Abstract:
An exhaust treatment system is provided including: a selective catalyst reduction (SCR) unit; a reducing agent dispensing unit configured to introduce a reducing agent into the exhaust; a first NO X  sensor upstream of the SCR unit; a second NO X  sensor at a location downstream of the SCR unit; a first temperature sensor at a location upstream of where the reducing agent is introduced into the exhaust; a second temperature sensor at a location downstream of where the reducing agent is introduced into the exhaust and upstream of the SCR unit; and a controller configured to determine a reductant quality indicator according to a NO X  differential between the first NO X  sensor and the second NO X  sensor relative to a predicted NO X  differential and a temperature differential between the first temperature sensor and the second temperature sensor relative to a predicted temperature differential.

Description:
TECHNICAL FIELD 
       [0001]    Embodiments of the present disclosure pertain to a diesel emission fluid quality detection system and method. 
       BACKGROUND 
       [0002]    Increasingly stringent government standards associated with combustion engine emissions have increased the burden on manufacturers to reduce the amount of nitrogen oxides (NO X ) and particulates that may be enitted from their developed engines. Along with this burden is the manufacturer&#39;s commitment to its customers to produce fuel efficient engines. 
         [0003]    One known type of NO X  reduction technique is selective catalytic reduction (SCR). This technique of reducing NO X  in a combustion engine generally includes the use of reductants, such as ammonia, aqueous urea, and other compounds, in conjunction with an appropriate catalyst material. 
         [0004]    In a conventional open loop control urea based SCR system, a urea pump may provide a pressurized supply of urea to an atomizer or injector, which then injects the a urea solution into the exhaust stream of a combustion engine. An SCR controller may control the rate of urea that is being applied to the atomizer. Within the exhaust stream, the urea solution may decompose into ammonia (NH 3 ) and water vapor above certain temperatures, such as 160 degrees C. When the exhaust gas mixture is passed over an SCR catalyst, the NO X  and NH 3  molecules react with the catalyst and generally produce diatomic nitrogen (N 2 ) and water (H 2 O. 
         [0005]    The ability of an SCR catalyst to reduce NO X  depends upon many factors, such as catalyst formulation, the size of the catalyst, exhaust gas temperature, and urea dosing rate. With regard to the dosing rate, the NO X  reduction efficiency tends to increase linearly until the dosing rate reaches a certain limit. Above the limit, the efficiency of the NO X  reduction may start to increase at a slower rate. One reason for the decline in the NO X  reduction efficiency is than the ammonia may be supplied at a faster rate than the NO X  reduction process can consume. The excess ammonia, known as ammonia slip, may be expelled from the SCR catalyst. 
         [0006]    In order for an optimal NO X  reduction to take place, the integrity of the reductant (e.g., urea) must be maintained. For instance, if the reductant is diluted (e.g., in water) or overly concentrated, an ideal reaction in the SCR system will not occur. Thus, to promote an optimal reaction, it is beneficial to ensure the quality of the reductant. 
         [0007]    Physical sensors are widely used in many products to measure and monitor physical phenomena, such as temperature, speed, and emissions from motor vehicles. Physical sensors often take direct measurements of the physical phenomena and convert these measurements into measurement data to be further processed by control systems. For example U.S. Pat. No. 7,216,478 describes a method of monitoring a dosing system. 
         [0008]    Although physical sensors take direct measurements of the physical phenomena, physical sensors and their associated hardware are often costly and, sometimes, unreliable. For instance, directly measuring the quality of a reductant, such as urea, with physical sensors in a field environment is difficult and may be unreliable. 
         [0009]    Instead of direct measurements, virtual sensors have been developed to process other various physically measured values and to produce values that were previously measured directly by physical sensors. For example, U.S. Pat. No. 5,386,373 (the &#39;373 patent) issued to Keeler et al. on Jan. 31, 1995, discloses a virtual continuous emission monitoring system with sensor validation. The &#39;373 patent uses a back propagation-to-activation model and a Monte Carlo search technique to establish and optimize a computational model used for the virtual sensing system to derive sensing parameters from other measured parameters. 
       SUMMARY 
       [0010]    According to aspects disclosed herein, a system and method are provided to detect the quality of a reductant according to sensor differentials. 
