Selective Catalytic Reduction Control Research Articles

Gain-scheduling Selective Catalytic Reduction Control in Diesel Engines with Switched H∞ Controllers

Gain-scheduling Selective Catalytic Reduction Control in Diesel Engines with Switched H∞ Controllers

Nonlinear control of a selective catalytic reduction unit for a bio-fueled cogeneration plant

The paper puts forward a method for control of selective catalytic reduction (SCR) units. First, a dynamic model of SCR performance including heat transfer, ammonia adsorption, NOx reduction, and ammonia oxidation is developed. The model is then more simplified to only include medium time scale dynamics which can be shaped using control. The obtained model is utilised in the design of a nonlinear control strategy for the regulation of the NOx emissions in the system. A robust observer of the system state is developed and exploited for control design. Numerical simulations verify the effectiveness of the proposed control system.

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.

Model-based optimal boundary control of selective catalytic reduction in diesel-powered vehicles

This paper is devoted to design a model-based boundary optimal controller for selective catalytic reduction system. The mathematical model consists of coupled parabolic-hyperbolic PDEs with an ODE. The main objective is to manipulate the ammonia gas at the inlet of the SCR in order to reduce the amount of NOx and ammonia slip as much as possible. The augmented infinite-dimensional state space representation has been used in order to solve the corresponding linear-quadratic control problem. The dynamical properties of both the linearized system and its augmented version have been studied. Under some technical conditions, it has been shown that the augmented system generates an exponentially stabilizable and detectable C0-semigroups. The linear-quadratic control problem has been solved for the augmented system. A decoupling technique has been implemented to decouple and solve the corresponding Riccati equation. An algorithm has been developed to describe the steps of solving the Riccati equation. Numerical simulations for the closed-loop system have been implemented to show the controller performances.

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.

Process identification of the SCR system of coal-fired power plant for de-NOx based on historical operation data

ABSTRACTThe selective catalytic reduction (SCR) system, as one principal flue gas treatment method employed for the NOx emission control of the coal-fired power plant, is nonlinear and time-varying with great inertia and large time delay. It is difficult for the present SCR control system to achieve satisfactory performance with the traditional feedback and feedforward control strategies. Although some improved control strategies, such as the Smith predictor control and the model predictive control, have been proposed for this issue, a well-matched identification model is essentially required to realize a superior control of the SCR system. Industrial field experiment is an alternative way to identify the SCR system model in the coal-fired power plant. But it undesirably disturbs the operation system and is costly in time and manpower. In this paper, a process identification model of the SCR system is proposed and developed by applying the asymptotic method to the sufficiently excited data, selected from the original historical operation database of a 350 MW coal-fired power plant according to the condition number of the Fisher information matrix. Numerical simulations are carried out based on the practical historical operation data to evaluate the performance of the proposed model. Results show that the proposed model can efficiently achieve the process identification of the SCR system.

Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V2O5/WO3–TiO2 Catalyst in SCR Control System Using an GS–PSO–SVM Algorithm

Selective catalytic reduction (SCR) is one of the most effective technologies used for eliminating NOx from diesel engines. This paper presents a novel method based on a support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose the degree of aging of the V2O5/WO3–TiO2 catalyst in the SCR system. This study shows the aging effect on the performance of a NH3 slip based closed-loop SCR control system under different aging factors (α), which are defined by the SCR reaction rate ( R scr ). A diagnosis of the performance of GS–PSO–SVM has been presented as compared to SVM, GS–SVM and PSO–SVM to get reliable results. The results show that the average prediction diagnosis accuracy of the degree of catalytic aging is up to 93.8%, 93.1%, 92.9% and 92.0% for GS–PSO–SVM, PSO–SVM, GS–SVM and SVM respectively. It is demonstrated that GS–PSO–SVM is able to identify the SCR catalyst’s degree of aging, to ultimately assist with fault tolerance in the aging of the SCR catalyst.

Characteristics-based model predictive control of selective catalytic reduction in diesel-powered vehicles

In heavy-duty diesel exhaust systems, selective catalytic reduction (SCR) is used to reduce NOx to nitrogen to meet environmental regulations. Diesel exhaust after-treatment involves a set of components that are best characterized as distributed parameter systems. Thus, the optimal ammonia dosage in the SCR is an important and challenging problem in diesel exhaust treatment. In this work, we propose a method to synthesize an optimal controller for the SCR section of the diesel exhaust after-treatment system, which is based on a system model consisting of coupled hyperbolic and parabolic partial differential equations (PDEs). This results in a boundary control problem, where the control objectives are to reduce the amount of NOx emissions and ammonia slip to the fullest extent possible using the inlet concentration of ammonia as the manipulated variable and assuming that the concentrations of nitric oxide and nitrogen dioxide and ammonia, are measured at the SCR inlet and outlet. The proposed method combines the method of characteristics, spectral decomposition and the model predictive control (MPC) approach. For performance comparison purposes, the open-loop dynamic optimization problem is solved via Direct transcription (DT) to compute the upper performance limit for the optimal SCR problem. The results show that the proposed approach is able to achieve a very high level of control performance in terms of NOx and ammonia slip reduction.

