Emissions Of Compression Ignition Engine Research Articles

Diesel engine performance fuelled with manifold injection of ethanol and water-in-diesel emulsion blends

Growing environmental concerns and rigorous emission rules have fuelled the search for an alternate fuel with cutting-edge engine modifications that can reduce pollutants from compression ignition (CI) engines. Adding water to diesel reduces the nitric oxide (NOx) and particulate matter (PM) emissions while increasing combustion efficiency is a promising standby approach that could help to meet global energy demands as well. Furthermore, injection of alcohol ensures the complete combustion of the fuel mixtures. In this case, two different emulsions with different water-to-diesel ratios are used to inject ethanol into the intake manifold at injection timing (IT) of 5°ATDC with injection duration (ID) of 3-ms (27°CA) to investigate the effects of the ethanol on the performance of modified diesel engine. The test results suggest that higher brake thermal efficiency (BTE) of 31.25% is obtained with 20% water in diesel emulsion along with manifold injected ethanol at 80% load conditions. 10.3% of increment in the BTE was noticed with engine operation on water-in diesel emulsion with ethanol manifold injection compared to baseline diesel operation. Lower smoke emission of 41 HSU was obtained when engine is fuelled with emulsion of 20% water in diesel with manifold injected ethanol at 80% load condition. The smoke emissions were deteriorated by 27.9% with ethanol injection comparatively to that obtained with baseline diesel operation. Declined emissions of NOx, carbon monoxide (CO) and hydrocarbon (HC) by 10.8%, 24.3% and 21% at 80% load were figured out when engine fuelled with an emulsion of 20% water in diesel with manifold injected ethanol compared to baseline diesel operation. Use of a 20% water-in-diesel emulsion with multiple injection of ethanol, delivered at the optimized IT and ID, yields better engine performance and emissions in CI engines.

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R2 of 0.97.

Comparative assessment of performance, combustion, and emission of compression ignition engine fuelled with different generations of biodiesel

ABSTRACT In the present research work, the performance, combustion and emission characteristics of compression ignition engine fuelled 22 different biodiesels categorised as (first, second, third generation, waste oil, and animal oil) biodiesel (B100) are evaluated numerically and compared with diesel fuel. Diesel-RK, a full-thermodynamic cycle engine analysis tool was used to carry out the numerical analysis. The engine power, fuel consumption, ignition delay, maximum cylinder pressure, oxides of nitrogen (NOx), and carbon dioxide (CO2) emission were investigated at four different engine operating loads. Power output was observed to be higher for Corn oil biodiesel, while Jatropha oil biodiesel produced lower fuel consumption among all other biodiesel. 9.1% increase in-cylinder peak pressure was found for waste transformer oil biodiesel than diesel, while ignition delay was found to be lowest for Rapeseed oil BD, Rice bran oil BD among all other biodiesel. The NOx emissions were higher for biodiesel by 30–32%, while CO2 emissions were reduced by 10–12% compared to diesel. The physicochemical properties of biodiesel were responsible for the diverse characteristics of the engine.

ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation

Compression ignition (CI) engines are popular in the transport sector because of their high compression ratio. However, in recent years, it has become a major concern from an environmental point of view because of the emission and depleting fossil fuel. The advanced combustion concept has been a popular research topic in the CI engine. Low-temperature combustion with alternate fuel has helped in reducing the oxides of nitrogen (NOx) and soot emission of the engine. Biogas is a popular substitute of energy especially deduced from biomass because of its clean combustion properties, as well it being a renewable energy source compared to non-renewable diesel resources. In experiments with dual fuel, i.e., conventional diesel and alternate fuel (biogas) were carried out through them. In the present study, an artificial neural network model was used to estimate emissions and check the attributes of performance. Different algorithms and training functions were used to train the models. However, the best training algorithm was Levenberge Marquardt and the training function was Tansig (Hyperbolic tangent sigmoid) and Logsig (logarithmic sigmoid), which showed the best result with regression coefficient (R > 0.98) and Mean square error (MSE < 0.001). The best model was trained by evaluating MSE and regression coefficient. Experimental results and artificial neural network (ANN) prediction showed that the experimental results were similar to each other and lie at the same intervals. The ANN model helped in predicting experimental data that were earlier difficult to experimentally perform using interpolation and extrapolations. It was observed that there was an increase in Brake Specific Energy Consumption (BSEC) and a decrease in Brake thermal efficiency (BTE) with improved biogas flow rate and reduced NOx emission in the combustion chamber. Carbon monoxide (CO) and hydrocarbon (HC) emissions increase linearly with the increase in biogas flow rate, whereas smoke opacity decreases. It could be concluded that this study helps in understanding the effect of dual fuel (diesel-biogas) combustion under different load conditions of the engine with the help of ANN, which could be a substitute fuel and help to protect the environment.

