.

13. The method of claim 12, wherein principal component analysis is used to derive the score value.

14. The method of any one of claims 11 to 13, wherein the method further includes repeating steps (a) to (c) for a plurality of different relative concentrations of the at least two known gases, and storing calibration data corresponding for 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 includes deriving score values from a plurality of the calibration data, and storing the score values in the database.

16. The method of claim 15, wherein the method further includes forming a spline model from the score values.

17. The method of any one of claims 11 to 16, wherein modulating the drive signal includes providing the drive signal as a pulse, and wherein the pulse is applied for a time of 50 ms or less.

18. A database of calibration model values obtained via the method of calibrating the multi-gas sensor of any one of claims 11 to 17.

19. A multi-gas sensing system including: a gas sensor device including at least: a gas sensitive element for sensing gases in a multi-gas sample; and a temperature control element for changing a temperature the gas sensitive element, the temperature control element controllable by modulating a drive signal supplied to the temperature control element, wherein the system further includes: a data acquisition system configured to record a transient impedance response of the gas sensitive element while a temperature of the gas sensitive element changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; and a processor or processors configured to use the transient impedance response to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including 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 processor or processors are configured to derive a score value from the transient impedance response, and use the score value to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including 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 including: receiving data representative of, or derived from, a transient impedance response from a gas sensitive element of a gas sensor; wherein the data is obtained by: exposing a gas sensitive 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 cause a temperature of the gas sensitive element to change from an initial temperature; and recording a transient impedance response while the temperature of the gas sensitive element changes to obtain a transient impedance response that is characteristic of the multi-gas mixture; the method further including: using the data to determine a type and corresponding concentration of at least one gas in the multi-gas sample from a database including calibration data corresponding to the at least one gas.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0076] FIG. 1 is a flow chart demonstrating processes for sensor calibration and sensor use, showing the inter-related components and the flow of information.

[0077] FIG. 2 is a schematic of a typical gas sensor system showing the elements of the gas sensor, the heater voltage supply, a data acquisition system, the computer processing system and a user application.

[0078] FIG. 3 shows the voltage measured across the sensor element during a 15 ms heater pulse for different gases (i) H.sub.2 (1% in N.sub.2), (ii) CH.sub.4 (100%), and (iii) H.sub.2S (56 ppm) in (A) 1.7% O.sub.2 environment and (B) 0% O.sub.2 environment.

[0079] FIG. 4(A) is a graph showing principal component analysis coefficient vectors (PCA vectors) for the first three dominant principal components for model gas tests in oxygen.

[0080] FIG. 4(B) is a graph showing principal component coefficient analysis vectors (PCA vectors) for the first three dominant principal components for model gas tests without oxygen.

[0081] FIG. 5(A) is a graph showing principal component (PC) scores for each gas concentration observation with oxygen.

[0082] FIG. 5(B) is a graph showing principal component (PC) scores for each gas concentration observation without oxygen.

[0083] FIG. 6 are charts illustrating the capability of the system in separating gases in aerobic (1.7% O.sub.2) and anaerobic (0% O.sub.2) environments: (A) Sensor output voltage data for several gas mixtures tested in oxygen and (B) the corresponding calculated concentrations of gases based on the response. (C) Sensor output voltage data for several gas mixtures tested without oxygen and (D) the corresponding calculated concentrations of gases based on the response.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] The invention broadly relates to a multi-gas sensing system, a method of calibrating the multi-gas sensing system, and a method of determining a type and corresponding concentration of at least one gas in a multi-gas sample. The system and method are adapted to sense (that is, determine the type and concentration) of a large number of different gases. Such gases may include, but are not limited to: NO.sub.x; SO.sub.x; CO.sub.2; CO; H.sub.2; H.sub.2S; NH.sub.3; O.sub.2; noble gases; halogens; hydrogen halides; volatile hydrocarbons such as alkanes, alkenes, alkynes, alcohols, organic acids (in particular volatile fatty acids), wherein the volatile hydrocarbons may be halogenated.

[0085] In various forms of the invention, the multi-gas system operates by modulating the temperature of a gas sensitive element in the presence of a multi-gas sample, sampling a transient output signal from the gas sensitive element as the temperature of the gas sensitive element changes over time, and extracting selective and sensitive data by applying mathematical algorithms to the digitally sampled data. This data can be obtained from a single gas element, but could also be applied to an array of different elements, each providing its own unique information based on its particular gas sensitivities. However, in preferred forms, the gas sensing device includes at least a single gas sensitive element that is capable of sensing a plurality of gases, such as more than one different type of gas.

