Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid

[Abstract]: A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400 rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that the maximum power and torque increased by 2.7 and 2.9% respectively, also the concentration of the CO and HC emissions have significantly decreased when biodiesel was used. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient enough in predicting the engine torque, specific fuel consumption and exhaust gas components for different engine speeds and different fuel blends ratios. It was found that the R2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption,CO and HC emissions, respectively

Estimation of biodiesel properties from chemical composition – an artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests

Artificial Neural Network Optimization Modeling On Engine Performance Of Diesel Engine Using Biodiesel Fuel

This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the incylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Development of chemometric multivariate calibration models for spectroscopic quality analysis of biodiesel blends

Thesis (Master)--İzmir Institute of Technology, Chemistry, İzmir, 2011Includes bibliographical references (leaves: 128-132)Text in English; Abstract: Turkish and Englishxiii, 132 leavesThe fact that the biodiesel is produced from renewable resources and environmentally friendly when compared to the fossil-based petroleum diesel, biodiesel has gained an increasing interest. It is mainly produced from a variety of different animal fat and vegetable oil combined with an alcohol in the presence of a homogeneous catalyst and the determination of the quality of the produced biodiesel is as important as its production. Industrial scale biodiesel production plants have been adopted the chromatographic analysis protocols some of which are standard reference methods proposed by official bodies of the governments and international organizations. However, analysis of multi component mixtures by chromatographic procedures can become time consuming and may require a lot of chemical consumption. For this reason, as an alternative, spectroscopic methods combined with chemometrics offer several advantages over classical chromatographic procedures in terms of time and chemical consumption. With the immense development of computer technology and reliable fast spectrometers, new chemometric methods have been developed and opened up a new era for processing of complex spectral data. In this study, laboratory scale produced biodiesel was mixed with methanol, commercial diesel and several different vegetable oils that are used to prepare biodiesels and then several different ternary mixture systems such as diesel-vegetable oil-biodiesel and methanol-vegetable oil-biodiesel were prepared and gas chromatographic analysis of these samples were performed. Then, near infrared (NIR) and mid infrared (FTIR) spectra of the same samples were collected and multivariate calibration models were constructed for each component for all the infrared spectroscopic techniques. Chemometric multivariate calibration models were proposed as genetic inverse least square (GILS) and artificial neural networks (ANN). The results indicate that determination of biodiesel blends quality with respect to chemometric modeling gives reasonable consequences when combined with infrared spectroscopic techniques

MODELING THE EFFECT OF TEMPERATURE ON ENVIRONMENTALLY SAFE OIL BASED DRILLING MUD USING ARTIFICIAL NEURAL NETWORK ALGORITHM

Due to increase in environmental legislation against the deposition of oil based mud on the environment, drilling companies have come up with an optimum drilling mud such as plant oil based mud with little or no aromatic content, which its waste is biodegradable. Optimum mud carry out the same function as diesel oil based drilling fluid and equally meets up with the HSE (Health, safety and environment) standard. It is expedient to determine the down hole mud properties such density in the laboratory or use of available correlation but most time; the range of data is not either reliable or unavailable. In this study, artificial neural network (ANN) was used to address the unreliable laboratory data and unavailable correlation for environmentally friendly oil based drilling mud such as jatropha and canola oil. The new artificial neural network model was developed for predicting the down hole mud density of diesel, jatropha and canola oil based drilling mud using 30 data sets. 60% of the data were used for training the network, 20% for testing, and another 20% for validation. The test results revealed that the back propagation neural network model (BPNN) showed perfect agreement with the experimental results in term of average absolute relative error returne

Predicting physical properties of alkanes with neural networks

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods

An Experimental Study of Variable Compression Ratio Engine Using Diesel Blend - A Computing Approach

Increase in the scarcity of the fossil fuels, prices and global warming have generated an interest in developing alternate fuel for engine. Technologies now focusing on development of plant based fuel, plant oils and plant fats as alternative fuel. The present work deals with finding the better compression ratio for the honne oil diesel blend fueled C.I engine at variable load and constant speed operation. In order to find out optimum compression ratio, experiments are carried out on a single cylinder four stroke variable compression ratio diesel engine. Engine performance tests are carried out at different compression ratio values. The optimum compression ratio that gives better engine performance is found from the experimental results. Using experimental data Artificial Neural Network (ANN) model was developed and the values were predicted using ANN. Finally the predicted values were validated with the experimentally

Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Transportation and power generation have historically relied upon Internal Combustion Engines (ICEs). However, because of environmental impact and inefficiency, considerable research has been devoted to improving their performance. Alternative fuels are necessary because of environmental concerns and the depletion of non-renewable fuel stocks. Biodiesel has the potential to reduce emissions and improve sustainability when compared to diesel fuel. Several researchers have examined using nanofluids to increase biodiesel performance in internal combustion engines. Due to their thermal and physical properties, nanoparticles in a host fluid improve engine combustion and efficiency. This comprehensive review examines three key areas for improving ICE efficiency: biodiesel as an alternative fuel, application of nanofluids, and artificial intelligence (AI)/machine learning (ML) integration. The integration of AI/ML in nanoparticle-infused biodiesel offers exciting possibilities for optimizing production processes, enhancing fuel properties, and improving engine performance. This article first discusses, the benefits of biodiesel concerning the environment and various difficulties associated with its usage. The review then explores the effects and characteristics of nanofluids in IC engines, aiming to know their impact on engine emissions and performance. After that, this review discusses the utilization of AI/ML techniques in enhancing the biodiesel-nanofluid combustion process. This article sheds light on the ongoing efforts to make ICE technology more environmentally friendly and energy-efficient by examining current research and emerging patterns in these fields. Finally, the review presents the challenges and future perspectives of the field, paving the way for future research and improvement

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel’s CN