Estimation and adaptive nonlinear model predictive control of selective catalytic reduction systems in automotive applications

The demands of high NOx conversion efficiency and low tailpipe ammonia slip for urea-based selective catalytic reduction (SCR) systems have been substantially increased in the past decade, as NOx emission legislations for Diesel engines are becoming more stringent than ever before. Since catalyst aging has a significant impact on SCR performance, robust and adaptive SCR control has been preferred for degraded SCR systems to realize emission control objectives. The purpose of this paper is twofold. Firstly, a robust ammonia coverage ratio observer was designed for estimating the ammonia coverage ratio reference for catalysts with different aging levels. An ammonia storage capacity observer was developed for estimating the actual ammonia storage capacity which can be reduced due to catalyst aging. An adaptive ammonia coverage ratio reference design was then developed to estimate the desired ammonia coverage ratio ranges at each instantaneous engine operating point for both single-cell and two-cell SCR systems at different aging levels based on a singular perturbation method. Secondly, to ensure the estimated ammonia coverage ratio falls in the desired ranges for most of engine operating conditions, robust nonlinear model predictive control (NMPC) algorithms were designed for both single-cell and two-cell SCR systems. Experimental data over US06 cycle were collected from a Diesel engine and aftertreatment system platform for controller verification. Simulation results under US06 test cycle demonstrate that the proposed NMPC algorithms were capable of consistently achieving high NOx conversion efficiency (>95.6%) and constrained tailpipe ammonia slip (<10ppm on average and <12ppm on the peak) for both fresh catalyst and aged catalyst with 30% loss of ammonia storage capacity.

Black Box SCR Modeling and Control for Small CI Engines

Selective catalytic reduction (SCR) has become widely used to reduce NOx emissions especially for medium and heavy duty CI engines, while lean NOx traps (LNT) are a typical choice for light duty engines. In the field of small engines, SCR is usually not applied, because an expensive additional AdBlue supply is necessary and the emission limits can be also achieved with less complex methods. Nevertheless, if the engine is used as range extender of a hybrid electric vehicle, in engine-only operation it must also fulfill the Diesel emission legislation. There SCR is a sensible and maybe in view of future legislation only choice. A key problem of this choice, is that for small engines, typically the available space for the SCR is limited and leads to a strongly reduced storage capability of the catalyst. Some of the features of a small system render a purely data based approach feasible as system dynamics are faster and phenomenon like storage are less pronounced. Thus in this work a black box modeling approach is presented and combined with an NMPC strategy for closed loop SCR control. The proposed control strategy is validated in simulation and experiments on a testbench.

Estimating tailpipe NOx concentration using a dynamic NOx/ammonia cross sensitivity model coupled to a three state control oriented SCR model

A dynamic NOx sensor model is developed to remove ammonia cross sensitivity from production NOx sensors mounted downstream of Diesel-engine selective catalytic reduction (SCR) systems. The model is validated for large amounts of ammonia slip during different engine transients. A three-state nonlinear control oriented SCR model is also developed to predict the NH3 concentration downstream of the SCR (NH3 slip). NH3 slip is then used as an input for modeling the cross sensitivity of a production NOx sensor and calculating the actual NOx concentration in the presence of NH3 contamination. The cross sensitivity is considered to be a function of temperature, normalized ammonia slip rate (NASR) and time. The validation results show that the developed model has an acceptable accuracy for the actual NOx concentration downstream of the SCR. This model should be of utility for engine emission control strategies such as SCR control.

