Experimental investigation on performance, combustion and emission characteristics of a single cylinder diesel engine fuelled by biodiesel derived from Cymbopogon Martinii

Biodiesel derived from nonedible feed stocks such as Mahua, Jatropha, Pongamia are reported to be feasible choices for developing countries including India. This paper presents the results of investigation of performance and emissions characteristics of diesel engine using Mahua biodiesel. In this investigation, the blends of varying proportions of Mahua biodiesel and diesel were prepared, analyzed compared with the performance of diesel fuel, and studied using a single cylinder diesel engine. The brake thermal efficiency, brake-specific fuel consumption, exhaust gas temperatures, Co, Hc, No, and smoke emissions were analyzed. The tests showed decrease in the brake thermal efficiencies of the engine as the amount of Mahua biodiesel in the blend increased. The maximum percentage of reduction in BTE (14.3%) was observed for B-100 at full load. The exhaust gas temperature with the blends decreased as the proportion of Mahua increases in the blend. The smoke, Co, and No emissions of the engine were increased with the blends at all loads. However, Hc emissions of Mahua biodiesels were less than that of diesel.

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Experimental Investigation of Performance and Emission Characteristics of Mahua Biodiesel in Diesel Engine

The present work examines the use of a non-edible vegetable oil namely honne
 oil, a new possible source of alternative fuel for diesel engine. Highly
 viscous honne oil can be reduced by blending it with diesel fuel. A direct
 injection diesel engine typically used in agricultural sector was operated on
 neat diesel and a blend of 50% honne oil with 50% diesel fuel (H50). Injector
 opening pressure was changed to study the performance, emission and
 combustion characteristics. It was observed that increasing the injector
 opening pressure with H50 from the rated injector opening pressure (200 bar)
 increased the brake thermal efficiency and reduced CO, HC, and smoke opacity
 emissions. However, NOx emission was increased. With H50, ignition delay
 decreased as injector opening pressure increased. Improved premixed heat
 release rate were observed with H50 when the injector opening pressure was
 advanced. The best injector opening pressure was 240 bar for H50 based on
 brake thermal efficiency and emissions.

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Effect of injector opening pressures on the performance, emission and combustion characteristics of DI diesel engine running on honne oil and diesel fuel blend

This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and
exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg–Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R2) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy

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Prediction of Marine Diesel Engine Performance by Using Artificial Neural Network Model

This study details the artificial neural network modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption, and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons, and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%), excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75% to 90%. This study shows that the artificial neural network approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

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Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine

Biodiesel plays a major role as one of the alternative fuel options in direct injection diesel engines for more than a decade. Though many feed stocks are employed for making biodiesel worldwide, biodiesel derived from domestically available non-edible feed stocks such as Jatropha curcas L. is the most promising alternative engine fuel option especially in developing countries. Since experimental analysis of the engine is pricey as well as more time consuming and laborious, a theoretical thermodynamic model is necessary to analyze the performance characteristics of jatropha biodiesel fueled diesel engine. There were many experimental studies of jatropha biodiesel fueled diesel engine reported in the literature, yet theoretical study of this biodiesel run diesel engine is scarce. This work presents a theoretical thermodynamic study of single cylinder four stroke direct injection diesel engine fueled with biodiesel derived from jatropha oil. The two zone thermodynamic model developed in the present study computes the in-cylinder pressure and temperature histories in addition to various performance parameters. The results of the model are validated with experimental values for a reasonable agreement. The variation of cylinder pressure with crank angle for various models are also compared and presented. The effects of injection timing, relative air fuel ratio and compression ratio on the engine performance characteristics for diesel and jatropha biodiesel fuels are then investigated and presented in the paper.

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An analytical and experimental study of performance on jatropha biodiesel engine

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

/pdf/artificial-neural-network-modeling-of-jatropha-oil-fueled-7cwadm6dvm.pdf

Parkash Rakesh Gakkhar

Papers

An analytical and experimental study of performance on jatropha biodiesel engine

Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions