Solveig Thorsteinsson. Control of Urea Injection for an Automotive SCR Application

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1 Solveig Thorsteinsson Control of Urea Injection for an Automotive SCR Application Master s Thesis, April 211

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3 Control of Urea Injection for an Automotive SCR Application This report was prepared by Solveig Thorsteinsson Supervisors John Bagterp Jørgensen Associate Professor, PhD., Department of Informatics and Mathematical Modeling, DTU Jakob Kjøbsted Huusom Assistant Professor, PhD., Department of Chemical and Biochemical Engineering, DTU Christophe Duwig Research Engineer, PhD., R&D Division, Haldor Topsøe A/S Department of Informatics and Mathematical Modelling Technical University of Denmark Building 321 DK-28 Kgs. Lyngby Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark Building 229 DK-28 Kgs. Lyngby Denmark Submission date: 15th of April 211 Comments: This report is part of the requirements to achieve the Master of Science in Engineering (M.Sc. Eng.) at the Technical University of Denmark. This report represents 4 ECTS points.

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5 Preface This thesis constitutes the final part of the requirements for awarding the Master degree in Chemical and Biochemical Engineering from the Technical University of Denmark. The project is carried out in collaboration between Haldor Topsøe A/S, the Department of Chemical and Biochemical Engineering, and the Department of Informatics and Mathematical Modelling at the Technical University of Denmark. I would like to thank my supervisors Jakob Kjøbsted Huusom, John Bagterp Jørgensen, and Christophe Duwig for their invaluable support through the project, for the beneficial and inspiring discussions, and for always devoting time to answer questions. Furthermore, I would like to thank Enric Senar and Christophe Duwig, for providing information on the process, and for their help in obtaining a profound understanding of the urea-scr process. Kongens Lyngby, April 15, 211 Solveig Thorsteinsson

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7 Abstract In this project, a soft model predictive controller (MPC) used to control the injection of urea in the urea-scr system, has been investigated. The control objectives have been specified in accordance with the emission limits of the Euro VI emission standard. Furthermore, an optimization problem reflecting the control requirements has been formulated. An offset-free model predictive controller based on ARX models has been implemented, and the implementation has been successfully validated. The corrective actions qualitatively have been shown to be appropriate, thus indicating that the control methodology is applicable for controlling the urea- SCR system. Tuning of the closed loop system has been carried out in simulations using a dynamic model developed at Haldor Topsøe A/S to approximate the actual physical system. The tuning has been complicated due to difficulties in reaching steady state conditions, and besides, long computation times have been limiting factor for the number of simulations conducted. Consequently, an optimal tuning has not been achieved for the offset-free soft MPC. This has been reflected in simulations of transient cycles, where a maximum NO x conversion of 75.4% has been achieved which is dissatisfactory. With the purpose of narrowing the problem, the optimization problem has been reduced to nominal MPC, and the behavior of the closed loop system has been investigated. This has revealed that the description of the dynamics by the estimated offset-free ARX is deficient. Despite the difficulties in meeting the control objectives, the qualitative results from the presented design activities can be used as a basis of further improvements and a number of possible future improvements is suggested.

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9 Contents 1 Introduction 1 2 Background Environmental regulations European regulations Diesel exhaust gas after treatment systems Lean NO x trap Selective catalytic reduction of NO x Ammonia as reductant Challenges of controlling automotive urea-scr systems Reported control systems Open loop control strategies Closed loop control strategies Model predictive controllers Discussion of reported control systems Design approach 18 4 The urea-scr system The dynamic model representing the physical system Control objectives

10 CONTENTS vii 4.3 Sensors and actuators Control strategy Identification of prediction model Step response analysis Methods of estimating gains and time constants Step response interpretation Step change model Validation of step response model ARX model Validation of ARX model Comparison of step change model and ARX model Controller structure The prediction model Soft MPC The soft MPC optimization problem Soft MPC tuning parameters Reference values and input constraints Validation of the controller implementation Preliminary tuning of the soft MPC controller Performance of the soft MPC controller Nominal MPC The nominal MPC optimization problem Tuning of the nominal MPC controller Controller evaluation 9 8 Future work 92 9 Conclusion 94

11 viii CONTENTS Nomenclature 95 References 1 Appendix 13 A Complete step response analysis 13 A.1 Step changes in NH A.2 Step changes in NO A.3 Step changes in NO A.4 Step changes in temperature A.5 Step changes in flow rate B Soft MPC tuning 129 B.1 Unit step responses of the ARX model B.2 Simulation with constant disturbances varying S u B.3 Simulation of transient cycle with soft MPC and H p = B.4 2-step prediction C MPC tuning 134 C.1 Simulation with higher prediction horizons C.2 Simulation to steady state only controlling NO D Matlab code 144 D.1 Step response model D.1.1 Numerical estimation of transfer function parameters. 144 D.1.2 Simulation with step change model D.2 ARX model D.2.1 Model estimation and validation D.2.2 Simulation with ARX model D.3 Control of urea-scr system D.3.1 Getting model

12 CONTENTS ix D.3.2 Defining input to mex-file D.3.3 Soft MPC D.3.4 MPC

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15 Chapter 1 Introduction Exhaust emissions from motor vehicles contribute significantly to air pollution. Worldwide, mounting concern of the health and environmental consequences, has resulted in enactments of regulations within exhaust emission control. With the tightening of the current emission regulations, by the enactment of the Euro VI emission standard, improvements are required to reach the emission levels of nitrogen oxides and ammonia from heavy duty vehicles. The urea-scr system is a widely used technology to reduce nitrogen oxides in the exhaust gas, and control of this system is the subject of this thesis. Hence, the overall objective of the report is the challenging task of controlling the urea-scr system in order to meet the regulation emission limits. In this report, model predictive control is investigated as control strategy, due to the dynamic behavior and complexity of the urea-scr process. The aim of this project is to validate a methodology of controlling the closed loop system, and thereby illustrate how soft output constrained model predictive control can be used to control the urea-scr system. The design activities described in the report follow a general design approach including system identification, model development, optimization problem formulation, controller tuning and evaluation. The presented design activities and the corresponding analyses are focused on the qualitative results and not on optimizing program formulation.

16 Chapter 2 Background 2.1 Environmental regulations Due to environmental and health considerations, actions have been made in many parts of the world in order to reduce adverse impacts caused by exhaust gas emissions. In the European Union (EU), directives have been enacted in the form of the Euro I to VI emission standards dealing with emission of nitrogen oxides (NO x ), total hydrocarbons (THC), non-methane hydrocarbons, carbon monoxide (CO) and particulate matter (PM). Thus, new vehicles can only be sold in the EU, if the exhaust emissions meet the currents standards for the specific vehicle type. Similar, the U.S. Environmental Protection Agency are enforcing emission regulations in U.S., though, in California more stringent regulations are set by the California Air Resources Board. In general, the regulations are different for different vehicle types, depending on duty and fuel European regulations With the topic of this report in mind, the focus in the following sections is regulation of heavy-duty diesel vehicle exhaust emissions.

17 2.1 Environmental regulations Vehicle classes The emission limits in the European standards are specified depending on the type of vehicles. In the EU, the vehicles are divided into classes according to the European Economic Community (EEC) and the United Nations Economic Commission for Europe (UNECE) regulations. The European vehicle classes are listed below in table 2.1. Class Appearance Weight (t) Seats M 1 Small passenger cars - 8 M 2 Large passenger cars < 5 > 8 M 3 Large passenger cars > 5 > 8 N 1 Light goods vehicles N 2 Heavy goods vehicles 3.5 < w 12 - N 3 Heavy goods vehicles < 12 - Table 2.1: European regulation vehicle classes [1, 2]. Generally, regulations based on the Euro V and Euro VI emission standards apply to all vehicles listed in table 2.1 [2] European emission standards In Europe, vehicles have to obtain EC type-approval, which imply meeting limitations on NO x, THC, CO, and PM emissions when running through special standardised test cycles. The current European regulation on exhaust emission uses the Euro V emission standard, however, in 214 a more stringent regulation using the Euro VI becomes effective. The Euro VI emission standard involves emission limits for the emission during test runs with the World Harmonized Steady-state Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC). These test cycles will be explained in sections and , respectively, and the emission limits can be seen in table 2.2. Test cycle CO THC NO x NH 3 PM mg/kwh mg/kwh mg/kwh ppm mg/kwh WHSC WHTC Table 2.2: Emissions limits for diesel vehicles for WHSC and WHTC test cycles of the Euro VI emission standard [3, 2].

18 4 Background The WHSC and WHTC standardized tests originate from the UNECE GRPE group (Working party on pollution and energy), more precisely from the proposed global technical regulation on a world wide harmonized heavyduty certification procedure for engine exhaust emissions. Both test cycles are constructed to simulate typical driving conditions in EU, USA, Japan, and Australia [3] World harmonized steady-state cycle The WHSC is a hot start steady state test cycle consisting of 13 modes, each lasting from 5 to 25 seconds. In table 2.3, the engine speed and the load are plotted with time. Mode Speed Load Weighting factor Mode duration - % % - s Motoring / /2 21 Sum Table 2.3: The WHSC [3] World harmonized transient cycle The WHTC is a transient cycle of 18 seconds, and the normalized engine speed and torque are plotted in figure 2.1.

19 2.2 Diesel exhaust gas after treatment systems 5 Engine torque, % Engine speed, % time, s Figure 2.1: Engine speed and torque for the WHTC test cycle [3]. The WHTC test consists of two test runs; a cold start and a test with hot start after a hot soaking period. The former has the purpose of determining the heat-up time of the after treatment system. The latter has the purpose of determining the possible temperature decrease of the after treatment system during the hot soaking period and investigate the influence on the NO x conversion of the selective catalytic reduction (SCR) after treatment system. With respect to the emission results of the WHTC cycle, the cold start test is weighted with 1% and the hot start test is weighted 9% [3]. 2.2 Diesel exhaust gas after treatment systems As explained in section 2.1, the demands for the exhaust emissions are continuously intensified, thus leading to the need of efficient combustion control and exhaust gas after treatment systems. Historically, catalysts were introduced in emission control in connection with the 197 Clean Air Act, as engine modifications alone could not meet the emission limits of CO, hydrocarbons (HC), and NO x for gasoline and diesel engine exhaust [29]. The following sections will focus on diesel exhaust gas after treatment systems. As described in section 2.1, the diesel exhaust gas emission limits are specified for both CO, the total amount of hydrocarbons, particulate matter (PM), and NOx. Both CO and THC can be oxidized in an oxidation catalyst (platinum (Pt), palladium (Pd) or vanadium-based (V)) to CO 2 and H 2 O using the oxygen in the exhaust gas. The main challenges in meeting the European emission standards are the

