Engine-out Hydrocarbon Emissions Research Articles

Parametric evaluation of ducted fuel injection in an optically accessible mixing-controlled compression-ignition engine with two- and four-duct assemblies

Ducted fuel injection (DFI) is a strategy to improve fuel/charge-gas mixing in direct-injection compression-ignition engines. DFI involves injecting fuel along the axis of a small tube in the combustion chamber, which promotes the formation of locally leaner mixtures in the autoignition zone relative to conventional diesel combustion. Previous work has demonstrated that DFI is effective at curtailing engine-out soot emissions across a wide range of operating conditions. This study extends previous investigations, presenting engine-out emissions and efficiency trends between ducted two-orifice and ducted four-orifice injector tip configurations. For each configuration, parameters investigated include injection pressure, injection duration, intake manifold pressure, intake manifold temperature, start of combustion timing, and intake-oxygen mole fraction. For both configurations and across all parameters, DFI reduced engine-out soot emissions compared to conventional diesel combustion, with little effect on other emissions and engine efficiency. Emissions trends for both configurations were qualitatively the same across the parameters investigated. The four-duct configuration had higher thermal efficiency and indicated-specific engine-out nitrogen oxide emissions but lower indicated-specific engine-out hydrocarbon and carbon monoxide emissions than the two-duct assembly. Both configurations achieved indicated-specific engine-out emissions for both soot and nitrogen oxides that comply with current on- and off-road heavy-duty regulations in the United States without exhaust-gas aftertreatment at an intake-oxygen mole fraction of 12%. High-speed in-cylinder imaging of natural soot luminosity shows that some conditions include a second soot-production phase late in the cycle. The probability of these late-cycle events is sensitive to both the number of ducted sprays and the operating conditions.

Experimental investigation on the effect of gaseous fuels energy share on reactivity controlled compression ignition mode of combustion operated with gaseous fuels and liquid fuels

The effect of gaseous fuels energy share (GFES) on reactivity controlled compression ignition (RCCI) mode of combustion operated with gaseous fuels and liquid fuels have been studied in this research work. Pure diesel, Thevetia Peruviana methyl ester (TPME) B20 and B100 fuels are used as high reactive fuels (HRFs) and these fuels injected directly during compression stroke while compressed natural gas (CNG) and compressed biogas (CBG) fuels are utilized as low reactive fuels (LRFs) and these fuels are injected during suction stroke with the help of electronic control unit (ECU). Experimental tests are carried-out on RCCI engine with 50% of rated power. The CNG and CBG fuels are varied in the form of GFES from 10% to 80%. From test results it is found that, brake thermal efficiency (BTE) of RCCI engine increased up to 40% GFES. The engine-out hydrocarbon (HC) and carbon monoxide (CO) emissions of CBG at all GFES are more as related to CNG. The engine-out nitric oxide (NOx) emissions of RCCI engine fueled with CNG at all GFES are higher as related to CBG.

Impact of methane energy fraction on emissions, performance and cyclic variability in low-load dual fuel combustion at early injection timings

This work experimentally examines the effect of methane (a natural gas surrogate) substitution on early injection dual fuel combustion at representative low loads of 3.3 and 5.0 bar BMEPs in a single-cylinder compression ignition engine. Gaseous methane fumigated into the intake manifold at various methane energy fractions was ignited using a high-pressure diesel pilot injection at 310 °CA. For the 3.3 bar BMEP, methane energy fraction sweeps from 50% to 90% were performed; while at 5.0 bar BMEP, methane energy fraction sweeps from 70% to 90% were performed. It is observed that minimum methane energy fraction is limited by maximum pressure rise rate leading to knock and maximum methane energy fraction is limited by a high coefficient of variation in netIMEP, which leads to high cyclic variations. For 3.3 bar BMEP, maximum pressure rise rate is 8 bar/°CA at 50% methane energy fraction while at 5 bar BMEP, it is 12 bar/°CA at 70% methane energy fraction. For 3.3 bar BMEP, engine-out NOx emissions decrease by 43 times when methane energy fraction increases from 50% to 90%, and it decreases by nearly 46 times when methane energy fraction increases from 70% to 90% at 5 bar BMEP. Engine-out unburned hydrocarbon emissions increase by nearly 9 times when methane energy fraction increases from 50% to 90% at 3.3 bar BMEP, and it increases by nearly 5 times when methane energy fraction increases from 70% to 90% at 5.0 bar BMEP. Engine-out carbon monoxide emissions increase by nearly 7 times when methane energy fraction increases from 50% to 90% at 3.3 bar BMEP and by nearly 5 times when methane energy fraction increases from 70% to 90% at 5.0 bar BMEP. In addition, cyclic combustion variations at both loads were analyzed to obtain further insights into the combustion process and identify opportunities to further improve fuel conversion efficiencies at low load operation.