         [0011]    According to an aspect of an embodiment herein, an exhaust treatment system for treating a flow of exhaust produced by an engine is disclosed. The exhaust treatment system for treating a flow of exhaust produced by an engine includes: a selective catalyst reduction (SCR) unit; a reducing agent dispensing unit configured to introduce a reducing agent into the exhaust; a first NO X  sensor configured to indicate a NO X  emission level of the exhaust at a location upstream of the SCR unit; a second NO X  sensor configured to indicate a NO X  emission level of the exhaust at a location downstream of the SCR unit; a first temperature sensor configured to indicate a temperature of the exhaust at a location upstream of where the reducing agent is introduced into the exhaust; a second temperature sensor configured to indicate a temperature of the exhaust at a location downstream of where the reducing agent is introduced into the exhaust and upstream of the SCR unit; and a controller configured to electronically communicate with the first NO X  sensor, the second NO X  sensor, the first temperature sensor, and the second temperature sensor, and to determine a reductant quality indicator according to a NO X  differential between the first NO X  sensor and the second NO X  sensor relative to a predicted NO X  differential and a temperature differential between the first temperature sensor and the second temperature sensor relative to a predicted temperature differential. 
         [0012]    According to an aspect of an embodiment herein, method for detecting a reducing agent quality is disclosed. The method for detecting a reducing agent quality includes: obtaining a first NO X  value indicating a NO X  level for an engine exhaust upstream of a selective catalyst reduction (SCR) unit; obtaining a second NO X  value indicating a NO X  emission level for the engine exhaust downstream of the SCR unit; obtaining a first temperature value indicating a temperature for the engine exhaust upstream of an introduction of a reducing agent; obtaining a second temperature value indicating a temperature for the engine exhaust downstream of the introduction of the reducing agent and upstream of the SCR unit; and computing a reductant quality indicator according to a NO X  differential between the first NO X  value and the second NO X  value relative to a predicted NO X  differential and a temperature differential between the first temperature value and the second temperature value relative to a predicted temperature differential. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]      FIG. 1  illustrates an exemplary machine according to a embodiment described herein; 
           [0014]      FIG. 2  is a block diagram of a reductant quality detection system in an after-treatment system according to an embodiment herein; 
           [0015]      FIG. 3  is a block diagram of a method of detecting reductant quality according to an embodiment herein. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]    Exemplary embodiments of the present invention are presented herein with reference to the accompanying drawings. Herein, like numerals designate like parts throughout. 
         [0017]      FIG. 1  illustrates an exemplary machine  100  according to a embodiment described herein. The machine  100  may refer to any type of stationary or mobile machine that performs some type of operation associated with a particular industry. The machine  100  may also include any type of commercial vehicle, such as cars, trucks, vans, boats, ships, and other vehicles or machines, such as power generators and stationary gas compressors. 
         [0018]      FIG. 2  is a block diagram of a reductant quality detection system in an after-treatment system according to an embodiment herein. According to  FIGS. 1 and 2 , a machine  100  may include an exhaust treatment system  200 . The exhaust treatment system  200  may include: an engine  102 , a selective catalytic reduction (SCR) unit  108 , a reductant system  106  (e.g., a urea reservoir/tank, a pump, and injection components), and sensor network  104 . 
         [0019]    The engine  102  generates an exhaust stream that is transmitted to the SCR unit  108 . Before reaching the SCR the exhaust may be optionally routed through one or more after treatment elements, e.g., a diesel particulate filter (DPF) configured to reduce the amount of particulates in the exhaust. By passing the exhaust through a DPF, prior to the SCR unit  108 , particulates in the exhaust may be removed. Removing particulates from exhaust prior to use of a physical NO X  sensor may increase the operational life of the sensor. 
         [0020]    The reductant system  106  is for holding and injecting a reductant, such as urea, ammonia, or any other reductant according to the specific SCR system. 
         [0021]    According to an embodiment herein, the reductant system  106  is configured to supply a urea reductant to the SCR unit  108  for reducing the exhaust NO X . For instance, the urea from the reductant system  106  may be combined with the exhaust from the engine  102  upstream of the SCR unit  108  in order to mix with the exhaust prior to entering the SCR unit  108 . 
         [0022]    The SCR unit  108  receives the exhaust from the engine  102 , and receives a reducing agent (also referred to as reductant) from the reductant system  106 . The SCR unit  108  and reductant unit  106  are configured to reduce the NO X  emission of the engine exhaust by using SCR unit  108 . 