20 6 Background diesel exhaust emission limits of NO x and PM. PM in diesel exhaust is small carbon particles, hydrocarbons, and inorganics, which are known for the potential of causing cancer and other health problems. An effective way to minimize emissions of PM is to use diesel particulate filters, as for instance cordierite wall flow filters, silicon carbide wall flow filters, or ceramic fiber filters. Another method of removing part of the PM is by using a diesel oxidation catalyst, where the hydrocarbon part of the PM can be oxidized. Thus, there are several effective methods of reducing diesel exhaust PM emission. Meeting the NO x emission limits are challenging. Due to the diesel combustion characteristics, the oxygen concentration in the exhaust gas is high favouring the HC and CO oxidation, but complicating the reduction of NO x. Therefore, different methods of NO x control in exhaust gas after treatment systems have been considered. The first after treatment systems, which reduced NO x significantly, was containing a rhodium/platinum based NO x reduction catalyst upstream the air injection and oxidation catalyst [29]. Current research is focusing on two different methods of NO x reduction which are considered as the most promising techniques. These are lean NO x -trap and selective catalytic reduction (SCR) of NO x [5, 13]. In order to reduce the emissions of both CO, HC, NO x, and PM and limit the NH 3 slip, the above mentioned after treatment methods can be combined. For instance, a SCR catalyst can be combined with a pre-oxidation catalyst and/or a slip oxidation catalyst and a PM filter, where the pre-oxidation catalyst has the purpose of oxidizing the HC s to CO 2 and H 2 O, the CO to CO 2, and some of the NO to NO 2, and thereby increasing the subsequent reduction of NO x by optimizing the reaction conditions for the fast SCR reaction. The slip oxidation catalyst has the purpose of oxidizing spare NH 3 to N 2, NO and H 2 O and thereby reducing the NH 3 slip. Another possibility is to combine the SCR catalyst with exhaust gas recirculation Lean NO x trap The lean NO x trap system consists of a catalyst with high NO x storage capacity at lean combustion conditions. Lean combustion conditions are referring to conditions with excess air promoting oxidizing conditions, and opposite, rich combustion conditions are referring to conditions with reducing conditions. The lean NO x -trap operation consists of two parts; a NO x adsorption process and a short regeneration process. During lean combustion conditions, the NO x is adsorbed on the catalyst, however, as the catalyst can have a finite amount of NO x, the NO x trap needs to be peri-

21 2.2 Diesel exhaust gas after treatment systems 7 odically regenerated. This is done under rich combustion conditions, where the desorption process is favoured. The lean period can for instance last for several minutes with rich periods lasting a few seconds [5]. In general, lean NO x traps are considered suitable for light-duty vehicles in comparison to SCR systems which are well suited for medium- and high-duty vehicles [5, 13, 32] Selective catalytic reduction of NO x In selective catalytic reduction (SCR), the NO x can be reduced despite the high oxygen concentration under the lean diesel combustion conditions, by use of an appropriate catalyst and an effective selective NO x reductant. One widely used reductant is ammonia which has high selectivity to NO x. Ammonia can, for instance, be supplied by injection of either gaseous or aqueous ammonia or as aqueous urea which is converted to ammonia after injection (see section ). In general, urea injection is preferred due to safety considerations. SCR of NO x in diesel exhaust is a well-known technology which has been used for more than 3 years in power generation [21]. However, for the use in the power generation industry, the stationary diesel engines operate at constant load which implies that the operating conditions for the SCR system is more or less constant with respect to exhaust flow and temperature [17]. In stationary SCR systems, the catalyst volume is often sized generously to ensure complete NO x reduction. In SCR systems used for automotive applications, this cannot be done due to space limitations. Thus, for automotive applications there are several challenges in obtaining the desired NO x conversions using SCR systems, due to limitations on catalyst volume and fluctuating operating conditions caused by changing engine loads Reactions in SCR catalyst In the SCR catalyst, NO and NO 2 react selectively with NH 3 as reductant. The overall reactions taking place in the reactor are the adsorptiondesorption equilibrium of ammonia and the reactions between NH 3 and the NO x species, expressed in equation (2.1)-(2.4), where NH 3 designates the ammonia adsorbed on a catalytic site [9]. NH 3 NH 3 (2.1) 4NO + 4NH 3 + O 2 4N 2 + 6H 2 O (2.2)

