Exhaust Gas Aftertreatment Systems Research Articles

Design and optimization of exhaust gas aftertreatment system for a heavy-duty diesel engine

Design and optimization of exhaust gas aftertreatment system for a heavy-duty diesel engine

Comprehensive kinetic model of a three-way catalyst for stoichiometric natural gas engines: Experiments and simulation

BackgroundKinetic modeling is an important technique for designing and optimizing automotive exhaust gas aftertreatment systems. However, few kinetic models can simultaneously predict the main pollutant removal and byproduct formation on three-way catalysts for stoichiometric natural gas engines. Therefore, further development of the TWC model was needed to address such issues. MethodsIn this study, a detailed global kinetic model for natural gas engines was developed by extending the classic TWC model by combining the investigation of reaction pathways and light-off experiments over a wide range of conditions. Significant findingsWith the help of a gradually simplified light-off experiment, the main side reactions between NO and CO/H2/O2 to form NH3/N2O/NO2 were well defined. Adding all observed reactions into the classic model, the extended kinetic model can capture all the experimental phenomena under a wide range of operating conditions in laboratory light-off tests. Importantly, this model can also describe the light-off phenomena of aged samples when aging factors are used. Apart from predicting reactor-scale phenomena, the extended model above describes and predicts the cold start emission under cold WHTC. This study provides a feasible and straightforward method to build a global kinetic model covering full-scale laboratory conditions to actual operating conditions.

Additive Manufacturing of Novel Hybrid Monolithic Ceramic Substrates

Additive manufacturing (AM) can revolutionise engineering by taking advantage of unconstrained design and overcoming the limitations of traditional manufacturing capabilities. A promising application of AM is in catalyst substrate manufacturing, aimed at the enhancement of the catalytic efficiency and reduction in the volume and weight of the catalytic reactors in the exhaust gas aftertreatment systems. This work addresses the design and fabrication of innovative, hybrid monolithic ceramic substrates using AM technology based on Digital Light Processing (DLP). The designs are based on two individual substrates integrated into a single, dual-substrate monolith by various interlocking systems. These novel dual-substrate monoliths lay the foundation for the potential reduction in the complexity and expense of the aftertreatment system. Several examples of interlocking systems for dual substrates were designed, manufactured and thermally post-processed to illustrate the viability and versatility of the DLP manufacturing process. Based on the findings, the sintered parts displayed anisotropic sintering shrinkage of approximately 14% in the X–Y direction and 19% in the Z direction, with a sintered density of 97.88 ± 0.01%. Finally, mechanical tests revealed the mechanical integrity of the designed interlocks. U-lock and Thread configurations were found to sustain more load until complete failure.

Performance evaluation of neural networks in modeling exhaust gas aftertreatment reactors

A comprehensive evaluation of neural network (NN) methods to model exhaust gas aftertreatment reactors is presented in this study. A combination of non-linear autoregressive model with exogenous inputs (NARX) network and static NN is utilized to capture the dynamic behavior of the state and outlet variables, such as coverage fractions, temperatures, and conversions, of monolith reactors used in the abatement of automobile emissions. The efficacy of this methodology is illustrated by modeling the diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) reactors of a diesel engine exhaust gas aftertreatment system. The final NN topology is designed by carefully comparing the model performances in the training and testing datasets. Model parsimony quantified by Akaike’s Information Criterion is employed for choosing the number of input and feedback delays in the NARX network. The novelty of this work lies in generating quality training and validation data using a physics-based system level model comprising of the engine and aftertreatment reactor components. The accuracy and generalizability of the NN models are demonstrated by comparing the model predictions with physical model simulation results in five disparate test datasets used in vehicle certification protocols. Overall, the NN models are found to reproduce all the qualitative trends in the various state and output variables while needing minimum computing resources resulting in better than real-time performance. Therefore, with appropriate training, these models can be embedded in the electronic control unit (ECU) and utilized for model-based predictive control systems.

