Patent Application
20210102925
Gas sensor arrays can be used to detect the presence of analytes in ambient environments surrounding the gas sensors. Detecting particular analytes in an ambient environment, e.g., volatile organic compounds can be useful for safety, manufacturing, and/or environmental monitoring applications. Individual gas sensors can be differently sensitized to a particular subset of analytes and nonreactive to other analytes.
This specification describes systems, methods, devices, and other techniques relating to a multi-modal gas sensing array. The array of differently-sensitized gas sensors can be used to generate a recognizable pattern of output signals unique to a variety of analyte compositions to which the multi-modal gas sensor array is exposed.
In general, one innovative aspect of the subject matter described in this specification can be embodied in methods for configuring a multi-modal gas sensor array including providing multiple gas sensors, wherein the multiple gas sensors include a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor. A test gas is provided to the multiple gas sensors, where the test gas includes multiple known analytes, and data generated by each gas sensor of the multiple gas sensors in response to the test gas is collected by a data processing apparatus, where the generated data from each gas sensor of the multiple sensors includes a response of the gas sensor to one or more analytes of the multiple known analytes in the test gas. A proper subset of gas sensors is selected by the data processing apparatus from the multiple gas sensors to include in an optimized gas sensor array, where the selection includes selecting a particular gas sensor from the multiple gas sensors based on the response of the particular sensor to the one or more analytes of the multiple known analytes in the test gas meeting a threshold response, and determining to include the selected gas sensor in the proper subset of gas sensors based on the selected gas sensor meeting a threshold temporal response, and configuring the multi-modal gas sensor array using the proper subset of gas sensors.
These and other embodiments can each optionally include one or more of the following features. In some implementations, the first type of gas sensor and the second type of gas sensor can be one of an organic-type gas sensor and an inorganic-type gas sensor. The first type of gas sensor and the second type of gas sensor can each have a different response to the plurality of known analytes of the test gas.
In some implementations, providing the test gas can include providing a first known concentration of an analyte of interest, providing a second known concentration of an analyte not of interest, and mixing, the first known concentration of the analyte of interest and the second known concentration of the analyte not of interest to formulate the test gas. Providing the test gas to the gas sensors can include providing the test gas in a controlled environment including a particular temperature and a particular relative humidity. Providing the test gas to the gas sensors can include providing the test gas at a flow rate of less than 5 cubic feet per hour for a known period of time.
In some implementations, the response of the gas sensor can be a measure of a change in electrical resistivity of the gas sensor, where a change in electrical resistivity of the gas sensor can be measured prior to exposure to the test gas, during exposure to the test gas, and after termination of exposure to the test gas.
In some implementations, the subset of gas sensors that each meet the threshold response includes meeting a threshold reactivity to one or more analytes of the multiple analytes in the test gas.
In some implementations, the threshold temporal response includes an amount of time between exposure of a particular gas sensor to the test gas and reaching the threshold response. The threshold temporal response can further include a recovery time for the particular gas sensor to reach a baseline reading after termination of exposure to the test gas.
In some implementations, configuring the multi-modal gas sensor array using the proper subset of gas sensors includes selecting, for inclusion in the multi-modal gas sensor array of a gas sensing apparatus, only the proper subset of gas sensors of the plurality of gas sensors. Configuring the multi-modal gas sensor array using the proper subset of gas sensors can include collecting, from only the proper subset of gas sensors of the plurality of gas sensors, data generated by each gas sensor of the proper subset of gas sensors in response to exposure to a second test gas.
In some implementations, the methods further include determining a first gas sensor and a second gas sensor selected based on the responses of the respective gas sensors, determining that the first gas sensor and the second gas sensor have similar responses to a same set of analytes of the plurality of analytes, determining that a first temporal response of the first gas sensor is less than a second temporal response of the second gas sensors, and determining to include only the first gas sensor in the subset.
In general, another innovative aspect of the subject matter described in this specification can be embodied in methods for training a multi-modal gas sensor array including generating training data for a plurality of test gases, where each test gas includes multiple analytes and is representative of a first environment. For each test gas, the training data is generated by exposing the multi-modal gas sensor array including multiple gas sensors to the test gas at a first set of operating conditions, where the gas sensors include a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor. A first set of sample data including response data for each of the gas sensors responsive to the test gas at the first set of operating conditions is collected by a data processing apparatus and from each of the gas sensors. A subset of gas sensors from the multiple gas sensors is selected for the test gas from the first set of sample data, where the response data collected for each gas sensor of the subset of gas sensors meets a threshold response. The multiple gas sensors of the multi-modal gas sensing array are exposed to the test gas to the test gas under a second set of operating conditions and a second set of sample data including response data for each of the subset of gas sensors responsive to the test gas under the second set of operating conditions is collected by the data processing apparatus and from each of the subset of gas sensors. Training data for the test gas representative of the first environment is generated from the first set of sample data and the second set of sample data from each of the subset of gas sensors and the training data is provided to a machine-learned model.
These and other embodiments can each optionally include one or more of the following features. In some implementations, the response data includes a measure of gas sensor electrical resistivity over a period of time during the exposure to the test gas.
In some implementations, the methods further include training the machine-learned model using the training data by providing, to the subset of gas sensors, a particular test gas of the plurality of test gasses under a third set of operating conditions, collecting, from each of the subset of gas sensors, a third set of sample data, providing, to the machine-learned model, the third set of sample data, and determining, by the machine-learned model, characteristics descriptive of the particular test gas under the third set of operating conditions. Determining characteristics descriptive of the test gas under the third set of operating conditions can include identifying, by the machine-learned model, a first concentration of a first analyte in the test gas under the third set of operating conditions.
In some implementations, the methods further include exposing the gas sensors to an inert gas for a period of time prior to exposing the plurality of gas sensors to the test gas under the second set of operating conditions. The period of time can be an amount of time for each of the plurality of sensors to reach a baseline resistivity reading.
In some implementations, the first set of operating conditions includes a first concentration of a first analyte of interest of the multiple analytes and a second concentration of a second analyte not of interest of the multiple analytes. The second set of operating conditions can include a third concentration of the analyte of interest of the multiple analytes and a fourth concentration of the analytes not of interest of the multiple analytes, where the third concentration is different than the first concentration.
In some implementations, the first set of operating conditions and the second set of operating conditions further include a respective temperature of the test gas and subset of sensors and a respective relative humidity of the test gas and subset of sensors.
In some implementations, the second set of operating conditions includes a first concentration of a second analyte of interest of the multiple analytes and the second concentration of the analyte not of interest of the multiple analytes.
In general, another innovative aspect of the subject matter described in this specification can be embodied in a multi-modal gas sensing apparatus including multiple gas sensors including a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor, where each of the first type of gas sensor and second type of gas sensor is sensitive to a respective set of analytes. The apparatus includes a housing configured to hold the multiple gas sensors and a gas inlet coupled to the housing and in fluidic contact with the multiple gas sensors, where the gas inlet is configured to expose the gas sensors to a gas introduced via the gas inlet. The apparatus includes an environmental controller coupled to the housing and configured to regulate a temperature of the housing, gas inlet, and multiple gas sensors to a particular temperature and a data processing apparatus in data communication with the gas sensors and the environmental controller.