[0086] The present invention has application in a range of different gas sensing systems, such as: micro-element sensors, CMOS sensors, multi-gas sensing, neural network, electronic nose, process monitoring, environmental monitoring, wastewater treatment monitoring, chemical process monitoring, bio-systems monitoring, ingestible sensors and personal monitoring. Systems and methods of the 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 multi-gas environment. A non-limiting disclosure of such applications includes: [0087] Industrial applications: plant monitoring; outgassing; power plants; volatile gas monitoring. [0088] Defence applications: personal or personnel safety; bodily data monitoring. [0089] Household appliance: monitoring the build-up of toxic gases in the house, such as carbon monoxide and NO.sub.2 [0090] Mobile phones: personal or personnel safety and monitoring; portable breath analysis systems; pollution monitoring. [0091] Environmental monitoring: monitoring the movements and concentrations of gases around cities, from cattle/livestock, from power production facilities as well as many other heavy industries (mining, oil, gas, etc). [0092] Automotive industries: monitoring of cabin air quality, monitoring of vehicle performance, etc. [0093] Aerospace industries: monitoring of cabin air quality, monitoring of vehicle performance, etc. [0094] Chemical and processing industries: monitoring of active chemical processes; personnel safety; community and environment monitoring and safety. [0095] Mining industries: Personnel safety; community and environment monitoring and safety.

[0096] In one particular form, the gas sensor is contained within an ingestible gas sensing capsule. This is useful to monitor the gases in the bodies of humans and animals. This application requires low power, but highly sensitive systems. In such cases, the gas sensor is contained within an ingestible capsule. The ingestible capsule is formed from a non-dissolvable material that contains a gas permeable but fluid selective membrane to protect the sensor from stomach acids, bile, or other digestive fluids within a digestive tract of a human or non-human animal (such as sheep, cow, goat, chicken, dog, cat, pig etc.). Permeation of the gaseous constituents through the membrane exposes the sensor to the environment of the digestive track, allowing the sensor to report gases detected in the digestive tract. In such instances, the multi-gas sensor includes wireless communication means (such as a wireless transmitter) to transmit information from the multi-gas sensor to a user interface at a remote location (for example, such as outside the body of the animal).

[0097] The process for measuring an unknown gas first requires calibration of the multi-gas sensing system using known gases and gas mixtures, and numerical modelling of the calibration data. This process results in unique models for each gas species for a specific gas sensitive element. The basic steps of the modelling process (which is also illustrated under heading 1 in FIG. 1) are as follows: [0098] 1.1. Apply a known gas to the sensor [0099] 1.2. Operate the temperature control element of the gas sensor and record the transient impedance response of the gas sensitive element in time [0100] 1.3. Generate a principal component (PC) model for all recorded calibration data and generate a PC score value [0101] 1.4. Repeat steps 1.1-1.3 until the PC model converges (that is, the addition of new observations has an effect on the model that is below a variance threshold) [0102] 1.5. For each gas species, a spline curve is fitted to the PC score values to generate a gas concentration vector.

[0103] Once an adequate model has been generated, the sensor can then be used for measuring unknown gases. This process (which is illustrated under heading 2 in FIG. 1) is as follows: [0104] 2.1. Apply an unknown gas to the sensor [0105] 2.2. Operate the temperature control element of the gas sensor and record the transient impedance response of the gas sensitive element in time [0106] 2.3. Using the calibration PC model, determine the PC scores for the unknown gas [0107] 2.4. Use regression fitting to assign the unknown gas to a spline curve from the model [0108] 2.5. Using the information from the curve in step 2.4, calculate a calibrated absolute concentration of the unknown gas by correlating the location of the unknown gas along the model curve.

[0109] The process will now be explained in more detail, relating directly to the steps presented above in FIG. 1.

[0110] Sensor Calibration and Modelling

[0111] 1.1: Apply a Known Gas Type and Concentration to the Sensor

[0112] FIG. 2 illustrates a gas sensor 200 that comprises a resistive gas sensitive element 202 and a heating element in the form of a micro-heater 204. The micro-heater 204 and gas sensitive element 202 are in thermal contact with one another. The gas sensitive element 202 is made of conductive electrodes coated in a gas sensitive film. The impedance of the sensing element changes when exposed to different gases at various applied temperatures. The various applied temperatures are modulated using a function generator 205 which applies a voltage to heat the heating element.

[0113] Examples of materials that can be used for gas sensitive element 202 are semiconducting metal oxides, such as tin oxides, zinc oxides and tungsten oxides; but many other metal oxides can also be incorporated. Other resistive or semi-conductive elements can be used for the sensing element, such as polymeric materials and graphitic elements; however, these materials may limit the range of heat modulation. The gas sensitive element 202 can also be modified by surface functionalization for improving gas sensitivity and selectivity.