The future of the internal combustion engine: actual development tasks

Introduction (statement of the problem and relevance). The forthcoming vehicle environmental regulations to be introduced in the European Union (EU) in 2025–2026 involve aggressive emission limit scenarios combined with new real-world test conditions. The new environmental concept sets ambitious goals in relation to the power line of traditional vehicles with an internal combustion engine (ICE), such as: the main source of energy for automotive technology; high efficiency (more than 50% efficiency); ultra-low fuel consumption and ultra-low emissions (CO2, NOx, PM).The purpose of the study was to analyze the prospective fuel efficiency and environmental safety requirements for wheeled vehicles to determine the main directions of internal combustion engines development to meet these requirements.Methodology and research methods. The analytical methods of research included: the analysis of the commercial engines market; the investigation of world projects to tighten the requirements for environmental safety of wheeled vehicles; the study of the main internal combustion engines development concepts. Results. The prospective requirements for internal combustion engines have been analyzed, and as result of it, the concept of ICE development, the promising internal combustion engine view and an exhaust gas after-treatment system (EGATS) were worked out.Practical significance. The results of the study can be used for determining the requirements and main directions of research and development work when creating the new generations of internal combustion engines.

A model-based analysis of washcoat distribution on zoned coated gasoline particulate filters

The integration of three-way catalysts (TWC) and gasoline particulate filters into a single so-called coated GPF (cGPF) can decrease the necessary packaging volume and costs in exhaust gas aftertreatment systems. Similar to classical TWCs, it is possible to introduce advanced coating technologies, in the presented case zoning, which creates zones of high and low washcoat loading, resulting in a filter with an axial non-uniformity of the washcoat distribution. In addition to zoning, a filter can either be coated solely in-wall or have a partially or fully on-wall coating, which leads to a variety of coating configurations. To investigate the impact of the coating configuration, a previously published validated cGPF model is applied to evaluate the pressure drop, high load conversion, and light-off performance of fresh cGPFs. The simulations reveal an enhanced light-off at steady-state conditions if more washcoat is deposited at the front part of the filter or on the inlet channel. Under transient conditions, however, a filter with a catalytic layer on the outlet channel performs best.

Concepts for Hydrogen Internal Combustion Engines and Their Implications on the Exhaust Gas Aftertreatment System

Hydrogen as carbon-free fuel is a very promising candidate for climate-neutral internal combustion engine operation. In comparison to other renewable fuels, hydrogen does obviously not produce CO2 emissions. In this work, two concepts of hydrogen internal combustion engines (H2-ICEs) are investigated experimentally. One approach is the modification of a state-of-the-art gasoline passenger car engine using hydrogen direct injection. It targets gasoline-like specific power output by mixture enrichment down to stoichiometric operation. Another approach is to use a heavy-duty diesel engine equipped with spark ignition and hydrogen port fuel injection. Here, a diesel-like indicated efficiency is targeted through constant lean-burn operation. The measurement results show that both approaches are applicable. For the gasoline engine-based concept, stoichiometric operation requires a three-way catalyst or a three-way NOX storage catalyst as the primary exhaust gas aftertreatment system. For the diesel engine-based concept, state-of-the-art selective catalytic reduction (SCR) catalysts can be used to reduce the NOx emissions, provided the engine calibration ensures sufficient exhaust gas temperature levels. In conclusion, while H2-ICEs present new challenges for the development of the exhaust gas aftertreatment systems, they are capable to realize zero-impact tailpipe emission operation.

Strategies for the Low Sulfur Policy of IMO—An Example of a Container Vessel Sailing through a European Route

Ships are an important part in international trade transportation and a major source of pollution. Therefore, the International Maritime Organization (IMO) implemented an amendment to the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex VI, which stipulates that the sulfur content in marine fuel oil shall not exceed 0.5 wt.% starting in 2020. In order to meet the IMO low sulfur policy, shipping lines could adopt one of the following strategies: (1) using very low sulfur fuel oil (VLSFO), i.e., with sulfur content less than 0.5 wt.%; (2) installing scrubbers or other exhaust gas aftertreatment systems; or (3) replacing current fuels with clean alternative fuels such as natural gas. This study evaluates the feasibility and benefits of these strategies for shipping lines in order to determine the most cost-effective measures. First, according to the feasibility of the strategies evaluated by SWOT analysis, although scrubbers can reduce emissions of sulfur oxides into the atmosphere, more and more countries are restricting the discharge of wastewater from open-loop scrubbers into their waters. Instead, VLSFO and liquefied natural gas (LNG) are good choices in terms of environmental protection and economic benefits. Therefore, this study further evaluates the two strategies of replacing high sulfur fuel oil (HSFO) with VLSFO and converting diesel engines to LNG engines based on a cost-benefit methodology. This study took an 8500 TEU container vessel, which is powered by a marine diesel engine with the nominal power of 61,800 kW, sailing the Asian-European route as an example, and calculated the total incremental costs, pollutant emission reductions, and cost benefits arising from the implementation of the VLSFO and LNG strategies, respectively. According to the results of this study, the total incremental cost of LNG is higher than that of VLSFO in the first 4.7 years, but this gradually decreases, making the gap of the total incremental costs between the two strategies wider year by year. In comparison with using HSFO without any improvement, the total incremental costs of the VLSFO and LNG strategies increase by 12.94% and 22.16% over the following five years, respectively. The use of LNG can significantly reduce SOx, PM, NOx, and CO2 emissions; on the other hand, it leads to more CH4 emissions than the VLSFO strategy. Compared to doing nothing, the cumulative reduction rates of SOx, PM, NOx, and CO2 emissions over the next five years after the adoption of the LNG strategy are 3.6%, 7.0%, 70.4%, and 15.7%, respectively. The higher emission reduction rates of LNG compared to VLSFO illustrate that the former has a good effect on the suppression of exhaust gas pollution. In terms of the cost-benefit evaluation of the two strategies, this study shows that the VLSFO strategy is more cost-effective than the LNG strategy in the first 2.5 years, but that the cost-benefit ratio of the latter increases year by year and exceeds that of the former, and the gap between them widens year by year. Based on the evaluation results of this study, the LNG strategy is suitable for ocean-going container vessels with fixed routes and younger or larger sized vessels to meet the IMO low sulfur policy. In contrast, the VLSFO strategy is appropriate for old merchant ships with fewer container spaces. LNG is a suitable medium- and long-term strategy, i.e., for more than 2.5 years, for shipping lines to meet the IMO low sulfur policy, while VLSFO is a suitable short-term strategy.

A causal supervisory control strategy for optimal control of a heavy-duty Diesel engine with SCR aftertreatment

Diesel engines continue to be the prime movers in heavy-duty and non-road applications. Optimising and controlling the engine and its exhaust-gas aftertreatment system (ATS) for low fuel consumption and low pollutant emissions remains a challenging task. Typically, the engine and ATS are calibrated separately and based on engineering experience. In order to exploit the full potential of the overall system, a so-called supervisory control strategy can be used. In this paper, we develop such a strategy based on Pontryagin’s minimum principle and show that its performance is close to the globally optimal solution. Compared to similar approaches, our concept does not require the optimal control problem to be solved online. Instead, we derive a simple, causal, and near-optimal control law that is easy to implement and tune. We present experimental results from the engine test-bench to demonstrate the practical feasibility and effectiveness of the proposed approach. Furthermore, we show how the strategy can be adapted online using sensor feedback in order to meet a given emission target during arbitrary and unknown engine operation.

Experimental investigation on reducing agents for catalytic converters of CI engine

High level pollution and emissions arising out of ever-increasing transport vehicles are wrecking a lot of havoc on the environment globally. Stringent emission norms and regulations force the research community across the globe to develop exhaust gas after-treatment systems for effective emission control. Catalytic converters are used to reduce the emissions of hydrocarbon, nitrogen oxides, carbon monoxide and particulate matter. Selective catalytic reduction (SCR) is a system used in IC engines, in which the NOx emission is reduced by injecting a reducing agent in the presence of catalyst. Normally adblue solution is used as the reducing agent for the dissociation of NOx into nitrogen and water. However, it has many drawbacks like NH3 slip, deposit formation and incomplete conversion of NOx. In the present study investigation has been carried out in a CI engine provided with a Vanadium based catalytic converter. Three different reducing agents were used for the dissociation of NOx into nitrogen and water and their effectiveness in controlling the NOx emission has been analyzed and reported.