Coordinated Active Thermal Management and Selective Catalytic Reduction Control for Simultaneous Fuel Economy Improvement and Emissions Reduction During Low-Temperature Operations

The low-temperature operations of diesel engines and aftertreatment systems have attracted increasing attention over the past decade due to the stringent diesel emission regulations and excessive tailpipe emissions at low temperatures. The removal of NOx emissions using selective catalytic reduction (SCR) systems during low-temperature operations remains a significant challenge. One of the popular techniques for alleviating this issue is to employ active thermal management via in-cylinder postinjection to promote aftertreatment system temperatures. Meanwhile, numerous studies have focused on ammonia coverage ratio controls with the aim to maintain high NOx conversion efficiency and low tailpipe ammonia slip. However, most of the active thermal management and SCR controls in the existing literatures were separately and conservatively designed, which can lead to higher cost of SCR operation than needed including diesel fuel consumption through active thermal management and urea solution consumption. The main purpose of this study is to design and coordinate active thermal management and SCR control using nonlinear model predictive control (NMPC) approach to minimize the total cost of SCR operation while obtaining high NOx conversion efficiency and low tailpipe ammonia slip. Simulation results demonstrate that, compared to the baseline control which consists of separated active thermal management and SCR control, the coordinated control is capable of reducing the total cost of SCR operation by 25.6% while maintaining the tailpipe NOx emissions and ammonia slip at comparable levels. Such an innovative coordinated control design concept shows its promise in achieving low tailpipe emissions during low-temperature operations in a cost-effective fashion.

Development of a variable resistance-capacitance model with time delay for urea-SCR system

Experimental research shows that the nitric oxides (NOX) concentration track at the outlet of selective catalytic reduction (SCR) catalyst with a transient variation of Adblue dosage has a time delay and it features a characteristic of resistance-capacitance (RC). The phenomenon brings obstacles to get the simultaneously NOX expected to be reduced and equi-molar ammonia available to SCR reaction, which finally inhibits NOX conversion efficiency. Generally, engine loads change frequently, which triggers a rapid changing of Adblue dosage, and it aggravates the air quality that are caused by NOX emission and ammonia slip. In order to increase the conversion efficiency of NOX and avoid secondary pollution, the paper gives a comprehensive analysis of the SCR system and tells readers the key factors that affect time delay and RC characteristics. Accordingly, a map of time delay is established and a solution method for time constant and proportional constant is carried out. Finally, the paper accurately describes the input-output state relation of SCR system by using variable RC model with time delay. The model can be used for a real-time correction of Adblue dosage, which can increase the conversion efficiency of NOX in SCR system and avoid secondary pollution forming. Obviously, the results of the work discover an avenue for the SCR control strategy.

Diesel Engine SCR Control: Current Development and Future Challenges

To meet increasingly stringent emission legislations, selective catalytic reduction (SCR) becomes an essential aftertreatment technique. This paper presents a comprehensive overview of the current development and future challenges of SCR control technologies. In this paper, SCR system hardware configurations are introduced, modeling and calibration of the SCR converter are provided, and current control strategies are summarized and reviewed. Finally, primary areas driving the future of SCR control research are discussed.

Sliding-mode observers for urea selective catalytic reduction system state estimations based on nitrogen oxide sensor measurements

The state estimations for urea-based selective catalytic reduction systems have received growing interest in the past few years because of their importance to selective catalytic reduction controls and onboard diagnostics, as well as the high cost and difficulties of direct sensor measurements. Most of the existing estimation techniques suffer because of the ammonia cross-sensitivity from the nitrogen oxide sensor and require an ammonia sensor to be available downstream of the selective catalytic reduction, which may increase the costs of production urea-based selective catalytic reduction systems. The purpose of this study is to design observers for estimating the key states (the nitrogen oxide concentration, the ammonia concentration, and the ammonia coverage ratio) of the urea-based selective catalytic reduction system at the selective catalytic reduction outlet in various situations, using only a nitrogen oxide sensor. The sliding-mode observers were designed to ensure high robustness against the ammonia cross-sensitivity from the nitrogen oxide sensor and uncertainties from the selective catalytic reduction inputs. Simulation verification results under the Federal Test Procedure 75 test cycle showed that the proposed observers were able to estimate the states accurately, in hot and cold exhaust conditions, for both single-cell selective catalytic reduction systems and two-cell selective catalytic reduction systems. The proposed observer-based estimation technique has the potential for reducing the cost of advanced selective catalytic reduction control systems and for applying it to onboard diagnostics.

A novel cost-effective robust approach for selective catalytic reduction state estimations using dual nitrogen oxide sensors

Urea-based selective catalytic reduction systems have become a promising technique in controlling nitrogen oxide emissions from diesel engines in the past decade. One of the continuing issues is the cross-sensitivity characteristics of the current nitrogen oxide sensors to ammonia in the exhaust, which makes nitrogen oxide sensor readings inaccurate in the presence of ammonia. Although prototype ammonia sensors for mobile selective catalytic reduction applications have been introduced for diesel emission control in the past few years, ammonia sensors are still too expensive to be available for mass-produced diesel engines and selective catalytic reduction systems. In order to address these issues, in this paper, a novel, cost-effective, and robust approach is proposed for estimating the key selective catalytic reduction states including the nitric oxide concentration, the nitrogen dioxide concentration, the ammonia concentration, and the ammonia surface coverage ratio, by utilizing two post-selective-catalytic-reduction nitrogen oxide sensors with different ammonia cross-sensitivity factors. Experimental verification results show that the proposed approach has the potential for precisely estimating the four selective catalytic reduction states. Such a novel approach can be instrumental in reducing the cost of selective catalytic reduction diagnosis, selective catalytic reduction estimation, and advanced selective catalytic reduction controls.