[0114] The gas sensitive element 202 can be thick or thin depending on the modulation and response time needed, as well as desired concentration ranges and gas sensitivities. Thicker gas sensitive element materials can improve the sensitivity of the material; however they will have a slower response time compared to thinner materials.

[0115] The thickness of the material should be chosen so as to optimise the dynamic response with respect to the gas sensitivity.

[0116] The gas sensitive element 202 parameters are measured using a data acquisition system 206, which records the analogue properties of the sensor element and converts them in to a digital signal. The digital signal is used for processing, and determining the gas type and concentration. This can be achieved using a computer processing step 208, which can be operated on any microprocessor, embedded system, mobile device or personal computer system. The information from this process can then be used in a desired user application 210, which may be in any suitable form from a simple graphical user interface (GUI) reading of the immediate gases to complex data logging and monitoring of long term changes.

[0117] 1.2: Pulse the Sensors Heating Element and Collect the Response

[0118] The gas sensitive element 202 provides different sensitivities and responses for various gases, which are directly measured as changes in the impedance of the sensing element. For instance, if the gas sensitive element 202 includes tin oxide, the impedance of the sensing element changes dramatically as it is heated from room temperature up to 400 C. Different gases affect the impedance profile of the gas sensitive element as it is heated and cooled. The invention is generally described in relation to the transient response behaviour of the sensor 200 as it is heated and cooled by applying a pulsed modulation signal to the heating element. However, other signals such as triangular, square, and sinusoidal waves can also be applied to the heating element to provide this transient response. This approach is contrary to current commercial systems, which 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.

[0119] The micro-heating element 204 of the sensor 200 can be modulated using a voltage pulse, which may be in the form of a sinusoid, a ramp; or a series of voltage pulses, which may be in the form of a sinusoidal wave or pseudo-random noise. The type, magnitude and frequency of the voltage pulses are adjustable, such as with function generator 205, and each combination can provide unique information on the gases present around the sensor. Therefore, the choice of heater voltage for the sensor 200 is important for the desired application, sensor material and target gas.

[0120] As an example, the micro-heating element 204 was operated with a pulse of several volts applied for 15 milliseconds for three different gases, H.sub.2 (1% in N.sub.2), CH.sub.4 (100%), and H.sub.2S (56 ppm). The resistance change in the gas sensitive element 202 as the heater is turned on and off when measuring each of the gases are recorded until the gas sensitive element 202 has returned to pre-heating equilibrium. FIG. 3 shows the results of the change in voltage measured across the sensor element during a 15 ms heater pulse for different gases (i) H.sub.2 (1% in N.sub.2), (ii) CH.sub.4 (100%), and (iii) H.sub.2S (56 ppm) in (A) 1.7% O.sub.2 environment and (B) 0% O.sub.2 environment. In this example, monitoring of the transient response occurred until the temperature returned to the pre-heating equilibrium temperature, which typically took around 100 ms.

[0121] The change in voltage was measured as an analogue signal which was digitised by sampling the analogue signal at an appropriate sampling rate. In this particular example, the sampling rate was 6 kHz, with a digital resolution of 15-bits from a 1.255 V reference voltage. The number of samples over the 100 ms monitoring period is thus 600 samples. The digitised results were then processed using a principal component analysis (PCA) algorithm.

[0122] 1.3: Use PCA to Process the Data: Record the Principal Component Scores for Each Test

[0123] In the present example the transient response of the gas sensitive element, along with post-processing using principal component analysis (PCA) and polynomial curve fitting and correlation, allows identification of types and concentrations of gases in a multi-gas sample. However, other mathematical algorithms can also be employed to extract the specific gas information. To study correlations (including predictive interactions) among gas profiles factor analysis, independent component analysis (ICA) and other methods and corresponding R functions are available. PCA is the preferred method for this, as it provides a simplified model of the data; however an issue with PCA is its poor performance in the presence of outlier data points. This may be overcome using additional algorithms to pre-filter the data to remove these outlier data points.

[0124] In order to determine the type and concentration of gas detected, the PCA algorithm must be trained by measuring known gases and mixtures. In this example, several gas mixtures of H.sub.2, CH.sub.4 and H.sub.2S are made and used as sensor training data. The PCA algorithm is capable of simplifying 100 ms of raw data down to a series of score values. The score values can be conveniently visualised as a coordinate in three-dimensional (3D) space, which are then used for the calculation of a spline curve to connect-the-dots and interpolate for missing observations in the gas sensing model. FIG. 4(A) and FIG. 4(B) illustrate three examples of sensor training data (PC observations), with the sensor detecting H.sub.2, CH.sub.4 and H.sub.2S gases respectively. FIG. 4(A) is a graph showing principal component analysis coefficient vectors (PCA vectors) for the first three dominant principal components for model gas tests (H.sub.2, CH.sub.4 and H.sub.2S) in oxygen, and FIG. 4(B) is a graph showing principal component coefficient analysis vectors (PCA vectors) for the first three dominant principal components for model gas tests (H.sub.2, CH.sub.4 and H.sub.2S) without oxygen.