         [0023]    The sensor network  104  may include a first NO X  sensor  202  for indicating a NO X  level of the exhaust prior to the SCR unit  108 ; a second NO X  sensor  206  for indicating a NO X  level of the exhaust after the SCR unit  108 ; a first temperature sensor  204  for indicating a temperature (e.g., a pre-urea injection temperature) of the exhaust prior to the SCR unit  108 ; a second temperature sensor  208  for indicating a temperature (e.g., a post-urea injection temperature) of the exhaust prior to the SCR unit  108 , but after the reducing agent has mixed with the exhaust from the engine  102 ; and a controller  210  configured to electronically communicate with the first NO X  sensor  202 , the second NO X  sensor  206 , the first temperature sensor  204 , and the second temperature sensor  208 , and to determine a quality indicator according to the differential between the first NO X  sensor  202  and the second NO X  sensor  206  and between the first temperature sensor  204  and the second temperature sensor  208 . Sensors  202 - 208  are electronically coupled to controller  210  and may be physical or virtual sensors. 
         [0024]    The controller  210  is configured to send or receive information to or from the sensors ( 202 - 208 ), and may be configured to send or receive information to or from other additional sensors. For instance the controller  210  may receive information from physical sensors (e.g., exhaust and/or reductant flow rate sensors, NO X  sensors, engine sensors, ambient condition sensors, etc.), or may generate or utilize preconfigured virtual sensors (e.g., a virtual NO X  sensor, a virtual urea sensor, etc.) at various points in the system. 
         [0025]    The controller  210  may be a processing system that monitors and controls operation of the machine  100 . Controller  210  may be configured to collect information from various sensors operating within the machine  100  and to provide control signals that affect the operations of devices within the machine  100 . In one embodiment of the present invention, the controller  210  may be part of an engine control module (ECM) that monitors and controls the operation of an engine  102  associated with machine  100 . For example, the controller  210  may be a module programmed or hardwired within an ECM that performs functions dedicated to certain embodiments described herein. For example, the controller  210  may be implemented in software that is stored as instructions and data within a memory device of an ECM and is executed by a processor operating within the ECM. Alternatively, the controller  210  may be a module that is separate from other components of the system, and may be in electronic communication with other components of the system. 
         [0026]    Controller  210  may include a processor, memory, and an interface. The processor may be a processing device, such as a microcontroller, that may exchange data with the memory and interface to perform certain processes consistent with features described herein. One skilled in the art would recognize that the controller  210  may include a plurality of processors that may operate collectively to perform functions consistent with certain embodiments presented herein. 
         [0027]    The controller may also be configured to interact with a plurality of sensors in addition to those shown in  FIGS. 1 and 2 . These sensors may include a combination of one or more physical and/or virtual sensors. For example, the sensors may include one or more physical sensors provided for measuring certain parameters of machine operating environment, such as physical sensors for measuring emissions of machine  100 , such as Nitrogen Oxides (NO X ), Sulfur Dioxide (SO 2 ), Carbon Monoxide (CO), total reduced Sulfur (TRS), etc. Physical sensors may include any appropriate sensors that are used with engine  102  or other machine components to provide various measured parameters about engine  102  or other components, such as temperature, speed, acceleration rate, fuel pressure, power output, etc. 
         [0028]    According to one embodiment, NO X  sensor  202  is a physical sensor which may be used by the controller  210  to predict a NO X  emission value. The sensor  202  may be a single sensor or may reflect a combination of sensors for detecting parameters such as ambient humidity, manifold pressure, manifold temperature, fuel rate, and engine speed associated with the engine. Additionally, a first NO X  sensor  202  may be a physical NO X  sensor located upstream of the SCR unit  108  or may be a virtual NO X  sensor generated by the controller  210  based on variables such as those provided by the other sensors. A second NO X  sensor  206  may be a physical NO X  sensor located downstream of the SCR unit  108  or may be a virtual NO X  sensor generated by the controller  210  based on variables such as those provided by the other sensors. 
         [0029]    The controller  210  may register variables such as temperature or time-of-last-fill of the reductant system  106  to help determine a cause of the deviation from the anticipated NO X  values. One or both of the first and second NO X  sensors  204 ,  206  may be a virtual sensor. 
         [0030]    Furthermore, during a steady state operation of the engine, the temperature sensor measurement may vary according to the diesel emission fluid (DEF) injection amount. Therefore, by comparing the differential between the first temperature sensor  204  and the second temperature sensor  208  to a predicted value, the quality of the DEF fluid and/or the presence of significant deposits in the system may be determined. For instance, a significant deviation from the predicted temperature differential is indicative that a non-standard DEF fluid (e.g., diluted DEF fluid, diesel, or water, etc.) is being used or that high levels of urea deposits have formed in the system. 
