Modeling of Neem Oil Methyl Esters Production using Artificial Neural Networks

Abstract

The objective of the present work is to develop models inculcating the effect of operating conditions of neem oil methyl esters (NOME) production in an oscillatory baffled reactor, namely temperature, time of reaction, oil to methanol ratio and catalyst concentration on the estimation of parameters like the viscosity of biodiesel produced by using Artificial Neural Networks technique. Experiments were conducted in the laboratory and the results obtained were used to develop the ANN model using MATLAB. The developed model was in good agreement with the experimental values (error within +1%).Based on the outcome of this demonstrative work, it can be concluded that ANN has a great potential in addressing the estimation of biodiesel properties. It is sincerely felt that the methodology adopted in the present work can be extended to more comprehensive data sets and various data from different experimental reactor design setups. KeywordsNeem Oil Methyl Ester, Oscillatory baffled reactor, artificial neural network.

Optimization of Cerbera manghas Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization

“…These neurons are arranged in layers and are interconnected to one another. The beauty of ANN is that it is not compulsory for the user to know the relationship between the data processing elements beforehand in order to establish a correlation [34,35]. The input data are manipulated by the neurons and the interconnections of these neurons during each step of the process in order generate the output [36].…”

Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester

“…The development of prediction methods are of great value in estimating properties of biodiesel. Relevant studies using ANN models have been found in recent literature . For example, Ramadhas et al .…”

“…More recently, Bhattacharyulu et al . modeled the effect of operating conditions on the neem oil biodiesel viscosity using the ANN technique with an acceptable accuracy.…”

“…The development of prediction methods are of great value in estimating properties of biodiesel. Relevant studies using ANN models have been found in recent literature [21][22][23][24][25][26][27][28][29]. For example, Ramadhas et al [21] evaluated four types of ANNs and found that the ANNs are effective and accurate in predicting the kinematic viscosity and CN of biodiesel by a combination of five FAMEs as inputs.…”

“…It was observed that the ANN models were more accurate and the need of detailed physical properties of the biodiesel mixture for the prediction of the kinematic viscosity was addressed, as well. More recently, Bhattacharyulu et al [24] modeled the effect of operating conditions on the neem oil biodiesel viscosity using the ANN technique with an acceptable accuracy.…”