WO2025072877A1

WO2025072877A1 - Systemsand methods for optimizing catalyst sizing and washcoat composition

Info

Publication number: WO2025072877A1
Application number: PCT/US2024/049129
Authority: WO
Inventors: Rohil DAYA; Anand Srinivasan; Karthik Venkata Rama Krishna DADI; Michael J. Cunningham; Arvind Suresh; Yuhui Zha
Original assignee: Cummins Inc
Current assignee: Cummins Inc
Priority date: 2023-09-29
Filing date: 2024-09-27
Publication date: 2025-04-03
Anticipated expiration: 2026-03-29

Abstract

Systems and methods disclosed estimate the degradation of a NOx reduction catalyst or other catalyst member as a function of real-world exposure to chemical contaminants and temperature. A system includes at least one processing circuit comprising at least one memory coupled to at least one processor, the at least one memory storing instructions therein that, when executed by the at least one processor, causes the at least one processor to perform operations. The operations include receiving at least one boundary condition indicative of a duty-cycle of an exhaust aftertreatment system, receiving at least one value indicative of an operating parameter of the exhaust aftertreatment system from at least one sensor, predicting an output value associated with an operation of a catalyst of the exhaust aftertreatment system over the duty-cycle, and determining, based on the output value, a reductant dosing value for the exhaust aftertreatment system.

Description

SYSTEMSAND METHODS FOR OPTIMIZING CATALYST SIZING AND WASHCOAT COMPOSITION

CROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/541,583, filed September 29, 2023, which is incorporated herein by reference in its entirety and for all purposes.

TECHNICAL FIELD

[0002] The present disclosure relates generally to the field of estimating and modeling, and particularly to estimating and modeling the degradation and performance of exhaust aftertreatment systems. More particularly, the present disclosure relates to systems, apparatuses, and methods for predicting or determining chemical contaminant accumulation on exhaust aftertreatment system active sites and using such predictions or determinations to improve operating parameters and to design catalysts, aftertreatment system components and/or sub-systems, and wash coat compositions to accommodate a variety of real-world applications and duty cycles.

BACKGROUND

[0003] Emissions regulations for internal combustion engines have become more stringent over recent years. Environmental concerns have motivated the implementation of stricter emission requirements for internal combustion engines throughout much of the world. Governmental agencies, such as the Environmental Protection Agency (EP A) or California Air Resources Board (CARB) in the United States, carefully monitor the emission quality of engines and set emission standards to which engines must comply. Consequently, the use of exhaust aftertreatment systems on engines to reduce emissions is increasing. Exhaust aftertreatment systems are generally designed to reduce harmful exhaust gas emissions such as nitrogen oxides (NOx), sulfur oxides (SOx), carbon oxides (CO and/or CO2) particulate matter, etc. [0004] Urea-selective catalytic reduction (SCR) utilizes ammonia (NH3) generated on-board through injection of diesel emission fluid (DEF) to convert nitrogen oxides (NOx) generated under lean conditions in diesel engines. Cu -Zeolites, Fe-Zeolites, and vanadia-based catalysts, among others, are widely utilized for the SCR of NOx to nitrogen. The performance of these catalysts deteriorates in real-world operation due to exposure to high temperatures and chemical contaminants in exhaust.

SUMMARY

[0005] One embodiment of the present disclosure relates to systems, computer-readable media, and methods to estimate and predict the influence of chemical contaminants and temperature on active site distribution and performance degradation of exhaust aftertreatment system catalysts. A system includes at least one processing circuit including at least one memory coupled to at least one processor. The at least one memory stores instructions therein that, when executed by the at least one processor, causes the at least one processor to perform operations. The operations include receiving at least one boundary condition indicative of a duty-cycle of an exhaust aftertreatment system; receiving at least one value indicative of an operating parameter of the exhaust aftertreatment system from at least one sensor; predicting an output value associated with an operation of a catalyst of the exhaust aftertreatment system over the duty-cycle; and determining, based on the output value, a reductant dosing value for the exhaust aftertreatment system.

[0006] Other embodiments relate to systems, computer-readable media, and methods for diagnosing an aftertreatment system operation. A method includes: receiving, by at least one processing circuit, boundary conditions indicative of a first duty cycle for the aftertreatment system; receiving, by the at least one processing circuit, boundary conditions indicative of a second duty cycle for the aftertreatment system; receiving, by the at least one processing circuit, an error threshold and an error objective relating to operation of the aftertreatment system; estimating, by the at least one processing circuit, output values for the aftertreatment system based on the boundary conditions indicative of the first duty cycle; estimating, by the at least one processing circuit, output values for the aftertreatment system based on the boundary conditions indicative of the second duty cycle; comparing, by the at least one processing circuit, the estimated output values based on the boundary conditions indicative of the first duty cycle with the estimated output values based on the boundary conditions indicative of the second duty cycle; and, in response to determining that the error objective meets or falls below the error threshold, reporting, by at the least one processing circuit, a degradation cycle parameter of the aftertreatment system.

10007] Other embodiments relate to systems, computer-readable media, and methods to the design and size diesel oxidation catalysts (DOCs), Urea-Selective Catalytic Reduction (SCR) catalysts, and/or dual-layer ammonia oxidation catalysts (AMOX), along with the material composition of such catalysts, to mitigate system degradation during real-world chemical contaminant exposure. A method includes: receiving, by at least one processing circuit, a contaminant loading threshold; receiving, by the at least one processing circuit, boundary conditions indicative of a duty cycle for the aftertreatment system; estimating, by the at least one processing circuit, a contaminant loading value for the aftertreatment system based on the boundary conditions indicative of the duty cycle; comparing, by the at least one processing circuit, the contaminant loading value and the contaminant loading threshold; and in response to the contaminant loading value meeting or falling below the contaminant loading threshold, reporting, by at the least one processing circuit, a catalyst design parameter.

[0008] This summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices or processes described herein will become apparent in the detailed description set forth herein, taken in conjunction with the accompanying figures, wherein like reference numerals refer to like elements. Numerous specific details are provided to impart a thorough understanding of embodiments of the subject matter of the present disclosure. The described features of the subject matter of the present disclosure may be combined in any suitable manner in one or more embodiments and/or implementations. In this regard, one or more features of an aspect of the invention may be combined with one or more features of a different aspect of the invention. Moreover, additional features may be recognized in certain embodiments and/or implementations that may not be present in all embodiments or implementations. BRIEF DESCRIPTION OF THE FIGURES

[0009] FIG. 1 A is a schematic view of an exemplary system, shown as a vehicle, having an aftertreatment system, according to an exemplary embodiment.

[0010] FIG. IB is a schematic diagram of a controller of the exemplary system of FIG. 1A, according to an exemplary embodiment.

[0011] FIG. 2 is a block diagram of a model for predicting the effects of chemical contaminants and temperature on aftertreatment system catalysts, according to an exemplary embodiment.

[0012] FIG. 3 A is a flowchart of a method of utilizing the model of FIG. 2 to estimate the degradation, output values, state of health values, and/or contaminant loading values of a catalyst or other aftertreatment system component, according to an exemplary embodiment.

[0013] FIG. 3B is a flowchart of a method utilizing sensors to update the catalyst or aftertreatment system component state of heath predictions of the model of FIG. 2, according to an exemplary embodiment.

[0014] FIG. 3C is a flowchart of a method utilizing sensors to update the catalyst or aftertreatment system component contaminant loading predictions of the model of FIG. 2, according to an exemplary embodiment.

[0015] FIG. 4 is a graph showing exemplary predictions of the model of FIG. 2, according to an exemplary embodiment.

[0016] FIG. 5 is a graph showing additional exemplary predictions of the model of FIG. 2, expanding on the predictions of FIG. 4, according to an exemplary embodiment.

[0017] FIG. 6 is a flow chart of a method of diagnosing an aftertreatment system operation using the model of FIG. 2, according to an exemplary embodiment.

[0018] FIG. 7 is a flow chart of an example implementation of the method of diagnosing an aftertreatment system operation of FIG. 6, according to an exemplary embodiment. [0019] FIG. 8 is a flow chart of a method for designing aftertreatment system components and catalysts to reduce real -world degradation using the model of FIG. 2, according to an exemplary embodiment.

[0020] FIG. 9 is a flow chart of an example implementation of the method of designing aftertreatment system components and catalysts to reduce real -world degradation of FIG. 8, according to another exemplary embodiment.

[00211 FIG. 10 is a flowchart of a method of calibrating engine operation to minimize real- world degradation of aftertreatment systems and components using the model of FIG. 2, according to an exemplary embodiment.

[0022] FIG. 11 is a flowchart of a method of calibrating reductant dosing strategies to minimize real-world degradation of aftertreatment systems and components using the model of FIG. 2, according to an exemplary embodiment.

[0023] FIG. 12 is a flowchart of a method of estimating the chemical contamination levels in engine fluids using the model of FIG. 2, according to an exemplary embodiment.

[0024] FIG. 13 is a flowchart of a method for identifying Xth percentile real -world degraded catalyst parts from population analysis using the model of FIG. 2, according to an exemplary embodiment.

DETAILED DESCRIPTION

]0025] Following below are more detailed descriptions of various concepts related to, and implementations of, methods, computer-readable media, apparatuses, and systems for creating improved accelerated degradation protocols and models to predict the chemical contamination, performance degradation, and active site distribution of real-world representative exhaust aftertreatment systems and their components. Before turning to the figures, which illustrate certain exemplary embodiments in detail, it should be understood that the present disclosure is not limited to the details or methodology set forth in the description or illustrated in the figures. It should also be understood that the terminology used herein is for the purpose of description only and should not be regarded as limiting. [0026] While Cu-Zeolite SCR catalysts demonstrate high activity and hydrothermal stability relative to other ion-exchanged zeolites and vanadia-based NOx reduction catalysts, the strong affinity of active copper towards chemical contaminants such as sulfur oxides leads to deactivation of SCR rates in presence of sulfur. Desorption of stable sulfates and bisulfates (desulfation) primarily occurs at temperatures above 500°C, forming an equilibrium- controlled mixture of gas-phase SO2 and SO3. Real -world sulfur exposure occurs cyclically with a combination of low temperature (less than approximately 300°C) and high temperature (greater than approximately 500°C) events.

[0027] Hydrothermal aging (e.g., exposure to high temperatures for long periods of time in the presence of moisture) may be used to degrade NOx catalysts and to create representative degraded catalyst parts in an accelerated way. However, significant quantitative differences often exist between representative catalysts from hydrothermal aging and catalysts that have degraded under real-world operating conditions in the presence of contaminants. The accuracy of previous systems and methods to estimate catalyst degradation, such as hydrothermal aging, have suffered from the failure of such systems to account for the influence of chemical contaminants (e.g., sulfur, potassium, sodium, phosphorus, etc.) and temperature on the components of the aftertreatment system. The systems, apparatuses, and computer-readable media described herein represent improved accelerated degradation models and methods to account for the exposure of catalysts to chemical contaminants. The systems, computer-readable media, and methods disclosed herein replicate real-world degraded catalysts to estimate and predict the distribution of active sites during temperature- cycled contaminant exposure. The systems, apparatuses, and computer-readable media also present methods of utilizing the improved degradation models to design catalysts and aftertreatment systems with improved resistance to degradation, longer service life, ability to maintain deNOx efficiency above the standards set by environmental regulations.

[0028] As utilized herein, the term “predicting” and like terms are used to refer to determining a future value, which may be based on data such as sensor data (e.g., historical sensor data, real-time sensor data, etc.), assumed boundary conditions, known static values, and the like. In some embodiments, the future value may be predicted/estimated using one or more models (e.g., physics-based plant models, statistical models, artificial intelligence models, machine learning models, etc.). For example, predicting a chemical contaminant value of a catalyst may include predicting a contaminant (e.g., sulfur, phosphorus, sodium, etc.) uptake value on the catalyst surface over time using data, such as historical sensor data, real-time sensor data, received boundary conditions, chemical reaction/interaction kinetics, etc. with a model to determine a future contaminant distribution over the catalyst active sites or future NOx conversion efficiency.

