Selective Catalytic Reduction Model Research Articles

A temporal discretization and spatial integration SCR model with dual temperature-related parameters

Developing an accurate and rapid Selective Catalytic Reduction (SCR) model is crucial for advanced control strategies, which is challenging due to the complex physicochemical processes. In this paper, a novel control-oriented SCR model is proposed, which exhibits faster and more accurate estimation performance for both NOx and NH3 emissions. Unlike traditional modeling methods, this research develops a temporal discretization and spatial integration (TDSI) SCR model with dual temperature-related parameters. By introducing NH3 recycling, the spatial integration model is improved, leading to an enhanced accuracy in model estimation. Moreover, A time discretization method based on current state can effectively address the issue of sampling time sensitivity. Additionally, dual temperature-related parameters can significantly enhance model accuracy, particularly in high-temperature regions. A simplified and clear flowchart is provided for parameter identification based on a limited number of engine bench test results. In a cold-start World Harmonized Transient Cycle (WHTC), the TDSI model can achieve NOx and NH3 accuracies of 92.26% and 92.52%, respectively, with a computation time of 1.47 s. Through comprehensive comparisons, the proposed model exhibits comparable accuracy to the GT model while significantly reducing computation time by 98.39%. Additionally, compared to the Continuous Stirred-Tank Reactor (CSTR) and θT models, there is an approximate 3% improvement in NOx and NH3 accuracies. This TDSI model provides a foundation for designing urea injection control strategies and offers new perspectives for simplifying the model design.

Adaptive time-step unscented kalman filtering (ATS-UKF) based observer design for urea selective catalytic reduction (SCR) performance of diesel engines

To reduce the number of sensors in the SCR catalyst, state feedback and fault diagnosis information are provided. Firstly, a model based on the coupling of flow, heat transfer, and gas-solid phase catalytic reaction in the SCR system is investigated in this paper. The parabolic partial differential equations are simplified by the variable substitution method and the method of lines approach (MOL). The simplified system of equations is solved by backward differentiation formulas (BDF) with adaptive adjustment time step strategy. Meanwhile, the chemical reaction parameters are accurately calibrated per second using the Levenberg-Marquardt method. Secondly, the ATS-UKF is designed in this paper, and to ensure the synchronisation between the ATS-UKF and the SCR model calculations, the time step of solving the BDF by the SCR model is taken as the time step of propagating the sigma points. Two observation scenarios are assumed: (1) no downstream NH3 concentration sensor, ammonia coverage and downstream NH3 concentration are observed by ATS-UKF; (2) no downstream NOx sensor, ammonia coverage and downstream NOx concentration are observed by ATS-UKF. Finally, the paper carries out bench tests. In the first case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R². The mean absolute error (MAE) between the observed and experimental values of the ATS-UKF for the downstream NH3 concentration was 2.76 ppm. In the second case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R², and the MAE between the observed and experimental values of the ATS-UKF for the downstream NOx concentration was 1.53 ppm. Environmental ImplicationThe Adaptive Time-Step Unscented Kalman Filtering (ATS-UKF) enhances urea Selective Catalytic Reduction (SCR) in diesel engines, improving environmental outcomes. This method minimizes sensor dependence, enabling more precise SCR system management and effective emission reduction. By advancing emission control technologies, ATS-UKF contributes to global air pollution mitigation efforts, supporting cleaner air and environmental sustainability. Its innovative approach in monitoring and predicting SCR performance marks a significant step towards eco-friendly diesel engine operation.

Effect and optimization of radial non-uniform porosity catalyst on SCR characteristics based on response surface method

Selective catalytic reduction (SCR) system is used to the denitration (de-NOx) of diesel engine exhaust after treatment. In this work, a three-dimensional SCR model with radial non-uniform distribution of catalysts is established to study the pressure drop and de-NOx performance. Firstly, the SCR converter model with uniform catalyst distribution is selected to verify its reliability. Then, the radius R1 and porosity ԑ1 of area 1, radius R2 and porosity ԑ2 of area 2 are selected as single factor variables to study the influence on the SCR system performance. Finally, response surface methodology (RSM) is employed to evaluate and optimize the above single factor parameters affecting de-NOx performance and pressure drop of SCR system. The results showed that the change of ԑ2 had the greatest impact on de-NOx efficiency and pressure drop. The optimum condition for maximum de-NOx efficiency was estimated at the R1 of 50 mm, R2 of 100 mm, porosity ԑ1 of 0.47 and porosity ԑ2 of 0.7. Under these conditions, the de-NOx efficiency by numerical simulation was 80.37 %, which was close to the predicted value (80.68 %), and the pressure drop was 218 Pa, which was close to the predicted value (246 Pa). Compared with the uniform distribution of catalysts, the de-NOx efficiency of the optimal Case of numerical simulation is increased by 3.74 %, and the pressure drop is reduced by 46 Pa. The research can provide theoretical and engineering references for the improvement of SCR catalyst coating process.