Investigation on mixture formation and combustion characteristics of a heavy-duty SI methanol engine

• A high-pressure PFI system is utilized on a heavy-duty methanol SI engine. • The mixture formation process and its homogeneity are studied theoretically. • The optimized injection strategy can improve the mixture homogeneity. • The optimized injection strategy can improve the engine brake thermal efficiency. Due to the simplicity of the exhaust gas aftertreatment system, the heavy-duty natural gas spark ignition (SI) engines play an important role in achieving the macro targets of the reduction of petroleum consumption, carbon and harmful emissions. Considering that power-to-methanol is economically more feasible than power-to-methane, methanol is proposed to continue the advantages of natural gas engines. Based on the fuel properties of methanol and our researching experience, methanol mixture preparation and flow within the cylinder are the most significant. A novel high-pressure port fuel injection (PFI) system is utilized to promote atomization and evaporation of methanol. The effects of the injection strategy, including injection pressure (p inj ) and injection timing (α inj ), and excess air–fuel ratio (λ) on the mixture formation were simulated and then studied by engine experiments. Three-dimensional simulation and engine tests were conducted under the condition of 1500 rpm and the brake mean effective pressure (BMEP) of 0.854 MPa. p inj varied from 8 MPa to 24 MPa by 4 MPa, α inj was from 240°CA BTDC to 400°CA BTDC by 40°CA and λ was set to be 1.1,1.2 and 1.3, respectively. Simulation results indicate that the mixture homogeneity can be promoted by the optimized injection strategy. The mixture is the most homogeneous at α inj = 340 °CA BTDC and p inj = 8 MPa and the most inhomogeneous at α inj = 400 °CA BTDC and p inj = 24 MPa when λ = 1.2. The experimental results indicate that the more homogeneous mixture can improve engine brake thermal efficiency by shortening CA 0-10 and CA 10-90 as well as methanol lean-burn. The optimized injection strategy can improve the brake thermal efficiency by 2.6%, 2.7% and 2.8% when λ = 1.1, 1.2 and 1.3, respectively.

Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests.

Securing Deep Learning Models with Autoencoder based Anomaly Detection

Deep learning models are on the rise in many scientific fields. Their ability to solve complex and nonlinear tasks has made them very popular. However, in comparison to physical models, they struggle with extrapolation. Hence, it is important that the input data in the production stage is similar to the data seen in training. Deviations to the training often occur in real world applications due to sensor delays and drifts, aging of the system, and communication errors such as noise. Especially in safety relevant applications, securing those models against these influences, which can be seen as anomalies, is essential.
 
 In the past, Autoencoders, especially Variational Autoencoders (VAEs), have been proven useful for anomaly detection. Many researches focus on improving the Autoencoder’s separation ability for an optimal set anomaly threshold. However, the setting of the threshold is not trivial and is crucial for a good anomaly detection. Setting the threshold optimal becomes especially challenging if the anomaly is unknown in the training process, which is often the case in a real world application.
 
 The proposed method combines a deep learning model with an Autoencoder. The input data is handed to the trained Autoencoder which reconstructs the input. If the data is similar to the training data, the Autoencoder should be able to reconstruct the input data accurately. Otherwise, an anomaly is suspected. The reconstruction and the original input data are both passed through the deep learning model, generating two predictions, which are then compared.
 
 For classification and reinforcement tasks with discrete result space the prediction of non-anomalous data should lead to the same class for both samples. For those tasks this allows us to sort out samples as anomalies for which the two results are not the same, hence the threshold becomes obsolete.
 
 For regression and continuous reinforcement tasks, the difference between the two predictions can be interpreted as a safety measure and is easier to grasp than the Autoencoder’s reconstruction error, which is typically used.
 
 The advantage of this method is the distinction between samples that can or cannot be handled by the subsequent application model instead of just deciding if the input is anomalous. This leads to a higher robustness of the joint model and a better usage of resources of the deep leaning model. Moreover, the Autoencoder and the deep learning model are trained separately which makes the training a lot more stable than using coupled training methods.
 