[0029] Also as used herein, a “parameter,” “parameter value,” and similar terms, in addition to the plain meaning of these terms, refer to an input, output, or other value associated with a component of the systems described herein. For example, a parameter may include a sensor value detected by an actual sensor or determined by a virtual sensor. A parameter may include a value, control setting, or other control signal used by the control system to control one or more components described herein. Thus, a parameter may include data or information, such as a temperature of the system component, a temperature of exhaust gas, a concentration of a parti cl e/component/species within a solution/mixture (e.g., exhaust), a flow rate, and the like.

[00301 Additionally, the quantitative predictions of the models disclosed herein are utilized to predict future parameters and/or output values of oxidation catalysts, NOx catalysts, and dual-layer ammonia oxidation (AMOX) catalysts. These predictions are utilized by the disclosed systems, computer-readable media, and methods to optimize the sizing of diesel oxidation catalysts (DOCs) (e.g., to increase life-time NOx conversion efficiency while decreasing life-time chemical contaminant uptake), SCR catalysts, dual-layer ammonia oxidation catalysts (AMOX), along with the material composition of catalysts, to mitigate system degradation during real -world chemical contaminant (e.g., sulfur) and temperature exposure. Analogous models and methods are also contemplated which can be applied to other NOx reduction catalysts (such as Fe-Zeolites and vanadia-based catalysts), oxidation catalysts, and AMOX catalysts for a range of chemical contaminants relevant to real-world operation. The chemical contaminants may include sulfur, potassium, phosphorus, sodium, and the like.

[0031] The material composition of the catalysts determines their reactivity in the presence of exhaust, DEF, and chemical contaminants. Based on the known material composition of the catalysts, their sizing, and their location relative to other components of the aftertreatment system, the computer-readable media, systems, and methods disclosed herein may model and predict the long-term and short-term active site distribution and performance degradation of catalysts in the presence of temperature, exhaust, and chemical contaminants.

10032] Additionally, the systems and methods disclosed herein may determine a quantity of chemical contaminant that an aftertreatment system and its components are exposed to based on the predicted parameters, such as events of reduced NOx conversion efficiency from a target NOx conversion efficiency. For example, certain fuels contain and release a known or average quantity of chemical contaminants when combusted in an engine. Accordingly, and as described herein, the quantity and rate of chemical contaminant exposure can be modeled, estimated, predicted, or back calculated based on the type of fuel consumed, its usage rate, and the predicted distribution of active sites present on a catalyst at a future time. The known chemical contaminant levels may include those of fuels such as diesel fuel, biodiesel fuel, hydrogen fuel, natural gas fuel, gasoline, etc. Known chemical contamination values may further include those from engine oils, lubricants, and the like.

[00331 Further embodiments relate to systems, computer-readable media, and methods to create or used to create real -world representative aftertreatment system parts (e.g., representative 50^th percentile degraded DOCs/SCR Catalysts/ AMOX catalysts, representative 90^th percentile degraded DOCs/SCR Catalysts/ AMOX catalysts, etc.) using the accelerated degradation protocols disclosed herein. For example, the model circuit described herein may determine an aftertreatment system accelerated degradation protocol configured to produce lab-accelerated aftertreatment system components that replicate real-world aged parts. The accelerated degradation protocol may be determined by the systems, methods, or computer- readable media described herein (e.g., model circuit) which receives boundary conditions indicative of a first duty cycle for the aftertreatment system and boundary conditions indicative of a second duty cycle for the aftertreatment system. The model circuit may then receive an error threshold and an error objective relating to the aftertreatment system operation. The model circuit may then estimate output values for the aftertreatment system based on the boundary conditions indicative of the first duty cycle and output values for the aftertreatment system based on the boundary conditions indicative of the second duty cycle. The model circuit may then compare the estimated output values based on the boundary conditions indicative of the first duty cycle with the estimated output values based on the boundary conditions indicative of the second duty cycle. In response to determining that the error objective meets or falls below the error threshold, the model circuit reports a degradation cycle parameter of the aftertreatment system. The systems and methods herein may then utilize the degradation cycle parameters to generate lab-accelerated parts, for example, behind an engine dynamometer or in a controlled synthetic gas bench environment. These and other features and benefits are described more fully herein below.

[0034] Referring now to FIG. 1 A, a schematic diagram of a system 100, shown as a vehicle, is depicted according to an example embodiment. The systems, computer-readable media, and methods disclosed herein may be utilized to predict the short term and long-term effects of exposure to chemical contaminants and temperature on components of the aftertreatment system 120 of the system 100. Further, the systems, computer-readable media, and methods disclosed herein may be utilized to design and optimize the components of the aftertreatment system 120 based on a variety of expected operating conditions of the system 100 over the course of its operational lifetime. For example, such operating conditions may include a defined real world duty cycle for a specific application. A duty cycle may be defined as a certain period of operation characterized by changes in the boundary conditions (e.g., fuel consumption, temperature profile, air flow rate, etc.) of the system 100 itself. A duty cycle may also represent a time spent in a certain operating range. For example, a duty cycle may represent the operating parameters of an on-highway truck travelling at a certain speed, at a certain elevation, for a certain time.

[0035] In brief overview, the system 100 can include an engine 110, an aftertreatment system 120, a controller 130 an operator input/output (I/O) device 140. The aftertreatment system 120 can include a diesel oxidation catalyst (DOC) 121, a diesel particulate filter (DPF) 122, a selective catalytic reduction (SCR) subsystem 123, an ammonia oxidation catalyst (AMOX) 124, a heater 125 and a diesel exhaust fluid (DEF) doser (or DEF pump) 126. The system 100 can include one or more sensors, such as an engine-out nitrogen oxide (NOx) sensor 127, a system-out NOx sensor 128 and a DEF dosing sensor 129, to monitor operational parameters or states of one or more systems or components of the system 100. The system/vehicle 100 may be an on-road or an off-road vehicle including, but not limited to, line-haul trucks, mid-range trucks (e.g., pick-up truck), cars (e.g., sedans, hatchbacks, coupes, etc.), buses, vans, refuse vehicles, fire trucks, concrete trucks, delivery trucks, locomotives, marine vehicles, aviation vehicles, and other types of vehicles. In another embodiment, the depicted components and systems of the system 100 can be a stationary piece of equipment, such as a power generator or genset, certain factory machinery, etc. In general, embodiments disclosed in the present disclosure can be applicable to vehicles and/or pieces of equipment that include internal-combustion engines.

[0036] The engine 110 may be any type of internal combustion engine that generates exhaust gas (e.g., compression ignition or a spark ignition engine that may utilize various fuels, such as natural gas, gasoline, diesel fuel, jet fuel, hydrogen, etc.). In some embodiments, the system 100 can be an at least partially hybrid vehicle where power from the internal combustion engine may be replaced by and/or supplemented with an electric motor. In either configuration, the engine 110 includes one or more cylinders and associated pistons. In this regard, air from the atmosphere is combined with fuel, and combusted, to power the engine 110. Combustion of the fuel and air in combustion chambers of the engine 110 produces exhaust gas that is operatively vented to an exhaust pipe and to the aftertreatment system 120. In the example shown in FIG. 1 A, the engine 110 is structured as an internal combustion engine and particularly, a compression-ignition engine powered by diesel fuel.

[0037] The aftertreatment system 120 is structured to receive exhaust-gas from the engine 110. Specifically, the DOC 121 is structured to receive the exhaust gas from the engine 110 and to oxidize hydrocarbons and carbon monoxide in the exhaust gas, such as NO oxidation to NO2, to promote passive DPF regeneration and fast SCR reaction. The DPF 122 is arranged or positioned downstream of the DOC 121 and structured to remove particulates, such as soot, from exhaust gas flowing in the exhaust gas stream. The DPF 122 includes an inlet, where the exhaust gas is received, and an outlet, where the exhaust gas exits after having particulate matter substantially filtered from the exhaust gas and/or converting the particulate matter into carbon dioxide. In some implementations, the DPF 122 may be omitted. [0038] The aftertreatment system 120 may further include a reductant delivery system which may include a decomposition chamber (e.g., decomposition reactor, reactor pipe, decomposition tube, reactor tube, etc.) to convert a reductant into ammonia. The reductant may be, for example, urea, diesel exhaust fluid (DEF), Adblue®, a urea water solution (UWS), an aqueous urea solution (e.g., AUS32, etc.), and other similar fluids. A diesel exhaust fluid (DEF) is added to the exhaust gas stream to aid in the catalytic reduction. The DEF doser 126 can inject the reductant upstream of the SCR subsystem 123, such that the SCR subsystem 123 receives a mixture of the reductant and exhaust gas. The reductant droplets then undergo the processes of evaporation, thermolysis, and hydrolysis to form gaseous ammonia within the decomposition chamber, e.g., the SCR subsystem 123 and/or the exhaust gas conduit system. The gaseous ammonia may leave the SCR subsystem 123. The aftertreatment system 120 may further include an oxidation catalyst (e.g., the DOC 121) fluidly coupled to the exhaust gas conduit system to oxidize hydrocarbons and carbon monoxide in the exhaust gas. In order to properly assist in this reduction, the DOC 121 may be required to be at a certain operating temperature. The operating temperature can be approximately between 200-500 °C. In some implementations, the operating temperature can be the temperature at which the hydrocarbon conversion efficiency of the DOC 121 exceeds a predefined threshold. The hydrocarbon conversion efficiency refers to the efficiency of the conversion of hydrocarbon to less harmful compounds.

[0039] The SCR subsystem 123 is configured to reduce or at least assist in the reduction of NOx emissions by accelerating a NOx reduction process between the DEF from the DEF doser 126 and the NOx of the exhaust gas into diatomic nitrogen, water, and/or carbon dioxide. If the SCR subsystem 123 is not at or above a certain temperature, the acceleration of the NOx reduction process may be limited and the SCR subsystem 123 may not be operating at a necessary level of efficiency to meet desired standards. In some implementations, the temperature can be approximately 250-300°C. The SCR subsystem 123 may be made from a combination of an inactive material and an active catalyst, such that the inactive material (e.g., ceramic metal) directs the exhaust gas towards the active catalyst. The active catalyst can be any sort of material suitable for catalytic reduction (e.g., base metals oxides like vanadium, molybdenum, tungsten, etc. or noble metals like platinum). The sizing, dimensions, composition, and location of the active catalyst and inactive material of the SCR subsystem relative to other components of the aftertreatment system 120 may be determined and otherwise stored as static values by the systems, computer-readable media, and methods described herein. These values may be varied by the systems, computer-readable media, and methods disclosed herein in order to determine catalyst sizing, location, composition, and/or the like to achieve a target operating parameter or parameters for a given duty cycle. For example, the systems, computer-readable media, and methods disclosed herein may determine sizing, location, and compositions of a catalyst in the aftertreatment system 120 that degrades less over the course of a given duty cycle compared to catalysts of different sizes, compositions, and the like.

[0040] In some implementations, the AMOX 124 is included with the aftertreatment system 120 and is structured to address ammonia slip by removing excess ammonia from the treated exhaust gas before the treated exhaust is released into the atmosphere.

[0041 ] Because the aftertreatment system 120 treats the exhaust gas before the exhaust gas is released into the atmosphere, some of the particulate matter or chemicals that are treated or removed from the exhaust gas may build up in the aftertreatment system 120 over time. For example, the soot filtered out from the exhaust gas by the DPF 122 may build up on the DPF 122 over time. Similarly, sulfur particles present in fuel may accumulate in the SCR subsystem 123 and deteriorate the effectiveness of the SCR subsystem 123. Further, DEF that undergoes incomplete thermolysis upstream of the catalyst may build up and form deposits on downstream components of the aftertreatment system 120. In this way, the active sites located on the surface of the catalysts may change over time and the systems and methods disclosed herein may predict the evolution of active sites based on the operating parameters, chemical interactions, redox/absorption/desorption kinetics, etc. during a given duty cycle.

[0042] Further, in some embodiments, these build-ups on (and subsequent deterioration of effectiveness of) the components of the aftertreatment system 120 may be reversible. In other words, the soot, sulfur, other chemical contaminants, and/or DEF deposits may be substantially removed from the DPF 122 and the SCR subsystem 123 by increasing a temperature of the exhaust gas running through the aftertreatment system 120 to recover performance (e.g., for the SCR subsystem 123, conversion efficiency of NOx to N2 and other compounds). These removal processes are referred to as regeneration events and may be performed for the DPF 122, SCR subsystem 123, and/or another component in the aftertreatment system 120 on which deposits develop. However, exposure to high temperatures during active regeneration may degrade the DOC, DPF, and SCR catalysts. This degradation may also be considered by the systems, computer-readable media, and methods disclosed herein to derive combustion cycles, regeneration operations, and the like that maintain a degradation value or a contaminant value above a target threshold. For example, the systems, computer-readable media, and methods disclosed herein may derive a regeneration cycle that maintains a deNOx efficiency value, sulfur storage value, contaminant storage value, etc. above a target threshold (e.g., an emissions regulation, a maximum contaminant loading value, and the like) for a given duty cycle.

[0043] An active regeneration event is a specifically commanded event by the controller 130, which may be based on a flow rate measurement through a DPF 122 being below a predefined threshold indicating a partially blocked DPF. The controller 130 may command a regeneration event to increase exhaust gas temperatures to raise the temperature of the DPF 122 and burn off the accumulated particulate matter (PM) and other components (e.g., raise engine power output, post-injection, and other means to increase exhaust gas temperatures to cause a regeneration event). In contrast, a passive regeneration event occurs naturally during operation of the vehicle (e.g., a high load condition that may be experienced while traversing a hill causes an increase in exhaust gas temperatures and regeneration event occurs naturally - not specifically commanded). The effects of both active and passive regeneration events on the components of the aftertreatment system 120 may be modeled, estimated, and predicted according to the systems, computer-readable media, and methods disclosed herein.

[0044] In some embodiments, the heater 125 can be located in the exhaust flow path before the aftertreatment system 120, in the aftertreatment system 120, and/or in a variety of positions (e.g., more than one heater). The at least one heater 125 can be structured to controllably heat the exhaust gas at the location of the heater 125, such as upstream of the aftertreatment system 120. In some embodiments, the heater 125 can located directly before the DOC 121, directly before the SCR subsystem 123, directly before the AMOX 124, etc. The heater 125 may be any sort of external heat source that can be structured to increase the temperature of passing exhaust gas, which, in turn, increases the temperature of components in the aftertreatment system 120, such as the DOC 121 or the SCR subsystem 123. The heater 125 may be an electric heater, a grid heater, a heater within the SCR subsystem 123, an induction heater, a microwave, or a fuel-burning (e.g., hydrocarbon fuel) heater. The heater 125 may be controlled by the controller 130 during an active regeneration event in order to heat the exhaust gas (e.g., by convection). In some implementations, the heater 125 may be positioned proximate a desired component to heat the component (e.g., DPF 122) by conduction and possibly convection. Multiple heaters may be used with the exhaust aftertreatment system 120. The multiple heaters may be structured the same or differently (e.g., conduction, convection, etc.).

[0045] The system 100 can include one or more sensors 142, 340 such as the engine-out NOx sensor 127, the system-out NOx sensor 128 and the DEF dosing sensor 129, for measuring parameters indicative of how various components of the exhaust aftertreatment system 120 are operating or performing. For instance, the engine-out NOx sensor 127 measures or acquires data or information indicative of the amount or rate of NOx release by the engine 110, while the system-out NOx sensor 128 measures or acquires data or information indicative of the amount or rate of NOx release by the aftertreatment system 120 or by the SCR subsystem 123. In one embodiment, the engine-out NOx sensor 127 is positioned immediately downstream of the engine 110 (e.g., on the exhaust manifold) and in other embodiments, the sensor is positioned further downstream or in other locations proximate the engine yet upstream of the aftertreatment system 120. The engine-out NOx sensor 127 is positioned in the exhaust flow downstream of the engine 110 and acquires data indicative of the NOx amount/rate at or approximately at its disposed location. The system-out NOx sensor 128 is positioned in the exhaust flow downstream of the aftertreatment system 120 and measures or acquires data indicative of the NOx amount/rate at or approximately at its disposed location. The DEF dosing sensor 129 measures or acquires data indicative of the pumping speed of the DEF doser 126 and/or the DEF flow rate from the DEF doser 126. In some implementations, the system 100 can include multiple DEF dosing sensors 129. For example, the system 100 can include a first DEF dosing sensor for measuring the pumping speed of the DEF doser 126 and a second DEF dosing sensor for measuring the DEF flow rate from the DEF doser 126. The DEF dosing sensor(s) 129 can positioned within or immediately downstream of the DEF doser 126. It should be understood that the depicted locations, numbers, and type of sensors is illustrative only. In other embodiments, different/additional sensors may also be included within the system 100 (e.g., a pressure sensor, a flow rate sensor, a temperature sensor, etc.). Those of ordinary skill in the art will appreciate and recognize the high configurability of the sensors in the system 100.

[0046] The sensors 142 may be real or virtual (i.e., a non-physical sensor that is structured as program logic in the controller 130 that makes various estimations or determinations). In this regard, any of the sensors described herein may be real or virtual. When structured as a virtual sensor, at least one input may be used by the controller 130 in an algorithm, model, lookup table, etc. to determine or estimate a parameter of the system 100.

100471 The controller 130 can be communicatively coupled to the sensors 142 and various components or systems of the system 100. As shown, the system 100 is included in a vehicle. As such, the controller 130 may be structured as or embedded in an onboard device such as one or more electronic control units (ECUs) or engine control modules (ECMs) (e.g., be or include one or more microcontrollers). The controller 130 may be separate from or included with at least one of a transmission control unit, an exhaust aftertreatment control unit, a powertrain control module, an engine control module, etc. In other embodiments, the controller 130 is embedded in a different onboard device such as an edge computing device.

[0048] The controller 130 can receive data, such as measurement data, from the sensors, and use the sensor data to diagnose components and/or systems of the system 100. For instance, the controller 130 can use data from the NOx sensors 127 and 128 and/or the DEF dosing sensor 129 to diagnose the aftertreatment system 120 or a component thereof. The controller 130 is communicatively coupled to systems and components of the system 100 and is structured to acquire operation data regarding one or more of the components or systems shown in FIG. 1 A. For example, the operation data may include data regarding operating conditions of the engine 110 (e.g., engine torque, engine speed, fuel injection rate, etc.) and/or the aftertreatment system 120 acquired by one or more sensors, such as the engine-out NOx sensor 127, the system-out NOx sensor 128 and/or the DEF dosing sensor 129. [0049] Components or systems of the system 100 may communicate with each other or remote components using any type and any number of wired or wireless connections. For example, a wired connection may include a serial cable, a fiber optic cable, a CAT5 cable, or any other form of wired connection. Wireless connections may include the Internet, Wi-Fi, cellular, radio, Bluetooth, ZigBee, etc. In one embodiment, a controller area network (CAN) bus provides the exchange of signals, information, and/or data. The CAN bus includes any number of wired and/or wireless connections. In some implementations, the controller 130 communicates with other components of the system 100 via the CAN bus.

[0050] The operator I/O device 140 may be coupled to the controller 130, such that information may be exchanged between the controller 130 and the I/O device 140. The exchanged information may relate to one or more components of FIG. 1 A or determinations (described below) of the controller 130. The operator VO device 140 enables an operator of the system 100 to communicate with the controller 130 and one or more components of the system 100 of FIG. 1 A. For example, the operator I/O device 140 may include, but is not limited to, an interactive display, a touchscreen device, one or more buttons and switches, voice command receivers, etc.

[00511 As shown in FIG. IB, the controller 130 includes processing circuitry 132 having a processor 134 and a memory device 136; a communications interface 138; and control and modeling circuitry including a model circuit 200. The model circuit 200 can include a Continuous Hydrothermal Aging SCR Redox (CHSR) model 204 (See FIG. 2), a contaminant model 208 (e.g., a sulfur model, a sodium model, etc.) (See FIG. 2), a NOx reduction catalyst degradation circuit 210 (See FIGs. 3 A, 3B, 7, 9-13), a real -world representativeness circuit 212 (See FIG. 7), a catalyst size optimization circuit 818 (See Fig. 9), among other circuits (e.g., an oxidation catalyst degradation circuit, an AMOX catalyst degradation circuit, etc.). The communications interface 138 is structured to facilitate communication between the controller 130, the system 100, and one or more remote computing systems or servers 144. Generally, the controller 130 is structured to monitor the data acquired from the sensors and/or the remote computing systems or server 144 and control or model various systems/components of the system/vehicle 100 based on the data, as described in more detail herein. [0052] In one configuration, the model circuit 200 is embodied as machine or computer- readable media storing instructions that are executable by a processor, such as the processor 134. As described herein and amongst other uses, the machine-readable media facilitates performance of certain operations to enable reception and transmission of data. For example, the machine-readable media may provide an instruction (e.g., command, etc.) to, e.g., acquire data. In this regard, the machine-readable media may include programmable logic that defines the frequency of acquisition of the data (or, transmission of the data). The computer readable media may include code, which may be written in any programming language including, but not limited to, Java or the like and any conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program code may be executed on one processor or multiple remote processors. In the latter scenario, the remote processors may be connected to each other through any type of network (e.g., CAN bus, etc.).

[0053] In another configuration, the model circuit 200 is embodied as one or more hardware units, such as electronic control units. As such, the model circuit 200 may be embodied as one or more circuitry components including, but not limited to, processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, the model circuit 200 may take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (IC), discrete circuits, system on a chip (SOCs) circuits, microcontrollers, etc.), telecommunication circuits, hybrid circuits, and any other type of “circuit.” In this regard, the model circuit 200 may include any type of component for accomplishing or facilitating achievement of the operations described herein. For example, a circuit as described herein may include one or more transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc ), resistors, multiplexers, registers, capacitors, inductors, diodes, wiring, and so on). The model circuit 200 may also include programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. The model circuit 200 may include one or more memory devices for storing instructions that are executable by the processor(s) of the model circuit 200. The one or more memory devices and processor(s) may have the same definition as provided below with respect to the memory device 136 and the processor 134. In some hardware unit configurations, the model circuit 200 may be geographically dispersed throughout separate locations in the system 100 relative to the controller 130. Alternatively, the model circuit 200 may be embodied in or within a single unit/housing with one or more of the circuits of the controller 130.

[0054] Additionally, as shown in Figure IB, the model circuit 200 or components thereof may be included in a remote computing system 144. In this way, the controller 130 may communicate or connect with the remote computing system 144 to perform one or more operations or functions of the model circuit 200. By including one or more components of the model circuit 200 in a remote computing system 144 (e.g., an offsite computing system, a network, a server, etc.), computing power may be offloaded from the controller 130 and delegated to the remote computing system 144. The remote computing system 144 may be associated with a provider or entity that provides a service or product. For example, the provider or entity may be an engine manufacturer, a telecommunications provider, etc.