SCR Ammonia Dosing Control by a Nonlinear Model Predictive Controller

To deal with the demanding goals of the restricted Diesel emission legislation the focus in the application of exhaust after treatment systems to fulfill these strict requirements moves more and more to model based design, optimization and control.Against this background a nonlinear model predictive control (MPC) for an exhaust after treatment system consisting of a DOC (Diesel oxidation catalyst), a DPF (Diesel particulate filter) and a SCR (selective catalytic reduction) is proposed. Similar to standard ammonia control schemes also in this study the dosing amount is the only adjustable input variable. But in contrast to these usually simple feed forward dosing approaches in this proposal also information about the nonlinear temperature behavior and the storage effects of the SCR - included in a nonlinear SCR model - can be considered for the ammonia control. Based on these improved knowledge of the system a nonlinear model predictive controller, also applicable for real time tests on the engine, is applied for SCR control leading to promising results in simulation.

Nonlinear and adaptive control of NO/NO2 ratio for improving selective catalytic reduction system performance

Reducing the NOx emissions from Diesel engines remains as a challenging issue as the emission standards for Diesel engine powered vehicles have become more stringent than ever before. Urea-based selective catalytic reduction (SCR) systems have emerged as a promising technique in addressing this issue. However, the SCR performance in terms of NOx reduction and ammonia slip continues as an ongoing challenge due to the engine exhaust gas temperature variations, Diesel emission characteristics (especially high NO/NO2 ratio), and immature SCR controls. The purpose of this study is to improve the SCR performance by feeding the SCR system with exhaust gas having the desired NO/NO2 ratio. The proposed complete active NO/NO2 ratio control consists of a low-level adaptive NO/NO2 ratio controller and a high-level nonlinear soot mass controller. The low-level controller utilizes the pre-SCR catalysts such as Diesel oxidation catalyst (DOC) to convert part of NO into NO2, while the high-level controller was designed and coordinated with the low-level controller to avoid NO2 reduction through the Diesel particulate filter (DPF). Simulation and experimental results show that the proposed active NO/NO2 ratio control has the potentials of regulating the NO/NO2 ratio to the desired value and thus considerably improving the SCR performance. Simulation results also illustrate that the active NO/NO2 ratio control can enable the SCR system size reduction by a half without a significant sacrifice on the overall tailpipe emission control performance. Such an integrated aftertreatment system control can be instrumental in reducing the cost and improving the performance of SCR systems, especially in low-temperature operations.

Model-based optimization of parameters for a diesel engine SCR system

A Selective Catalytic Reduction (SCR) system simulation model for diesel engines was established based on MATLAB/Simulink. The model includes four subsystems: signal acquisition, DEF (Diesel Exhaust Fluid) dosing control, catalytic chemistry reaction and NOX emissions reduction. The SCR control strategy was optimized based on ETC simulation results. Simulation and test results indicate that the model developed in this paper can predict ETC NOX emissions accurately.

A Two-Cell Backstepping-Based Control Strategy for Diesel Engine Selective Catalytic Reduction Systems

This paper presents a diesel engine selective catalytic reduction (SCR) ammonia surface coverage ratio profile control design for a two-cell SCR system, which enables a staircase ammonia coverage ratio profile for simultaneously achieving high NOx conversion efficiency and low ammonia slip. Such a novel two-cell SCR architecture greatly increases the control system complexity. A nonlinear backstepping-based control law is proposed to regulate the ammonia coverage ratio of the upstream cell to a desired (high) value in order to maintain high-SCR NOx conversion efficiency, and to keep the ammonia coverage ratio of the downstream cell below a low upper limit for high-ammonia adsorption capability. The control law thus possesses the advantages of being able to simultaneously maintain SCR high-NOx conversion efficiency and guarantee low ammonia slip, as well as being robust to the SCR ammonia storage capacity uncertainty. Theoretical analyses and FTP75 driving cycle simulations based on an experimentally validated full-vehicle simulator were conducted to evaluate the effectiveness of the proposed control strategy. Comparisons with conventional SCR controllers are presented to show the advantages of the proposed two-cell backstepping-based SCR control strategy.