NOx control systems and methods for controlling NOx emissions

An exhaust aftertreatment system comprising a selective reduction catalyst, a NOx sensor or an NH3 sensor, a urea injector, and a dosing control unit, wherein the dosing control unit calculates the rate of urea injection by estimating the concentrations of NO and NO2 in the exhaust downstream of the SCR catalyst.https://digitalcommons.mtu.edu/patents/1013/thumbnail.jp

A Kalman Filter estimator for a Diesel Oxidation Catalyst during active regeneration of a CPF

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This paper focuses on the development of an Extended Kalman Filter (EKF) based state estimator for a Diesel Oxidation Catalyst (DOC) during active regeneration of a catalyzed particulate filter (CPF). The DOC estimator is critical to predict the exhaust gas states entering the downstream aftertreatment components such as a CPF and NOx reduction catalysts such as urea-selective catalytic reduction (SCR) in heavy duty diesel vehicles. The internal states of the DOC that are important for the performance of the CPF and SCR systems include NO and NO2 concentration states, that participate in the passive oxidation of particulate matter (PM) in the CPF and that are important for urea injection control system design in the SCR catalyst. During CPF active regeneration, the DOC is used to achieve a temperature exotherm by oxidizing the injected diesel fuel resulting in hydrocarbon slip into the CPF and an increased CPF inlet temperature (550-600°C) which promotes the PM oxidation. The results show that HC and temperature states in the DOC can be estimated using an EKF estimator with NOx and temperature measurements upstream of the DOC and temperature measurements downstream of the DOC. © 2012 AACC American Automatic Control Council)

Optimal SCR control using data-driven models

In this paper, we develop a method for optimizing urea dosing to minimize the downstream readings from a production NOx sensor that has cross-sensitivity to ammonia. This approach favors high NOx conversion and reduced ammonia slip. The motivation for this work is to define a process to identify the maximum selective catalytic reduction SCR performance bounds for a given drive cycle. The approach uses a model structure that has a closed-form optimal solution for the urea injection. Every aftertreatment system has its own, unique model, which must be identified and validated. To demonstrate the approach, a model is identified and validated using experimental SCR input/output NOx sensor data from a 2010 Cummins 6.7L ISB production engine. The optimal control law is then simulated and its performance compared against the simulated performance of the SCR using experimental data for its inlet conditions. The example case showed an optimal NOx conversion efficiency of 92.71% and an optimal NH3 conversion efficiency of 98.67% for a transient drive cycle

Adequacy of reduced order models for model-based control in a urea-SCR aftertreatment system

Model-based control strategies are important for meeting the dual objective of maximizing NOx reduction and minimizing NH3 slip in urea-SCR catalysts. To be implementable on the vehicle, the models should capture the essential behavior of the system, while not being computationally intensive. This paper discusses the adequacy of two different reduced order SCR catalyst models and compares their performance with a higher order model. The higher order model assumes that the catalyst has both diffusion and reaction kinetics, whereas the reduced order models contain only reaction kinetics. After describing each model, its parameter identification and model validation based on experiments on a Navistar I6 7.6L engine are presented. The adequacy of reduced order models is demonstrated by comparing the NO, NO2 and NH3 concentrations predicted by the models to their concentrations from the test data. It is observed that the 4 state model is a good representative of the physical system and is sufficient for model based control in urea-SCR aftertreatment systems. Copyright © 2008 SAE International

Model-based estimation and control system development in a Urea-SCR aftertreatment system

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea- SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design. An experimentally validated, higher or- der simulation is used to evaluate the performance of the closed loop system. For the cases considered, the control strategy uses less urea, produces less NH3 slip, and less tailpipe NOx than a similar strategy where NO and NO2 are assumed as all NO during estimation and control law implementation