These and other embodiments can each optionally include one or more of the following features. In some implementations, the first type of gas sensor is sensitive to a first set of analytes of interest and the second type of gas sensor is sensitive to a second, different set of analytes of interest. The first type of gas sensor can be sensitive to organic-type analytes and the second type of gas sensor can be sensitive to inorganic-type analytes.
In some implementations, the housing includes a material that is non-reactive to multiple analytes of interest. The housing can further include a heat-exchange component configured to interact with the gas and regulate a temperature of the gas to the particular temperature and prior to entering the gas inlet, where the gas inlet can be configured to regulate a flow of the gas at a flow rate less than 5 cubic feet per hour.
In some implementations, the data processing apparatus is configured to collect time-dependent measurements of operating conditions of the multiple sensors, gas inlet, and environmental controller. The operating conditions can include a response of each of the sensors prior to exposure to the gas via the gas inlet, during exposure to the gas, and after termination of exposure to the gas.
In some implementations, the environmental controller is further configured to regulate a relative humidity of the housing, gas inlet, and multiple gas sensors to a particular relative humidity.
In general, another innovative aspect of the subject matter described in this specification can be embodied in a training system for a multi-modal gas sensing array including multiple gas sensors where the multiple gas sensors include a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor and each gas sensor is sensitive to a respective set of analytes. The system includes a gas manifold fluidically coupled to the multi-modal gas sensing array and configured to provide a gas to the multi-modal gas sensing array where the gas manifold includes a gas source including an analyte of interest and a confounding gas source including an analyte that is not of interest. The system includes a data processing apparatus in data communication with the multi-modal gas sensing array and the gas manifold and configured to record a first label describing a first state of the multi-modal gas sensing array, provide, by the gas manifold and to the multi-modal gas sensing array, a first test gas, where the first test gas includes a first concentration of the analyte of interest and a second concentration of the analyte that is not of interest, collect a set of sample data from each of the gas sensors of the multi-modal gas sensing array responsive to the first test gas, and record a second label describing a second state of the multi-modal gas sensing array.
These and other embodiments can each optionally include one or more of the following features. In some implementations, the first type of gas sensor and the second type of gas sensor are each sensitive to a different set of analytes of multiple analytes.
In some implementations, the first label includes a first timestamp and a baseline response measurement for each gas sensor of the gas sensors prior to providing the first test gas. The baseline response measurement can be a resistivity measurement for the gas sensor. The set of sample data from each of the plurality of gas sensors of the multi-modal gas sensing array responsive to the first test comprises a time-dependent response of each of the plurality of gas sensors to the first test gas. The set of sample data from each of the gas sensors of the multi-modal gas sensing array responsive to the first test can include a time-dependent response of each of the gas sensors to the first test gas.
In some implementations, the time-dependent response includes a measure of resistance of the gas sensor over time. The second label can be a second timestamp and a response measurement for each gas sensor at a termination of exposure of the first test gas to the gas sensor.
In some implementations, the operations of the data processing apparatus further include collecting a second set of sample data from each of the gas sensors, where the second set of sample data includes a time-dependent recovery response of each of the gas sensors after terminating exposure of the gas sensors to the first test gas.
In some implementations, the operations of the data processing apparatus further include recording a third label for each gas sensor of the multiple gas sensors including a timestamp, where the third label is recorded when a response measurement for each gas sensor of the multiple gas sensors matches the baseline response measurement prior to providing the first test gas.
In some implementations, the operations of the data processing apparatus further include identifying a responsive subset of gas sensors from the multiple gas sensors to the first test gas, where identifying the responsive subset of gas sensors includes identifying each gas sensor having a threshold response recorded in the set of sample data and selecting, from the responsive subset, a proper subset of gas sensors having a difference between the third timestamp and first timestamp that is less than a threshold time.
Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. For example, an advantage of this technology is that it allows for the development of fully-customizable gas sensor arrays for a particular set of gases unique to a use-case by using an automated simulation of environmental variables in a controlled laboratory setting. The custom “e-nose” system can be generated from a multi-modal system generic to many different use cases and using automated sample collection and analysis, where a machine-learned model for a particular use-case is trained in the controlled environment. Additionally, a custom sub-set of gas sensors can be selected from the multi-modal generic system to create a best-fit e-nose for the particular use-case. Rather than requiring the creation of the custom device prior to collecting the data and training the model for the particular use-case, a custom sensor array can be created in real-time using the generic multi-modal sensor array and custom software feedback loops. An automated high-throughput system allows for the collection of massive amounts of pertinent odor data for the particular use-case and enables the e-nose system to properly classify odors automatically in the field.
The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below.
Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
Like reference numbers and designations in the various drawings indicate like elements.
The technology of this patent application utilizes a multi-modal gas sensing array of differently-sensitized gas sensors included in a same gas sensing apparatus to generate a recognizable pattern of output signals unique to a variety of analyte compositions to which the gas sensor array is exposed.
More particularly, the technology utilizes a multi-modal array of gas sensors, where the array of differently-sensitized sensors is exposed to a composition of analytes in a controlled environment. The output signals of the gas sensors in the multi-modal gas sensor array under exposure are used to train a machine-learned model to recognize and classify particular analytes in the field. Moreover, in certain implementations, the system identifies a subset of gas sensors of the full multi-modal array of gas sensors that are optimal for a particular use case based on a feedback loop measuring in real-time. Additionally, optimized sampling conditions, e.g., minimum sampling time, recovery time, etc., to collect insight for a particular gas mixture including an analyte of interest is determined for the optimized subset of the sensors. The system is then trained using the optimized subset of sensors and optimized sampling conditions to reduce data collection and processing.
The gas sensing apparatus 102 includes a housing 104 including an environment regulator 106. Environmental regulator 106 can include a heat-exchange component, e.g., cylindrical heaters inserted into the housing, and/or heat transfer fins for controlling the temperature of gases that are introduced through the gas inlet 110. The heat-exchange component can be configured to interact with the gas and regulate a temperature of the gas to the particular temperature and prior to entering the gas inlet.
Environmental regulator 106 can be configured to control a temperature within the housing 104, gas sensors 108, and a gas within the housing 104, for example, to a temperature between 40-45° C., to a temperature above dew point >16° C., or at a temperature relevant to an environment of interest (e.g., room temperature 23° C.). In some implementations, environmental regulator 106 can be configured to regulate a relative humidity within the housing 104, gas sensors 108, and a gas within the housing 104, e.g., to a relative humidity below 10%, to a relative humidity between 10-30%, or to a relative humidity relevant to an environment of interest.
The housing 104 and environmental regulator 106 can be in thermal contact such that the gas introduced through the gas inlet 110, the gas sensors 108, and the housing 104 are all maintained at a same temperature during operation of the apparatus 102.
Housing 104 can be composed of various materials that are selected to be non-reactive to a set of analytes to which the housing 104 will be exposed. Materials for the housing 104 can include, for example, Teflon, Teflon-coated aluminum or stainless steel, Delrin, or other materials that are resistant to the set of analytes.
Housing 104 includes fixtures to hold a set of gas sensors 108 within the housing 104. The fixtures can be configured to accommodate particular dimensions of the gas sensors, and a layout of the fixtures within the housing 104 can be configured to designate particular locations for different types of gas sensors 108 within the housing 104.
Housing 104 further includes a gas inlet 110 and a gas outlet 112, where the gas inlet 110 is configured to allow for the introduction of gases into the housing 104 and to flow gas across the gas sensors 108. Gas outlet 112 is configured to allow for the purge of the gases from the housing 104.