[0125] 1.4: Repeat Steps 1-3 Until the PC Model Converges

[0126] The gas sensor's calibration model must be made robust by repeating the measurements with a large variety of gas types and concentrations. More results included in the model will reduce the error for gas correlation when measuring unknown gases. For this example, each gas mixture was measured at five (5) different concentration values. The scores given to each gas test are shown as points in FIG. 5(A) and FIG. 5(B).

[0127] 1.5: For Each Gas Species, a Spline Curve is Fitted to the PC Score Values to Generate a Gas Concentration Vector

[0128] The process for generating the model must be done individually for each gas concentration and gas type/mixture. Example cubic spline vectors are shown in FIG. 5(A) and FIG. 5(B) for the sensor model data at various concentrations of H.sub.2, CH.sub.4 and H.sub.2S. Three sets of data are shown in each plot for mixtures of CH.sub.4 and H.sub.2, CH.sub.4 and H.sub.2S, and for H.sub.2 and H.sub.2S. The curves are there to connect-the-dots between the known measurement points (from the previous step), and to give an estimate for any gases found in-between the known measurement points. This spline curve helps to give a direct relationship between PC score and gas concentration values, and is used for the measurements of unknown gases.

[0129] 2: Sensor Usage

[0130] Using the information obtained from (i) the PCA analyses, (ii) the subsequent gas mixture PCA model and (iii) gas concentration vectors, it is possible to obtain the types and concentrations of gases (for which calibration has been previously done) in an unknown multi-gas mixture.

[0131] 2.1: Apply an Unknown Gas Type and Concentration to the Sensor

[0132] This step is similar to step 1.1, except that the sensing element is exposed to a multi-gas mixture including a gas or gases of unknown types and concentrations.

[0133] 2.2: Pulse the Sensor's Heating Element and Collect the Response

[0134] This step is similar to step 1.2. The application of the voltage to the heater element is preferably the same as that used in the calibration phase. FIG. 6(A) and FIG. 6(C) show the sensor response to various gas mixtures in the presence of 1.7% and 0% O.sub.2 respectively.

[0135] 2.3: Using the Calibration PC Model, Determine the PC Scores for the Unknown Gas

[0136] This step relies on the developed PCA model in the calibration phase (step 1.3). For a PCA-based algorithm, the PCA model is a series of principal component curves. Example principal component curves are shown in FIG. 4(A) and FIG. 4(B). The response from the unknown gas is compared to these curves, and a score value is generated for the unknown gas.

[0137] 2.4: Use Regression Fitting to Assign the Unknown Gas to a Spline Curve from the Model

[0138] Regression fitting is then used on the score values of the unknown gas to determine which gas mixture type it belongs to. This step reveals only the type of gas measured.

[0139] 2.5: Calculate a Calibrated Absolute Concentration of the Unknown Gas by Correlating the Location of the Unknown Gas Along the Model Curve.

[0140] This last step is for calculating the concentration of the unknown gas. The spline curves generated from the model are used, where the score values from the unknown gas are compared to the spline curves, and a concentration value for the gas is determined. FIG. 6(B) shows the corresponding calculated concentrations of gases based on the sensor response illustrated in FIG. 6(A), and FIG. 6(D) shows the corresponding calculated concentrations of gases based on the sensor response illustrated in FIG. 6(C).

[0141] In this example, tests were repeated 40 times, and the error bars are shown (see FIG. 6(B) and FIG. 6(D)). The error includes sensor error, PCA algorithm error and vector calculation and correlation errors. The errors are all less than 20%the highest is for separation between CH.sub.4 and H.sub.2S. The error can be improved through more thorough training of the gas sensor model to produce a very good separation of gases in both aerobic and anaerobic environments.

[0142] It should be noted that even though the example tin oxide sensor performs poorly in 0% O.sub.2 environments, it was still possible to identify and measure gases. The exceptions appear to be when measuring pure H.sub.2 or pure H.sub.2S, where the error bars are larger. This can be ameliorated, for example, through selection of different materials for the gas sensitive element, or by operating an array of gas sensitive elements.

[0143] 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 invention.