         [0031]    The controller  210  may be further configured to generate a signal when the quality index of the reducing agent indicated by the controller  210  is not within a tolerance level (e.g., a predefined tolerance level). For example, the controller  210  may be configured to trigger a warning light or adjust the flow rate of the reductant. For instance, if the NO X  reduction is less than expected, the controller  210  may generate a signal to increase the amount of reductant to send to the SCR unit  108 . 
         [0032]    If the temperature drop is low and the NO X  reduction is also low, the controller  210  may generate a signal indicating that a clogged injector is likely or that deposits are being formed. And if there is no temperature drop and no NO X  reduction then the controller  210  may generate a signal indicating that injector failure is likely. 
         [0033]    Additionally, if the temperature differential is zero or and/or slightly increasing and there is a moderate, but less than anticipated NO X  reduction, than the controller  210  may generate a signal indicating that the DEF tank may be filled with diesel fluid or another fluid (e.g., a non-urea fluid). 
         [0034]    Additionally, the exhaust treatment system  200 , the first NO X  sensor  202  may further indicates a NO X  level for the engine exhaust after treatment by a filter. The filter may be a diesel particulate filter (DPF). 
         [0035]      FIG. 3  is a block diagram of a method of detecting reductant quality according to an embodiment herein. According to  FIG. 3 , a method for detecting a reducing agent (e.g., urea or urea mixture) quality  300  includes an obtaining a pre-injection (e.g., pre-urea injection) temperature step  302 , an obtaining a pre-SCR NO X  value step  304 , an obtaining a post-injection (e.g., post-urea injection) temperature step  306 , an obtaining a post-SCR NO X  value step  308 , a computing a change in temperature step  310 , a computing a change in NO X  step  312 , an evaluating change in temperature and NO X  step  314 , a computing the reductant quality step  316  (also referred to as a predicting DEF status step  316 ). Optionally, the method  300  may also include a generating a warning step  318 . The warning step  318  may further include, but is not limited to, triggering a warning light or adjusting the flow rate of the reductant. For instance, if the NO X  reduction is less than expected, the controller may generate a signal to increase the amount of reductant to send to the SCR unit  108 . 
         [0036]    During the evaluating change in temperature and NO X  step  314 , a differential between the pre-SCR NO X  value obtained in step  304  and the post-SCR NO X  obtained in step  308  is compared against a predicted NO X  differential. Additionally, during step  314   a  differential between the pre-injection temperature value obtained in step  302  and the post-injection temperature value obtained in step  306  is compared against a predicted temperature differential. 
         [0037]    The obtaining a pre-SCR NO X  value step  304  includes determining the first NO X  value according to a first NO X  sensor indicating a NO X  level for engine exhaust prior to treatment by the SCR unit  108 . The obtaining a second NO X  value (a post-SCR NO X  value) step  308  includes determining a value according to a second NO X  sensor indicating a NO X  level for engine exhaust after treatment by the SCR unit  108 . 
         [0038]    The computing the reductant quality step  316  includes generating a quality indicator signal, and may also include generating a virtual urea quality sensor according to the NO X  and temperature values. 
         [0039]    According to an embodiment herein, the first NO X  sensor  202  may further indicate a NO X  level for the engine exhaust after treatment by a filter. Additionally, the method for detecting a reducing agent quality  300  may further include generating a signal when the reductant quality indicator indicated is not within a tolerance range. 
         [0040]    The controller  210  may be configured to generate a first signal indicating that the reducing agent low has a low urea concentration when the NO X  differential is lower than the predicted NO X  differential and the temperature differential is approximate to the predicted temperature differential; generate a second signal indicating likely clogged injector when the NO X  differential is lower than the predicted NO X  differential and the temperature differential is lower than the predicted temperature differential; generate a third signal indicating likely injection failure when the NO X  differential is approximately zero and the temperature differential is approximately zero; and generate a fourth signal indicating that the reducing agent is likely diesel when the NO X  differential is moderately lower than the predicted NO X  differential and the temperature differential is higher than the predicted temperature differential (e.g., the increase in temperature between the first and second temperature sensors is greater than the expected change in temperature.) 
