Constant Speed Direct Injection Diesel Research Articles

Prediction efficiency of artificial neural network for CRDI engine output parameters

Considering the environmental and energy concerns, the need for finding an alternative fuel is increasing greatly. But also, the toil associated with finding the suitable fuel for the engine under study is very high. As a measure to reduce this work, theoretical analysis can be used in the search of suitable fuel blends. The present study deals with the creation of an Artificial Neural network (ANN), training, testing the network and finally comparing the predicted values with the experimental values. The test engine was operated with lemon peel oil, with different ratios of Di tertiary butyl peroxide (DTBP), rice husk nanoparticles and water injection. Six engine output parameters (BTE, BSFC, HC, NOx, CO & Smoke) were predicted for different input values of engine load, DTBP proportions, RH nanoparticle concentration and water injection percentage. A single cylinder constant speed direct injection diesel engine is used for obtaining all the experimental values. From the obtained results a data set of 70 experimental values were used to train and test the neural network formed. The predicted values were correlated with the experimental values and the R2 was found for each correlation. The R2 values, thus obtained is in the range of 0.98209 to 0.99744 (which is very close to unity) thus proving that the network created were able to predict the performance and emissions of engine accurately. This work shows the prediction of multi component fuel mixture using ANN, which is new and also helps to reduce the experimental works needed to find the engine output parameters.

Availability analysis, performance, combustion and emission behavior of bael oil - diesel - diethyl ether blends in a variable compression ratio diesel engine

The aim of the present work is to experimentally investigate the effect of injection pressure (IP) and injection timing (IT) on the performance, combustion, and emissions of a compression ignition (CI) engine with aegle marmelos oil (bael oil) blends. This work includes the exergy analysis of diesel engine to maximize the work availability. The tests were conducted on a constant speed direct injection diesel engine fueled with ternary blends of bael oil, diethyl ether (DEE) and neat diesel (D) at various engine loads. When the engine was operated with B2 blend (60%D+30%bael oil+10%DEE), there was an increase in brake thermal efficiency of 3.5% accompanied by a declination in oxides of nitrogen emissions by 4.7% at full load with 250bar IP. The B2 blend showed lower hydrocarbon emission by 7% as compared to that of neat diesel at full engine load with fuel IT of 23° before top dead center. With Increase in engine load, augmentation exhaust gas and cooling water availabilities lead to amplification of exergy efficiency with increasing load. The exergy efficiency of B2 fuel has found as 62.17% of fuel input at 230bar IP with 100% load. From results, B2 fuel exhibits the best performance and combustion characteristics.

A genetic algorithm-based artificial neural network model with TOPSIS approach to optimize the engine performance

ABSTRACTThis paper describes the application of an artificial neural network, genetic algorithm and analytic network process-technique for order preference by similarity to ideal solution for the selection of the optimum fuel blend. A single-cylinder, constant-speed direct-injection diesel engine with a rated output of 4.4 kW was used for exploratory analysis at different load conditions. Ten objectives – brake thermal efficiency, maximum rate of pressure rise, NOx, CO2, CO, HC, smoke, exhaust gas temperature, ignition delay and combustion delay were considered. The proposed ANN model is integrated with GA and the hybrid multi-criteria decision-making technique of ANP-TOPSIS to evaluate the optimum blend. First the ANN model was developed to predict the performance, combustion and emission parameters of the engine. A multi-layer perception network was used for non-linear mapping between input and output parameters. The performance of the ANN model is determined and shows the efficiency of the model to predict the performance, emission and combustion parameters, with a determination coefficient of 0.9627. Second, GA was used to determine the optimum load and blend based on the predicted ANN output parameter. Third, an approach based on the TOPSIS method was used for finding the best blend from the observed optimum parameters of GA.

Experimental Investigation of Performance and emission characteristics of Various Nano Particles with Bio-Diesel blend on Di Diesel Engine

Present study provides the effect of Zinc Oxide (ZnO) and Cerium Oxide (CeO2) nanoparticles additives on the Performance and emission uniqueness of Jatropha. Jatropha blended fuel is prepared by the emulsification technique with assist of mechanical agitator. Nano particles (Zinc Oxide (ZnO)) and Cerium Oxide (CeO2)) mixed with Jatropha blended fuel in mass fraction (100 ppm) with assist of an ultrasonicator. Experiments were conducted in single cylinder constant speed direct injection diesel engine for various test fuels. Performance results revealed that Brake Thermal Efficiency (BTE) of Jatropha blended Cerium Oxide (B20CE) is 3% and 11% higher than Jatropha blended zinc oxide (B20ZO) and Jatropha blended fuel (B20) and 4% lower than diesel fuel (D100) at full load conditions. Emission result shows that HC and CO emissions of Jatropha blended Cerium Oxide (B20CE) are (6%, 22%, 11% and 6%, 15%, 12%) less compared with Jatropha blended Zinc Oxide (B20ZO), diesel (D100) and Jatropha blended fuel (B20) at full load conditions. NOx emissions of Jatropha blended Cerium Oxide is 1 % higher than diesel fuel (D100) and 2% and 5% lower than Jatropha blended Zinc Oxide, and jatropha blended fuel.