Partially Premixed Combustion Application for Diesel Power Improvement

A partially premixed combustion (PPC) approach was applied in a single cylinder diesel research engine in order to characterize engine power improvements. PPC is an alternative advanced combustion approach that generally results in lower engine-out soot and oxides of nitrogen (NOx) emission, with a moderate penalty in engine-out unburned hydrocarbon (UHC) and carbon monoxide (CO) emissions. In this study, PPC is accomplished with a minority fraction of jet fuel injected into the intake manifold, while the majority fraction of jet fuel is delivered directly to the combustion chamber near the start of combustion (SOC). Four compression ratios (CR) were studied. Exhaust emissions plus exhaust opacity and particulate measurements were performed during the experiments in addition to fast in-cylinder combustion metrics. It was seen that as CR increased, the soot threshold equivalence ratio decreased for conventional diesel combustion; however, this afforded an increased opportunity for higher levels of port injected fuel leading to power increases from 5% to 23% as CR increased from 14 to 21.5. PPC allowed for these power increases (defined by a threshold opacity level of 3%) due to smaller particles (and lower overall number of particles) in the exhaust that influence measured opacity less significantly than larger and more numerous conventional diesel combustion exhaust particulates. Carbon monoxide levels at the higher PPC-driven power levels were only modestly higher, although NOx was generally lower due to the overall enriched operation.

Impact of propane energy fraction on diesel-ignited propane dual fuel low temperature combustion

This paper focuses on quantifying the effects of percent energy substitution of propane on Diesel-ignited Propane Dual Fuel Low Temperature Combustion (DPDFLTC) performed in a single cylinder research engine (SCRE). All the experiments were performed with the engine load and speed held constant at 1500rev/min and 3.3bar BMEP, respectively. The intake pressure and temperature were held constant at 150kPa and 30°C, respectively, the diesel injection timing and injection pressure were maintained at 310 CAD (50deg. BTDC) and 500bar, while the propane energy fraction (PEF) was varied throughout the experiment. The results indicate that the maximum PEF was limited by to 90% due to high cyclic combustion variability (COV imep∼11%), and the minimum PEF was limited to 53% due to onset of engine knock (MPRR∼10.5bar/deg). Additionally, the engine-out unburned hydrocarbon emissions decreased from nearly 40g/kWh at 90%PEF to about 10g/kWh at 53% PEF, the engine-out carbon monoxide emissions decreased from nearly 30g/kWh 90% PEF to about 5g/kWh at 53% PEF and the engine-out nitrogen oxide emissions increased from 0.1g/kWh 90% PEF to 1.5g/kWh at 53% PEF; whereas, engine-out smoke emissions remained low throughout the experimental program.

A holistic investigation of natural gas\u2013diesel dual fuel combustion with dual direct injection for passenger car applications

This publication covers the investigation of a dual fuel combustion process for passenger car applications using natural gas and diesel as fuels. In the literature a widely studied dual fuel concept is the combination of port fuel injection of natural gas and direct injection of diesel. The challenge of this concept is a high emission of unburned hydrocarbons at low load operation as previous publications show. The proposed concept features a low pressure direct injection of natural gas in combination with direct injection of diesel to circumvent this problem. The acronym DDI—dual direct injection is introduced for this concept. It enables charge stratification of the air–natural gas mixture. This allows for a significant reduction of the unburned hydrocarbon emissions as earlier studies already demonstrated. The focus of this publication is on hardware variations which were performed on the engine test bench. Results are presented of a variation of the compression ratio and of different charge motion patterns which were studied. The results are compared with a conventional diesel and a gasoline spark ignited engine. The investigations demonstrate that a CO2 reduction of 20–29% is feasible as compared to conventional engines. Finally, investigations of exhaust gas aftertreatment with a three-way catalyst are published. The aftertreatment of the remaining engine-out hydrocarbon emissions is still the key challenge due to the low exhaust gas temperature during low load operation.

Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control

In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon (HC) emissions of an automotive engine over the coldstart period. As the engine’s behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and represented by a number of separate linear models generated based on a coldstart model verified by experimental data. An independent LQTS is then implemented for each of these linear models. In this way, several control laws are created, and the corresponding gains are calculated for each of the independent control laws. ES is then used to tune the adjustable parameters of LQTSs to calculate the control inputs, namely air/fuel ratio (AFR) and spark timing (Δ), such that the resulting exhaust gas temperature (Texh) and engine-out HC emissions (HCraw) be close to a set of optimum profiles. This enables the controller reduce the cumulative tailpipe hydrocarbon emissions (HCcum) to the highest possible extent. To demonstrate the acceptable performance of the proposed controller, an optimal controller derived from the Pontryagin’s minimum principle (PMP) is also taken into account. Based on the results of the conducted comparative study, it is shown that the proposed control technique has a very good performance, and also, can be easily used for real-time applications, as it consumes a remarkably trivial computational time for calculating the controlling commands.

A Nonlinear Model Predictive Controller With Multiagent Online Optimizer for Automotive Cold-Start Hydrocarbon Emission Reduction

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature $T_{\mathrm{exh}}$ and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions $\text{HC}_{\mathrm{cum}}$ . To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem.

An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions

The main objective of this study is to propose a novel algorithmic framework for the implementation of a nonlinear controller for reducing the amount of tailpipe hydrocarbon emissions in automotive engines over the coldstart period. To this aim, the control problem for a given engine is formulated in the form of the standard Bolza problem, and then, the concepts of Euler–Lagrange equation and Hamiltonian function are taken into account to calculate the optimal states, co-states, and control input signals. An extreme learning machine is also linked to an experimentally validated nonlinear state-space representation of the engine during the coldstart to approximate the values of exhaust gas temperature and engine-out hydrocarbon emissions, which are two key variables for the considered control problem. To solve the resulting system of equations, a cellular variant of the particle swarm optimization technique is implemented and the existing nonlinear system of equations is solved heuristically. In addition, some constraints are exerted on the control signals to guarantee the smooth operation of the engine by applying the calculated controlling commands. Finally, the authenticity of the resulting optimal controller is validated against a classical Pontryagin’s minimum principle-based control system. Generally, the findings demonstrate the effectiveness of the proposed control methodology to reduce coldstart hydrocarbon emissions in automotive engines.

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the real-time behavior of the engine over coldstart periods. Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bio-inspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

Application of a Phenomenological Model for the Engine-Out Emissions of Unburned Hydrocarbons in Driving Cycles

Abstract In this work, the application of a phenomenological model to determine engine-out hydrocarbon (HC) emissions in driving cycles is presented. The calculation is coupled to a physical-based simulation environment consisting of interacting submodels of engine, vehicle, and engine control. As a novelty, this virtual calibration methodology can be applied to optimize the energy conversion inside a spark-ignited (SI) internal combustion engine at transient operation. Using detailed information about the combustion process, the main origins and formation mechanisms of unburned HCs like piston crevice, oil layer, and wall quenching are considered in the prediction, as well as the in-cylinder postoxidation. Several parameterization approaches, especially, of the oil layer mechanism are discussed. After calibrating the emission model to a steady-state engine map, the transient results are validated successfully against measurements of various driving cycles based on different calibration strategies of engine operation.

Online optimization of automotive engine coldstart hydrocarbon emissions control at idle conditions

In this work, we propose a model-based scheme for the online optimization of coldstart hydrocarbon emission control strategy of an automotive spark-ignited engine during its idle operating conditions. First, the existing model-based control schemes to reduce the coldstart emissions are reviewed and classified. Then, the proposed scheme and the related control-oriented engine model validated by experimental data are introduced. At the heart of the suggested scheme, there is a new optimal control problem formulation for the coldstart that can be solved rapidly using the Pontryagin’s minimum principle. This formulation is based on a redefined objective function with weighted terms for the coldstart key variables, that is, the engine-out hydrocarbon emission and the exhaust gas temperature. The weighting numbers for these dominant factors in the redefined objective function are tuned such that the optimum solution becomes close to the minimum of the cumulative tailpipe hydrocarbon emissions. The use of the redefined objective function reduces significantly the computational efforts, resulting in an order of magnitude faster convergence rate than solving the original coldstart hydrocarbon emission minimization problem with a complex form. This important property is demonstrated with some simulation results based on the above-mentioned engine model. This feature makes the new formulation a good fit to the proposed scheme, where measured or estimated state variables of the engine model can be fed back to the control unit to re-calculate online the input profiles, such as the spark timing and the air/fuel ratio. Through online modification of the pre-determined input trajectories obtained from off-line calculations, the control performance degradation in practice due to the plant/model mismatch, which is equivalent to a significant increase of the tailpipe hydrocarbon emissions, can be reduced considerably.