22 8 Background 3NO 2 + 4NH 3 3.5N 2 + 6H 2 O (2.3) NO + NO 2 + 2NH 3 2N 2 + 3H 2 O (2.4) The reaction in equation (2.2) is called the standard SCR reaction, the reaction in equation (2.3) the slow SCR reaction, and the reaction in equation (2.4) is referred to as the fast SCR reaction. The standard SCR reaction occurs at temperatures above 2 C, however, the reaction rate is significantly faster for higher temperatures [8]. The fast SCR reaction is favoured by an equimolar mixture of NO and NO 2, and the reaction occurs at temperatures as low as C [8]. Thus, the effect of NO 2 in the exhaust gas has higher significance at low temperatures due to the low reaction rate of the standard SCR reaction at low temperatures. According to [17] and [8] the NO comprise above 9% of the NO x in the exhaust gas, and therefore, actions have to be made if the optimal 1:1 NO:NO 2 ratio is desired, favouring the fast SCR reaction. Thus, one way to improve the reaction rate at low temperatures is by increasing the amount of NO 2 in the exhaust gas. This can be done by use of an oxidation catalyst placed upstream the SCR catalyst. Furthermore, the oxidation of NO to NO 2 has been reported to occur over iron exchanged zeolites following the reaction given in equation (2.5) [24]. 2NO + O 2 2NO 2 (2.5) The slow SCR reaction occurs at temperatures above 275 C if excess NO 2 is present [8]. Besides the reduction reactions, there are several unwanted side reactions occurring as well. Ammonia can be oxidized by the reactions given in equations (2.6) and (2.7), leaving less ammonia for NO x reduction. 4NH 3 + 3O 2 2N 2 + 6H 2 O (2.6) 4NH 3 + 5O 2 4NO + 6H 2 O (2.7) The ammonia oxidation described in equation (2.6) has been reported to occur at temperatures above 4 C on a Fe-zeolite [24], and the reaction in equation (2.7) has been reported to be significant at high temperatures i.e. above 45 C on a V-based catalyst [32], thus resulting in an upper temperature limit for NO x reduction. Furthermore, formation of ammonium nitrate (NH 4 NO 3 ) has been reported to occur below 2 C and formation

23 2.2 Diesel exhaust gas after treatment systems 9 of nitrous oxide (N 2 O) above 45 C both on V-based catalyst [32]. The reaction mechanisms of the NO x reduction reactions have been investigated by several research groups, however, the results will not be discussed in this report as it is not the main focus Factors influencing SCR catalyst performance Several factors have influence on the degree of NO x conversion in a SCR catalyst using ammonia as reductant: Space velocity NO to NO 2 ratio Temperature Availability of NH 3 In general, the higher residence time the higher conversion. Thus, the relation between the exhaust gas flow rate and the catalyst bed volume, the normal hourly space velocity (Nm 3 /h/m 3 catalyst bed) has direct influence on the conversion. Depending on the NO:NO 2 ratio, different NO x reduction reactions occur, and according to Ciardelli et al. [8], the highest conversions are obtained at a NO:NO 2 ratio of 1:1, which corresponds to the stoichiometry of the fast SCR reaction. The lower temperature limit, above which NO x reduction occurs, depends strongly on the NO:NO 2 ratio. Thus, for NO:NO 2 ratio of 1:1 the lower temperature limit is C in comparison to an exhaust gas with only NO present, where the lower temperature limit for NO x reduction is 2 C [8]. Furthermore, the upper temperature limit for the NO x reduction is caused by the oxidation of ammonia which occurs at temperatures above 425 C Ammonia as reductant Ammonia can be supplied either as gaseous ammonia, as aqueous ammonia, as ammonia stored in a solid (Amminex s AdAmmine), or as aqueous urea (In Europe sold as AdBlueR) which is converted to ammonia in situ. Furthermore, for automotive applications aqueous urea is the choice of ammonia supply, due to safety considerations.

24 1 Background Ammonia formation from urea Ammonia is formed from aqueous urea (H 2 CONH 2 ) in three steps after the urea injection, which involves evaporation of water, thermal decomposition of urea, and finally hydrolysis of iso-cyanic acid. Evaporation of water is initiated when the aqueous urea is injected into the exhaust gas pipe, where the temperature is above 1 C. Even though the melting point of urea is approximately 133 C, the physical state of the urea at this point is not known, however, it is suggested either to be solid, molten, or a highly concentrated aqueous solution [28, 4]. The thermal decomposition of urea occurs upstream of the SCR catalyst forming ammonia and iso-cyanic acid (HNCO), as expressed in equation (2.8) [32]. H 2 CONH 2 NH 3 + HNCO (2.8) In the literature, the temperature, at which the thermal decomposition of urea begins, is reported to be between 133 and 16 C [28]. The conversion efficiency of urea to ammonia and iso-cyanic acid is depending on temperature, and at temperatures above 25 C cyanurates, ammeline, ammelide, and melamine will be formed instead [28]. The iso-cyanic acid is hydrolysed forming ammonia and carbon dioxide following equation (2.9). HNCO + H 2 O NH 3 + CO 2 (2.9) The hydrolysis of iso-cyanic acid has to be catalysed, hence, a part of the ammonia, up to 5%, is not released upstream the SCR catalyst for gas phase temperatures below 3 C where no significant hydrolysis occur [32, 28] Ammonia coverage As expressed in section , the ammonia, which is used in the reduction of NO x, is initially adsorbed on the catalyst. This is caused by the strongly acidic properties of the SCR catalyst surface by activity of both Brønsted and Lewis acid sites [16]. In the literature, there is disagreement on whether the ammonia adsorbed on the Brønsted or the Lewis acid sites is active in the reduction of NO x. The amount of adsorbed ammonia is referred to as the ammonia coverage. Several factors influence the ammonia coverage, with the most important listed below: Catalyst material