Investigation on the applicability of parameters identification of marine diesel engine SCR kinetic model based on Discrete-Grid algorithm

Selective catalytic reduction (SCR) technology is the most widely used denitrification method in industry. However, the calibration of its kinetic model is very problematic due to the large number of parameters involved and the nonlinear characteristics, especially for the undesired reactions such as formation-decomposition of ammonium nitrate (AN), ammonium sulfate (AS), ammonium bisulfate (ABS), etc. In this paper, the synthetic gas test bench and the SCR system of WD10 marine diesel engine burning high-sulfur fuel oil (HSFO) and low-sulfur fuel oil (LSFO) are taken as the research objects. Firstly, based on the experimental data obtained from the synthetic gas test bench, the pre-exponential multiplier and activation energy of each reaction were identified using the Discrete-Grid algorithm. In this work, considering that the production of N2O, SO3, AN, AS, and ABS in the undesired reactions is very low, not on the same order of magnitude as that of NO and NH3, it is easy to overlook their reaction characteristics in the optimization process. We have adopted an approach that amplifies the error between the predicted concentration and the observed concentration of the species to solve this problem. Secondly, a detailed and complete SCR kinetic model was constructed. Finally, with the parameter identification results obtained from the synthesis gas bench as initial values, all the kinetic parameters of the WD10 marine engine SCR model was calibrated simultaneously using the Discrete-Grid algorithm. The results showed that the SCR kinetic model derived from the synthetic gas test bench was suitable for the real marine engine SCR system when the range of temperature and space velocity (SV) of the synthetic gas bench test is similar to that of the marine SCR system.

Investigation of the complex 3D flow structure within a selective catalytic reduction (SCR) reactor of a coal-fired power plant

A selective catalytic reduction (SCR) reactor is commonly used to remove nitrogen oxides (NOx) from coal-fired boilers. Uniformity of the flow passing through the catalyst layer is important for increasing denitrification (de-NOx) efficiency. In order to examine flow uniformity, this study conducted an experimental and numerical analysis of the complex internal flow within a realistic SCR model. Magnetic resonance velocimetry (MRV) was utilized to obtain non-invasive measurements of three-dimensional three-component average velocity and validate Reynolds-averaged Navier-Stokes (RANS) numerical simulations. The computational results showed similar overall flow structure compared with the MRV results. Parameters representing flow quality such as relative standard deviation (RSD) and recirculation zone strength (RZS) were calculated by integrating the flow field. These parameters have the largest value after the straightener area and decrease towards the catalyst reactor, and are not significantly affected by Reynolds number upstream of the catalyst layer. The recirculation zone size was analyzed using spanwise uniformity and skewness indicators. As the recirculation zone induces biased flow, the non-reacted NOx concentration was more prominent in the outlet zone opposite of the recirculating area in the corresponding actual on-site SCR reactor. Based on this finding, a meaningful correlation between flow maldistribution and de-NOx reaction could be deduced.

Nitrogen Oxides and Ammonia Removal Analysis Based on Three-Dimensional Ammonia-Diesel Dual Fuel Engine Coupled with One-Dimensional SCR Model