Gas inlet 110 and gas outlet 112 can be configured for gas flow rates ranging between 0-10 cubic feet/hour, e.g., 5 cubic feet/hour. A particular flow rate for a gas into the gas inlet 110 can be selected, for example, based on an amount of time it takes for the environmental regulator 106 to bring a gas introduced at the gas inlet 110 to a test temperature, e.g., how long the gas will have to be in the fins to get it to the temperature of the housing 104.
In some implementations, gas sensing apparatus 102 includes a fan 114 configured to generate a negative pressure at the gas inlet 110 and within the housing 104 which can draw a gas into the housing 104 via the gas inlet 110, move the gas across the gas sensors 108, and purge the gas from the gas outlet 112. One or more operating parameters of the fan 114, e.g., a rotational speed of the fan, can be selected to regulate a desired flow rate of the gas through the gas sensing apparatus 102.
Gas sensors 108 include a multi-modal array of gas sensors that can be sensitive to various different organic and/or inorganic compounds. In other words, the multi-modal array of gas sensors 108 can include gas sensors that are responsive to certain analytes in a test gas and not sensitive to others. Types of gas sensors 108 can include gas sensors having different sensing mechanisms, e.g., metal oxide (MOx) sensors, photoionization detector (PID) sensors, electrochemical sensors, nondispersive infrared (NDIR) sensors, or other types of gas sensors. For example, gas sensors included in a gas sensing apparatus 102 include MOx sensors 108a-c, PID sensors 108d-f, electrochemical sensor 108g, and NDIR sensor 108h.
In some implementations, types of gas sensors 108 include gas sensors having a same sensing mechanism, e.g., oxidation-based, resistivity-based, optical-based, etc., but can have different sensitivities to the multiple analytes. In other words, a first type of gas sensor 108a and a second type of gas sensor 108b have a same mechanism for gas sensing, e.g., MOx sensors that make resistivity-based measurements, but are configured to have different performance parameters, e.g., a MOx sensor operating at a first voltage bias and a MOx sensor operating at a second voltage bias, such that the respective sensitivities to certain analytes are different.
The multi-modal array of gas sensors 108 can include multiple gas sensors, e.g., 38 total gas sensors, 25 total gas sensors, greater than 40 total gas sensors. A number of each type of gas sensor relative to respective numbers of each other type of gas sensor included in the multi-modal array of gas sensors 108 can depend in part on dimensional/size considerations, cost-benefit of each type of sensor, responsivity, signal-to-noise ratio of each sensor, or the like. A field of application of the sensor array, e.g., agricultural, industrial, etc., can determine a number of each time of gas sensor relative to a respective number of each other type of gas sensor in the multi-modal array of gas sensors 108. For example, for applications that may have a fainter signal-to-noise ratio, e.g., more background analytes not of interest, more sensors will be included overall in the multi-modal array of gas sensors 108.
In some implementations, multiple different gas sensors can each have a same sensitivity to a particular analyte, e.g., a first type of gas sensor and a second type of gas sensor can each be sensitive to the particular analyte. For example,
The array of gas sensors 108 can be configured within the house 104 such that a test gas introduced via an inlet 110 can be sampled simultaneously by all of the gas sensors 108 in the array of gas sensors. In other words, the test gas is exposed to the multiple gas sensors in the array of gas sensors simultaneously such that data collection from each of the multiple gas sensors 108 can be performed in parallel.
In some implementations, the multi-modal array of gas sensors can include multiple MOx sensors 108a-c that are each configured to operate at a different temperature, e.g., biased at a different operating voltage, such that they have different responses to particular analytes based on the operating parameters.
Each gas sensor 108 is connected to a data processing apparatus 116 which is configured to collect data from the gas sensors 108, e.g., response data 118. Response data 118 can include a time-dependent response of each of the gas sensors 108 to a test gas. Additionally, the data processing apparatus 116 can be configured to collect time-dependent measurements of operating conditions of the plurality of sensors 108, gas inlet 110, and environmental controller 124, e.g., temperature and relative humidity, gas flow rate, and the like.
In some implementations, response data 118 includes a measure of the resistivity of the gas sensor 108 versus time. Further discussion of the response data is discussed with reference to
Data processing apparatus 116 can be hosted on a server or multiple servers in data communication with the gas sensors over a network. The network may include, for example, one or more of the Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a public switched telephone network (PSTN), Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (DSL)), radio, television, cable, satellite, or any other delivery or tunneling mechanism for carrying data. The network may include multiple networks or subnetworks, each of which may include, for example, a wired or wireless data pathway. The network may include a circuit-switched network, a packet-switched data network, or any other network able to carry electronic communications (e.g., data or voice communications). For example, the network may include networks based on the Internet protocol (IP), asynchronous transfer mode (ATM), the PS TN, packet-switched networks based on IP, X.25, or Frame Relay, or other comparable technologies and may support voice using, for example, VoIP, or other comparable protocols used for voice communications. The network may include one or more networks that include wireless data channels and wireless voice channels. The network 505 may be a wireless network, a broadband network, or a combination of networks including a wireless network and a broadband network.
Data processing apparatus 116 can be configured annotate the response data 118 received from the gas sensors 108 responsive to a test gas. In some implementations, annotation data 120 includes timestamps, e.g., a start time label, a stop time label, and/or respective composition data 122 of the test gases, e.g., a set of known analytes of interest and set of known analytes not of interest, being evaluated using the gas sensing apparatus 102.
In some implementations, the data processing apparatus 116 is configured to generate annotation data 120 before, during, and after exposure of a test gas to the gas sensors 108, where the annotation data 120 includes recording a first label describing a first state of the multi-modal gas sensing array, e.g., a start time of the gas exposure. The gas manifold 126 can provide a first test gas to the multi-modal array of gas sensors 108, where the first test gas includes a first concentration of a known analyte of interest and a second concentration of a known analyte that is not of interest, e.g., a confounding gas. The data processing apparatus 116 can then collect a set of sample data 118 from each of the gas sensors 108 of the multi-modal gas sensing array responsive to the first test gas and then record annotation data 120, e.g., a second label, describing a second state of the multi-modal gas sensing array, e.g., a stop time of gas exposure. Further details of processes for the gas sensing apparatus 102 are discussed with reference to
In some implementations, the data processing apparatus 116 further generates, from the response data 118, the annotation data 120, and the composition data 122 for the known test gases, training data for a machine-learned model. Details described in further detail below with reference to
In some implementations, the data processing apparatus 116 is in data communication with an environmental controller 124. The environmental controller 124 may be a part of the data processing apparatus 116. The environmental controller 124 can be configured to provide operating instructions to the environmental regulator 106, gas sensors 108, data processing apparatus 116, and gas manifold 126. In some implementations, the environmental controller 124 can be configured to receive operational feedback, e.g., solenoid valve status, temperature control, etc., from one or more of the environmental regulator 106, gas sensors 108, data processing apparatus 116, and gas manifold 126.
In some implementations, the environmental controller 124 can receive operating conditions feedback, e.g., temperature, humidity readings, from a temperature and/or humidity gauge 128. The gauge 128, e.g., a thermocouple, hygrometer, or the like, can be in physical contact with the housing 104 or gas sensors 108 to measure a temperature and/or relative humidity. The gauge 128 can additionally or alternatively measure a temperature and/or relative humidity of the gas present within the housing 104.