Taguchi‐based optimization of design and fuel parameters of partially premixed charge CI engine fuelled with biodiesel and butanol blends

AbstractSimultaneous control of NOx and smoke emission of compression ignition engine was the major challenge for the researchers as their formation mechanisms are inversely proportional to each other. In the present study, combustion process was modified as partially premixed charge combustion (PPCC) which supplies a premixed charge of fuel and air through inlet valve in addition to the fuel injected into the cylinder. This resulted in reduction of combustion temperature with lean air fuel ratio. To investigate the influence of various factors on these emissions, six design factors and two fuel factors of the engine were selected as factors and NOx, smoke, and brake thermal efficiency were selected as responses for the investigation. Experimental test matrix was designed based on Taguchi L18 orthogonal array. Multi response Signal to Noise (MRSN) ratio was calculated for the response variables and the optimum combination level of factors was obtained simultaneously using Taguchi's parametric design and the same was tested experimentally. Influence of each factor and most influencing factor on the chosen objective was determined by analyzing the variance of the MRSN ratio. This investigation reveals that among the factors tested, main injection fuel is the most influencing factor with a maximum percentage contribution of 44.5. It has been observed that normal combustion mode parameters have more influence with a percentage of contribution of 80 when compared with the parameters of PPCC mode. It has also been observed that fuel factors have higher percentage contribution of 53 compared with the design factors contribution of 43.6.

Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM

The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to model the performance and the emissions of a single cylinder diesel engine fueled by diesel and propylene glycol additive. The evaluations are performed using the correlation coefficient (CC), and the root mean square error (RMSE) values. The best model for prediction of the dependent variables is reported ANN-GA with the RMSE values of 0.0398, 0.0368, 0.0529, 0.0354, 0.0509 and 0.0409 and CC 0.988, 0.987, 0.977, 0.994, 0.984, 0.990, respectively for brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), CO, CO2, NOx and SO2. The proposed hybrid model reduces BSFC, NOx, and CO by −30.82%, 21.32%, and 11.32%, respectively. The model also increases the engine efficiency and CO2 emission by 17.29% and 31.05%, respectively, compared to a single RSM in the optimized level of independent variables (69% of biodiesel's oxygen content and 32% of the oxygen content of propylene glycol).

NOx Reduction in Ag/Al2O3-SiO2 Converters in the Exhaust of a Compression-Ignition Engine

NOx emissions in compression-ignition engines can be reduced, either through the application of engine-internal methods, i.e., making sure that as little NOx as possible is produced as a result of the mixture combustion processes, or through the use of catalytic converters designed to reduce nitrogen oxides, including NH3-SCR or HC-SCR converters. Converters using ammonia offer high conversion rates, but they tend to be problematic in terms of their operation. For this reason, converters using hydrocarbons for the purpose of NOx reduction have been gaining in popularity. An Ag/Al2O3-SiO2 converter is an example of such a catalytic converter. This paper describes the process of preparing such a converter and characterises the porosity and acidity of its surface. The dispersion of silver was assessed based on oxygen absorption tests on crystallites of sliver and based on TEM images of crystallites of silver. The conversion of NO2, NO and propane was assessed depending on the temperature. Additionally, NO2, NO and propane conversion was assessed at 500 °C, depending on the admixture of a reducer, and propane selectivity in the process of reduction of NO to N2 was calculated. The test results indicate that the developed converter can be considered the basis for further research into the development of this compression-ignition engine exhaust after-treatment technology.