Experimental investigation on Performance and Emission Characteristics of J20, P20, N20 Biodiesel blends and Sound Characteristics of P20 Biodiesel blend Used in Single Cylinder Diesel Engine

Present work provides the effect of biodiesel blends and Sound Characteristics of P20 Biodiesel blend compared with Performance and emission Characteristics of diesel. Methods and analysis biodiesel blends was prepared by the Transesterification Process. Experiments were conducted in single cylinder constant speed direct injection diesel engine for various test fuels. Research is mainly focused on pongamia oil. It was observed that a 20% Pongamia oil blends and its properties were similar to diesel. The results showed that 20% Pongamia oil blends gave better performance, less in noise and emission compared with ester of Jatropha and neem oil blends. Hence Pongamia blends can be used in existing diesel engine without compromising the engine performance.

Prediction of CI engine performance, emission and combustion characteristics using fish oil as a biodiesel at different injection timing using fuzzy logic

The present study investigates the potential of Fuzzy Inference System (FIS) to predict the performance, combustion and exhaust emissions of the Compression Ignition (CI) engine at different injection timings (21°, 24°, 27°bTDC) using fish oil biodiesel. The experimental investigations are carried out on a single cylinder constant speed direct injection diesel engine under variable load conditions. Here Multi Input Multi Output (MIMO) fuzzy models are developed, trained and validated to predict the parameters like Brake Thermal Efficiency (BTE), Hydrocarbon (HC), Exhaust Gas Temperature (EGT), Oxides of Nitrogen (NOx), Carbon monoxide (CO), Smoke, Carbon dioxide (CO2), Ignition Delay (ID), Combustion Delay (CD) and Maximum Rate of Pressure Rise (MRPR) with the experimental performance data sets using Trapezoidal membership function. The developed MIMO fuzzy model is capable of predicting the performance, emissions and combustion parameters of the engine with better correlation coefficients in the range of 0.946–0.999, mean absolute percentage error in the range of 0.06–4.5% with noticeably low root mean square errors. The validation results confirm the applicability of the developed FIS models with high degree of accuracy and minimum time demand that can effectively replace the costly and time consuming real life experiments and trails.

Optimisation of compression ignition engine performance with fishoil biodiesel using Taguchi-Fuzzy approach

Due to the increase in demand of energy, there is a steep rise in petroleum product, which led to the depletion of conventional energy. This gave rise to the need of biodiesel as an alternate for diesel fuel. This paper deals with the study of biodiesel blend's performance and different emission behaviour using the Taguchi and fuzzy logic method. Experimental investigations on a single cylinder constant speed direct injection diesel engine were carried out under two parameters: blend proportion and percentage load for six and five levels, respectively. As per this method, an L30 orthogonal array was used to collect data for 30 trials, which were experimented at different engine load and blend proportion. The optimisations are done on brake thermal efficiency, carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), oxides of nitrogen (NOx), smoke, exhaust gas temperature, ignition delay, combustion delay and maximum rate of pressure rise. The signal-to-noise ratio was used for data analysis and the result is verified by the Fuzzy control system model with multi input multi output (MIMO) to predict the performance of engine. The developed model produced idealised results with the correlation coefficients of 0.897–0.998 and root mean square error and found to be useful for predicting the engine performance characteristics with limited number of available data.

EFFECT OF INJECTION PRESSURE ON PERFORMANCE AND EMISSION ANALYSIS OF CI ENGINE USING NON EDIBLE VEGETABLE OILS BIODIESEL AND THEIR BLENDS WITH DIESEL

Gradual depletion of world petroleum reserves and increase in the exhaust emissions day by day have led to an urgent need for alternative fuels to replace diesel. Vegetable oils biodiesel is considered as an alternative for diesel because of their properties which have been close to pure diesel. In the present study non edible vegetable oils like Honge and Jatropha oils biodiesel and their blends were used as fuel in a constant speed direct injection diesel engine. Further effect of injection pressure on the performance parameters such as brake thermal efficiency, brake specific fuel consumption, brake power and emission parameters such as HC, CO and NOX were investigated in a constant speed direct injection diesel engine with varied injection pressures of 180, 200 and 220 bar.The test results showed that Honge and Jatropa oil biofuel blends are having good performance and emission results at 200 bar injection pressure when compared to 180 and 200 bar injection pressure. The test results also showed that performance and emission results of Honge and Jatropa biofuel blends are near to that of the results obtained for pure diesel and they can be used to replace pure diesel.