[0055] In the example shown, the controller 130 includes the processing circuitry 132 having the processor 134 and the memory device 136. The processing circuitry 132 may be structured or configured to execute or implement the instructions, commands, and/or control processes described herein with respect to the model circuit 200. The depicted configuration represents the model circuit 200 as machine or computer-readable media. However, as mentioned above, this illustration is not meant to be limiting as the present disclosure contemplates other embodiments where the model circuit 200 is configured as a hardware unit. All such combinations and variations are intended to fall within the scope of the present disclosure.

[0056] The communications interface 138 may include any combination of wired and/or wireless interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals) for conducting data communications with various systems, devices, or networks structured to enable in-system communications (e.g., between and among the components of the system 100) and out-of-system communications (e.g., with a remote server 144). For example, and regarding out-of-system communications, the communications interface 138 may include an Ethernet card and port for sending and receiving data via an Ethernet-based communications network and/or a Wi-Fi transceiver for communicating via a wireless communications network. The communications interface 138 may be structured to communicate via local area networks or wide area networks (e.g., the Internet) and may use a variety of communications protocols (e.g., IP, LON, Bluetooth, ZigBee, radio, cellular, near field communication).

(0057] The processing circuitry 132 may be structured or configured to execute or implement the instructions, commands, and/or control processes described herein with respect to model circuit 200. The depicted configuration represents the model circuit 200 as machine or computer-readable media. However, as mentioned above, this illustration is not meant to be limiting as the present disclosure contemplates other embodiments where the model circuit is configured as a hardware unit, or a combination of hardware, software, computer-readable media, etc. All such combinations and variations are intended to fall within the scope of the present disclosure.

100581 The hardware and data processing components used to implement the various processes, operations, illustrative logics, logical blocks, modules and circuits described in connection with the embodiments disclosed herein (e.g., the processor 134) may be implemented or performed with a general purpose single- or multi-chip processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, or, any conventional processor, or state machine. A processor also may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some embodiments, the one or more processors may be shared by multiple circuits (e.g., model circuit 200, processing circuitry 132, remote computing systems 144 may comprise or otherwise share the same processor which, in some example embodiments, may execute instructions stored, or otherwise accessed, via different areas of memory). Alternatively or additionally, the one or more processors may be structured to perform or otherwise execute certain operations independent of one or more co-processors. In other example embodiments, two or more processors may be coupled via a bus to enable independent, parallel, pipelined, or multi-threaded instruction execution. All such variations are intended to fall within the scope of the present disclosure. [0059] The memory device 136 (e.g., memory, memory unit, storage device) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage) for storing data and/or computer code for completing or facilitating the various processes and modules described in the present disclosure. The memory device 136 may be communicably connected to the processor 134 to provide computer code or instructions to the processor 134 for executing at least some of the processes described herein. Moreover, the memory device 136 may be or include tangible, non-transient volatile memory or non-volatile memory. Accordingly, the memory device 136 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described herein.

[0060] It should be understood that the controller 130 may include any number of circuits for completing the functions described herein. For example, the activities and functionalities of the model circuit 200 may be combined in multiple circuits or as a single circuit. Additional circuits with additional functionality may also be included. Further, the controller 130 may further control other activity beyond the scope of the present disclosure.

[00611 As mentioned above and in one configuration, the “circuits” may be implemented in machine-readable medium for execution by various types of processors, such as the processor 134. An identified circuit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified circuit need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the circuit and achieve the stated purpose for the circuit. Indeed, a circuit of computer readable program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within circuits and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. [0062] While the term “processor” is briefly defined above, the term “processor” and “processing circuit” are meant to be broadly interpreted. In this regard and as mentioned above, the “processor” may be implemented as one or more general-purpose processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), or other suitable electronic data processing components structured to execute instructions provided by memory. The one or more processors may take the form of a single core processor, multi-core processor (e.g., a dual core processor, triple core processor, quad core processor, etc.), microprocessor, etc. In some embodiments, the one or more processors may be external to the apparatus, for example the one or more processors may be a remote processor (e.g., a cloud-based processor). Alternatively or additionally, the one or more processors may be internal and/or local to the apparatus. In this regard, a given circuit or components thereof may be disposed locally (e.g., as part of a local server, a local computing system, etc.) or remotely (e.g., as part of a remote server such as a cloud-based server). To that end, a “circuit” as described herein may include components that are distributed across one or more locations.

[0063] Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine- readable media. Machine-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0064] Turning now to FIG. 2, a block diagram for a model circuit 200 to predict the evolution of active sites and corresponding performance degradation of a catalyst based on exposure to chemical contaminants and temperature is shown, according to an example embodiment. The model circuit 200 may be stored in the memory of the controller 130 and/or stored on a remote computing system 144, and selectively executed by the at least one processor.

[0065] Via the model circuit 200, the controller 130 may be configured to predict the active site distribution and performance degradation of a variety of catalysts in the presence of a variety of contaminants. For example, using the model circuit 200, the controller 130 may estimate the degradation of a NOx reduction catalyst such as a Cu-Zeolite catalyst, an Fe- Zeolite catalyst, a vanadia-based catalyst, or a catalyst of another composition (e.g., another NOx reduction catalyst, an oxidation catalyst, an AMOX catalyst, etc.). Further, the controller 130 may use the model circuit 200 to determine the effect of the presence of a particular chemical contaminant such as sulfur, potassium, phosphorus, sodium, etc. on the catalyst. The controller 130 may also predict the active site distribution and performance degradation of NOx reduction catalysts based on predicting the output parameters of oxidation catalysts positioned upstream of the NOx reduction catalysts. The controller 130 and model circuit 200 may further be expanded to predict the output parameters of AMOX catalysts utilizing the predicted output parameters of the NOx reduction catalysts disposed upstream of the AMOX catalyst.

[0066] In the particular embodiment shown in FIG. 2, the model circuit 200 is a physicsbased plant model to predict the evolution of active sites and performance degradation of a copper-zeolite NOx reduction catalyst in the presence of sulfur as the chemical contaminant. Accordingly, the model circuit 200 in this embodiment may be a continuous sulfur aging SCR redox model (a CSSR model). The model circuit 200 includes two subsystems, each with multiple components. The first subsystem is a Continuous Hydrothermal Aging SCR Redox (CHSR) model 204 and the second subsystem is a contaminant model, here, a sulfur model 208. The CHSR model 204 simulates the impact of hydrothermal aging on active site distribution and catalyst performance (e.g., changes in the NOx conversion efficiency of a Cu-zeolite NOx reduction catalyst based on exposure to temperature and moisture). The CHSR model 204 includes an evolution of NH3 adsorption-desorption kinetics component 212, an SCR redox kinetics component 214, an N0/NH3 oxidation redox kinetics component 216, and an NCh chemistry component 218.

(0067] The sulfur model 208 (i.e., a contaminant model) simulates the influence of the presence of a contaminant (e.g., sulfur) on the active site distribution and catalyst performance. The contaminant model 208 includes an adsorption of sulfur oxides (SOx) and sulfur dioxide (SO2) oxidation kinetics component 222, an NH3-SOx interaction kinetics component 224, an NH3-SOx-NOx interaction kinetics component 226, and a contaminant- NO2 chemistry component (e.g., a sulfur-NCh chemistry component 228).

(0068] Together, the CHSR model 204 and the contaminant model (e.g., the sulfur model 208) form the kinetic model circuit 200 that captures contaminant (e.g., sulfur) adsorptiondesorption, along with the influence of contaminant (e.g., sulfur) uptake on the performance of a NOx reduction catalyst. Further, as discussed below, the physics-based plant model circuit 200 can include additional components to predict the output parameters of an oxidation catalyst. For example, the model circuit 200 for an oxidation catalyst includes a kinetics for SOx adsorption and SO2 oxidation component, along with an influence of sulfur uptake on the oxidation of NO to NO2 component, and an oxidation of hydrocarbons (HC) to carbon dioxide and water component.

[0069] In aftertreatment system configurations that include an oxidation catalyst upstream of a NOx reduction catalyst, the oxidation catalyst can influence the contaminant (e.g., sulfur) exposure, form, and uptake of the NOx reduction catalyst. Real-world duty cycles and accelerated degradation cycles can be simulated by providing boundary conditions to the oxidation catalyst model to acquire outputs that are then utilized as inputs in the NOx reduction catalyst model. In this way, one or more model circuits 200 may be utilized to predict both the outputs of the oxidation catalyst and the active site evolution and performance degradation of the NOx reduction catalyst. For example, a first model circuit 200 is used to predict the output parameters of an oxidation catalyst. The output parameters may include exhaust values, temperature values, contaminant values, etc. The exhaust values may include a NOx concentration, an exhaust flow rate, and the like. Similarly, temperature values may include a temperature of the exhaust gas entering the SCR subsystem 123, a temperature profile of a component of the aftertreatment system 120, etc. Contaminant values may include a concentration, quantity, flow rate, and the like of a contaminant such as sulfur, potassium, sodium, etc. The outputs of the oxidation catalyst model circuit 200 are then utilized as inputs to a second model circuit 200 used to predict NOx reduction catalyst output parameters, active site distributions, and performance degradation. In this way, the model circuits 200 may simulate a duty cycle operating on a system having an oxidation catalyst operating upstream from a NOx reduction catalyst.

[0070] The continuous contaminant aging SCR redox model circuit 200 may be applied to a wide range of degradation states, material compositions and metal loadings. Measured material properties and limited experimental data on a fresh catalyst (e.g., an oxidation catalyst, a NOx reduction catalyst, etc.) can be supplied to the model circuit 200 to simulate the output of oxidation catalysts and degradation of a variety of NOx reduction catalysts with differing compositions. The model circuit 200 may also be utilized to develop accelerated aging protocols that can be applied to create representative degraded parts in a lab or test setting. For example, the model circuit 200 may develop accelerated degradation protocols that can be applied to fresh or new aftertreatment system parts, catalysts, or components to create representative aged parts, catalysts, or components.

[00711 Turning to FIG. 3 A, an example flowchart illustrating the process of utilizing the model circuit 200 of FIG. 2 is shown, according to an example embodiment. The model circuit 200 (here, shown as a NOx reduction catalyst degradation circuit 210) may receive inputs in the form of boundary conditions. The boundary conditions are a set of constraints or rules applied which define the behavior of the system 100. The boundary conditions are used to represent the interactions between the system 100 and its surrounding environment or other adjacent systems. The boundary conditions may include static inputs 310, fuel consumption values (e.g., a fuel consumption rate 312), air flow values (e.g., an air flow rate 316), temperature values 320, (e.g., a temperature profile of a component, a temperature of exhaust, etc.), engine-out values (e.g., engine-out O2 324, engine-out NOx 328), engine fluid consumption values 330 (e.g., an oil consumption rate), etc. The boundary conditions may also include reductant dosing strategies and/or a reductant dosing value such as reductant injection rate 332. [0072] Static input 310 boundary conditions may include fixed values or values that are unlikely to significantly change over time. For example, a value that does not significantly change over time may be the same or within ,01%-l% of an initial starting value. Exemplary static inputs 310 include material properties such thermal conductivity values, the composition of components and catalysts within the aftertreatment system 120, metal loading, sizing and dimensions of the aftertreatment system 120 and components therein, constants (e.g., maximum fuel storage quantity, gravitational constants, etc.), locations of the components of the aftertreatment system 120 relative to one another, etc. Fuel consumption values may include one or more fuel consumption rates for hybrid fuel systems, fuel consumption values, average contaminant values associated with fuel consumption, etc. Air flow values may include an air flow rate/volume/amount at one or more of various locations in the system 100, such as an airflow provided to the engine 110 or aftertreatment system 120. Temperature values likewise may include a temperature data of components, exhaust, air, or a catalyst, a temperature profile of any component of the aftertreatment system 120, a temperature of the engine 110, an ambient temperature, an exhaust gas temperature, the temperature of the heater 125, or the like. Further, engine out values may include values indicative of an exhaust gas parameter or input values to the aftertreatment system 120. For example, engine out values may include an engine-out NOx amount or flow rate, an engine- out oxygen flow rate or amount, a torque, speed, pressure associated with the engine 110, etc. Boundary conditions may also include engine fluid consumption values such as a rate of oil consumption, a flow rate of lubricant, an average amount of contaminant released per quantity of fluid used/burned, and the like. Additional boundary conditions may include a reductant injection rate, a volume/type/amount/quantity of reductant injected into the system, etc. Other boundary conditions not shown in FIG. 3 A may include a regeneration strategy (e.g., a time spent operating the aftertreatment system 120 above a certain temperature to remove impurities (an amount of time spent in regeneration or a regeneration state), a fuel injection strategy to increase temperature to remove impurities, etc.), a frequency of activating regeneration events, or other various temperatures, concentrations, and flow rates associated with the aftertreatment system 120.

[0073] By varying the boundary conditions, the controller 130, via the model circuit 200, can simulate various duty cycles. For example, the boundary conditions may simulate a real- world duty cycle, such as the operating conditions and parameters of a stop-and-go bus driving on a given route over a specified time, the operating conditions and parameters of a truck travelling down the highway, etc. The boundary conditions may also define duty cycles to simulate operating an aftertreatment system in a reactor environment, in a test cell, etc. Predictions from different simulation cases and duty cycles can then be compared and matched as needed to increase the accuracy of accelerated degradation protocols.

[0074] The model circuit 200 (e.g., a NOx reduction catalyst degradation circuit 210) receives the boundary conditions indicative of one or more duty cycles and utilizes the same to predict output values 336 associated with the aftertreatment system 120. For example, the output values 336 may include outlet gas values and surface site distributions. Outlet gas values may include NH3 slip amounts, NOx slip amounts, N2 concentrations or amounts, NOx values, NOx concentrations, oxygen values, oxygen concentrations, ammonium nitrate (AN) storage, NOx conversion efficiency, tailpipe NH3 slip and tailpipe N2O slip during real-world degradation of a NOx reduction catalyst in presence of temperature and chemical contaminants. Surface site distributions may include the composition and location of different types of sites present on the surface of an aftertreatment system catalyst or component. For example, the surface site distributions may include NH3 storage values, contaminant (e.g., sulfur, sodium, etc.) storage values, locations on the catalyst that remain active catalytic sites, locations on the catalyst where NH3 is stored, locations where contaminants are stored, etc.

|0075| Additionally, as shown in Figure 3B and 3C, the model circuit 200 may update and review (e.g., compare to measured/known/expected conditions of an aftertreatment system 120) the output values 336 (e.g., tailpipe NOx estimate values 336) to determine one or more state-of-health values 337 that define a degradation level, a NOx reduction efficiency, or otherwise qualitatively define the degradation level experienced by the catalyst over a certain period of time. Similarly, the model circuit 200 may update or review the output values 336 (e.g., tailpipe NOx estimate values 336) to determine one or more contaminant loading values 347. The contaminant loading values 347 may define the amount or concentration of contaminants that have accumulated on the surface of the catalyst over a period of operation (e.g., a sulfur concentration, a percentage of active sites blocked by sulfur, etc.), define the surface chemistry of the catalyst, or similarly define the degradation or estimated remaining useful life of the catalyst.

(0076] To predict the output values 336, the model circuit 200 may utilize conservation equations such as mass conservation equations, energy conservation equations, species conservation equations, reaction simulations, empirical relationships, kinetics simulations, predictive algorithms, look-up tables, etc. to simulate how the presence of contaminants influences the performance of the catalyst. For example, the model circuit 200 may predict and compare, for various duty cycles, NOx conversion efficiency in the presence of a contaminant to a baseline NOx conversion efficiency in the absence of contaminant to determine catalyst sizing, location, reductant dosage strategies, etc. that produce NOx conversion efficiencies closest to that of the baseline NOx conversion efficiency. The model circuit 200 may then report the output values 336 as respective state-of-health values 337 and/or contaminant loading values 347. Further, the model circuit 200 may update revise the estimated output values 336 based on other data to arrive at updated/modified state-of-health values 337 and contaminant loading values 347 that reduce the error between the estimated values and measured/actual aftertreatment system 120 parameters.

[0077] As shown in Figure 3B, the model may compare the output values 336 (e.g., tailpipe NOx estimate values, tailpipe NH3 slip values, etc.) to one or more sensor values (here, tailpipe NOx sensor values) to define a state-of-health value 337 of an aftertreatment system 120 and/or a catalyst. The model circuit 200 may also include a state of health modification circuit 344 to revise or update the model’s estimated output values 336 in view of physically/virtually measured operating conditions of the aftertreatment system 120. For example, the model may estimate output values 336 comprising a percentage of active sites degraded, an amount of chemical contaminant uptake, a surface site distribution comparison between active sites and sites of NH3, contaminant, and the like. At step 341, the model circuit may receive an indication of a sensor value 339 from an aftertreatment system 120 (e.g., a tailpipe NOx sensor value 336) and may compare the sensor value 339 to the predicted output value 336. For example, the model circuit 200 may determine the gradient associated with the output value 336 from the NOx reduction catalyst degradation circuit 210 and determine whether the gradient in the error objective exceeds an error threshold. If the gradient does not exceed the error threshold, the model circuit 200 may report that the output values 336 are indicative of the state-of-health values 337 (e.g., the output values sufficiently correspond to sensor values 339 indicative of health/operational parameters of the catalyst). However, if the gradient does exceed the error threshold, the model circuit may utilize the state of health modification circuit 344 to modify the catalyst output values 336 to reduce or minimize the error between the sensor values 339 and the model predicted output values 336. In this way, the model circuit 200 may utilize the state of health modification circuit 344 and measurements (e.g., sensor values 339) or other data indicative of actual aftertreatment system 120 conditions (e.g., telemetry data) to update, refine, or otherwise increase the accuracy of the state-of-health values 337.

[0078] In some embodiments, the model circuit 200 may predict output values 336 that estimate the axial distribution of chemical contaminants (including sulfur, potassium, sodium, etc.) on chemically distinct active sites present in a NOx reduction catalyst during real-world operation. The model circuit 200 may also estimate the axial distribution of chemical contaminants (e.g., sulfur) and corresponding degradation in NH3 oxidation function of a dual-layer ammonia (NH3) oxidation catalyst during real-world operation or of additional catalyst components.

[0079] In other embodiments, the model circuit 200 may utilize virtual and/or physical sensor values 339 received from one or more sensors 340 as inputs to estimate the degradation of a NOx reduction catalyst as a function of real-world exposure to chemical contaminants and temperature. The model circuit 200 may receive information from one or more sensors 340 (virtual or real) that provide information or data regarding operation of the aftertreatment system 120 and inform the estimations of the model circuit 200, improve the accuracy of the estimations of the model circuit 200, or correct/update the estimations of the model circuit 200. The sensors 340 are structured to detect operational parameters (e.g., temperature, pressure, outlet/inlet gas concentrations, etc.) of certain components of the aftertreatment system 120 such at the oxidation catalyst, NOx reduction catalyst, AMOX catalyst, inlet, outlet, and the like. The number, placement, and type of sensors 340 communicating with the model circuit 200 is highly configurable. The sensors 340 may include, but are not limited to, one or more of a moisture sensor, pressure sensor, temperature sensor (e.g., fluid temperature sensor, solid surface temperature sensors, IR sensor, etc.), a fluid sensor (e.g., exhaust gas flow rate, coolant flow rate, etc.), torque sensor, speed sensor (e.g., to determine at least one of an engine speed or a vehicle speed), exhaust gas concentration sensors, NOx, SOx, NH3, 02, H2, hydrocarbon sensor, and so on. For example, a condensation sensor may determine whether condensation is present within the exhaust aftertreatment system 120. As another example, a mass flow sensor may be disposed upstream of the aftertreatment system 120 and structured to determine a flow rate of exhaust gas entering the aftertreatment system 120.

[0080] As shown in Figure 3C, the model circuit 200 may also utilize sensor values 339 to update the catalyst or aftertreatment system component contaminant loading predictions of the model circuit 200. According to an exemplary embodiment, the sensors 340 may correct the NOx reduction catalyst output values 336 (e.g., tailpipe out NOx values) to reflect more accurate or updated contaminant loading values 347 using physical/virtual sensor data (e.g., NOx emission measurement data) downstream of a catalyst (e.g., a NOx reduction catalyst). Additionally, the degradation predictions of the model circuit 200 may utilize physical sensors measuring NOx concentration, flow rates and temperatures upstream and downstream of the NOx reduction catalyst. Like in Figure 3B, at step 341 of Figure 3C, the model circuit 200 may determine the gradient associated with the output value 336 from the NOx reduction catalyst degradation circuit 210 and determine whether the gradient in the error objective exceeds an error threshold. If the gradient does not exceed the error threshold, the model circuit 200 may report that the output values 336 are indicative of the contaminant loading values 347 (e.g., the output values sufficiently correspond to sensor values 339 indicative of contaminants on the catalyst). However, if the gradient does exceed the error threshold, the model circuit may utilize the contaminant loading modification circuit 346 to modify the catalyst output values 336 to reduce or minimize the error between the sensor values 339 and the model predicted output values 336. In this way, the model circuit 200 may utilize the contaminant loading modification circuit 346 and measurements (e.g., sensor values 339) or other data indicative of actual aftertreatment system 120 conditions (e.g., telemetry data) to update, refine, or otherwise increase the accuracy of the contaminant loading values 347.

[0081] Accordingly, the model circuit 200 can track the evolution of chemical contaminants on a catalyst (e.g., a NOx reduction catalyst). For example, the model circuit 200 may use sensor data from a NOx value sensor, an exhaust sensor, a temperature sensor and the like over a predefined time period to predict or measure how chemical contaminant values on the catalyst change over the time period. FIGS. 4 and 5 illustrate exemplary model predictions of contaminant (e.g., sulfur) uptake during an accelerated degradation cycle simulation with varying copper-zeolite NOx reduction catalyst sizes. Here, D refers to the catalyst diameter, L refers to the catalyst length, and V refers to the catalyst volume. The normalized sulfur uptake for a Cu-Zeolite based NOx reduction catalyst can be quantified based on a contaminant value that represents the amount of contaminant present on the catalyst. Accordingly, the contaminant value may be representative of a ratio of the contaminant (e.g., sulfur) uptake compared to the catalyst material and is displayed on the y-axis of the graphs in FIGS. 4 and 5. Analogous normalized chemical contaminant uptake values can be defined for Fe-Zeolites, vanadia-based NOx reduction catalysts, and the like. The model can perform similar predictive simulations on such catalysts.

[0082] As shown in FIG. 4, the model circuit 200 may make predictions regarding multiple NOx reduction catalysts with various dimensions. For example, chart 400 shows the results of the model circuit 200 predicting a contamination value (e.g., an amount of sulfur uptake, a total amount of contaminated surface sites, etc.) of a catalyst having varying dimensions (e.g., varying lengths, varying diameters, varying volumes, etc.). For example, line 401 represents a catalyst having a first length/diameter/volume, line 402 represents a catalyst having a second length/diameter/volume, and line 403 represents a catalyst having a third length/diameter/volume. As shown in chart 400, over an aging process and as time increases, the model circuit 200 predicts, based on catalyst dimensions, and boundary conditions, that the catalyst represented by line 401 will have a higher contaminant uptake than the catalyst depicted by line 403. Accordingly, the model circuit 200 may indicate that the catalyst represented by line 403 has an increased resistance to degradation as compared to the catalyst represented by line 401. As shown in chart 500, a cumulative or final contaminant value may be determined over the catalyst lifetime. For example, line 401 corresponds to bar 501, line 402 corresponds with bar 502, and line 403 corresponds with bar 503. As shown in Figure 5, the catalyst represented by line 403 and bar 503 resulted in the least lifetime contaminant uptake. Results of the model may be compared to a target or desired contaminant uptake (e.g., bar 504). Similar catalyst sizing studies can be performed for the oxidation catalyst across a range of application specific duty cycles. Additionally, optimization of the catalyst sizing for specific application and duty cycles (e.g., determining a smallest catalyst size for a defined NOx reduction efficiency, determining a catalyst geometry resulting in the least degradation over a given period of time for a specific duty cycle, etc.) can be automated through the utilization of non-linear regression algorithms.

[0083] Referring to FIGS. 6 and 7, a method 600 and a flow chart 700 of an example implementation of the method of diagnosing an aftertreatment system operation (e.g., diagnosing a contaminant value associated with a catalyst via sensor data obtained over a predefined period of time) using the model circuit 200 are shown.

[0084] At step 604, the model circuit 200 receives boundary conditions indicative of a real- world duty cycle for an aftertreatment system. For example, as shown in FIG. 7, the boundary conditions may include fuel values 712 associated with the aftertreatment system 120 (e.g., fuel consumption rate, fuel consumption volume, type of fuel consumed, average chemical contaminants per amount of fuel consumed, etc. of the engine 110 or any other source providing exhaust to the aftertreatment system 120) and air flow values 716 (e.g., an air flow rate at a component of the aftertreatment system 120, the engine 110, etc.). The boundary conditions may also include temperature values 720 such as temperature profiles of a catalyst, temperatures of exhaust, ambient temperature, and the like. Further, boundary conditions may include engine-out values including but not limited to engine-out oxygen values 724 and engine-out NOx values 728. In some embodiments, the boundary conditions received by the model may include static values such as material properties, dimensions, locations of components relative to one another, etc. as discussed above with respect to FIG. 3 A and FIG. 3B. Other boundary conditions may include reductant dosing strategies or a reductant dosing rate 732, regeneration strategies, combustion strategies, and the like. For example, regeneration strategies may include a designated time spent above temperatures of 500°C, a rate of desulfation over time, or an increased fuel injection or dosing quantity configured to increase the temperature of the aftertreatment system components and promote regeneration. The boundary conditions may be indicative of a real-world duty cycle such as the parameters of an engine and how the engine is being used (e.g., changes to the engine’s speed, torque, load, etc. over time). The boundary conditions may also represent a time spent in a certain operating range such as a time spent operating in on-highway conditions. The duty cycle may reflect a certain period of operation characterized by changes in the boundary conditions of the system/vehicle/engine over time.

[0085] At step 608, the model circuit 200 receives boundary conditions indicative of a lab- accelerated duty-cycle for an aftertreatment system. As shown in FIG. 7, the boundary conditions may be similar to those received and representative of a real -world duty cycle. For example, boundary conditions of an exemplary lab accelerated duty cycle may include fuel values 712, air flow values 716, temperature values 720, engine-out values (e.g., engine-out oxygen values 724 and engine-out NOx values 728), static values, reductant dosing strategies or a reductant dosing rate 732, regeneration strategies, combustion strategies, and the like. Lab accelerated duty cycles may simulate real-world operating conditions in a controlled laboratory environment. For example, lab accelerated duty cycles may include reactor based, test cell based, dynamometer based, or synthetic gas bench-based duty cycles. In this way, the model may compare the results of the real-world duty cycle accelerated degradation with the results of accelerated degradation for a corresponding lab accelerated duty cycle.

[0086] At step 612, the model circuit 200 receives an error threshold and an error objective. The error threshold may be a predetermined value used to determine when the error of the model’s predictions reaches a target minimum error value. The error threshold identifies a value at which to stop re-parameterization, as discussed with respect to step 632 below. The error objective may quantify the discrepancy or error between the predicted output values of the model based on the real-world boundary conditions and lab accelerated boundary conditions. The error objective may designate a minimum error amount at which the results are sufficiently similar such and re-parameterization is not required.

[0087] At step 616, the model circuit 200, after receiving the real -world boundary conditions, estimates and/or predicts output values for the aftertreatment system and duty-cycle defined by the real-world boundary conditions. The output values may include outlet gas values and surface site distributions of the aftertreatment system as a function of real-world exposure to chemical contaminants. For example, output values may include outlet gas concentrations, NH3/N0x slip values, N2/oxygen output concentrations, outlet gas flow rates, volumes, etc. In some embodiments, the model circuit 200 may determine surface site distributions of one or more components of the aftertreatment system 120 and/or a catalyst thereof. Surface site distributions may include the composition and location of different types of sites present on the surface of an aftertreatment system catalyst or component. A site may include a specific location on a surface of a component of the aftertreatment system (e.g., a specific location on a surface of a catalyst) where chemical reactions occur. Such sites are typically composed of metal ions or active metal particles that facilitate the conversion of harmful exhaust gases (e.g., NOx, CO, etc.) into less harmful substances (e.g., nitrogen (N2), carbon dioxide (CO2), and water). Further, a type of the site may refer to a nature of the sites of the aftertreatment system, such as their accessibility to the exhaust gases. Types of sites may include contaminated sites (e.g., inactive sites that are less effective at converting exhaust gases), active sites (e.g., sites that are not contaminated with sulfur, etc.), and the like. For example, the surface site distributions may include NH3 storage values, contaminant (e.g., sulfur, sodium, etc.) storage values, locations on the catalyst that remain active catalytic sites, locations on the catalyst where NH3 is stored, locations where contaminants are stored, etc. As shown in FIG. 7, the output values may be degradation metric values 736 which represent the amount of degradation on the aftertreatment system or a component thereof (e.g., a NOx catalyst, an oxidation catalyst, a AMOX catalyst, etc.). The degradation metric values may include a deNOx efficiency value, a baseline deNOx efficiency value, a location of active sites, an amount of contaminant uptake, and the like. The degradation metrics may indicate a catalyst degradation state (e.g., quantified by an amount of contaminant uptake) and catalyst performance (e.g., quantified by a NOx conversion efficiency).

[0088] At step 620, the model circuit 200, after receiving the lab-accelerated boundary conditions, likewise estimates and/or predicts output values for the aftertreatment system and duty-cycle defined by the lab-accelerated boundary conditions. The output values may include those described above with respect to step 616.

[0089| At step 624, the model circuit 200 (e.g., via a real-world representativeness circuit 212) compares the estimated or predicted output values from the real -world boundary conditions derived from the real-world duty cycle with the estimated or predicted output values from the lab -accelerated boundary conditions derived from the lab-accelerated duty cycle. The real-world representativeness circuit 212 may generate an output indicative of the closeness between the predicted and/or estimated output values from the real-world duty cycle and the lab-accelerated duty cycle. For example, the real -world representativeness circuit 212 may determine an error value associated with the predicted and/or estimate output values from the model circuit 200. In some embodiments, the real-world representativeness circuit 212 selects a specific mathematical metric or formula that quantifies the difference between predicted values and actual (e.g., observed or expected) values. This metric serves as a measure of the model circuit’s performance and helps assess how well the model circuit’s predictions match the actual outcomes. In this way, the real-world representativeness circuit 212 collects the degradation metrics received from the model circuit 200 (e.g., the NOx reduction catalyst degradation circuit 210) and may determine an error objective and/or determine the appropriate objective function definition for error estimation.

[0090] At step 628, the model circuit 200 may determine the gradient associated with the output generated from the real -world representativeness circuit 212 and determine whether the gradient in the error objective exceeds the error threshold. For example, model circuit 200 may determine the gradient in the error objective received from the real -world representativeness circuit 212 and compare it to the error threshold. If the gradient in the error objective exceeds the error threshold, the model circuit may proceed to step 632. However, if the gradient in the error objective is less than or equal to the error threshold, the model circuit 200 may proceed to step 636.

[0091 | At step 632, in response to the gradient in the error objective exceeding the error threshold, the model circuit 200 may re-parameterize the lab-accelerated boundary conditions. For example, a re-parameterization subsystem 744 may alter the lab-accelerated boundary conditions based on the gradient in the error objective in order to reduce the error between the lab-accelerated model results and the real-world boundary condition model results. The re-parameterization subsystem 744 may utilize an iterative or automated process to re-calculate model results based on the gradient in the error objective. When the gradient in the error objective meets or falls below the error threshold, the model circuit 200 may proceed to step 636.

[0092] At step 636, in response to the gradient in the error objective falling below or meeting the error threshold, the model circuit 200 may report degradation cycle parameters. The degradation cycle parameters represent optimized (e.g., improved accuracy as compared to other modeling techniques such as hydrothermal aging) output values and degradation protocols for NOx reduction, oxidation, and/or AMOX catalysts.

[0093] Turning to FIGS. 8 and 9, a method 800 and a flow chart 900 of an example implementation of the method to design aftertreatment system components and catalysts based on the results of the model circuit 200 are shown. The method provided may predict a wide variety of output values such that numerous design choices associated with an aftertreatment system, and its catalysts can be modeled, varied, and results compared to select the design with the most desirable features (e.g., least chemical uptake, highest durability, highest average NOx reduction efficiency over time, etc.). For example, as shown in Figure 9, the model circuit 200 may be used to select a catalyst sizing, chemical composition, washcoat composition, and component location to minimize the uptake of chemical contaminants and corresponding NOx conversion performance degradation during real-world operation.

(0094] Additional uses of the model circuit 200 include selecting a position and location of a NOx reduction catalyst to minimize NOx conversion performance degradation during exposure to chemical contaminants and temperature. Such methods may include optimizing NOx reduction catalyst location, sizing, material composition, distribution of active sites on the NOx reduction catalyst, and the like to minimize N2O formation during real-world exposure to chemical contaminants and temperature.

[0095] Analogously, the systems and methods disclosed herein may be utilized to predict and selected improved designs for oxidation catalysts, AMOX catalysts, etc. For example, the model circuit 200 may be used to optimize the position and size of an oxidation catalyst to minimize the uptake of chemical contaminants and corresponding NOx conversion performance of a downstream NOx reduction catalyst during real-world operation. Other methods improve the metal loading, metal distribution and metal oxidation state of an oxidation catalyst to minimize the uptake of chemical contaminants and NOx conversion performance degradation of a downstream NOx reduction catalyst during real-world exposure to chemical contaminants and temperature and optimize the position, location and sizing of a dual-layer ammonia (NH3) oxidation catalyst to minimize NOx conversion performance degradation during exposure to chemical contaminants and temperature, utilizing predictions of the model circuit 200.

(0096] At step 804, the model circuit 200 receives a contaminant loading threshold. The contaminant loading threshold may correspond to a target operating parameter or performance objective of the aftertreatment system or components thereof. Further, the contaminant loading threshold may comprise a value indicative of a value regarding emissions associated with the aftertreatment system. For example, the contaminant loading threshold may correspond to a NOx emission regulation such as a US or EU limit of the amount of NOx released from the aftertreatment system 120. The contaminant loading threshold may be emission rate limitation such as approximately less 0.51bs./mm BTU to approximately 0.051bs./mm BTU. In some embodiments, the limitation may be particularly less than 0.15 lbs. /mm BTU. The contaminant loading threshold may be translated and correlated to the contaminant loading values and compared with the contaminant loading values in order to determine whether the contaminant loading values of a specific duty-cycle meet or exceed the contaminant loading threshold. For example, chemical contaminant uptake on a catalyst can be utilized to determine a predicted NOx emission value or NOx reduction efficiency. The contaminant loading threshold may be, for example, contaminant loading value that causes an aftertreatment system to satisfy or comply with a specific contaminant loading threshold (e.g., a regulated emission value, a target emission value, a target NOx reduction efficiency, and the like).

[0097] At step 808, model circuit 200 receives boundary conditions indicative of a real-world duty cycle for an aftertreatment system. As discussed above, the boundary conditions may include fuel values 912, air flow values 916, temperature values 920, engine-out values (e.g., engine-out oxygen values 924 and engine-out NOx values 928), static values (such as catalyst dimensions, length, cross-sectional area, diameter, wash-coat composition, etc.), reductant dosing strategies or a reductant dosing rate 932, regeneration strategies, combustion strategies, and the like. Boundary conditions may also include oxidation catalyst dimensions 904, such as the wash-coat composition, material properties, sizing, location relative to the NOx reduction catalyst, surface area, volume, etc. Similarly, the boundary conditions may include NOx reduction catalyst dimensions 908 and NOx reduction catalyst composition values 929. For example, NOx reduction catalyst composition values 929 may define a catalyst substrate material, wash coat, chemical composition, or the like. Analogous boundary conditions may be defined for AMOX catalysts. The boundary conditions may also be indicative of a particular test design, configuration, dosing strategy for an aftertreatment system, component, or catalyst. For example, a first set, second set, third set, etc. of boundary conditions may represent on-highway conditions for a long-haul truck over its estimated lifetime. Each set may be identical except for changes made to the sizing, wash coat composition, reductant dosing strategy, active site distribution, and/or location of the catalyst in the aftertreatment system 120. In this way, the varied boundary conditions can be modeled and contaminant loading values can be compared to determine which design of the aftertreatment system 120 achieves the most desirable (e.g., least chemical contaminant uptake, highest NOx reduction efficiency, lowest NOx emissions) results.

[0098] At step 812, the model circuit 200, based on the real-world boundary conditions and using the model circuit 200, estimates the contaminant loading value 936 of the aftertreatment system and its components as a function of real-world exposure to chemical contaminants. The contaminant loading value 936 may represent the uptake of chemical contaminants on the surface a catalyst as a function of time, temperature, and the like. For example, as shown in Figure 9, the model circuit 200 utilizes the NOx reduction catalyst degradation circuit 210 to estimate one or more contaminant loading values 936. The contaminant loading value 936 may indicate a distribution of active sites, a NOx emission value, a N0x/NH3 slip value, or other output gas values and surface site distribution values indicative of a degradation state of the aftertreatment system 120 and its components (e.g., oxidation catalysts, NOx reduction catalysts, AMOX catalysts).

[0099] At step 816, the model circuit 200 determines whether the contaminant loading value exceeds the contaminant loading threshold. The model circuit 200 may compare the contaminant loading values at a specific point in time to determine whether a maximum contaminant loading value, an average contaminant loading value, a median contaminant loading value, or any single contaminant loading value exceeds the contaminant loading threshold. A contaminant loading value exceeding the contaminant loading threshold may indicate that the specific aftertreatment system design (e.g., catalyst size, wash-coat composition, fuel usage, reductant dosing strategy, and the like reflected by the boundary conditions) was unable to meet the emission regulation/standard, target NOx reduction efficiency, minimum NOx emission rate, etc. represented by the contaminant loading threshold. Accordingly, if the comparison of the contaminant loading values and contaminant loading threshold indicates that the contaminant loading values exceed the contaminant loading threshold, the model circuit 200 may proceed to step 820. However, if the contaminant loading values meet or fall below the contaminant loading threshold, this may indicate that the aftertreatment design complies with the emission threshold or target operating parameter represented by the contaminant loading threshold. Accordingly, if the contaminant loading values meet or fall below the contaminant loading threshold, the model circuit 200 may proceed to step 824.

[0100] At step 820, in response to the contaminant loading value exceeding the contaminant loading threshold, the model circuit 200 (e.g., via a catalyst size optimization circuit 818) reparameterizes the catalyst boundary conditions. Re-parameterizing the catalyst boundary conditions may occur via a catalyst size optimization circuit 818. In such an embodiment, the static input values and/or boundary conditions associated with the physical dimensions of the catalyst may be altered and the model may be utilized again in an iterative process, comparing the new contaminant loading values with the contaminant loading threshold. In some embodiments, re-parameterizing the catalyst boundary conditions may include changing a value representative of a catalyst length, width, cross-sectional area, volume, active site distribution, wash-coat composition, location relative to other aftertreatment system components, metal loading, metal distribution, etc. For example, the at least one processing circuit of the model circuit 200 may adjust the boundary conditions to change a catalyst size of the aftertreatment system. In this way, the design of the catalyst and/or the design of the aftertreatment system 120 and its components may be varied and simulated until a result meets or falls below the contaminant loading threshold.

(0101 ] At step 824, in response to the contaminant loading value meeting or falling below the contaminant loading threshold, the model circuit 200 reports the final catalyst boundary conditions. At this step, the model circuit 200 may complete the iterative process and report a set of boundary conditions that meet the desired target operating parameters, emissions standards, durability, and the like represented by the contaminant loading threshold. For example, reporting the final catalyst boundary conditions may include reporting the physical dimensions (length, diameter, cross-sectional area, volume, etc.) of a catalyst, the wash-coat composition of the catalyst, a location of the catalyst in the system relative to other catalysts, a metal loading, active site distribution, regeneration strategy, reductant dosing strategy, etc. of the aftertreatment system 120.

[0102] As discussed herein, in addition to predictions of the accumulation of chemical contaminants, the model circuit 200 can predict the influence of temperature and chemical contaminant exposure on the active sites of the NOx reduction catalyst. These predictions can analogously be utilized to optimize the material composition (such as the metal loading and wash coat composition), catalyst sizing, and distribution of active sites on an upstream oxidation catalyst, a downstream AMOX catalyst, etc. For example, a method for optimizing catalyst material composition may determine a catalyst AMOX configuration/sizing/loading/etc. that would minimize real-world degradation of catalyst performance caused by chemical contaminants for a specific duty cycle or application.

|0103| Other optimization and estimation methods are also disclosed. For example, Figure 10 shows a flow chart illustrating a method of utilizing the model circuit 200 to find optimal boundary conditions (e.g., operating parameters, static dimensions, and the like) for oil consumption in an automotive engine, fuel consumption, combustion strategy, regeneration strategy, etc. to minimize chemical contaminant exposure and real-world degradation of an oxidation, NOx reduction, and/or AMOX catalyst. The model circuit 200 may receive boundary conditions indicative of a duty cycle for an aftertreatment system. As discussed above, the boundary conditions may include oil consumption values 1010, fuel consumption values 1012, air flow values 1016, temperature values 1020, engine-out values (e.g., engine- out oxygen values 1024 and engine-out NOx values 1028), static values (such as catalyst dimensions, length, cross-sectional area, diameter, wash-coat composition, etc.), reductant dosing strategies or a reductant dosing rate 932, regeneration strategies, combustion strategies, and the like. Reductant dosing strategies, regeneration strategies, and the like may include a predetermined reductant dosage rate/amount/etc. over time or a regeneration schedule such as a predetermined regeneration temperature and conditions that initiate aftertreatment system catalyst regeneration. Accordingly, the model circuit 200 may generate and implement a regeneration strategy, for example, to regenerate a catalyst of the aftertreatment system based on the determined surface site distribution (e.g., to minimize or eliminate contaminated surface sites in response to a predetermined percentage of the sites in the surface site distribution becoming contaminated).

101041 The model circuit 200 may then estimate state-of-health values 1037 for an aftertreatment system 120 (e.g., a NOx reduction catalyst) based on the duty-cycle indicative of engine operating parameters and a reductant injection rate 1032. At step 1041, the model circuit 200 may compare one or more state-of-health values 1037 to a threshold. For example, the model circuit 200 may receive an estimated NH3 slip value or sulfur concentration value based on the duty cycle, and compare those values to target values (e.g., thresholds aimed at providing operating conditions which results in an NH3 slip value lower than a certain amount for a predefined time, a maximum sulfur concentration on the catalyst, etc.). If the predicted state of health values 1037 exceed the threshold, the model circuit 200 may utilize an engine operation optimization circuit 1045 to modify the engine combustion parameters (e.g., fuel consumption rate 1012, air flow rate 1016) to re-evaluate the state of health values 1037. The model circuit 200 may then report a set of boundary conditions indicative of engine operating parameters that satisfy the desired threshold (e.g., an engine fuel consumption strategy/regime that prevents sulfur uptake from rising above a certain threshold).

[0105] Turning to Figure 11, another method disclosed herein varies the boundary conditions and compares contaminant loading values to predesignated threshold in order to optimize the reductant dosing (e.g., predict a reductant dosing strategy that results in the least chemical contamination, performance degradation, etc.) upstream of the NOx reduction catalyst to minimize performance degradation during real-world exposure to chemical contaminants and temperature. The model circuit 200 may additionally detect the extent of dilution of reductant dosed upstream of the NOx reduction catalyst utilizing NOx reduction catalyst state of health diagnostic predictions. Analogously to Figure 10 above, if the predicted state of health values 1037 exceed a target threshold, the model circuit 200 may utilize a reductant dosing optimization circuit 1047 to modify the reductant dosing rate 1032 (e.g., an amount of reductant dosed over time, a type or quantity of reductant dosed, etc.) to re-evaluate the state of health values 1037. The model circuit 200 may then report a set of boundary conditions indicative of a reductant dosing strategy 1032 that satisfies the desired threshold (e.g., a reductant dosing strategy/regime that prevents sulfur uptake from rising above a certain threshold, that maintains NOx conversion efficiency above a certain percentage, etc.).

10106] Turning to Figure 12, still further methods may utilize the model circuit 200 and the systems described herein to detect a concentration of chemical contaminants in engine oils and engine fuels, including diesel fuel, biodiesel fuel, hydrogen fuel, natural gas fuel, and gasoline fuel, utilizing predictions of the model circuit 200 along with physical NOx emission sensor measurements downstream of the NOx reduction catalyst. As shown in Figure 12, the model circuit 200 may utilize predictions of tailpipe output values 336 and update/compare the output values 336 via physical or virtual sensor values 1239 to detect events of high chemical contaminant presence in an engine fluid (e.g., an engine oil, fuel, lubricants, etc.). As discussed above, the model circuit 200 may also determine whether the estimated tailpipe output values 336 satisfy an error threshold at step 1041. If the error exceeds the error threshold, the state of health modification circuit 1044 may adjust the output values 336 and/or modify the boundary conditions to achieve tailpipe output values 336 that satisfy the error threshold. In other embodiments, the model circuit 200 may also utilize integrated telematic approaches in place of or in addition to the physical or virtual sensor values 1239 to estimate or predict events of high chemical contaminant presence. For example, the model circuit 200 may detect events of high chemical contaminant presence and estimate a level, contaminant concentration, or contaminant identity in an engine fluid via an engine fluid contaminant estimation circuit 1251. The model circuit 200, for example, may predict, detect, or measure events such as a decrease in NOx conversion efficiency, an increase in chemical contaminant storage on the surface of one or more catalysts, a decrease in active site distribution, and the like and flag that event as an event caused by an increase in a chemical contaminant amount. Detections of events of high chemical contaminant presence in engine fluids (e.g., oil, lubricants, and/or fuel, etc.) may be further improved by utilizing physical NOx emission sensor measurements downstream of the NOx reduction catalyst in addition to the predictions of the model circuit 200. Further, the predictions of the model circuit 200 may utilize (e.g., via a look up table, by pulling from pre-defined values, etc.) known chemical contaminant levels for various fuel types including diesel fuel, biodiesel fuel, hydrogen fuel, natural gas fuel and gasoline fuel.

[0107] Like Figure 3C described above, the model circuit 200 may utilize sensor values 1239 to update the catalyst or aftertreatment system component contaminant loading values 1039. According to an exemplary embodiment, the sensors 1239 may correct the estimated output values 336 (e.g., tailpipe out NOx values) to reflect more accurate or updated contaminant loading values 1039 using physical/virtual sensor data (e.g., NOx emission measurement data) downstream of a catalyst (e.g., a NOx reduction catalyst). Additionally, the degradation predictions of the model circuit 200 may utilize physical sensors measuring NOx concentration, flow rates and temperatures upstream and downstream of the NOx reduction catalyst. At step 1041, the model circuit 200 may determine the gradient associated with the output value 336 from the NOx reduction catalyst degradation circuit 210 and determine whether the gradient in the error objective exceeds an error threshold. If the gradient does not exceed the error threshold, the model circuit 200 may report that the output values 336 are indicative of the contaminant loading values 1039 (e.g., the output values sufficiently correspond to sensor data 1239 indicative of contaminants on the catalyst). Accordingly, the contaminant loading values 1039 may then be provided to the engine fluid contaminant estimation circuit 1251, which may estimate the contaminants present in engine oils or fuels burned/ consumed based on the predicted output values 336, the actual measurement values 1239, the updated contaminant loading values 1039, and known or estimated average quantities of contaminants associated with certain fuels and fluids.

[0108] As shown in Figure 13, the model circuit 200 may identify representative Xth percentile (e.g., 90th percentile, 50th percentile) degraded NOx reduction catalyst parts in real-world operation via the use of population analysis. The model circuit 200 may estimate catalyst degradation metric values 736 (e.g., output values 336) based on sets of boundary conditions exemplary of lab accelerated duty cycles (e.g., degradation protocols determined via the model circuit 200) and/or real -world duty cycles. For example, the first set of boundary conditions may be exemplary of a real-world duty cycle experienced by an actual vehicle, aftertreatment system 120, etc. For instance, the real -world duty cycle may designate fuel consumption values 712, air flow values 716, and other metrics of a passenger vehicle over the course of a designated time or operation (e.g., a designated number of years, a designated travel route, etc.), The second exemplary duty cycle may be exemplary of a lab accelerated duty cycle such as a degradation protocol predicted by the model circuit 200 and may be configured to be representative of the real -world duty cycle (e.g., generate degraded parts having substantially similar contaminant loading values, state of health values, and the like as compared to the corresponding real-world part). The model circuit 200 may compare the degradation metric values 736 between the lab-accelerated and real -world parts to identify an Xth percentile degraded part. The model circuit 200 may utilize a telemetric degradation metric statistic circuit 1312 to analyze the distributions of degradation values obtained from the lab-accelerated and real-world data sets. For example, the telemetric degradation metric statistic circuit 1312 may calculate or receive summary statistics (e.g., mean, median, standard deviation) of the degradation values over a population of parts for each duty cycle and predict a distribution of parts based on the degradation values. The telemetric degradation metric statistic circuit 1312 may then identify the degradation value that corresponds to the Xth percentile (e.g., 90^th percentile, 50^th percentile) degraded part 1328 in each data set.

[0109] As utilized herein with respect to numerical ranges, the terms “approximately,” “about,” “substantially,” and similar terms generally mean +/- 10% of the disclosed values. When the terms “approximately,” “about,” “substantially,” and similar terms are applied to a structural feature (e.g., to describe its shape, size, orientation, direction, etc.), these terms are meant to cover minor variations in structure that may result from, for example, the manufacturing or assembly process and are intended to have a broad meaning in harmony with the common and accepted usage by those of ordinary skill in the art to which the subject matter of this disclosure pertains. Accordingly, these terms should be interpreted as indicating that insubstantial or inconsequential modifications or alterations of the subject matter described and claimed are considered to be within the scope of the disclosure as recited in the appended claims.

[0110] It should be noted that the term “exemplary” and variations thereof, as used herein to describe various embodiments, are intended to indicate that such embodiments are possible examples, representations, or illustrations of possible embodiments (and such terms are not intended to connote that such embodiments are necessarily extraordinary or superlative examples).

[0111 ] Embodiments within the scope of the present disclosure include program products comprising computer or machine-readable media for carrying or having computer or machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a computer. The computer readable medium may be a tangible computer readable storage medium storing the computer readable program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and/or store computer readable program code for use by and/or in connection with an instruction execution system, apparatus, or device. Machine-executable instructions include, for example, instructions and data which cause a computer or processing machine to perform a certain function or group of functions.

[0112] The computer readable medium may also be a computer readable signal medium. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport computer readable program code for use by or in connection with an instruction execution system, apparatus, or device. Computer readable program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), or the like, or any suitable combination of the foregoing.

[0113] In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as an electro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor.

[0114] Computer readable program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more other programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone computer- readable package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0115] Although the figures and description may illustrate a specific order of method steps, the order of such steps may differ from what is depicted and described, unless specified differently above. Also, two or more steps may be performed concurrently or with partial concurrence, unless specified differently above. Such variation may depend, for example, on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure.

[0116] The foregoing description of embodiments has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from this disclosure. The embodiments were chosen and described in order to explain the principals of the disclosure and its practical application to enable one skilled in the art to utilize the various embodiments and with various modifications as are suited to the particular use contemplated. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions and arrangement of the embodiments without departing from the scope of the present disclosure as expressed in the appended claims. Additionally, any element disclosed in one embodiment may be incorporated or utilized with any other embodiment disclosed herein.

[0117] Accordingly, the present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the disclosure is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

WHAT IS CLAIMED IS:

1. A system comprising: at least one processing circuit comprising at least one memory coupled to at least one processor, the at least one memory storing instructions therein that, when executed by the at least one processor, causes the at least one processor to perform operations comprising: receiving at least one boundary condition indicative of a duty-cycle of an exhaust aftertreatment system; receiving at least one value indicative of an operating parameter of the exhaust aftertreatment system from at least one sensor; predicting an output value associated with an operation of a catalyst of the exhaust aftertreatment system over the duty-cycle; and determining, based on the output value, a reductant dosing value for the exhaust aftertreatment system.

2. The system of claim 1, wherein the at least one memory stores instructions therein that, when executed by the at least one processor, causes the at least one processor to perform operations further comprising: simulating a hydrothermal aging process on the catalyst of the exhaust aftertreatment system.

3. The system of claim 2, wherein the catalyst is at least one of an oxidation catalyst, a NOx reduction catalyst, or an AMOX catalyst.

4. The system of claim 2, wherein simulating the hydrothermal aging process on the catalyst of the exhaust aftertreatment system includes simulating an active site distribution of the catalyst.

5. The system of claim 1, wherein the at least one memory stores instructions therein that, when executed by the at least one processor, causes the at least one processor to perform operations further comprising: simulating a presence of a contaminant on the catalyst.

6. The system of claim 5, wherein the contaminant includes at least one of sulfur, potassium, sodium, or phosphorous.

7. The system of claim 5, wherein the catalyst is at least one of an oxidation catalyst, a NOx reduction catalyst, or an AMOX catalyst.

8. The system of claim 1, wherein the at least one boundary condition includes at least one of a fuel value, an air flow value, a temperature value, an engine-out value, a size of an aftertreatment system component, a reductant dosing rate, or an amount of time spent in a regeneration state.

9. The system of claim 1, wherein the output value includes an outlet gas value and a surface site distribution of the catalyst.

10. A method for designing an aftertreatment system degradation protocol, the method comprising: receiving, by at least one processing circuit, boundary conditions indicative of a first duty cycle for the aftertreatment system; receiving, by the at least one processing circuit, boundary conditions indicative of a second duty cycle for the aftertreatment system; receiving, by the at least one processing circuit, an error threshold and an error objective relating to operation of the aftertreatment system; estimating, by the at least one processing circuit, output values for the aftertreatment system based on the boundary conditions indicative of the first duty cycle; estimating, by the at least one processing circuit, output values for the aftertreatment system based on the boundary conditions indicative of the second duty cycle; comparing, by the at least one processing circuit, the estimated output values based on the boundary conditions indicative of the first duty cycle with the estimated output values based on the boundary conditions indicative of the second duty cycle; and in response to determining that the error objective meets or falls below the error threshold, reporting, by the at least one processing circuit, a degradation cycle parameter of the aftertreatment system.

11. The method of claim 10, further comprising: in response to the error objective exceeding or failing to satisfy the error threshold, adjusting, by the at least one processing circuit, the boundary conditions indicative of the second duty cycle based on a gradient in the error objective.

12. The method of claim 11, wherein: the error threshold comprises a predetermined value indicating that the gradient in the error objective falls below a target minimum error value; and the error objective comprises a value indicative of a degree of similarity between the estimated output values based on the boundary conditions indicative of the first duty cycle and the estimated output values based on the boundary conditions indicative of the second duty cycle.

13. The method of claim 10, wherein: the boundary conditions indicative of the first duty cycle for the aftertreatment system comprise at least one of a first value indicative of a fuel consumption associated with the aftertreatment system, a first value indicative of an air flow of the aftertreatment system, a first value indicative of a temperature of the aftertreatment system, or a first value indicative of a dimension of the aftertreatment system; and the boundary conditions indicative of the second duty cycle for the aftertreatment system comprise at least one of a second value indicative of the fuel consumption associated with the aftertreatment system, a second value indicative of the air flow of the aftertreatment system, a second value indicative of the temperature of the aftertreatment system, or a second value indicative of the dimension of the aftertreatment system.

14. The method of claim 10, further comprising: determining, by the at least one processing circuit, a surface site distribution of one or more components of the aftertreatment system, the surface site distribution indicative of a composition and a location of a type of a site present on a surface of the one or more components of the aftertreatment system; and regenerating a catalyst of the aftertreatment system based on the determined surface site distribution.

15. The method of claim 10, wherein: the output values for the aftertreatment system based on the boundary conditions indicative of the first duty cycle comprise at least one of a first outlet gas concentration, a first outlet gas flow rate, or a first value indicative of a surface site distribution of a catalyst of the aftertreatment system; and the output values for the aftertreatment system based on the boundary conditions indicative of the second duty cycle comprise at least one of a second outlet gas concentration, a second outlet gas flow rate, or a second value indicative of the surface site distribution of the catalyst of the aftertreatment system.

16. A method for designing aftertreatment system components or catalysts, the method comprising: receiving, by at least one processing circuit, a contaminant loading threshold; receiving, by the at least one processing circuit, boundary conditions indicative of a duty cycle for the aftertreatment system; estimating, by the at least one processing circuit, a contaminant loading value for the aftertreatment system based on the boundary conditions indicative of the duty cycle; comparing, by the at least one processing circuit, the contaminant loading value and the contaminant loading threshold; and in response to the contaminant loading value meeting or falling below the contaminant loading threshold, reporting, by the at least one processing circuit, a catalyst design parameter.

17. The method of claim 16, further comprising: in response to the contaminant loading value exceeding the contaminant loading threshold, adjusting, by the at least one processing circuit, the boundary conditions indicative of the duty cycle, wherein adjusting the boundary conditions includes changing a catalyst size of the aftertreatment system.

18. The method of claim 16, wherein the contaminant loading threshold comprises a value indicative of a value regarding emissions associated with the aftertreatment system.

19. The method of claim 16, wherein the boundary conditions indicative of the duty cycle for the aftertreatment system comprise at least one of a value indicative of a fuel consumption associated with the aftertreatment system, a value indicative of an air flow in the aftertreatment system, a value indicative of a temperature of the aftertreatment system, or a value indicative of a dimension of the aftertreatment system.

20. The method of claim 16, wherein the catalyst design parameter comprises a value regarding a physical dimension of a catalyst of the aftertreatment system.