Ammonia, as an alternative fuel for internal combustion engines, can achieve nearly zero carbon emissions. Although the development of the pure ammonia engine is limited by its poor combustion characteristics, ammonia–hydrocarbon mixed combustion can effectively improve the combustion of ammonia fuel. With the increase in the ammonia fuel proportion in the fuel mixture, a large number of nitrogen oxides (NOX) and unburned ammonia may be discharged, which have a poor impact on the environment. In this study, the performance of selective catalytic reduction (SCR) aftertreatment technology in reducing NOX and ammonia emissions from ammonia–diesel dual-fuel engines was investigated using simulation. A good cross-dimensional model was established under the coupling effect, though the effect of a single-dimensional model could not be presented. The results show that when the exhaust gas in the engine cylinder is directly introduced into the SCR without additional reducing agents such as urea, unburned ammonia flowing into SCR model is in excess, and there will be only ammonia at the outlet; however, if the unburned ammonia fed into the SCR model is insufficient to reduce NO, the ammonia concentration at the outlet will be 0. NOX can be 100% effectively reduced to N2 under most engine conditions; thus, unburned ammonia in exhaust plays a role in reducing NOX emissions from ammonia–diesel dual-fuel engines. However, when the concentration of unburned ammonia in the exhaust gas of an ammonia–diesel dual-fuel engine is large, its ammonia emissions are still high even after the SCR. In addition, the concentrations of N2O after SCR do not decrease, but increase by 50.64 in some conditions, the main reason for which is that by the action of the SCR catalyst, NO2 is partially converted into N2O, resulting in an increase in its concentration at the SCR outlet. Adding excessive air or oxygen into the SCR aftertreatment model can not only significantly reduce the ammonia concentration at the outlet of the model without affecting the NOX conversion efficiency of SCR, but inhibit N2O production to some extent at the outlet, thus reducing the unburned ammonia and NOX emissions in the tail gas of ammonia–diesel dual-fuel engines at the same time without the urea injection. Therefore, this study can provide theoretical guidance for the design of ammonia and its mixed-fuel engine aftertreatment device, and provide technical support for reducing NOX emissions of ammonia and its mixed fuel engines.

Coupling selective catalytic reduction process inside a coke oven regenerator: The influence of mixing parameters on NO reduction

Many coking plants have adopted the process of preheating flue gas to apply medium and low temperature selective catalytic reduction (SCR) denitrification technology, which increases the NOx treatment cost, fuel consumption, and generates secondary pollutants. Based on the unique temperature distribution characteristics of the coke oven regenerator, this work proposes a method for synergizing SCR inside the regenerator to remove NOx. A 3-D numerical model of the synergy between heat storage and SCR reaction was constructed to study the influence of mixing parameters (atomization angle, injection velocity, nozzles number) on the NO reduction and NH3 distribution. The validity of the model was verified. The results showed that the uneven distribution of NH3 near the catalytic layer and the small SCR coating area limited the NO reduction efficiency. Increasing the number of nozzles was more beneficial than the injection velocity and atomization angle for improving the NH3 distribution uniformity and NO reduction efficiency, achieving a maximum NO reduction efficiency of 37.1% (reduced to 251.6 mg/m3). Further research found that the NO reduction capacity of the co-operative SCR model cannot exceed 36% without changing the thermal storage brick structure. When the inlet NO concentration is less than 235 mg/m3, the reaction model ensures that the outlet NO concentration meets the national emission standards.

LPV Controller Design for Diesel Engine SCR Aftertreatment Systems Based on Quasi-LPV Models

This letter presents linear parameter-varying (LPV) controller design for the urea-based selective catalytic reduction (SCR) system in diesel engines to reduce nitrogen oxides (NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> ) and ammonia (NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) emissions. Although such LPV SCR controller design has been previously developed, this letter extends it in various ways. The extension includes the usage of NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> slip sensor for feedback LPV control, the adoption of NOX and NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> measurements downstream of the catalyst as gain-scheduling parameters, the simultaneous design of feedforward and feedback LPV controllers, and a robustness analysis of the LPV controllers. Quasi-LPV SCR models derived from an existing control-oriented nonlinear parameter-varying model are utilized in the LPV controller design. The LPV controller performance is demonstrated based on an SCR simulation utilizing experimentally obtained engine-out NOX, and exhaust gas temperatures and flow rates. It is shown that the LPV controller provides satisfactory emission performance, as well as robustness against sensor noise and model parameter uncertainty.

Global kinetic modeling of NH3-SCR with two sites of NH3 storage on Cu-SSZ-13

A comprehensive selective catalytic reduction (SCR) model is developed to detail the global kinetic reactions in the catalyst which is involved in two type of sites for high- and low-temperature NH3 adsorption/desorption, as well as a low temperature H2O storage. The model accounts for the formation of ammonium nitrate in fast SCR and the decomposition formation of ammonium nitrate in the absence of NO, and addresses N2O relative to NO and NO2 reactions with NH3. The model has been validated against commercial Cu-SSZ-13 measurements of NH3 inventory, NH3 oxidation, NO oxidation, standard SCR, fast SCR and NO2 SCR at both comprehensive steady-state and transient conditions. In addition, the application of the SCR model for emissions control over a transient drive cycle at cold start was demonstrated based on diesel oxidation catalyst (DOC) and SCR aftertreatment system for a passenger car. The results show that the SCR model can provide the detailed emissions estimations for steady-state and transient conditions, can assist with understanding the impact of the SCR reaction mechanism, and can assist with the design and optimization of SCR catalysts to develop innovative technologies for co-optimization of engine fuel economy and emissions control technologies.