 The proposed method is proven on both, a classification and a regression task. For the classification, the publicly available UEA multivariate time series classification dataset was used. For regression, a dataset simulating a SCR catalyst as part of an automotive exhaust gas aftertreatment system was evaluated. For both tasks common anomalies such as delay and noise were applied to the data.

A novel method for lubrication oil consumption measurement for wholistic tribological assessments of internal combustion engines

The research and development of combustion engines will face multiple challenges during the next years. Increasing reliability while further reducing emissions and life cycle costs are just a few of them. The reduction of the lubrication oil consumption (LOC) addresses all the aforementioned challenges. Reducing the oil consumption of an engine, reduces emissions like particle emissions, hydrocarbon emissions and combustion anomalies. However, the biggest incitement for optimizing the oil consumption would be to lower the life cycle cost through extending the durability of the exhaust gas aftertreatment system, as contaminants coming from the oil are considered to lead to poisoning of catalytic materials. In addition, it is also important to take the cost of the lubrication oil itself into account as well. Thus, especially for manufacturers of large stationary gas engines, reducing – or rather, optimizing – the lube oil consumption is becoming more and more of a focus. However, a further reduction of the LOC causes state-of-the-art measurement methods to reach their limitations regarding resolution and lower detection limit (LDL). Hence, new methods need to be developed. Most currently available systems are based on the tracer technology, where a naturally abundant or synthetically added substance of the oil is traced down in the exhaust gas. By quantifying the amount of tracer in the exhaust gas, the LOC can be calculated. The newly developed and patented (Rossegger and Engelmayer, 2018) [1] method presented in this article is based on the use of the stable hydrogen isotope deuterium (2H) as a tracer. It is added to the oil by conducting a hydrogen/deuterium (1H/2H) exchange process with synthetic oil, which is then blended into the oil. When operating the engine, the deuterium can be detected in the exhaust gas water vapor using an isotopic water analyzer based on the cavity ring-down spectroscopy (CRDS). The present article will first focus on the synthesis of the tracer, then on the design of a prototype and finally present measurement results collected on a passenger car engine.

Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems

This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in a complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.

Influence of Low Ambient Temperatures on the Exhaust Gas and Deposit Composition of Gasoline Engines

Abstract To comply with future emission regulations for internal combustion engines, system-related cold start conditions in short-distance traffic constitute a particular challenge. Under these conditions, pollutant emissions are seriously increased due to internal engine effects and unfavorable operating conditions of the exhaust aftertreatment systems. As a secondary effect, the composition of the exhaust gases has a considerable influence on the deposition of aerosols via different deposition mechanisms and on fouling processes of exhaust gas-carrying components. Also, the performance of exhaust gas aftertreatment systems may be affected disadvantageously. In this study, the exhaust gas and deposit composition of a turbocharged three-cylinder gasoline engine are examined in situ upstream of the catalytic converter at ambient and engine starting temperatures of −22 °C to 23 °C using a Fourier-transform infrared spectrometer and a particle spectrometer. For the cold start investigation, a modern gasoline engine with series engine periphery is used. In particular, the investigation of the behavior of deposits in the exhaust system of gasoline engines during cold start under dynamic driving conditions represents an extraordinary challenge due to an average lower soot concentration in the exhaust gas compared to diesel engines and so far has not been examined in this form. A novel sampling method allows ex situ analysis of formed deposits during a single driving cycle. Both, particle number concentration and the deposition rate are higher in the testing procedure of real driving emissions (RDEs) than in the inner city part of the worldwide harmonized light vehicles test cycle (WLTC). In addition, reduced ambient temperatures increase the amount of deposits, which consist predominantly of soot and to a minor fraction of volatile compounds. Although the primary particle size distributions of the deposited soot particles do not change when boundary conditions change, the degree of graphitization within the particles increases with the increasing exhaust gas temperature.

