Application of Artificial Neural Networks for Emission Modelling of Biodiesels for a C.I Engine under Varying Operating Conditions

Abstract

<p class="MsoNormal" style="margin: 0cm 0cm 4pt; line-height: 12pt; mso-line-height-rule: exactly;"><span style="font-size: 10pt; mso-bidi-font-size: 11.0pt;" lang="EN-US"><span style="font-family: Times New Roman;">The technical analysis conducted in this study deals with the modelling of diesel engine exhaust emissions using artificial neural networks. Objective of this study is to understand the effectiveness of various biodiesel fuel properties and engine operating conditions on diesel engine combustion towards the formation of exhaust emissions. The experimental investigations have been carried out on a single cylinder Direct Injection (DI) combustion ignition (CI) engine using blends of biodiesel methyl esters from Pongamia, Jatropha and Neem oils. The performance parameters such as brake power (BP), brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), volumetric efficiency, exhaust gas temperature (EGT) were measured along with regulated and unregulated exhaust emissions of CO, HC and NO_x. An Artificial neural network (ANN) was developed based on the available experimental data. Multi layer perceptron neural network was used for nonlinear mapping between input and output parameters of ANN. Biodiesel blend percentage, calorific value, density, Cetane number of each biodiesel blend and operating load were used as inputs to train the neural network. The exhaust gas emissions - NO_x, CO and HC are predicted for the new fuel and its blends. Different activation functions and several rules were used to train and validate the normalized data pattern and an acceptable percentage error was achieved by Levenberg-Marquardt design optimization algorithm. The results showed that training through back propagation was sufficient enough in predicting the engine emissions. It was found that R (Regression Coefficient) values were 0.99, 0.95 and 0.99 for NO_x, CO and HC emissions, respectively. Therefore, the developed model can be used as a diagnostic tool for estimating the emissions of biodiesels and their blends under varying operating conditions.</span></span></p

Biodiesel as an alternative fuel for direct injection diesel engines: A review

“…Manjunatha et al 102 developed a ANN model to analyze the effect of fuel properties of various biodiesel namely pongamia methyl ester, neem oil methyl ester, and Jatropha methyl ester on emission parameters of DI diesel engine. Biodiesel blend percentage, calorific value, density, cetane number of each biodiesel blend, and operating load were used as input to train the ANN model.…”

A Review on Optimization of C. I. Engine Performance Parameters for Biodiesel Fuel Using Suitable Software

“…Mechanical Efficiency of sunflower biodiesel blends has been compared with diesel fuel at various loads. He had observed that the Mechanical Efficiency for B20 blend was higher than the diesel and other blends operation over entire load range [8]. Also Researcher [10] had shows that the volumetric efficiency for diesel is higher than the other blend operations over entire load range.…”

“…These techniques are computationally efficient for optimization requiring hundreds of function evaluations, this tool that saves cost and time in developing new models and methodologies for overall engine management [8]. Some research work has shown that, for analyzing complex problems of emission analysis of biodiesels and their blends, artificial neural network is suitable, adaptable and flexible computing tool that can be used for diagnostic purposes.…”

“…Some authors found that there was no difference in PM emissions for biodiesel relative to diesel fuel. biodiesel from sunflower and olive oil on a single cylinder stationary diesel engine found the maximum PM emissions for the 10% blends and the minimum PM emissions for the 50% blends at the different loads [8]. The trend which PM emissions of biodiesel will be reduced is due to lower aromatic and sulfur compounds and higher cetane number for biodiesel [5], but the more important factor is the higher oxygen content.…”

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

“…ANN has also been used to predict diesel engine emissions from in-cylinder pressure along with engine operating parameters [173,174] investigated the effectiveness of various biodiesel fuel properties and engine operating conditions on diesel engine combustion towards the formation of exhaust emissions using ANN. They conducted experiments on a single-cylinder direct injection (DI) diesel engine using blends of biodiesel from pongamia, jatropha and neem oils.…”