WITHDRAWN: Computational analysis on combustion, characteristics and ignition analysis on IC engine using mahua oil

Abstract In this research work, the main objective is to enhance the performance, combustion characteristics and reduce emissions of compression ignition engine using alternative fuel. Four experimental investigations and one numerical analysis are carried out to identify the optimum biodiesel blend and modification methods. Mahua oil issued. In this study due to its ease of availability compared with other non-edible oils. Two types of engine modifications were made. First one is Low Heat Rejection (LHR) and second one is Low Temperature Combustion (LTC). In the LHR modification the engine parts such as the cylinder head and the piston head is coated by the alumina (Al2O3) with a thickness of 350 μm. For the LTC modification the Exhaust Gas Recirculation (EGR) process was adopted with help of control valves. CFD analysis was done through ANSYS FORTE software comparing with the experimental results. The variation between the CFD analysis and experiment results was 4% to 9%. Hence from this study, ANSYS FORTE is one of the suitable CFD software for evaluating the performance of the IC engine. The biodiesel blends B20 and the LHR modification technique by ceramic coating, along with LTC modification of 15% EGR yields better performance and lower emissions.

Soot reduction for cleaner Compression Ignition Engines through innovative bowl templates

Considering the need of pollutant emissions reduction and the high cost of the after-treatment systems, in-cylinder solutions for pollutant reduction are becoming more and more relevant. Among different proposals, new piston geometries are considered an attractive solution for reducing both soot and nitrogen oxides emissions in compression ignition engines. For this reason, this paper evaluates the soot formation and combustion characteristics of a novel piston geometry proposal, called stepped lip-wave, for light-duty engines. It is compared with other two well-known bowl geometries: re-entrant and stepped lip. The study was performed in an optical single-cylinder direct injection compression ignition engine. Two optical techniques (2 color pyrometry and OH* chemiluminescence) were applied for analyzing soot formation in each piston geometry. Test were performed at different engine loads, fuel injection characteristics and exhaust gas recirculation configuration. The re-entrant piston presents higher soot formation and a slower late oxidation process in comparison with the other two geometries. Stepped lip and stepped lip-wave present similar soot formation levels. However, stepped lip-wave showed a more efficient and faster soot oxidation process during the final combustion stages. Results confirm the potential of the stepped lip-wave concept to reduce soot emissions and achieve a cleaner energy production system.

Numerical investigation and prediction of effects of applying Anatase TiO2 nano-particle on performance and emissions of CI engine by using WNN-GA

This study analyzed the effects of Anatase titanium dioxide (TiO2) nano-catalysts addition on performance and emissions of a compression ignition (CI) engine. By making a one-dimensional model of the engine in GT-power software and conducting the simulation process by using the model, this has been done. The performance and emissions of the engine in partial loads were investigated in three selected speeds for engine with diesel and again under the same conditions by adding TiO2 nanoparticles of Anatase to fuel. Performance parameters which would be examined for two types fuel, diesel and synthetic, are BSFC, power, torque, NOx, HC and Co, respectively. Then, in regarding to experimental data, a wavelet neural network (WNN) model was developed. We used four different kinds of mother wavelet functions and the best one was chosen in neurons of the network layer to predict the engine performance. Using genetic algorithm (GA), optimization coefficients and WNN parameters were determined. The numerical and prediction results were in a good match with experimental result. Therefore, results show that modeling by GT-power and WNN have very high ability to predict the performance and emissions of the engine, without needing any expensive and time-consuming engine tests.

On the possibilities to reduce compression ignition engine emissions by controlling the reactivity of diesel-biofuel mixtures

In most European countries the use of different biodiesel fuels has been proved to be a sustainable method to reduce the pollutant emissions of the operating Diesel engines. An in-debt analysis of combustion characteristics is necessary to give a detailed perspective on the advantages of biodiesel use in compression ignition engines. Among all the essential parameters characterizing the compression ignition engine operation, the ignition delay is the most difficult to be estimated. This parameter offers the possibility to have more control over the combustion process inside the cylinder. This gives the ability to better quantify and ultimately to decrease the pollutant emission for this type of engine. An experimental study has been conducted on a tractor Diesel engine using different biofuel mixtures with 7% and 20% volumetric fractions of fatty acid methyl esters in Diesel fuel. No modifications were made to the fuel injection system in order to have a better image of the direct impact of biodiesel fuel on performance and emissions of the engine. The obtained results showed some reductions of total unburned hydrocarbons and nitrogen oxide emissions over the entire engine speed domain for both tested biodiesel fuels. The estimation of the ignition delay was experimentally carried out and correlated with the properties of the biodiesel fuel; a proper control of the combustion process by fuel reactivity ensured thus the mitigation of carbon monoxide, carbon dioxide and smoke. The link between the experimentally determined ignition delay, mainly it’s reduction, and fuel reactivity is what the present work tries to analyse. A better evaluation of the ignition delay parameter, shorter when using biodiesel mixtures, should lead to better control of combustion and further lowering the exhaust pollutant emissions, without major impact on engine performance. An in-debt analysis of the combustion process starting from the injection characteristics correlated with the fuel reactivity is necessary for shortening the ignition delay and reducing carbon dioxide emissions.