In some implementations, the operating conditions feedback received by the environmental controller 124 can be provided to the data processing apparatus 116 and recorded as annotation data 120. For example, temperature of the housing 104, gas sensors 108, and test gas can be recorded as annotation data 120 and included with the response data 118 for the particular test gas.
The system 100 further includes a gas manifold 126 having multiple gas sources 130. The multiple gas sources 130 can each include multiple known analytes of interest and multiple known analytes not of interest. For example, gas manifold 126 can include gas source 130a, 130b each having analytes of interest and gas sources 130c and 130d each having analytes not of interest (e.g., confounding gases).
Gas manifold 126 can be a closed-loop system, where no external compounds are present within the gas manifold 126 that could interfere with the test gases provided by the gas manifold to the gas sensing apparatus 102 via the gas inlet 110. The test gases provided by the gas manifold 126 to the gas sensing apparatus 102 can be composed only of the known analytes of interest and known analytes not of interest from the one or more gas sources 130.
Gas manifold 126 can further include regulatory components 132 which can be operable to selectively allow a controlled flows (e.g., 0-5 cubic feet/hour) of one or more of the gas sources 130 from the gas manifold 126 and into the gas inlet 110 to generate a particular test gas for the gas sensing apparatus 102. Further details of the gas manifold 126 are discussed with reference to
As depicted in
One or more of the gas sources 130, e.g., gas source 130b, can further include a mixing vessel 210. Mixing vessel 210 can be a vessel containing an item, e.g., coffee grounds, or a secondary gas source (e.g., a methane tank) that provides an analyte to the gas source 130.
Pressure regulator 202 can regulate a pressure from a purge gas source 212, e.g., a compressed gas cylinder, from a high pressure to a low pressure to generate a desired flow rate of the purge gas into the gas sources 130, e.g., into a mixing vessel 210. The purge gas source 212 can include a compressed air source, e.g., clean dry air, and/or an inert gas source, e.g., nitrogen, argon, or the like. The purge gas source 212 can be regulated by an electronic flow meter 214 which controls a flow rate of the purge gas source 212 into the various gas sources 130, where pressure regulator 202 for each gas source 130 can further regulate a flow of the purge gas into the particular gas source 130.
In some implementations, a gas source 130a is only composed of purge gas from gas source 212, e.g., compressed air and/or inert gas, which is provided to a gas inlet 110 without any analytes of interest or analytes not of interest present.
In some implementations, a gas source, e.g., gas source 130b, is a first gas source, where the first gas source is generated by mixing at least the purge gas source 212 with an analyte present in the mixing vessel 210b, e.g., coffee grounds, soil, methane, etc. A gas source, e.g., gas source 130b, can be a second gas source, where the second gas source is generated by mixing at least the purge gas source 212 with an analyte present in the mixing vessel 210c, e.g., ethanol, apples, charcoal, etc.
In some implementations, the gas manifold 126 further includes a solenoid valve 216 and a gas stream mixer 218. Solenoid valve 216 can be configured to control the flow of gases from the gas manifold 126 prior to entering the gas inlet 110. Solenoid valves 216 can receive operational instructions from the environmental controller 124 and/or from the data processing apparatus 116.
In some implementations, gas stream mixer 218 can be used to mix gases from multiple gas sources 130. For example, a first gas source 130b and a second gas source 130c can be allowed to flow to the gas stream mixer 218 and blended together to form a test gas that is provided to the gas inlet 110. Operation of the gas stream mixer 218 can be controlled by the environmental controller 124 and/or the data processing apparatus 116. Other methods for blending gases from multiple gas sources 130 prior to the test gas being provided to the gas inlet 110 are possible.
In some implementations, some or all of the pressure regulators 202, solenoid valves 204, adjustable orifices 206, mixing vessels 210, check valves 208, solenoid valve 216, electronic flow meter 214, and gas stream mixer 218 can be operated automatically and/or semi-automatically by the environmental controller 124. For example, the environmental controller 124 can be configured to provide process control instructions to the gas manifold 126 to provide, at the gas inlet 110, a particular test gas including a composition of analytes.
In some implementations, the environmental controller 124 can open and close solenoid valves 204 and 216 to determine which gas sources 130 flow into the gas inlet 110. The environmental controller 124 can control a flow rate for each pressure regulator 202 to establish a concentration of the particular analyte present in the mixing vessel 210 for the gas source 130. For example, mixing vessels 130b and 130c can include a same analyte (e.g., coffee grounds) and the environmental controller 124 can set pressure regulator 202b to a first flow rate and pressure regulator 202c to a second flow rate to achieve a gas source 130b with a first concentration of a given analyte and a gas source 130c with a second, different concentration of the given analyte.
Referring back to
In some implementations, response of a gas sensor 108 to a test gas including one or more analytes can be measured as a change in gas sensor electrical resistivity before, during, and after exposure to the particular test gas (e.g., as in MOx sensors). In some implementations, response of a gas sensor 108 can be measured as an electrical signal generated by one or more analytes of interest that are present in the test gas (e.g., as in PID sensors).
A baseline response R0 can be a response measurement for the gas sensor A prior to providing the first test gas, where T3 is the time stamp after termination of the gas exposure when the response 302 of the gas sensor A matches the baseline response measurement R0. The baseline response measurement R0 can be a resistivity measurement for the gas sensor, where the resistivity of the gas sensor A is below a minimum threshold resistivity.
Additional timestamps can be recorded in the annotation data 120 for the plot 300, for example, a timestamp for threshold time Tt, when the response 302 of the gas sensor A reaches a threshold response Rt. In another example, a timestamp can be annotated when the response 302 of the gas sensor A reaches a saturation point Ts, in other words, when the response 302 of the gas sensor A saturates to the exposure of the test gas.
Threshold response Rt can be selected based in part on a concentration of the one or more analytes of interest in the test gas, e.g., for very low concentrations of analytes the threshold response Rt can be set lower than for relatively higher concentrations of analytes. Threshold response Rt can be selected based in part on the characteristics of the particular analytes in the test gas, e.g., a lower threshold response can be set for a highly dangerous and/or toxic analyte versus a higher threshold response can be set for a non-toxic analyte.
In some implementations, a threshold response Rt can depend in part on the one or more types of gas sensors of the multiple gas sensors 108 that are exposed to the test gas. For example, if all of the gas sensors have some amount of sensitivity to a particular analyte in the test gas, the threshold response may be set at a higher threshold response to select only the gas sensor(s) with a highest relative sensitivity to the particular analyte.
In some implementations, a threshold response Rt can depend in part on a type of gas sensor, for example, depending on a mechanism of detection for the type of gas sensor, in order to normalize the sensitivities of each type of gas sensor relative to each other type of gas sensors in the multi-modal array of gas sensors 108. A minimum response threshold can be defined as a lowest detectable limit of a particular analyte. For example, a threshold of detectability can be 1 part per million (ppm) for most MOx sensors when detecting acetone vapor.
As depicted in
In some implementations, one or more of the gas sensors in the array of gas sensors 108, e.g., gas sensor 108b, do not meet a threshold response Rt to the particular test gas.
In some implementations, gas sensor B can have negligible response R to a test gas, where the response 312 of the gas sensor B is not measurable, e.g., below the sensitivity of the sensor. Gas sensor B can have zero response to a test gas, where the gas sensor 130 is nonreactive to analytes of interest in the test gas, e.g., an inorganic gas sensor 130 can have zero reactivity to an organic compound being tested.