         [0041]    A virtual sensor network (also referred to as a virtual sensor network system), as used herein, may refer to a collection of virtual sensors integrated and working together using certain control algorithms such that the collection of virtual sensors may provide more desired or more reliable sensor output parameters than discrete individual virtual sensors. A virtual sensor network system may include a plurality of virtual sensors configured or established according to certain criteria based on a particular application. Additional sensors may provide information about the ambient environmental conditions, such as humidity, air temperature, and elevation. 
         [0042]    A virtual sensor, as used herein, may refer to a mathematical algorithm or model that produces output measures comparable to a physical sensor based on inputs from other systems. For example, a physical NO X  sensor may measure the level of NO X  present in the exhaust stream of the engine  102  and provide values of the NO X  level to other components, such a controller  210 ; while a virtual NO X  sensor may provide calculated values of the NO X  level to a controller  210  based on other measured or calculated parameters, such as such as compression ratios, turbocharger efficiency, after cooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, and engine speeds, etc. The term “virtual sensor” may be used interchangeably with “virtual sensor model.” 
         [0043]    The virtual sensor network system may also facilitate or control operations of the virtual sensors. The virtual sensors may include any appropriate virtual sensor providing sensor output parameters corresponding to one or more physical sensors in machine  100 . 
         [0044]    Further, the virtual sensor network system may be configured as a separate control system or, alternatively, may coincide with other control systems such as an ECM. The virtual sensor network system may also operate in series with or in parallel to an ECM. Virtual sensor network system and/or ECM may be implemented by any appropriate computer system. Thus, the virtual sensor network system may be implemented on the controller  210 , or e.g., may be implemenedt elsewhere and communications therewith may be relayed through the controller  210 . Additionally, a computer system may also be configured to design, train, and validate virtual sensors in virtual sensor network and other components of machine  100 . 
         [0045]    A virtual sensor process model may be established to build interrelationships between physical and virtual sensors. After the virtual sensor process model is established, values of input parameters may be provided to the virtual sensor process model (e.g., the controller  210 ) to generate values of output parameters based on the given values of input parameters and the interrelationships between input parameters and output parameters established by the virtual sensor process model. 
         [0046]    In certain embodiments, the virtual sensor system may include a NO X  virtual sensor to provide levels of NO X  emitted from an engine  102 , and a virtual reductant sensor to provide a quality level (or quality index) of the reductant stored in the reductant system  106  and transmitted to the SCR unit  108 . Input parameters may include any appropriate type of data associated with NO X  levels. For example, input parameters may include parameters that control operations of various response characteristics of engine  102  and/or parameters that are associated with conditions corresponding to the operations of engine  102 . For instance, input parameters may include fuel injection timing, compression ratios, turbocharger efficiency, after cooler characteristics, temperature values (e.g., intake manifold temperature), pressure values (e.g., intake manifold pressure), ambient conditions (e.g., ambient humidity), fuel rates, and engine speeds, etc. Other parameters, however, may also be included. For example, parameters originated from other vehicle systems, such as chosen transmission gear, axle ratio, elevation and/or inclination of the vehicle, etc., may also be included. Further, input parameters may be measured by certain physical sensors, or created by other control systems such as an ECM. 
         [0047]    A virtual sensor process model may include any appropriate type of mathematical or physical model indicating interrelationships between input parameters and output parameters. For example, the virtual sensor process model may be a neural network based mathematical model that is trained to capture interrelationships between input parameters and output parameters. Other types of mathematic models, such as fuzzy logic models, linear system models, and/or non-linear system models, etc., may also be used. Virtual sensor process model may be trained and validated using data records collected from a particular engine application for which virtual sensor process model is established. That is, the virtual sensor process model may be established according to particular rules corresponding to a particular type of model using the data records, and the interrelationships of virtual sensor process model may be verified by using part of the data records. 
         [0048]    After the virtual sensor process model is trained and validated, virtual sensor process model may be optimized to define a desired input space of input parameters and/or a desired distribution of output parameters. The validated or optimized virtual sensor process model may be used to produce corresponding values of output parameters when provided with a set of values of input parameters. 
         [0049]    Thus, a controller  210  may be configured to generate or to utilize a preconfigured virtual sensor model to determine predicted NO X  values based on a model reflecting a predetermined relationship between control parameters and NO X  emissions, wherein the control parameters include ambient humidity, manifold pressure, manifold temperature, fuel rate, and engine speed associated with the engine. Additional sensors may provide information about the ambient environmental conditions, such as humidity, air temperature, and elevation. Additionally, the virtual sensor network can utilize additional sensors for detecting the flow rate of the exhaust through the SCR and the flow rate of the reductant through the SCR. 