Deep kernel learning approach to engine emissions modeling

Abstract We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NO x emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.

Impact of Biogas Blends with Diesel on Emission of Compression Ignition Engine

Main objective of the work was to investigate the output like emission from compression ignition engine which has been run by diesel as well as the blends of biogas with diesel. Volume flow rate of biogas with petrol as a major parameter to reach the expected outcome. The engine was operated with diesel, and blends of biogas 15%, 25% and 35% with petrol. The study focused on the variation of outputs hydrocarbon, carbon monoxide(CO) Nitrous oxides(NOX) and smoke for the brake power generated by the engine. The engine exhibits better results when the proportion of biogas was increased.

Influence of blends of diesel and renewable fuels on compression ignition engine emissions over transient engine conditions

To reduce the amount of carbon dioxide released from transportation the EU has implemented legislation to mandate the renewable content of petrol and diesel fuels. However, due to the complexity of the combustion process the addition of renewable content, such as biodiesel and ethanol, can have a detrimental effect on other engine emissions. In particular the engine load can have a significant impact on the emissions. Most research that have studied this issue are based on steady state tests that are unrealistic of real world driving and will not capture the difference between full and part loads. This study aims to address this by investigating the effect of renewable fuel blends of diesel, biodiesel and ethanol on the emissions of a compression ignition engine tested over the World Harmonised Light Vehicle Test Procedure (WLTP). Diesel, biodiesel and ethanol were blended to form binary and ternary blends, the ratios were determined by Design of Experiments (DoE). The total amount of emissions for CO, CO2 and NOx as well as the fuel consumption, were measured from a 2.4 L compression ignition (CI) engine running over the WLTP drive cycle. The results depicted that percentages smaller than 10% of ethanol in the fuel blend can reduce CO emissions, CO2 emissions as well as NOx emissions, but increases fuel consumption with increasing percentage of ethanol in the fuel blend. Blends with biodiesel resulted in minor increases in CO emissions due to the engine being operated in the low and medium load regions over the WLTP. CO2 emissions as well as NOx emissions increased as a result of the high oxygen content in biodiesel which promoted better combustion. Fuel consumption increased for blends with biodiesel as a result from biodiesel’s lower heating value. All the statistical models describing the engine responses were significant and this demonstrated that a mixture DoE is suitable to quantify the effect of fuel blends on an engine’s emissions response. An optimised ternary blend of B2E9 was found to be suitable as a ’drop in’ fuel that will reduce harmful emissions of CO emissions by approximately 34%, NOx emissions by 10% and CO2 emissions by 21% for transient engine operating scenarios such as the WLTP drive cycle.

Prediction of Properties, Engine Performance and Emissions of Compression Ignition Engines Fuelled with Waste Cooking Oil Methyl Ester - A Review of Numerical Approaches

Given the challenges of renewability, affordability, and sustainability that ultra-low sulfur diesel (ULSD) fuel and first-generation biodiesel currently face, a niche clearly exists for biodiesel sourced from waste cooking oil. Exploring these potentials requires insights into a range of properties as well as performance and emission characteristics. Real-time experimental determination of fatty acid methyl ester (FAME) properties, engine performance and emissions of compression ignition (CI) engines fuelled with unblended FAME is technical, time-consuming, expensive, and requires sophisticated laboratory infrastructure, unlike numerical prediction techniques. Emerging trends and consensus indicate that utilization of numerical simulation and prediction techniques are cost-effective, less laborious, innovative, and flexible. Although FAME properties, engine performance, and emissions have been accurately predicted via various numerical techniques in the recent past, important gaps such as using numerical approaches to determine the optimal FAME mix still exist. The objective of this study was to review the numerical techniques employed in predicting FAME properties, engine performance and emissions of CI engine and investigate whether an optimal FAME candidate can be unearthed through these numerical prediction techniques. This review reports that the outcomes of these numerical predictions agree with experimental data and fall within acceptable average relative deviation but a precise approach to determining an optimal FAME candidate was not achieved. The use of numerical tools like MATLAB and computational fluid dynamics (CFD) in amalgamation with some new measurement techniques including high-speed cameras to accurately predict FAME properties, engine performance, emission characteristics and gain access to the activities in the combustion chambers, were highlighted. These high capacity modeling tools, high-speed cameras, and techniques can be used to accurately forecast an optimal FAME mix to improve engine performance, meet emission benchmarks and advanced engine research, if well harmonized.