In some implementations, multiple gas sensors 108 can have at least a threshold response Rt to a particular test gas, where a first gas sensor can have an optimized response to the particular test gas versus a second gas sensor. An optimized response can be defined as a shorter period of time between exposure to the test gas T1 and a time to reach a threshold response Rt. Referring to
In some implementations, an optimized response can be a period of recovery time for the gas sensor between the exposure of the gas being terminated T2 and the gas sensor reaching the baseline responsivity R0, T3. Referring to
Referring now to
In some implementations, selecting a particular gas sensor with an optimized response is based on the particular gas sensor exhibiting a shorter period of time to reach a threshold response after exposure to the particular test gas and/or a shorter period of time to recover a baseline response R0, e.g., selecting gas sensor A instead of gas sensor C because P1 is less than P3 and/or because P2 is less than P4.
In some implementations, each gas sensor having at least a threshold response Rt to the particular test gas can be compared to each other gas sensor having at least the threshold response to the particular test gas, and a particular gas sensor or set of two or more gas sensors of the multiple gas sensors can be selected based in part on the respective responses, e.g., response 302 and 322, of each gas sensor. The response data 118, e.g., including plots 300, 310, 320 of responses 302, 312, 322 respectively, for each gas sensor 108 of the multiple gas sensors can be analyzed and gas sensors having at least a threshold response Rt recorded in the set of response data 118 can be selected. A proper subset of gas sensors can include the gas sensors that have a difference between the third timestamp and first timestamp, e.g., between T3 and T1 that is less than a threshold time. A range of time for T3-T1 can be, for example, 5-120 seconds. Further discussion of the selection of the subset of gas sensors is discussed with reference to
The gas inlet 110 is coupled to the housing 104 and configured to expose the multiple gas sensors 108 to an unknown gas mixture 404 that is introduced through the gas inlet 110. Unknown gas mixture 404 can be introduced passively via the environment surrounding the gas inlet 110. For example, the unknown gas mixture 404 can be introduced via the gas inlet 110 when the gas sensing apparatus 402 is deployed in a testing environment, e.g., in a factory setting, Passive introduction of the unknown gas mixture 404 into the gas inlet 110 can be, for example, by diffusion of the unknown gas mixture into the gas inlet 110.
In some implementations, the unknown gas mixture 404 can be introduced actively into the gas inlet 110, for example, by generating a negative pressure within the housing 104 using a fan 114 or other similar device. In another example, the unknown gas mixture 404 can be introduced actively to the gas inlet 110 by a positive pressure of the gas at the gas inlet, e.g., by a person blowing into the gas inlet 110, a gas exhaust from a piece of equipment, or the like.
Multiple gas sensors 108 including a first type of gas sensor, e.g., gas sensor 108a, and a second type of gas sensor, e.g., gas sensor 108b, that is different from the first type of gas sensor are located within the housing 104 where each of the first type of gas sensor and second type of gas sensor is sensitive to a respective set of analytes. The first type of gas sensor 108a and the second type of gas sensor 108b can have different methods for gas sensing, e.g., where the first type of sensor is a MOx sensor and the second type of sensor is a PID sensor.
In some implementations, the first type of gas sensor 108a and the second type of gas sensor 108b have a same method for gas sensing but are configured to have different performance parameters, e.g., a MOx sensor operating at a first operating temperature and a MOx sensor operating at a second operating temperature. Operating the MOx sensors at different temperatures can cause each respective MOx sensor to respond differently to analytes in a test gas, even when sensing a same test gas at a same concentration.
Gas sensing apparatus 402 further includes an environmental controller 124 coupled to the housing 104 and configured to regulate temperatures of the housing 104, gas inlet 110, and gas sensors 108 to a particular temperature. The environmental controller 124 can include an environmental regulator 106, for example, a heating source and heat-transfer fins, embedded into the housing 104 where gas introduced at the gas inlet 110 passes through heating channels within the heat-transfer fins of the housing 104 to stabilize a temperature of the gas 404 to the particular temperature. In some implementations, the environmental controller 124 can include temperature and/or humidity gauges 128 to measure a temperature and/or relative humidity of the unknown gas mixture 404 that is received at the gas inlet 110.
Gas sensing apparatus 402 includes a data processing apparatus 116 in data communication with the gas sensors 108 and the environmental controller 124. The data processing apparatus 116 can be an onboard computer that is affixed to the housing 104 of the apparatus 402. In some implementations, a portion or all of the data processing apparatus 116 can be hosted on a cloud-based server that is in data communication with the gas sensing apparatus 402 over a network.
Data processing apparatus 116 can include a user device 410, where a user can interact with the gas sensing apparatus 402 via the user device 410, e.g., receive data, provide testing instructions, receive testing information, or the like. User device 410 can include, for example, a mobile phone, tablet, computer, or another device including an application environment through which a user can interact with the gas sensing apparatus 402. In one example, user device 410 is a mobile phone including an application environment configured to display gas mixture test results for the unknown gas mixture 404, allow for user interaction with the gas sensing apparatus 402, and the like.
In some implementations, the apparatus 402 includes a display 406 configured to communicate information 408 to a user of the gas sensing apparatus 402 and/or allow for user interaction with the gas sensing apparatus 402. Information 408 can include, for example, the operational status of the apparatus 402, e.g., on/off, testing, processing, etc.
In some implementations, information 408 includes test results for the unknown gas mixture 404. Information 408 can be presented to a user based on user preferences, e.g., to highlight a particular set of analytes that the user is interested in discovering in the unknown gas mixture 404. Information 408 can be additionally or alternatively provided to one or more user devices 410 in data communication with the apparatus 402.
In some implementations, display 410 is configured to respond to user interaction, e.g., a touch-screen functionality. Display 410 can further include audio feedback, e.g., an alert, to notify a user of the status of the apparatus 402. For example, the apparatus 402 can provide an audio and/or visual update to the user of a testing status. In another example, the apparatus 402 can provide an audio and/or visual alarm to the user, e.g., if a particular analyte is detected above/below a preset threshold, e.g., a threshold concentration of the analyte is detected in the ambient.
In some implementations, imaging data can be captured of an environment surrounding the apparatus 402, e.g., using a camera or video recording device. The imaging data of the surrounding environment can be displayed on display 410 to identify, to a user, one or more objects in the surrounding environment that may be included in the unknown gas mixture 404 being sampled by the apparatus 402. A blend ratio of the various analytes that are being sensed in the unknown gas mixture 404 can be displayed on display 410.
Further details of the gas sensing apparatus 402 are discussed with reference to
The e-nose multi-modal gas sensing apparatus can operate in various modes, including a configuration mode, e.g., determining a subset of gas sensors to include in the gas sensing apparatus for a particular environment including a set of analytes of interest, a training mode, e.g., training a machine-learned model to identify various analytes of interest, and detection mode, e.g., where the e-nose multi-modal gas sensing apparatus is deployed in a testing environment.
In a first step, multiple gas sensors are provided, where the multiple gas sensors include multiple types of gas sensors, e.g., a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor (502). As depicted in
In some implementations, the first type of gas sensor can be an organic-type gas sensor and the second type of gas sensor can be an inorganic-type gas sensor. For example, a volatile organic compound (VOC) sensor is sensitive to organic compounds, e.g., hydrogen, carbon dioxide, etc. In another example, PID sensors are sensitive to inorganic compounds, e.g., chlorine, tin oxide, etc.
A test gas is provided to the multiple gas sensors, where the test gas includes multiple known analytes (504). The test gas can be produced using a gas manifold, e.g., gas manifold 126, including multiple gas sources, e.g., gas sources 130. Each of the multiple gas sources 130 can include a different analyte, where the analytes can be mixed together using gas manifold 126 to generate a particular composition of known analytes in the test gas. The test gas can include a first concentration of a known analyte of interest and a second concentration of a known analyte not of interest. For example, 0.01% of methane and 0.99% of nitrogen or air.
The analytes of interest can be selected based on an intended environment for the apparatus with the selected set of gas sensors. For example, an industrial setting may have an interest in solvent analytes or processing gases. In another example, in an agricultural setting, analytes of interest are methane, pesticide compounds, or other agricultural by-products. In yet another example, in a laboratory setting, an analyte of interest can be a toxic or hazardous gas, e.g., chlorine gas. The analytes not of interest, or “confounding” gases can be selected to be inert gases, e.g., nitrogen, or as gases that are typically present in the intended environment for the apparatus with the selected set of gas sensors. For example, in a laboratory environment, an analyte not of interest can be nitrogen, solvent, e.g., isopropyl alcohol, or carbon dioxide. In another example, in an agricultural setting, analytes not of interest can be carbon dioxide, equipment exhaust, e.g., vehicle exhaust, or gasoline.
In some implementations, providing the test gas of known analytes to the multiple gas sensors 108 includes providing the test gas in a controlled environment including a particular temperature and a particular relative humidity. An environmental regulator, e.g., environmental regulator 106 including heat transfer fins, can be used to alter a temperature and/or relative humidity of the test gas prior to the test gas reaching the multiple gas sensors 108. Temperature and humidity of the test gas can be regulated, for example, to room temperature and a relative humidity below dew point.
In some implementations, providing the test gas to the gas sensors 108 includes providing the test gas at a flow rate of less than 5 cubic feet per hour for a known period of time. A flow rate can be, for example, as low as 1 cubic centimeter per minute. In another example, a flow rate can be up to 2 liters per minute. A particular flow rate can be selected based in part on a desired temperature and/or relative humidity of sampling and a temperature and/or relative humidity of the test gas prior to entering the gas inlet 110. In other words, an amount of time required to regulate the temperature and/or relative humidity of the test gas prior to exposure to the gas sensors 108 by the environmental regulator 106, e.g., heating fins, can determine a flow rate of the test gas within the environmental regulator 106.
A data processing apparatus collects data generated by each gas sensor of the multiple gas sensors in response to the test gas, where the generated data from each gas sensor of the multiple gas sensors includes a response of the gas sensor to one or more analytes of the multiple known analytes in the test gas (506). Data processing apparatus 116 can collect response data 118, annotation data 120, and composition data 122 for each gas sensor 108 in response to the test gas.
In some implementations, response data includes a response of the gas sensor 108 over a period of time. In one example, response data includes a measure of a change in electrical resistivity of the gas sensor over time. Each of a first type of gas sensor and the second type of gas sensor can have a different response to the multiple known analytes of the test gas. For example, an organic-type gas sensor may react to an organic analyte, e.g., methane, in the test gas and not react to an inorganic analyte, e.g., tin oxide, in the test gas, and an inorganic-type gas sensor may not react to the organic analyte in the test gas and react to the inorganic analyte in the test gas.
As described above with reference to
Referring back to
In some implementations, the proper subset of gas sensors 108 can include sensors that are responsive to at least one of the multiple analytes of the test gas. Each of the gas sensors of the proper subset of gas sensors 108 can be responsive to one or more of the multiple analytes of the test gas. For example, gas sensor 108a can be responsive to a first analyte, gas sensor 108c can be responsive to the first analyte and a second analyte, gas sensors 108d and 108h can both be responsive to the second analyte and a third analyte.
In some implementations, the proper subset of gas sensors 108 can include sensors that are unresponsive to one or more of the multiple analytes of the test gas. Sensors that are unresponsive may have zero response to a particular analyte or can have a response to an analyte below a threshold responsivity, e.g., Rt as discussed with reference to
A proper subset is selected including selecting a particular gas sensor from the multiple gas sensors based on the response of the particular sensor to the one or more analytes in the multiple known analytes in the test gas meeting a threshold response (510). As described with reference to
In some implementations, the subset of gas sensors that each meet the threshold response can include meeting a threshold reactivity to one or more analytes of the multiple analytes in the test gas. In one example, the threshold reactivity includes a threshold change in resistivity of the gas sensor in response to the one or more analytes, e.g., for a MOx sensor. In another example, the threshold reactivity includes a threshold oxidation or reduction of the one or more analytes at an electrode of the sensor, e.g., for an electrochemical sensor.
In some implementations, the threshold reactivity can be defined by a change of at least 0.1% relative to a total response range of a particular gas sensor. The threshold reactivity can be defined in part by what is considered a standard detectable signal for the particular gas sensor and can be different depending on the total response range of the particular gas sensor, e.g., can be different between a MOx sensor and an electrochemical sensor.
Selecting a proper subset further includes determining to include the selected gas sensor in the proper subset of gas sensors based on the selected gas sensor meeting a threshold temporal response (512). A threshold temporal response can include an amount of time between exposure of a particular gas sensor to the test gas and the particular gas sensor reaching the threshold response Rt. In one example, as depicted in
As depicted in
In some implementations, a first gas sensor and a second gas sensor can be selected for inclusion in the proper subset of gas sensors based on the respective responses of the gas sensors, where the first gas sensor and the second gas sensor are determined to have similar responses to a same set of analytes of the plurality of analytes, e.g., both meet a threshold response Rt within a period P1. A first temporal response of the first gas sensor can be determined to be less than a second temporal response of the second gas sensors, where only the first gas sensor is then determined to be utilized in the subset of gas sensors. In one example, as depicted in
In some implementations, a threshold temporal response and threshold response for selecting the proper set of sensors depends in part on the subset of sensors of the multiple sensors that have non-zero responsivity to the test gas. For a test gas with a very low concentration of analytes of interest and/or analytes that are relatively difficult to detect, the subset of sensors that have a non-zero sensitivity to the test gas may have a low sensitivity and long temporal response, e.g., sampling time to reach threshold response Rt is relatively long. In this example, the threshold temporal response may be set to a relatively long period of time and threshold response may be set to a relatively low response R. In contrast, for a test gas with high concentrations of analytes of interest and/or analytes that are relatively simple to detect, the subset of sensors that have a non-zero sensitivity may be a large subset of the multiple gas sensors, where at least some of the subset of gas sensors have high sensitivity to the test gas and quick temporal response, e.g., sampling time to reach threshold response Rt is relatively short. In this example, the threshold response may be set to a relatively short period of time and threshold response may be set to a relatively high response R.
In some implementations, the proper subset of gas sensors is selected by selecting an optimized set of gas sensors that collectively have a best response to the multiple analytes of the test gas. Each individual gas sensor may be nonresponsive to one or more analytes of the multiple analytes, but overall can have a best response to a particular analyte of the multiple analytes.
In some implementations, selecting the proper subset of gas sensors depends in part on cost considerations, reliability of each type of gas sensors of the gas sensing array, and a repeatability requirement of the particular environment and application. In one example, an environment having safety critical aspects, e.g., toxic gases, redundancy of sensing can factor into a selection of the proper subset of gas sensors, where multiple sensors having a same sensor response can be selected.
In some implementations, selecting the proper subset of gas sensors includes selecting only the gas sensors of the proper subset of gas sensors to be included in a gas sensing apparatus, e.g., gas sensing apparatus 402, that is deployed in a particular test environment, e.g., a factory setting. Fewer than the total available number of gas sensors, e.g., the multi-modal array of gas sensors discussed in
In some implementations, selecting the proper subset of gas sensors includes collected data from only the gas sensors of the proper subset of gas sensors for a gas sensing apparatus, e.g., gas sensing apparatus 402, that is deployed in a particular test environment, e.g., a factory setting. The total available number of gas sensors, e.g., the multi-modal array of gas sensors 108 discussed in
Training data can be generated using system 100 and provided to train a machine-learned model which can then be deployed in a test environment to detect one or more analytes. Multiple sets of training data can be generated, where each set of training data can be customized for a particular test environment, e.g., a factory environment, an agricultural environment, a home environment, etc., such that the machine-learned model is trained to recognize a set of analytes that are relevant to the environment, e.g., process gases to a fabrication environment, and of importance to the particular environment, e.g., detecting toxic chemicals rather than inert chemicals. The process 600 described with reference to
Training data is generated for multiple test gases, where each test gas includes multiple analytes and is representative of a first environment (602). The first environment, e.g., an industrial environment, an agricultural environment, a residential environment, etc., can have a particular set of relevant analytes of interest and analytes not of interest, depending on the particulars of the environment. The test gases each include a particular composition of the set of multiple analytes, where the particular composition can include known concentrations of a subset of analytes of interest and analytes not of interest.
Training data is generated for each test gas including exposing the multi-modal gas sensor array including multiple gas sensors to the test gas at a first set of operating conditions, where the multiple gas sensors include a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor (604). The first set of operating conditions includes a first concentration of a first analyte of interest of the multiple analytes and a second concentration of a second analyte not of interest of the multiple analytes. For example, the first concentration of the first analyte that is of interest is 0.2% of coffee and the second concentration of the second analyte that is not of interest is 1% of ethanol, where the first analyte and the second analyte are mixed with an inert gas, e.g., nitrogen, in the test gas.
Exposing the multi-modal gas sensor array includes flowing the test gas from the gas manifold 126 through gas inlet 110 into the environmental regulator 106. Within the environmental regulator 106, e.g., heating/cooling fins, the test gas is regulated to a particular temperature, e.g., as measured by temperature gauge 128 and monitored by environmental controller 124. The test gas is then provided to the array of gas sensors 108 and exhausted through gas outlet 112. A flow of the test gas within house 104 can be controlled by the environmental controller 124 and regulated in part using regulatory components of the gas manifold 126, e.g., flow meter 214, pressure regulator 202, by a negative pressure generated by fan 114 within the housing 104, or a combination thereof
For each test gas, a first set of sample data including response data for each of the multiple gas sensors responsive to the test gas at the first set of operating conditions is collected by a data processing apparatus and from each of the multiple gas sensors (606). Response data 118 can be collected by data processing apparatus 116 from each of the multiple gas sensors 108 of the gas sensing apparatus 102.
The response data 118 can include multiple different formats of responses depending in part on a type of gas sensor 108 of the multiple different types of gas sensors. Formats of response data can include optical response data, e.g., from PID sensors, electrical resistivity data, e.g., from MOx sensors, and oxidation/reduction response data, e.g., from electrochemical sensors. In some implementations, the response data 118 includes a measure of electrical resistivity of the gas sensor over a period of time during the exposure to the test gas, e.g., for MOx gas sensors.
Additionally, annotation data 120, e.g., timestamps, temperature/humidity data, etc., delineating when the test gas is exposed to the gas sensors 108 can be recorded, e.g., when the test gas is provided from the environmental regulator 106 to the array of gas sensors 108. Further details of annotation data 120 are described in further detail with reference to
Composition data 122 describing a particular composition of the test gas including the respective concentrations of one or more analytes of interest and one or more analytes not of interest in the test gas can be recorded for each test gas. The known analytes of interest and known analytes not of interest can be associated with the response data 118 generated by each gas sensor of the multi-modal array of gas sensors 108.
In some implementations, the known concentrations of known analytes can be used to label the response data 118, e.g., response outputs, of the multiple gas sensors. The labeled response data 118 can be provided to a machine-learned model for training. The labeled response data 118 can be processed, e.g., calibrated to a standard baseline, to ensure consistency between different sensing apparatuses 102 having a same design.
A subset of gas sensors from the multiple gas sensors is selected from the first set of sample data for the test gas, where the response data collected for each gas sensor of the subset of gas sensors meets a threshold response (608). As described above with reference to
In some implementations the multiple gas sensors are 38 or more gas sensors in the multi-modal array of gas sensors 108, where the subset of gas sensors includes fewer than all of the total number of gas sensors in the gas sensing apparatus 102. For example, the total number of gas sensors in the gas sensing apparatus 102 is 30 gas sensors of multiple different types, e.g., MOx sensors, PID sensors, electrochemical sensors, and the subset of selected gas sensors are 15 gas sensors, 20 gas sensors, or 8 gas sensors. The subset of gas sensors that is selected represents an optimized subset of the total available gas sensors in the gas sensing apparatus 102 for responding to the particular test gas under the first set of operating conditions.
The multiple gas sensors of the multi-modal gas sensing array are exposed to the test gas under a second set of operating conditions (610). The second set of operating conditions can differ from the first set of operating conditions based on sampling conditions, e.g., temperature/relative humidity, relative concentrations of the analytes in the test gas, and/or the set of one or more analytes of interest and analytes not of interest included in the test gas.
In some implementations, the second set of operating conditions includes a first concentration of a second analyte of interest of the multiple analytes and the second concentration of the analyte not of interest of the multiple analytes. Continuing the example from above, the second set of operating conditions can include a first concentration of a second analyte of interest, e.g., 0.1% vanilla, combined with 1% of ethanol as the analyte not of interest and mixed with the inert gas, e.g., nitrogen.
In some implementations, the second set of operating conditions includes a third concentration of the analyte of interest of the multiple analytes and a fourth concentration of the analytes not of interest of the multiple analytes, where the third concentration is different than the first concentration. Continuing the example from above, the third concentration of the analyte of interest can be 0.5% coffee and the fourth concentration of the analyte not of interest can be 5% of ethanol, where the two analytes are then mixed with an inert gas, e.g., nitrogen, to generate the test gas.
In another example, the third concentration of the analyte of interest can be 0.5% coffee and the fourth concentration of the analyte not of interest can be the same as the second concentration, e.g., 1% of ethanol, where the two analytes are then mixed with a purge gas, e.g., nitrogen, to generate the test gas.
In some implementations, prior to exposing the gas sensors to the test gas under the second set of operating conditions, the multiple gas sensors 108 are exposed to a purge gas, e.g., nitrogen or compressed clean dry air, for a period of time. The purge gas, e.g., gas source 130a depicted in
In some implementations, the period of time of exposure of the multiple gas sensors 108 to the purge gas can be an amount of time for each of the sensors to reach a baseline resistivity reading. The period of time of exposure can be selected based on a longest recovery time of all the respective recovery times for the multiple gas sensors 108. The period of time exposure of the multiple gas sensors 108 to the purge gas can be, for example, 1 minute, 5 minutes, 30 seconds, or the like.
A second set of sample data including response data is collected by the data processing apparatus and from each of the subset of gas sensors responsive to the test gas under the second set of operating conditions (612). Response data 118 can be collected by data processing apparatus 116 from only the selected subset of gas sensors 108 of the multiple gas sensors of the gas sensing apparatus 102 that were selected as responsive to the test gas under the first set of operating conditions in Step 608. The gas sensors 108 of the multiple gas sensors 108 not selected for inclusion in the subset of gas sensors 108 can be, for example, switched “off” during the sampling process. In another example, the gas sensors 108 not selected for inclusion in the subset of gas sensors 108 can be active, e.g., “on”, during the sampling process but the data generated by those particular gas sensors may not be collected by the data processing apparatus 116.
Training data is generated for the test gas representative of the first environment from the first set of sample data and the second set of sample data from each of the subset of gas sensors (614). In some implementations, training data is generated by recording the captured response for each sensor to the test gas, an amount of time that the response was captured, e.g., a “sniff” time, as well as an amount of time that a baseline measurement of no gas exposure was recorded prior to exposure of the test gas and an amount of time that the response was captured after termination of the exposure to the test gas. In other words, training data includes sensor response as well as labeled timestamps denoting baseline measurement, exposure to test gas, and recovery measurement, e.g., as described with reference to
The process described with reference to Steps 602-614 can be repeated for multiple test gases, where each test gas is representative of the test environment. The multiple test gases can be selected based on a range of compositions and/or environmental conditions, e.g., temperature, over which the response of the gas sensors 108 are measured to generate the training data.
The training data is provided to a machine-learned model (616). A machine-learned model can be trained for each intended test environment using a particular set of test gases and environmental conditions. For example, a machine-learned model for an industrial applications can be different that a machine-learned model for a food production facility, where both sets of training data are generated using system 100 described with reference to
In some implementations, training the machine-learned model using the training data can further include providing, to the subset of gas sensors, a particular test gas of the multiple test gasses under a third set of operating conditions. A third set of sample data is collected from each of the subset of gas sensors and provided to the machine-learned model. The machine-learned model determines characteristics descriptive of the particular test gas under the third set of operating conditions, e.g., determines the analytes included in the test gas and relative concentrations of each of the analytes included in the test gas. For example, the third set of operating conditions can include a fifth concentration of the analyte of interest, e.g., 0.6% of coffee, and a sixth concentration of the analyte not of interest, e.g., 1% of ethanol, where the two analytes are mixed with an inert gas, e.g., nitrogen, to generate the test gas under the third set of operating conditions.
In some implementations, the operating conditions of the first set of operating conditions, the second set of operating conditions, and the third set of operating conditions are defined as a particular temperature and relative humidity of the test gas and the gas sensors 108 when the test gas is exposed to the subset of gas sensors 108. For example, the first set of operating conditions can be 20 degrees C. at 10% humidity, the second set of operating conditions can be 40 degrees C. at 33% humidity, and the third set of operating conditions can be 35 degrees C. at 33% humidity.
In some implementations, determining characteristics descriptive of the test gas under the third set of operating conditions includes identifying, by the machine-learned model, a first concentration of a first analyte in the test gas under the third set of operating conditions.
A footprint, e.g., width and length, of the gas sensing apparatus 700 can be, for example, smaller than 2×4 inches, smaller than 10×12 inches, smaller than 20×20 inches, or the like. Though depicted in
Gas sensing apparatus 720 includes a gas inlet 726, e.g., gas inlet 110, to receive an unknown gas mixture, e.g., unknown gas mixture 404, and provide the unknown gas mixture to a multi-modal gas sensing array 724. Gas sensing apparatus 720 can route the unknown gas mixture from the gas inlet 726 via internal tubing and/or fins to regulate the flow of the unknown gas mixture across the gas sensors 724. In some implementations, the gas sensing apparatus 720 includes heating fins or another temperature control component, e.g., as described with reference to the environmental regulator 106 in
In some implementations, gas sensing apparatus 720 includes a display 728
The memory 820 stores information within the system 800. In one implementation, the memory 820 is a computer-readable medium. In one implementation, the memory 820 is a volatile memory unit. In another implementation, the memory 820 is a non-volatile memory unit.
The storage device 830 is capable of providing mass storage for the system 800. In one implementation, the storage device 830 is a computer-readable medium. In various different implementations, the storage device 830 can include, for example, a hard disk device, an optical disk device, a storage device that is shared over a network by multiple computing devices (for example, a cloud storage device), or some other large capacity storage device.
The input/output device 840 provides input/output operations for the system 800. In one implementation, the input/output device 840 can include one or more network interface devices, for example, an Ethernet card, a serial communication device, for example, a RS-232 port, and/or a wireless interface device, for example, a 802.11 card. In another implementation, the input/output device can include driver devices configured to receive input data and send output data to other input/output devices, for example, keyboard, printer and display devices 860. Other implementations, however, can also be used, such as mobile computing devices, mobile communication devices, set-top box television client devices, etc.
Although an example processing system has been described in
This specification uses the term “configured” in connection with systems and computer program components. For a system of one or more computers to be configured to perform particular operations or actions means that the system has installed on it software, firmware, hardware, or a combination of them that in operation cause the system to perform the operations or actions. For one or more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, for example, a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.
The term “data processing apparatus” refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can also be, or further include, special purpose logic circuitry, for example, an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can optionally include, in addition to hardware, code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
A computer program, which may also be referred to or described as a program, software, a software application, an app, a module, a software module, a script, or code, can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, for example, files that store one or more modules, sub-programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a data communication network.
In this specification the term “engine” is used broadly to refer to a software-based system, subsystem, or process that is programmed to perform one or more specific functions. Generally, an engine will be implemented as one or more software modules or components, installed on one or more computers in one or more locations. In some cases, one or more computers will be dedicated to a particular engine; in other cases, multiple engines can be installed and running on the same computer or computers.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, for example, an FPGA or an ASIC, or by a combination of special purpose logic circuitry and one or more programmed computers.
Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, for example, magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, for example, a universal serial bus (USB) flash drive, to name just a few.
Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, for example, EPROM, EEPROM, and flash memory devices; magnetic disks, for example, internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, for example, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, for example, a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, for example, visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's device in response to requests received from the web browser. Also, a computer can interact with a user by sending text messages or other forms of messages to a personal device, for example, a smartphone that is running a messaging application and receiving responsive messages from the user in return.
Data processing apparatus for implementing machine learning models can also include, for example, special-purpose hardware accelerator units for processing common and compute-intensive parts of machine learning training or production, that is, inference, workloads.
Machine learning models can be implemented and deployed using a machine learning framework, for example, a TensorFlow framework, a Microsoft Cognitive Toolkit framework, an Apache Singa framework, or an Apache MXNet framework.
Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server, or that includes a front-end component, for example, a client computer having a graphical user interface, a web browser, or an app through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, for example, a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), for example, the Internet.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data, for example, an HTML page, to a user device, for example, for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, for example, a result of the user interaction, can be received at the server from the device.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any features or of what may be claimed, but rather as descriptions of features specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.