Model-Based Ammonia Slip Observation for SCR Control and Diagnosis

The control of selective catalytic reduction (SCR) systems, via NH $_{\text{3}}$ injection, requires from a precise estimation of the SCR load in order to ensure NOx reduction by minimizing ammonia slip. This article aims to resolve the cross-sensitivity of current NOx sensors at the outlet of the SCR, by providing the control unit with an estimation of NOx and ammonia slip. The problem of discerning between NOx and ammonia slip is solved by identifying an intermediate variable representing the SCR load. The SCR load is estimated by combining the mass conservation principle between the inlet and the outlet of the SCR and a NOx reduction model, via an extended Kalman filter. Current models and observers have several limitations to represent the real behavior of the SCR along all the operating conditions; on one hand, when relying on the mass conservation, small errors at models are integrated, leading to important bias on the SCR load and on the other hand, the dynamics at the SCR must be precisely modelled for an adequate adaptation of the model. The main focus of the developed algorithm is to use a simplified model which might be used for ammonia slip estimation, being aware of current limitations of SCR models in real operation. Experimental results in a EURO 6 compression ignited (CI) engine show the potential of such observation in transient conditions and an adequate correlation with external ammonia measurements provided by additional sensors available on the test bench.

Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants

This study develops an adaptive selective catalytic reduction (SCR) model in a coal-fired power plant with typical operating data to tackle two issues: selecting appropriate samples for model training and maintaining model accuracy under new operating conditions. First, an index of representing the information contained in the operating data of SCR is defined by considering three factors including variation span, distribution status, and information redundancy. Next, the genetic algorithm (GA) is applied to select typical operating data from SCR operational database by maximizing the information index. These data are taken as the training set to develop SCR models and predict NOx emissions with artificial intelligence techniques, including least square support vector machine and artificial neural network. Furthermore, typical operating data are managed adaptively to cover information from new operating conditions, and SCR models are updated according to the data change. SCR models trained with data from other common selections are compared. Results show that the typical operating data selected by GA can contain large information, and the developed models perform better than those trained with data from other selections. In addition, data management and model update can make the model maintain high prediction accuracy when new operating conditions occur.

Compact research for maritime selective catalytic reduction reactor based on response surface methodology

Selective Catalytic Reduction (SCR) system provides an effective approach to removing NOx emission, in order to meet stringent emission legislation of NOx generated by internal combustion engine. However, the oversize SCR system would increase its operation costs and occupy more space, which restricts its widespread application to a certain extent. Therefore, the compact structure design of SCR system is worthy of research considering the limited installation space. In the present study, one-dimensional SCR model is established, and the effect of structural parameters on the SCR performance has been investigated by AVL BOOST. Then, based on the Response Surface Methodology (RSM), the coupling relationship among these structural parameters is explored. The optimal structural parameter values of SCR are calculated through the coupling relationship function. The SCR volume of the optimal structural parameters is reduced by 23.82% and the pressure drop generated by SCR reactor is reduced by 10.38%, which not only lead to the reduction of fuel consumption and also save the space and energy on ship. Meanwhile, and the NOx conversion is decreased slightly to 0.51%.

Estimation of Ammonia Storage Nonuniformity for Urea-Based Selective Catalytic Reduction Systems

Ammonia storage nonuniformity has a significant impact on the emission reduction performance of urea-based selective catalytic reduction (SCR) systems. In this paper, a unique SCR platform with two catalysts in a parallel configuration was created for investigating the impact of ammonia storage nonuniformity on the emission reduction performance in a simulation environment. The established two-cell SCR platform allows users to independently control the ammonia-to-NOx ratio (ANR) for each catalyst using two independent urea solution injectors. Simulation results over US06 cycle demonstrate that, compared to the case without ammonia storage nonuniformity, the tailpipe NOx and ammonia emissions can be increased by 6.73% and 22.0%, respectively, due to the nonuniform ammonia storage in the case of an ANR nonuniformity index (NUI) at 0.2. Furthermore, an innovative model-based method was proposed for estimating the ammonia coverage ratio nonuniformity (i.e., ammonia storage nonuniformity if storage capacity is known) by utilizing a control-oriented SCR model and the tailpipe NOx and ammonia measurements at the confluence point. Simulation results proved the effectiveness of the proposed method in estimating the ammonia coverage ratio nonuniformity.