25 2.2 Diesel exhaust gas after treatment systems 11 Temperature Presence of other adsorbates First of all, the catalyst material is of importance, as zeolites can have a higher coverage than V-based catalysts. According to Willems et al. [32] a steady state ammonia storage of a zeolite catalyst can be 1.4 g/l at 2 C compared to a vanadium catalyst for which a steady state storage can be.4 g/l at the same temperature. Furthermore, the ammonia storage potential is highly temperature dependent. At low temperatures large amounts of ammonia can be stored, however, the ammonia storage potential is decreasing significantly with increasing temperature between 2 C and 3 C, and at temperatures above 35 C and 4 C almost no ammonia is stored in the vanadium and zeolite catalysts, respectively [32]. Thus, temperature ramps in the catalyst bed can lead to significant ammonia slip. Other compounds, such as H 2 O, can adsorb on the catalyst and thereby have an influence on the adsorption of NH Urea injection system The main purpose of the injection system is to ensure proper preparation of the reductant upstream of the SCR catalyst, independent on the operating conditions. This involves sufficient spatial distribution of the reductant and as high ammonia formation upstream the catalyst as possible. The injected solution of urea typically has a concentration of 32.5 wt% urea. There are several factors that can influence the release of NH 3. Among these are poor atomization and spatial distribution as well as deposition of solid urea in the equipment. Thus, proper spray properties or possibly use of a mixing device is important [4]. In order to increase the amount of NH 3 formed upstream the SCR catalyst, a hydrolysis catalyst can be used, or the gaseous ammonia can be formed before injection [32] Challenges of controlling automotive urea-scr systems In order to control urea-scr system as a diesel exhaust after treatment system, the objective of the controlling action has to be defined. Thus, for a urea-scr system, the design objectives are to reduce the emission of NO x and ammonia, according to the Euro VI emission standard: Minimizing NO x emission

26 12 Background Minimizing NH 3 slip Through the preceding sections, the urea-scr system, including reactions, and physical and dynamic effects, have been covered. The use and control of urea-scr systems in automotive applications involve many challenges, due to the transient characteristics of engine combustion. In the following, the specific challenges with urea-scr systems for automotive applications will be highlighted. SCR catalysts are exposed to a demanding environment when used in automotive exhaust control systems. Temperature fluctuations, normal low operating temperature, presence of catalyst poisonous species, mechanical vibrations, and flow variations are some of the factors that are influencing the system [29]. Furthermore, the catalysts degenerates over time and thereby become less efficient. Due to the combustion characteristics of diesel engines, the exhaust gas temperature is low, especially during low load periods. This has direct influence on the reactions in the SCR catalyst, which are slower at low temperatures for typical exhaust gas composition. The variations in engine load through a driving cycle result in continuous changes of the exhaust gas flow rate, composition, and temperature. The varying temperature has significant influence on the system performance due to lower NO x conversion at low temperatures and due to risk of high NH 3 slip at high temperatures. Thus, considering the temperature dependency on NH 3 storage potential, it is necessary to keep the stored ammonia significantly below the steady state storage capacity at low catalyst temperatures. Thereby, high ammonia slip at large temperature ramps in the catalyst bed can be prevented. All these parameters have significant influence on the performance of the SCR system and complicates the process of controlling the system. Therefore, advanced control of the reductant injection is important, in order to optimize the trade off between NO x reduction and NH 3 slip. 2.3 Reported control systems In order to meet the emission limits of both NO x and NH 3, control of urea injection is important. Due to the transient dynamics of the SCR system and the varying operating conditions caused by changing engine load, control of urea-scr systems is challenging. Through the last decade, different methods of controlling such a system have been proposed, all with the same control objective; to minimize NH 3 slip and minimize the NO x emission. In many of the proposed control systems, either NH 3 coverage maps or trip

27 2.3 Reported control systems 13 systems are used in order to avoid urea injection for conditions causing risk of large ammonia slip [2]. In the following sections, an overview of the research within this field will be given Open loop control strategies Use of open loop control strategies have been reported to be sufficient to meet the Euro IV and V emission standards with respect to NO x reduction [32]. The reported open loop strategies are either based on the stoichiometry of the SCR surface reactions, on ammonia storage control, or on a combination of both, however, most of the open loop control strategies are based on the first mentioned [6, 32] Stoichiometry based Open loop control systems, where the urea injection rate is based on stoichiometry of SCR surface reactions, are depending on good estimates of the exhaust gas composition - more specific the concentrations of NO and NO 2. However, due to uncertainties in the exhaust gas NO x concentrations, in the exhaust gas mass flow rate and temperature, in urea solution properties, and in NO 2 to NO x ratio in catalyst inlet, the SCR catalyst NO x reduction efficiency will vary with the open loop feedforward control strategy [6]. Most of the open loop based control systems use nominal stoichiometric ratio maps to determine the urea injection needed to achieve a certain tailpipe NO x concentration. However, it is difficult to achieve higher NO x conversions, while keeping an acceptable ammonia slip using open loop control strategies, due to the system behaviour during transient conditions [32] Ammonia storage based As explained in section , the capacity of NH 3 stored in the SCR catalyst vary significantly with temperature. Thus, effective management of the ammonia storage is necessary to fulfill the objective of the controller system [6]. However, it is difficult to find an optimal strategy which compensates for the NH 3 stored in the catalyst, the storage capacity, and the target storage level. Especially, as the amount of stored ammonia cannot be measured, but has to be estimated [6]. Schuler et al. [24] has proposed a feedforward controller where the NH 3 dosing rate is determined from the inlet NO concentration by use of parameter mapping of maximum NO conversion, maximum NH 3 slip at steady state, and maximum NH 3 slip peak.

28 14 Background Closed loop control strategies In closed loop control strategies, feedback controllers are used to compensate for errors and variation in the feedforward control strategies [6]. Challenges in closed loop control strategies involve time delay in urea injection, NO x conversion from NH 3 stored in the catalyst, and problems of not getting reliable NO x feedback signals [6]. In the literature, there has been focus on how to overcome the problem with cross sensitivity of NH 3 in NO x sensors. Use of an extended Kalman filter to estimate the actual NO x concentration from the NO x sensor feedback has been proposed, while another approach is the use of NH 3 sensors for the feedback control [14, 26, 31, 12, 1]. The reported closed loop control systems are here divided into model based, adaptive, and model predictive controllers. The feedback loops are often combined with feedforward loops, which are either map or model based Model based controllers The reported model based controllers are often using either NO x sensor feedback or NH 3 sensor feedback. NO x sensor feedback Song and Zhu [27] have proposed one of the early model based closed loop control systems, consisting of an inverse dynamics feedforward loop, based on a simple control model, and a NO x sensor feedback loop using PI control. The feedback loop is based on the error between setpoint and target NO x in the catalyst outlet. Schär et al. [23] have compared four different feedforward controllers in combination with a NO x sensor feedback loop. The feedforward controllers are a map based (maximum NO x reduction at constant 1 ppm NH 3 slip), a surface coverage estimating controller, a model based controller with constant NH 3 slip, and a model based controller with constant NO x /NH 3 ratio. The most promising control strategy of these four controllers, is the model based feedforward controller with constant NO x /NH 3 ratio. Schär et al. [23] have reported on cold start European Transient Cycle (ETC) tests using this closed loop controller, which results in overall NO x removel of 75%, and argues for a 85% NO x removal potential in European Steady Cycle (ESC) and ETC test cycles. Devarakonda et al. [11] have presented a control strategy consisting of a

29 2.3 Reported control systems 15 linear 4 state model, used for state estimation, and a nonlinear version of the 4 state model, used for control design based on individual NO and NO 2 concentrations. This is compared with a 3 state model assuming that all NO x is NO. The two methods were compared by simulation of a cold Federal Test Procedure (FTP) test cycle. The resulting average NH 3 slip was 44 ppm using the 4 state model, which is 27% less than the similar simulation using the 3 state model, and the NO x reduction was 36.2% higher for the 4 state model compared to the 3 state model. NH 3 sensor feedback Wang et al. [31] have proposed a closed loop control system combining NH 3 slip feedback control and map based NH 3 coverage control. Based on NH 3 coverage estimates and the NH 3 slip measurements, the controller switches between feedback control and coverage control. Thus, the feedback control is active at high and increasing temperatures, where NH 3 slip is expected and where the feedback control is possible. In return, the map based NH 3 surface coverage control is active when the temperatures are low and decreasing. The control strategy is validated with tests using ESC, ETC and FTP cycles with 3% urea overdosing as disturbance. In the ESC test, NO x conversion of up to 91% were achieved with average NH 3 slip of 6 ppm and a peak slip of 24 ppm. The ETC tests resulted in decreasing NO x conversions during three consecutive test runs from 87% to 72% with average NH 3 slip of 5.8% and 1.2%, respectively. The hot FTP tests resulted in NO x conversion up to 77% with average NH 3 slip of 1.4 ppm and a peak slip of 5 ppm. Shost et al. [26] have presented a combined feedforward SCR model and NH 3 sensor feedback controller on a FTP cycle achieving 74% NO x conversion with.2 ppm average NH 3 slip and a 3.4 ppm peak slip Adaptive controllers Chi and DaCosta [7] have proposed a closed loop self-tuning controller consisting of two parts. One part, the NH 3 slip control, is identifying the desired NO x conversion in order to control the NH 3 slip. Thus, the objective of the algorithm is to keep the amount of NH 3 adsorbed in the catalyst below the saturation point, and thereby taking advantage of the NO x reduction potential of the stored NH 3. On the basis of measurements of temperature on both sides of the SCR catalyst and the exhaust gas mass flow, estimations of the catalyst bed temperature, the catalyst temperature ramp rate, and the catalyst space

30 16 Background velocity are determined. Thus, based on maps of the temperature dependency of the nominal stoichiometric ratio and the NO x conversion at a given catalyst space velocity, the maximum desired NO x reduction target can be identified. The second part of the control algorithm, the NO x reduction control, has the objective to determine the urea injection rate. This is based on Model- Reference adaptive control, where the NO x reduction is modeled as a first order system with online adaption of model parameters based on measurement of system operating conditions. The reference signal in the Model- Reference adaptive control control system is the output from the NH 3 slip control algorithm, and the adaption mechanism is based on the reference signal and the tracking error as well as the prediction error. Thereby, the control law used to determine the urea injection rate has a feedforward component and a feedback component. With this control strategy a NO x reduction of 84% has been achieved with mean ammonia slip below 7 ppm (peak below 55 ppm) in tests on a fresh V-based catalyst using a FTP cycle [7]. Herman et al. [12] have presented a closed loop PI controller, based on real time NH 3 surface coverage computations, using NH 3 sensor feedback. An adaptive control algorithm is used to adjust target ammonia coverage with mid catalyst NH 3 sensor in feedback control loop. ESC test cycle results of 9.6% NO x reduction and 7.9 ppm average NH 3 slip (3.4 ppm peak slip), have been achieved Model predictive controllers McKinley and Alleyne [2] have proposed an approach using model predictive control on the urea-scr system. The model predictive controller (MPC) is based on a first order linear approximation of a fourth order nonlinear model. The cost function consists of two parts; a part expressing the output tracking error, and a part expressing the control effort. Furthermore, the algorithm uses feedback from NO x and NH 3 catalyst outlet sensors. Thus, the controller includes online determination of simplified, linear models, and in addition, the controller uses urea dosing limits in order to prevent NH 3 slip. In validation, of the controller on ESC test cycle, NO x conversion of 95.7% was achieved, with a peak NH 3 slip of 2.3 ppm. In a similar test using the FTP test cycle, NO x conversion of 82.4% with a peak NH 3 slip of 2.4 ppm was achieved.

31 2.3 Reported control systems Discussion of reported control systems Control of automotive urea-scr systems is challenging due to variation in operating conditions through typical driving cycles. In the literature, different methods of optimizing of the tradeoff between maximizing NO x conversion and minimizing NH 3 slip have been proposed. In table 2.4, the reported controller performances using ESC test cycles can be seen. Controller NO x avg. NH 3 slip NH 3 peak % ppm ppm CL NH 3 slip & coverage, Wang et al. [31] 91% 6 24 Adaptive, Herman et al. [12] MPC, McKinley and Alleyne [19] Table 2.4: Overview of controller performance in ESC test cycle. As can be seen in table 2.4, the controllers achieve NO x conversions above 9% and NH 3 slip below 8 ppm. In table 2.5, similar test results using the FTP test cycle can be seen. Controller NO x avg. NH 3 slip NH 3 peak % ppm ppm CL NH 3 slip & coverage, Wang et al. [31] FF and NH 3 sensor FB, Shost et al. [26] Adaptive, Chi and DaCosta [7] MPC, McKinley and Alleyne [19] Table 2.5: Overview of controller performance in FTP test cycle. In table 2.5, the maximum NO x conversion is 84%. The reason for the better performance in the ESC cycle can be explained by the higher catalyst temperature during the cycle. In the FTP cycle, the gas temperature is too low for NH 3 injection in a large part of the test cycle, while this is not a problem during the ESC cycle. Generally, the reported conversions depend on the catalyst used, and especially the catalyst activity has been increased as a result of the continuous research within this field. Hence, the reported controller performances and thereby the NO x conversions are not completely comparable. However, the reported results of the model predictive controller for the urea-scr system results in high conversions, while ensuring low peak NH 3 slip. Thus, model predictive control seems to be the most advantageous control strategy having the objective to meet the emission limits in the Euro VI emission standard.

32 Chapter 3 Design approach The process of developing and implementing a controller in a complex system, involves a sequence of engineering activities that should be carried out in a certain order. For a model based design approach, the major activities in the design procedure are illustrated in figure 3.1. Figure 3.1: Illustration of model based design approach.

33 19 Process understanding Process understanding is essential for the final design and performance of the controller. The understanding could be based on plant data or computer simulations. Furthermore, it is advantageous to have a good process model describing the system with the purpose of facilitating and improving the process understanding and the design process, especially for complex processes. The dynamic model could be based on physical and chemical principles, or be empirical based on plant data. Control objectives and identification of sensors and actuators Based on process knowledge, the control objectives should be formulated, and sensors and actuators carefully identified. These decisions are critical and determining for the controller design. Development of controller Development of the controller is an iterative process that involves a series of steps. Initially, the control strategy has to be decided, considering the process behavior, the control objective, and on the sensors and actuators. On this basis, the controller structure has to be build up. The next step is identification of the model used in the controller. This part involves determining a test plan (lab or simulation experiments), conducting the experiments, estimation of model parameters, and finally model evaluation. The quality of the model is of great importance, and the process of identifying the model should be repeated until the model accuracy is acceptable. After model identification, the controller should be implemented. This implementation should be validated before preliminary tuning and evaluation of the controller is carried out by computer simulations. Hence, the performance of the controller can be evaluated in accordance to the control objective. If the controller performance does not meet the requirements, the control strategy and model should be reconsidered. Hence, the controller development cycle should be repeated until a satisfying controller design is achieved. Implementation in process and final adjustment Finally, the controller can be implemented in the real system and the final tuning can be conducted.

34 Chapter 4 The urea-scr system The system under investigation is a urea-scr exhaust gas after treatment system on a heavy duty diesel engine. The system consists of a urea injection system, a SCR catalyst system, and a controller to regulate the urea injection. Thus, exhaust gas from the diesel engine is mixed with urea upstream the SCR catalyst, where formation of NH 3 begins and continues in the catalyst. In the SCR catalyst, NO x reduction takes place according to the description in section A simplified sketch of the urea-scr system can be seen in figure 4.1. Figure 4.1: Simple sketch of the urea-scr system. 4.1 The dynamic model representing the physical system Prior to this project, a dynamic model of the urea-scr system has been developed and implemented in Fortran at Haldor Topsøe A/S. The Topsøe

35 4.2 Control objectives 21 Fortran model is based on the reaction kinetics of NO x reduction and the ammonia adsorption on the catalyst. Furthermore, the catalyst is modeled with a tank-in-series approach. Thus, the Topsøe Fortran model is a valuable tool, in order to investigate the behavior of the urea-scr system and to gain process understanding. In this project, the Topsøe Fortran model is used as an approximation the actual physical system. 4.2 Control objectives The purpose of this project is to design a controller on the urea-scr system that can be used to meet the emission limits for heavy duty vehicles of the Euro VI emission standard described in section 2.1. Hence, the controller objective is to reduce NO x emission to at least 4 mg/kwh and minimize ammonia slip to maximum 1/11 ppm (depending on the test cycle), as expressed in table 4.1. Table 4.1: Test cycle NO x NH 3 mg/kwh ppm WHSC 4 1 WHTC 4 11 NO x and NH 3 emissions limits for diesel vehicles for WHSC and WHTC test cycles of the Euro VI emission standard [3, 2]. 4.3 Sensors and actuators Identification of the sensors and the actuators in the urea-scr system is an important design activity which has large influence on the final controller design. Based on the system description, it is evident that there is one actuator, which is the injection of urea. As the system behavior is highly dependent on inlet concentrations, the exhaust gas flow rate and the temperature, sensors measuring these parameters are necessary. Furthermore, measurements of the outlet concentrations of NO, NO 2 and NH 3 are advantageous to use as feedback signals. On the basis of the choice of the actuator and sensors in the system, the controlled variables, the manipulated variables, and the disturbance variables can be identified.

36 22 The urea-scr system The controlled variables are the output variables which have a desired setpoint. Hence, in the urea-scr system, these are the tailpipe NO, NO 2, and NH 3 concentrations. The manipulated variables correspond to the actuators, and are hence the input variables which are changed in order to keep the controlled variables at the setpoint. Finally, the disturbance variables are measured input variables which has influence on the controlled variable, but cannot be manipulated. In the urea-scr system, the disturbance variables are the exhaust gas flow rate, the gas temperature, and the exhaust NO and NO 2 concentrations. A list of these definitions for the urea-scr system, can be seen in table 4.2. Variable type Controlled Manipulated Disturbance Variable description NH 3 outlet concentration NO outlet concentration NO 2 outlet concentration NH 3 injection rate Exhaust gas NO concentration Exhaust gas NO 2 concentration Exhaust gas temperature Exhaust gas flow rate Table 4.2: The controlled, manipulated and disturbance variables of the urea- SCR system. 4.4 Control strategy The choice of control strategy strongly depends on the choice of actuators and sensors. Hence, sensors on process output variables can, for instance, be applied as feedback signals to the controller, and similar measured disturbances can be used as feedforward signals. As described previously, the reactions in the SCR system depend highly on temperature and inlet concentrations. Furthermore, the fact that ammonia is accumulated in the catalyst complicates the process additionally. Thus, due to the dynamic behavior of the urea-scr system it cannot be expected that control with feedback, feedforward, or a combination of those will result in a controller design that meets the control objectives. In return, a model based control strategy could be advantageous in a complex system as the urea-scr system. Therefore, it is decided to use model predictive control (MPC).

37 4.4 Control strategy 23 Model predictive control is an advanced control technique, which is especially advantageous to use with multi-variable control problems. In model predictive control, a model is used to predict the process output for a future horizon, based on the current measurements. Hence, the appropriate change in the input is calculated on the basis of the process output measurements and the model predictions. There are several advantages connected with the use of model predictive control. First of all, the use of an accurate prediction model to describe the dynamics of input, output, and disturbances can enhance the performance by early identification of possible problems. Thereby, the accuracy of the prediction model is critical for the performance of the MPC, as inaccuracy can result in erroneous corrective actions and deteriorating problems. Furthermore, constraints on input and output can be considered systematically, and the use of the MPC enables operation closer to these constraints than conventional control [18, 22].

38 Chapter 5 Identification of prediction model With the choice of using MPC to control the urea-scr system, the next step is to develop the prediction model that should be used in the controller. As mentioned, the accuracy of the prediction model is very important to achieve satisfactory control. Furthermore, the model complexity, and thereby computation effort needed, should be balanced with how often the predictions should be updated. In the urea-scr system, fast fluctuations occur, as for instance, when testing on WHTC cycles. Therefore, frequent updates of the predictions are required. Hence, the prediction model should be a good description of the actual system, while still be simple to reduce the calculation effort and thereby the computation time. In the following, two possible prediction models are identified and compared. Initially, a step response analysis is carried out to enhance process understanding and with the purpose of identifying a step response model. In addition, an ARX model (AutoRegressive model with exogeneous input) is estimated. 5.1 Step response analysis The system behavior is investigated by analysis of the step responses from step change simulations with the Topsøe Fortran model. The aim of the step response analysis is to create a linear step response transfer function