Multiscale Modeling and Analysis of Pressure Drop Contributions in Catalytic Filters

Catalytic monolith filters with a honeycomb structure represent a key component of modern automotive exhaust gas aftertreatment systems. In this paper, we present and validate a multiscale modeling methodology for the prediction of filter pressure loss depending on the monolith channel geometry as well as the microscopic structure of the wall including catalytic coating. The approach is based on the combination of a 3D pore-scale model of flow through the wall reconstructed from X-ray tomography and a 1D+1D model of the filter channels. Several cordierite and SiC filter samples with varying substrate pore sizes and catalyst distributions are examined. A series of experiments are performed at different gas flow rates and filter lengths in order to validate the model predictions and to distinguish individual pressure drop contributions (inlet and outlet, channel, and wall). The predicted pressure drop shows a strong impact of the coating location and agrees well with the experiments.

Impact of gas phase reactions and catalyst poisons on the NH3-SCR activity of a V2O5-WO3/TiO2 catalyst at pre-turbine position

With stricter regulations and decreasing exhaust gas temperature due to more efficient engine management, NOx abatement remains a critical issue despite many technical and catalytic advances. To counterbalance these challenges the positioning of exhaust gas aftertreatment systems in front of the turbocharger has gained significant interest in recent years. In this context, we report a systematic study on the impact of propylene, n-dodecane, and o-xylene as well as of SO2 on the gas phase reactions and NH3-SCR activity of a conventional vanadium-based catalyst at increased pressure. An elevated pressure is shown to dramatically promote homogeneous reactions of hydrocarbons, which subsequently contribute to an increased NO oxidation but also to parasitic NH3 overconsumption. The hydrocarbons are also faster catalytically oxidized and affect NOx reduction at all investigated temperatures. Their presence in the exhaust gas further leads to the formation of the toxic byproducts HCHO and HCN. Upon short term exposure to 20 ppm SO2 in the feed gas, no significant catalyst poisoning was observed. However, for longer exposure times an enhanced formation of sulfur containing deposits was encountered at low temperatures for elevated pressures. Notably, their poisonous effect was reversible after heating to high temperatures.

Compliance of modern agricultural tractors presented on Russian market with global emission standards

The paper discusses the problem of compliance of diesel engines of agricultural tractors that are available on the Russian market with international environmental regulations and standards. Based on the data presented on the official websites of agricultural machinery manufacturers, an analysis of modern domestic and foreign tractors is performed. The performed studies allow concluding that in Russia there is a serious problem associated with the mismatch of agricultural tractors available on the Russian market with the of Stage and Tier global emission standards; 87 % of domestically produced agricultural tractors correspond to low emission classes (Euro 0, Euro 2, Tier 2, Tier 3); 78 % of the examined imported models of tractors from leading world manufacturers comply with emission classes below Tier 4. Methods for improving diesel engines towards reducing the amount of harmful substance emissions into the environment are described: adjustable turbocharging, aftercooling, partial exhaust gas recirculation, electronically controlled Common Rail, and exhaust gas purification and aftertreatment systems are used.

Ammonia Emissions in SI Engines Fueled with LPG

Ammonia is a toxic exhaust component emitted from internal combustion engines. Both pure ammonia and the products of its reaction with nitrogen and sulfur compounds, being the source of particulate matter (PM) emissions, are dangerous for human health and life. The aim of the article was to demonstrate that NH3 can be produced in exhaust gas after-treatment systems of spark-ignition (SI) engines used in light-duty vehicles. In some cases, NH3 occurs in high enough concentrations that can be harmful and dangerous. It would be reasonable to collect research data regarding this problem and consider the advisability of limiting these pollutant emissions in future regulations. The article presents the results of the spark-ignition engine testing on an engine test bench and discusses the impact of the air–fuel ratio regulation and some engine operating parameters on the concentration of NH3. It has been proven that in certain engine operating conditions and a combination of circumstances like the three-way catalytic reactor (TWC) temperature and periodic enrichment of the air–fuel mixture may lead to excessive NH3 emissions resulting from the NO conversion in the catalytic reactor. This is a clear disadvantage due to the lack of limitation of these pollutant emissions by the relevant type-approval regulations. This article should be a contribution to discussion among emissions researchers whether future emission regulations (e.g., Euro 7 or Euro VII) should include a provision to reduce NH3 emissions from all vehicles.