Hydrothermal Aging Factor Estimation for Two-Cell Diesel-Engine SCR Systems via a Dual Time-Scale Unscented Kalman Filter

Selective catalytic reduction (SCR) systems have been widely used to meet the emission regulations and two-cell SCR systems have shown the advantages of high NO $_{x}$ conversion efficiency and low NH $_{3}$ slip simultaneously. However, it is noteworthy that the catalyst performance of SCR device would degrade over the service time gradually. If the performance degrading is not well-compensated, the NO $_{x}$ conversion efficiency would reduce significantly. Different from detecting the aging issue in the laboratory, a practical method is to design an observer to estimate the aging factor online such that the urea injection can be modified accordingly. In this paper, we aim to construct an aging-factor observer for two-cell SCR systems. In order to reduce the algorithm computational load and guarantee the implementation performance, we propose a dual time-scale algorithm based on two-cell SCR model and unscented Kalman filter. There is a fast time scale and a slow time scale in the algorithm. Two simulation studies of constant aging factor and time-varying aging factor are investigated in the simulation environment of MATLAB/SIMULINK. The simulation results indicate that the proposed dual time-scale observer works well under different conditions and the calculational load is reduced significantly.

Structure Optimization for SCR Reactor Based on RSM Method

Selective Catalytic Reduction (SCR) system provides an effective approach to removing NOx emission, to meet stringent emission legislation of NOx generated by internal combustion engine. However, the oversized SCR system would increase its operation costs and occupy more space, which restricts its widespread application to a certain extent. Therefore, the compact structure design of SCR system is worthy of research considering the limited installation space. In the present study, one-dimensional SCR model is established, and the effect of structural parameters on the SCR performance has been investigated. Then, based on the Response Surface Methodology (RSM), the coupling relationship among these structural parameters is explored. The optimal structural parameter values of SCR are calculated through the multi-objective optimization function. The SCR volume of the optimal structural parameters is reduced by 23.82% and the pressure drop generated by SCR reactor is reduced by 10.38%. Meanwhile, the decrease of NOx conversion efficiency could be negligible under the same condition.

Simulation of the Transient Thermal Response of a High Pressure Selective Catalytic Reduction Aftertreatment System for a Tier III Two-Stroke Marine Diesel Engine

The IMO tier III legislation, applicable to vessels with a keel laying date from Jan. 1, 2016, has compelled engine builders to apply new technologies for NOx abatement. One of the most promising technologies for tier III compliance is the selective catalytic reduction (SCR) of nitrogen oxides (NOx). Despite that SCR technology has been applied in powerplants and heavy duty truck engines for years, there are challenges that stem from its applications in large two-stroke marine diesel engines. In this paper, an SCR model applicable to large two-stroke marine diesel engines is introduced. The goal of the model is to predict the thermal response of a marine SCR aftertreatment system when the engine undergoes transient loading. The model has been developed and validated using testbed measured data from a large two-stroke marine diesel engine. The output of the model is the SCR outlet temperature. It is shown that the model can accurately predict the transient inertial response of the SCR during engine acceleration, deceleration, and low load operation.

Development and Validation of a Two-Site Kinetic Model for NH3-SCR over Cu-SSZ-13. Part 1. Detailed Global Kinetics Development Based on Mechanistic Considerations