In-cylinder flame luminosity measured from a stratified lean gasoline direct injection engine

Abstract In-cylinder flame luminosity has been used to diagnose combustion process and the formation of soot emissions in compression-ignition engines. Increased soot particle emissions from spark-ignited gasoline direct injection (GDI) engines, especially those with stratified fuel-air mixtures, requires investigation of responsible anomalous flame behavior. In this work, spatiotemporal in-cylinder flame luminosity is reported from a lean stratified operating condition in a modern GDI engine. Significant fuel mass injected near top dead center led to detectable piston-top pool fires that have previously correlated to significant soot particle emissions. The demonstrated result shows that measuring in-cylinder flame luminosity is a viable tool for identifying soot-causing flames in GDI engines.

Hydrothermal conversion of biomass to higher alcohol fuels for compression ignition engine

Abstract Higher alcohol (C5/C6 alcohol) blended with diesel or bio-diesel is a promising alternative clean fuel for the compression ignition engine. The most important puzzle is the technology development of biomass to higher alcohol fuels. In this paper, a novel approach of hydrothermal conversion of biomass into C5/C6 higher alcohol fuels was proposed. High yield of higher alcohol fuels was obtained from the real biomass with the C5/C6 alcohol selectivity of 77% under the mild hydrothermal conditions. The properties of the produced higher alcohol fuels are similar to that of 0# diesel. The exhaust emission of compression ignition engine decreased vigorously by blending the higher alcohol fuels with diesel at weight ratio of 20/80.

Biodiesel with nano additives from coconut shell for decreasing emissions in diesel engines

The aim of this work is to decrease emissions in compression ignition engines fueled with diesel and biodiesel blends and also addition of coconut shell (CS) nano particles were prepared by mechanical ball milling process. The organic nano particle was characterized by Transmission Electron Microscopy (TEM), and energy dispersive analysis of X-rays (EDAX) technique. The biodiesel was prepared by using Pungamia pinnata non-edible oil through transesterification process. The average particle size of coconut shell nano particles was found as 20 nm in diameter. Without changing the engine design the performance and emission test was conducted in single cylinder diesel engine with fuels of 100%DF, 20%BD80%DF and 20%BD80%DF with CS.

Analysis and comparison of performance and emissions of compression ignition engine fuelled with diesel and different bio-fuels blended with Methanol

The present fossil fuel crisis and increasing vehicle population made us to think of alternate fuels. The abundance of the fossil fuels is expected to be exhaust in another 30-40 years. The cost of the fossil fuels is day by day increasing and also the emission from these fuels increases the air pollution. With keeping in view of all the above said points, it is made us to think of alternate fuels for all CI Engines. Among alternate fuels, the Mango Seed oilblended with Ethanolwill promise for substituting the diesel. With vegetable oils there is combustion problems and which leads to more emissions in the exhaust.To overcome the above problems we use combustion boosters at the time of combustion. So here we use Mango Seed oilblended with Ethanolas alternative fuels in semi adiabatic diesel Engine and perform various tests and evaluate its performance. The present investigation evaluates Methanol blended with Palma oil, Mahua oil, Coconut oil in CI Engine. A Twin cylinder C I engine adapted to study the brake thermal efficiency, brake specific energy consumption, and emissions. In this study, the diesel engine was tested using Diesel and Methanol blended with Palma oil, Mahua oil,Coconut oil Palma oil, Mahua oil,Coconut oil. From this study the emissions like HC,CO2,CO,NOx,Aldehydes,Smokehas been reduced and Methanol blended with vegetable oils are substitute to diesel fuel.

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives was modeled by regression analysis, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods. Cetane number (CN) and engine speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results.