A hybrid multi-criteria decision modeling approach for the best biodiesel blend selection based on ANP-TOPSIS analysis

The ever increasing demand and depletion of fossil fuels had an adverse impact on environmental pollution. The selection of appropriate source of biodiesel and proper blending of biodiesel plays a major role in alternate energy production. This paper describes an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of optimum fuel blend in fish oil biodiesel for the IC engine. The proposed model, Analytical Network Process (ANP) is integrated with Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian) (VIKOR) to evaluate the optimum blend. Evaluation of suitable blend is based on the exploratory analysis of the performance, emission and combustion parameters of the single cylinder, constant speed direct injection diesel engine at different load conditions. Here the ANP is used to determine the relative weights of the criteria, whereas TOPSIS and VIKOR are used for obtaining the final ranking of alternative blends. An efficient pair-wise comparison process and ranking of alternatives can be achieved for optimum blend selection through the integration of ANP with TOPSIS and VIKOR. The obtained preference order of the blends for ANP-VIKOR and ANP-TOPSIS are B20>Diesel>B40>B60>B80>B100 and B20>B40>Diesel>B60>B80>B100 respectively. Hence by comparing both these methods, B20 is selected as the best blend to operate the internal combustion engines. This paper highlights a new insight into MCDM techniques to evaluate the best fuel blend for the decision makers such as engine manufactures and R& D engineers to meet the fuel economy and emission norms to empower the green revolution.

A neural network model for the prediction of compression ignition engine performance at different injection timings

Rapid depletion of fossil fuel and continuous increase in gasoline prices have stimulated the search of alternative fuels. This paper deals with the prediction of engine performance, emission and combustion characteristics of compression ignition engine fuelled with fish oil biodiesel using artificial neural network (ANN). Experimental investigations are carried out in a single cylinder constant speed direct injection diesel engine under variable load conditions at different injection timings−210, 240 and 270 bTDC. The performance, combustion and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. For training the neural network, feed-forward back propagation algorithm is used. The developed ANN model predicts the performance, combustions and exhaust emissions with a correlation coefficients (R) of 0.97–0.99 and a mean relative error of 0.62–4.826%. The root mean square errors are found to be low. The developed model has found to predict accurately the engine performance, combustion and emission parameters at different injection timings.

Development of fuzzy logic model to predict the engine performance of fish oil biodiesel with diethyl ether

Multiple Inputs and Multiple Outputs (MIMO) fuzzy logic model is developed to predict the engine performance of fish oil biodiesel blended with diethyl ether. Engine performance and emission characteristics such as brake thermal efficiency, hydrocarbon, exhaust gas temperature, oxides of nitrogen (NO x), carbon monoxide, Smoke and carbon dioxide were considered. Experimental investigations on single-cylinder constant-speed direct-injection diesel engine are carried out under variable load conditions. The performance and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. In this model, triangular membership function is used to predict the performance. Computational results clearly demonstrated that the proposed fuzzy models produced less deviations and exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.96–0.99 with experimental values. The developed model produces the idealised results and has been found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

Predicting the engine performance using ethyl ester of fish oil with the aid of artificial neural network

This article describes an application of an artificial neural network (ANN) model for predicting the performance, combustion and emission characteristics of a compression ignition engine using fish oil biodiesel. Experimental investigations are carried out in a single-cylinder constant speed direct injection diesel engine under variable load conditions. The performance, combustion and emission characteristics are measured using an exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. The obtained data are recorded for each experiment and the associated data are used to train the simulation model using the back-propagation algorithm. The developed ANN model predicts the performance, combustion and exhaust emissions with a correlation coefficient (R) of 0.957–0.999 and a mean relative error of 0.02–3.97%. The root-mean-square errors were found to be low. The developed model has been found to predict the engine performance, combustion and emission parameters accurately for the range of data trained.

RETRACTED: Effect of Exhaust Gas Recirculation (EGR) on Performance and Emission characteristics of a Three Cylinder Direct Injection Compression Ignition Engine

Exhaust Gas Recirculation (EGR) is being used widely to reduce and control the oxides of nitrogen (NOx) emission from diesel engines. EGR controls the NOx because it lowers oxygen concentration and flame temperature of the working fluid in the combustion chamber. However, the use of EGR leads to a trade-off in terms of soot emissions moreover it exhausted more unburned hydrocarbons (20-30%) compared to conventional engines. Present experimental study has been carried out to investigate the effect of EGR on performance and emissions in a three cyl- inders, air cooled and constant speed direct injection diesel engine, which is typically used in agri- cultural farm machinery. Such engines are normally not operated with EGR. The experiments were carried out to experimentally evaluate the performance and emissions for different EGR rates of the engine. Emissions of hydrocarbons (HC), NOx, carbon monoxide (CO), exhaust gas temperature, and smoke opacity of the exhaust gas, etc. were measured. Performance parameters such as thermal efficiency, brake specific fuel consumption (BSFC) were calculated. Reductions in NOx and exhaust gas temperature were observed but emissions of particulate matter (PM), HC, and CO were found to have increased with usage of EGR. The engine was operated for normal running conditions with EGR and the performance and emission readings were observed.