In this study, we present the first of two parts in the development and validation of a two-site detailed global kinetic model for NH3 Selective Catalytic Reduction (SCR) over a well-characterized Cu-SSZ-13 catalyst. Based on fundamental literature studies and experimental data for two distinct hydrothermal aging conditions, it was observed that at least two distinct sites were necessary to describe the storage, oxidation, and SCR behavior for this catalyst. The site definitions were allocated based on analysis of simulated net desorption rates during the NH3-TPD experiment. The S1 sites were associated with extra framework copper ions stabilized by -OH ligands (ZCuOH), likely located near the eight-membered ring CHA cages, along with Bronsted acid sites, while S2 sites were associated with copper ions attached directly to the repeating units (Z2Cu) near the six-membered rings, along with physisorbed NH3 sites and low temperature transient copper dimers. The selective NH3 oxidation and NO oxidation reactions were only modeled over S1, in line with the expected catalytic sites for these reactions. Standard, fast, and NO2 SCR reactions were modeled on both sites, with different activation energies. Finally, noticeable nitrate-based hysteresis effects were observed, in both N2O concentrations and fast SCR NO conversions. These were accounted for by explicitly modeling nitrate formation, titration by NO, and thermal decomposition to N2O. The developed SCR model was validated with additional reactor data at nominal inlet NH3-to-NOx ratios (ANRs) of 0.8 and 1.2. In general, the model showed good predictability in the temperature range of 150–550 °C for both hydrothermal ageing conditions and space velocities. Further full-scale engine dynamometer validation and development of a downstream NH3 slip catalyst (ASC) reaction-diffusion model will be reported in the second part of the paper.

Typical condition library construction for the development of data-driven models in power plants

The performance of data-driven models to describe processes in thermal power plants highly depends on the operating data that are used for model training. Models developed using representative operating data with rich process information are more likely to produce accurate predictions. This paper presents a concept of typical condition library to collect informative operating data from power plants. First, a quantitative criterion to measure the condition information of operating data is defined by three factors including variation span, distribution status, and redundancy. Additionally, an analytic expression is provided to describe the condition information metric. Then, the genetic algorithm is used to search operating data samples with the largest information for the condition library construction. A numerical case is given to validate the proposed metric and the sample selection strategy. Finally, the typical condition library is applied to develop a model for a selective catalytic reduction (SCR) system in a coal-fired power plant. The results indicate that the SCR model trained using samples in the library can give accurate predictions, and the typical condition library is effective for initial data preparation of model development.

An NOx Sensor-Based Direct Algebraic Approach-Newton Observer for Urea Selective Catalytic Reduction System State Estimations

NOx sensor-based state estimations for urea-based selective catalytic reduction (SCR) systems have attracted much attention in the past several years because of their significant importance in achieving high NOx conversion efficiency and low ammonia slip at low operation cost. Most of the existing SCR state estimation techniques require sophisticated design processes and significant tuning efforts, which may prevent them from widespread applications to production urea-SCR systems. In addition, the existing SCR state observers may not be able to achieve fast and accurate estimations due to the corresponding slow estimation error dynamics. The purpose of this study was to design a straightforward and effective NOx sensor-based SCR state estimation algorithm for decoupling post-SCR NOx sensor signals (NOx concentration, ammonia concentration), and for estimating ammonia coverage ratio of the urea-SCR systems. A singular-perturbation-based approach was applied to attain the reduced-order SCR model by decoupling the fast NO and NH3 concentration dynamic models from the slow ammonia coverage ratio dynamics model. Based on the reduced-order model, a direct algebraic approach (DAA)-Newton observer was proposed for estimating ammonia coverage ratio. The achieved ammonia coverage ratio estimation was applied to estimate the post-SCR NOx and NH3 concentrations. Simulation verification results under US06 cycle proved the effectiveness of the proposed method in accurately estimating the aforementioned key SCR states. The proposed observer can potentially be popularly applied to the production SCR systems for the advanced SCR control systems and on-board diagnostics.

NH&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$_{3}$&lt;/tex-math&gt; &lt;/inline-formula&gt; Coverage Ratio Estimation of Diesel-Engine SCR Systems by a Dual Time-Scale Extended Kalman Filter

For the sake of reducing the computational load and enhancing the implementation possibility, a dual time-scale extended Kalman filter is developed in this paper. First, a conventional multistate mathematical selective catalytic reduction (SCR) model is simplified into a single-state SCR model. This simplification would reduce the computational cost a lot. Inspired by the fact that the variation of NH $_{3}$ coverage ratio between two cells changes relatively slow, then, the dual time-scale extended Kalman filter is proposed. The dual time-scales include one fast time-scale for the average NH $_{3}$ coverage ratio and a slow time-scale for the difference of NH $_{3}$ coverage ratios between the actual cell and the average value. In the dual time-scale scenario, it is unnecessary to estimate the NH $_{3}$ coverage ratio of each SCR cell every time. Finally, simulation results for a medium-duty diesel engine through the MATLAB/SIMULINK show the proposed method has good performance and lower computational load.