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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.

Urea-selective catalytic reduction (SCR) catalysts are the leading aftertreatment technology for diesel engines, but there are major challenges associated with meeting future NOx emission standards, especially under transient drive cycle conditions that include large swings in exhaust temperatures. Here we present a simplified, transient, one-dimensional integral model of NOx reduction by NH₃ on a commercial small-pore Cu-zeolite urea-SCR catalyst for which detailed kinetic parameters have not been published. The model was developed and validated using data acquired from bench reactor experiments on a monolith core, following a transient SCR reactor protocol. The protocol incorporates NH₃ storage, NH₃ oxidation, NO oxidation and three global SCR reactions under isothermal conditions, at three space velocities and at three NH₃/NOx ratios.

Although the urea-SCR technology exhibits high NO reduction efficiency over a wide range of temperatures among the lean NO reduction technologies, further improvement in low-temperature performance is required to meet the future emission standards and to lower the system cost. In order to improve the catalyst technologies and optimize the system performance, it is critical to understand the reaction mechanisms and catalyst behaviors with respect to operating conditions. Urea-SCR catalysts exhibit poor NO reduction performance at low-temperature operating conditions (T ≺ 150°C). We postulate that the poor performance is either due to NH₃ storage inhibition by species like hydrocarbons or due to competitive adsorption between NH₃ and other adsorbates such as H₂O and hydrocarbons in the exhaust stream. In this paper we attempt to develop one-dimensional models to characterize inhibition and competitive adsorption in Fe-zeolite-based urea-SCR catalysts based on bench reactor experiments.

Due to its high hydrogen storage capacity (up to 16% by weight for the release of 2.5 molar equivalents of hydrogen gas) and its stability under typical ambient conditions, ammonia borane (AB) is a promising material for chemical hydrogen storage for fuel cell applications in transportation sector. Several systems models for chemical hydride materials such as solid AB, solvated AB and alane were developed and evaluated at Pacific Northwest National Laboratory (PNNL) to determine an optimal configuration that would meet the 2010 and future DOE targets for hydrogen storage. This paper presents an overview of those systems models and discusses the simulation results for various transient drive cycle scenarios.

Synergies between various catalytic converters such as SCR and DPF are vital to the success of an integrated aftertreatment system for simultaneous NO and particulate matter control in diesel engines. Several issues such as hydrocarbon poisoning, thermal aging and other coupled aftertreatment dynamics need to be addressed to develop an effective emission control system. This work is significant especially in an integrated DPF-SCR aftertreatment scenario where the SCR catalyst on the filter substrate is exposed to un-burnt diesel hydrocarbons during active regeneration of the particulate filter. This paper reports an experimental and modeling study to understand the effect of hydrocarbons on a Fe-zeolite urea-SCR catalyst. Several bench-reactor tests to understand the inhibition of NO oxidation, to characterize hydrocarbon storage and to investigate the impact of hydrocarbons on SCR reactions were conducted.

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 NO 2 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.

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.