Effect of Exhaust Gas Recirculation (EGR) on performance, emissions, deposits and durability of a constant speed compression ignition engine

To meet stringent vehicular exhaust emission norms worldwide, several exhaust pre-treatment and post-treatment techniques have been employed in modern engines. Exhaust Gas Recirculation (EGR) is a pre-treatment technique, which is being used widely to reduce and control the oxides of nitrogen (NOx) emission from diesel engines. EGR controls the NOx because it lowers oxygen concentration and flame temperature of the working fluid in the combustion chamber. However, the use of EGR leads to a trade-off in terms of soot emissions. Higher soot generated by EGR leads to long-term usage problems inside the engines such as higher carbon deposits, lubricating oil degradation and enhanced engine wear. Present experimental study has been carried out to investigate the effect of EGR on soot deposits, and wear of vital engine parts, especially piston rings, apart from performance and emissions in a two cylinder, air cooled, constant speed direct injection diesel engine, which is typically used in agricultural farm machinery and decentralized captive power generation. Such engines are normally not operated with EGR. The experiments were carried out to experimentally evaluate the performance and emissions for different EGR rates of the engine. Emissions of hydrocarbons (HC), NOx, carbon monoxide (CO), exhaust gas temperature, and smoke opacity of the exhaust gas etc. were measured. Performance parameters such as thermal efficiency, brake specific fuel consumption (BSFC) were calculated. Reduction in NOx and exhaust gas temperature were observed but emissions of particulate matter (PM), HC, and CO were found to have increased with usage of EGR. The engine was operated for 96h in normal running conditions and the deposits on vital engine parts were assessed. The engine was again operated for 96h with EGR and similar observations were recorded. Higher carbon deposits were observed on the engine parts operating with EGR. Higher wear of piston rings was also observed for engine operated with EGR.

Experimental investigation of control of NO x emissions in biodiesel-fueled compression ignition engine

Biodiesel is an alternative fuel consisting of the alkyl esters of fatty acids from vegetable oils or animal fats. Vegetable oils are produced from numerous oil seed crops (edible and non-edible), e.g., rapeseed oil, linseed oil, rice bran oil, soybean oil, etc. Research has shown that biodiesel-fueled engines produce less carbon monoxide (CO), unburned hydrocarbon (HC), and particulate emissions compared to mineral diesel fuel but higher NO x emissions. Exhaust gas recirculation (EGR) is effective to reduce NO x from diesel engines because it lowers the flame temperature and the oxygen concentration in the combustion chamber. However, EGR results in higher particulate matter (PM) emissions. Thus, the drawback of higher NO x emissions while using biodiesel may be overcome by employing EGR. The objective of current research work is to investigate the usage of biodiesel and EGR simultaneously in order to reduce the emissions of all regulated pollutants from diesel engines. A two-cylinder, air-cooled, constant speed direct injection diesel engine was used for experiments. HCs, NO x , CO, and opacity of the exhaust gas were measured to estimate the emissions. Various engine performance parameters such as thermal efficiency, brake specific fuel consumption (BSFC), and brake specific energy consumption (BSEC), etc. were calculated from the acquired data. Application of EGR with biodiesel blends resulted in reductions in NO x emissions without any significant penalty in PM emissions or BSEC.

Experimental investigations of heavy metal addition in lubricating oil and soot deposition in an EGR operated engine

Exhaust gas recirculation (EGR) is a technique, which is being used widely to reduce and control the NO x emissions from diesel engines. However, the use of EGR leads to rise in soot emission because of soot–NO x trade-off. This EGR generated soot leads to several other problems inside the engine like degradation of lubricating oil, enhanced engine wear etc. In the present study, an experimental investigation has been carried out to evaluate the effect of EGR on characteristics of lubricating oil with time of its usage. A two cylinder, air cooled, constant speed direct injection diesel engine of 9 kW rating was used for conducting the experiments. The experiments were conducted in two phases for a comparative study on the normally operated engine (i.e. without EGR) and the engine operated with EGR for 96 h in each phase. Lubricating oil samples were collected after every 24 h interval and were analysed for soot loading (total carbon content) and various metal addition due to wear of the engine. Higher metal contents were found in the lubricating oil drawn from the engine using EGR. Higher carbon deposits were also observed on vital parts of the engine operating with EGR.