An ensemble neuro-fuzzy radial basis network with self-adaptive swarm based supervisor and negative correlation for modeling automotive engine coldstart hydrocarbon emissions: A soft solution to a crucial automotive problem

In this paper, the authors propose a novel intelligent framework to identify the exhaust gas temperature (Texh) and the engine-out hydrocarbon emission (HCraw) during the coldstart operation of an automotive engine. These are two key variables affecting the cumulative tailpipe emissions (HCcum) over the coldstart phase, which is the number one emission-related problem for today's spark-ignited (SI) engine vehicles. The coldstart operation is regarded as a highly nonlinear, transient and uncertain phenomenon. The proposed identifier integrates different soft computational strategies, i.e. neuro-fuzzy computing, fuzzy controller, swarm intelligent computing, and ensemble network design, beneficial for capturing both uncertainty and nonlinearity of the problem at hand. Furthermore, concepts of negative correlation topology design and hierarchical pair competition based parallel training are extracted from literature to form a diverse and robust ensemble identifier. Training of each neuro-fuzzy sub-component in ensemble network is carried out using a hybrid learning scheme. One feature of the antecedent part of neuro-fuzzy system, i.e. number of linguistic terms for each variable, as well as characteristics of rules in rule base are adjusted using hierarchical fair competition-based parallel adaptive particle swarm optimization (HFC-APSO) and the rest of features, i.e. the shape of (membership functions) MFs and the consequent variables of each rule, are tuned using back-propagation (BP) and steepest descent techniques. As it was mentioned, the authors try to design an ensemble identifier with acceptable rate of generalization, robustness and accuracy. These features help them to tame the intuitive uncertainties associated with the rate of Texh and HCraw emission over the coldstart period. To do so, the potential characteristics of sub-components (solution domain of network design) are divided into a set of partitions and then HFC-APSO is utilized to explore/exploit each of those partitions. The exploration/exploitation rate of PSO (the core of HFC-APSO) is dynamically controlled by a fuzzy logic based controller. Hence, it is expected that HFC-APSO yields a set of accurate sub-identifiers with different operating characteristics. To further foster the diversity of the ensemble, negative correlation criterion is considered which obstructs the integration of identical sub-identifiers. The identification results demonstrate that the method is highly capable of providing an authentic model for estimation of Texh and HCraw emission during the coldstart period.

Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission

In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems.

Real-Time Hybrid Switching Control of Automotive Cold Start Hydrocarbon Emission

Reduction of cold start hydrocarbon (HC) emissions requires a proper compromise between low engine-out HC emission and fast light-off of the three way catalytic converter (TWC). In this paper, a hybrid switching system is designed and optimized for reducing HC emissions of a mid-sized passenger car during the cold start phase of FTP-75 (Federal Test Procedure). This hybrid system has the benefit of increasing TWC temperature during the early stages of the driving cycle by switching between different operational modes. The switching times are optimized to reduce the cumulative tailpipe HC of an experimentally validated automotive emission model. The designed hybrid system is tested in real-time on a real engine control unit (ECU) in a model-in-the-loop structure. The results indicate the new hybrid controller reduces the HC emissions over 6.5% compared to nonswitching cold start controller designs.

Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification

In this investigation, the authors test the efficacy and reliability of optimally pruned extreme learning machine with ensemble of regularization techniques to identify the exhaust gas temperature (Texh) and the engine-out hydrocarbon emission (HCraw) during the coldstart operation of automotive engines. These variables have significant impact on the cumulative tailpipe emissions (HCcum) during a coldstart phenomenon, which is the number one emission-related problem for today's spark-ignited (SI) engine vehicles. To do so, the concepts of ensemble computing with negative correlation learning (NCL) and pruning of neurons are used in tandem to cope with difficulties associated with extracting knowledge from collected database. In the proposed framework, the regularization strategies are adopted to help us increasing the numerical stability of identifier while mitigating the redundant complexity of hidden neurons. Moreover, to increase the generalization of identifier and also reduce the effects of uncertainty, an ensemble of independent OP-ELM with NCL selection criterion called OP-ELM-ER-NCL is taken into account. To endorse the valid performance of OP-ELM-ER-NCL for modeling the characteristics of engine over the coldstart phenomenon, its performance is compared to a set of well-known identification systems, i.e. standard extreme learning machine (ELM), back-propagation neural network (BPNN), OP-ELM with different types of regularization, ensemble of regularized OP-ELM without negative correlation (OP-ELM-ER), and an ensemble ELM with a constrained linear system of leave-one-out outputs (E-LL), in terms of both accuracy and computational complexity. The simulation results indicate that the proposed identifier is really capable of capturing the knowledge of collected database. It is observed that its resulted accuracy and robustness are comparable with those obtained by identification methods available in the literature. Besides, using NCL strategy aids the ensemble to select the most effective regularization techniques and remove the redundant (ineffective) ones, which consequently decreases the complexity of final ensemble.

A generalized renormalization group turbulence model and its application to a light-duty diesel engine operating in a low-temperature combustion regime

A generalized renormalization group closure model based on the “dimensionality” of the flow strain rate is proposed and applied to engine flows in this study. In the model, the model coefficients C1, C2, and C3 are constructed as functions of the flow strain rate. Computations were made for compressing/expanding flows in a diesel engine under motoring conditions. It was found that the generalized renormalization group model performs better than the standard renormalization group k−ε model in terms of its predictions of turbulent kinetic energy and model length scales. Computations in a diesel engine operating under low-temperature combustion conditions were also investigated to further assess the performance of the present model. Predictions of the spatial distributions of unburned hydrocarbons were significantly improved and agreed well with available experimental images. Engine-out unburned hydrocarbon emission was predicted to be smaller with the generalized renormalization group model than with the standard renormalization group k−ε model.

The Impact of Ethanol Fuel Blends on PM Emissions from a Light-Duty GDI Vehicle

This study explores the influence of ethanol on particulate matter (PM) emissions from gasoline direct injection (GDI) vehicles, a technology introduced to improve fuel economy and lower CO2 emissions, but facing challenges to meet next-generation emissions standards. Because PM formation in GDI engines is sensitive to a number of operating parameters, two engine calibrations are examined to gauge the robustness of the results. As the ethanol level in gasoline increases from 0% to 20%, there is possibly a small ( 30%, there is a statistically significant 30%–45% reduction in PM mass and number emissions observed for both engine calibrations. Particle size is unaffected by ethanol level. PM composition is primarily elemental carbon; the organic fraction increases from ∼5% for E0 to 15% for E45 fuel. Engine-out hydrocarbon and NOx emissions exhibit 10–20% decreases, consistent w...

Spark timing and fuel injection strategy for combustion stability on HEV powertrain

Stringent regulations on exhaust emission and fuel economy for vehicles have become major issues in the automotive industry. Hybrid electric vehicles (HEVs) are one of the crucial alternative plans over current conventional vehicles, but they have drawbacks, such as the increase of engine-out total hydrocarbon (THC) emission and the deterioration of combustion stability with frequent engine stopping and restarting. This study is an experimental investigation to obtain a better understanding of THC emission characteristics during engine restart and deceleration driving on a HEV powertrain of a parallel motor/generator type. Using a fast response flame ionization detector (FFID) and a specified tool for cycle by cycle and cylinder to cylinder investigation, the combustion stability and engine control characteristics were analyzed during the transient state of the HEV powertrain. The experimental conditions cover variations of the high voltage battery state of charge (SoC), spark ignition timing and injected fuel mass. As an experimental result, the effect of the spark timing control was slight, but the reasonable injection duration for stable combustion was more than 7ms at the first cycle shortly after engine start-up, although the injection rate was 2.7–3ms in the idle state. The reason for the greater volume of fuel being injected was that the fuel deposited in the cylinder and intake port was reduced due to discharge and vaporization during the deceleration step before the idle stop. A basic control algorithm for combustion stability under engine restart conditions was built by considering the experimental results.

Effect of Multifunctional Fuel Additive Package on Fuel Injector Deposit, Combustion and Emissions using Pure Rape Seed Oil for a DI Diesel

This work investigates the effect of a multifunctional diesel fuel additive package used with RapeSeed Oil (RSO) as a fuel in a DI heavy duty diesel engine. The effects on fuel injectors’ cleanliness were assessed. The aim was to maintain combustion performance and preventing the deterioration of exhaust emissions associated with injector deposit build up. Two scenarios were investigated: the effect of deposit clean-up by a high dose of the additive package; and the effect of deposit prevention using a moderate dose of the additive package. Engine combustion performance and emissions were compared for each case against use of RSO without any additive. The engine used was a 6 cylinder, turbocharged, intercooled Perkins Phaser Engine, fitted with an oxidation catalyst and meeting the Euro II emissions limits. The tests were conducted under steady state conditions of 23kW and 47kW power output at an engine speed of 1500 rpm. The in-cylinder pressure, gaseous and particulate exhaust emissions were measured. The injectors were inspected using SEM (Scanning Electronic Microscopy). The results show that the use of the multifunctional fuel additive package reduces the ignition delay (ID), increases the premixed combustion duration (PCD) and improves the combustion stability. The multifunctional fuel additive package also reduced the deposit build up on the fuel injectors and prevented the deterioration of engine-out particulate, CO and hydrocarbon emissions