         [0050]    If the controller  210  (or the ECM or processor operating the virtual network) determines that any individual input parameter or output parameter is out of the respective range of the input space or output space, the controller may send out a notification to other computer programs, control systems, or a user of machine  100 . 
         [0051]    Optionally, controller  210  (or the ECM or processor operating the virtual network) may also apply any appropriate algorithm to maintain the values of input parameters or output parameters in the valid range to maintain operation with a reduced capacity. For instance, reducing the engine speed to reduce the flow rate of the exhaust, or increase the flow rate of the reductant in order to increase the reduction of NO X . 
         [0052]    The controller  210  (or the ECM or processor operating the virtual network) may also determine collectively whether the values of input parameters are within a valid range. For example, a processor may use a Mahalanobis distance to determine normal operational condition of collections of input values. 
         [0053]    During training and optimizing of virtual sensor models, a valid Mahalanobis distance range for the input space may be calculated and stored as calibration data associated with individual virtual sensor models. In operation, a processor may calculate a Mahalanobis distance for input parameters of a particular virtual sensor model as a validity metric of the valid range of the particular virtual sensor model. If the calculated Mahalanobis distance exceeds the range of the valid Mahalanobis distance range stored in the virtual sensor network, the controller  210  may send out a notification to other computer programs, control systems, or a user of machine  100  to indicate that the particular virtual sensor may be unfit to provide predicted values. 
         [0054]    Other validity metrics may also be used. For example, a processor may evaluate each input parameter against an established upper and lower bounds of acceptable input parameter values and may perform a logical AND operation on a collection of evaluated input parameters to obtain an overall validity metric of the virtual sensor model. 
         [0055]    After monitoring and controlling individual virtual sensors, the controller  210  (e.g., virtual sensor network processor) may also monitor and control collectively a plurality of virtual sensor models. That is, the controller  210  may determine and control operational fitness of the virtual sensor network. A processor may monitor any operational virtual sensor model. The processor may also determine whether there is any interdependency among any operational virtual sensor models including the virtual sensor models becoming operational. If the controller  210  determines there is interdependency between any virtual sensor models, the controller  210  may determine that the interdependency between the virtual sensors may have created a closed loop to connect two or more virtual sensor models together, which may be neither intended nor tested. 
         [0056]    The controller  210  may then determine that the virtual sensor network may be unfit to make predictions, and may send a notification or report to control systems, such as ECM, or users of the machine  100 . That is, the controller (e.g., a processor) may present other control systems or users with the undesired condition via a sensor output interface. Alternatively, the controller may indicate as unfit only the interdependent virtual sensors, while keeping the remaining virtual sensors in operation. 
         [0057]    As used herein, a decision that a virtual sensor or a virtual sensor network is unfit is intended to include any instance in which any input parameter to or any output parameter from the virtual sensor or the virtual sensor network is beyond a valid range or is uncertain, or any operational condition that affects the predictability and/or stability of the virtual sensor or the virtual sensor network. An unfit virtual sensor network may continue to provide sensing data to other control systems using virtual sensors not affected by the unfit condition, such as interdependency, etc. 
         [0058]    The controller  210  may also resolve unfit conditions resulting from unwanted interdependencies between active virtual sensor models by deactivating one or more models of lower priority than those remaining active virtual sensor models. 
         [0059]    For instance, if a first active virtual sensor model has a high priority for operation of machine  100  but has an unresolved interdependency with a second active virtual sensor having a low priority for operation of machine  100 , the second virtual sensor model may be deactivated to preserve the integrity of the first active virtual sensor model. 
       INDUSTRIAL APPLICABILITY 
       [0060]    The disclosed reductant quality sensing system may be implemented in an exhaust after-treatment system in various machines. A reductant quality sensing system provides for enhanced reliability of the NO X  reduction process by verifying the integrity of the reductant and/or an indication as to the proper operation of the system that adds reductant to the exhaust stream (e.g., a prediction of the status of urea injectors). 
         [0061]    Although certain embodiments have been illustrated and described herein for purposes of description, it will be appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent embodiments or implementations calculated to achieve the same purposes may be substituted for the embodiments shown and described without departing from the scope of the present disclosure. Those with skill in the art will readily appreciate that embodiments in accordance with the present invention may be implemented in a very wide variety of ways. This application is intended to cover any adaptations or variations of the embodiments discussed herein. Therefore, it is intended that embodiments in accordance with the present invention be limited only by the claims and the equivalents thereof.