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

Direct Calophyllum oil extraction and resin separation with a binary solvent of n-hexane and methanol mixture

This study investigated the use of a mixture of n-hexane and methanol as a binary solvent for the direct oil extraction and resin separation from Calophyllum seeds, in a single step. Optimal oil and resin yields and physicochemical properties were determined by identifying the best extraction conditions. The solvent mixture tested extracted oil and resin effectively from Calophyllum seeds, and separated resin from oil. Extraction conditions affected oil and resin yields and their physicochemical properties, with the n-hexane-to-methanol ratio being the most critical factor. Oil yield improved as n-hexane-to-methanol ratio increased from 0.5:1 to 2:1, and resin yield increased as methanol-to-n-hexane ratio increased from 0.5:1 to 2:1. Physicochemical properties of oil and resin, particularly for acid value and impurity content, improved as the n-hexane-to-methanol ratio decreased from 2:1 to 0.5:1. The best oil (51% with more than 95% triglycerides) and resin (18% with more than 5% polyphenols) yields were obtained with n-hexane-to-methanol ratios of 2:1 and 0.5:1, respectively, at a temperature of 50 °C, with an extraction time of 5 h. The best values for physicochemical property of oil were a density of 0.885 g/cm3, a viscosity of 26.0 mPa.s, an acid value of 13 mg KOH/g, an iodine value of 127 g/100 g, an unsaponifiable content of 1.5%, a moisture content of 0.8% and an ash content of 0.04%

Optimisation of bio-oil extraction process from Beauty Leaf (Calophyllum inophyllum) oil seed as a second generation biodiesel source

The Beauty Leaf tree (Calophyllum inophyllum) is a potential source of non-edible vegetable oil for producing future generation biodiesel because of its ability to grow in a wide range of climate conditions, easy cultivation, high fruit production rate, and the high oil content in the seed. This plant naturally occurs in the coastal areas of Queensland and the Northern Territory in Australia, and is also widespread in south-east Asia, India and Sri Lanka. Although Beauty Leaf is traditionally used as a source of timber and orientation plant, its potential as a source of second generation biodiesel is yet to be exploited. In this study, the extraction process from the Beauty Leaf oil seed has been optimised in terms of seed preparation, moisture content and oil extraction methods. The two methods that have been considered to extract oil from the seed kernel are mechanical oil extraction using an electric powered screw press, and chemical oil extraction using nhexane as an oil solvent. The study found that seed preparation has a significant impact on oil yields, especially in the screw press extraction method. Kernels prepared to 15% moisture content provided the highest oil yields for both extraction methods. Mechanical extraction using the screw press can produce oil from correctly prepared product at a low cost, however overall this method is ineffective with relatively low oil yields. Chemical extraction was found to be a very effective method for oil extraction for its consistence performance and high oil yield, but cost of production was relatively higher due to the high cost of solvent. However, a solvent recycle system can be implemented to reduce the production cost of Beauty Leaf biodiesel. The findings of this study are expected to serve as the basis from which industrial scale biodiesel production from Beauty Leaf can be made

Real-time PCR assay and rapid diagnostic tests for the diagnosis of clinically suspected malaria patients in Bangladesh

<p>Abstract</p> <p>Background</p> <p>More than 95% of total malaria cases in Bangladesh are reported from the 13 high endemic districts. <it>Plasmodium falciparum </it>and <it>Plasmodium vivax </it>are the two most abundant malaria parasites in the country. To improve the detection and management of malaria patients, the National Malaria Control Programme (NMCP) has been using rapid diagnostic test (RDT) in the endemic areas. A study was conducted to establish a SYBR Green-based modified real-time PCR assay as a gold standard to evaluate the performance of four commercially-available malaria RDTs, along with the classical gold standard- microscopy.</p> <p>Methods</p> <p>Blood samples were collected from 338 febrile patients referred for the diagnosis of malaria by the attending physician at Matiranga</p> <p>Upazila Health Complex (UHC) from May 2009 to August 2010. Paracheck RDT and microscopy were performed at the UHC. The blood samples were preserved in EDTA tubes. A SYBR Green-based real-time PCR assay was performed and evaluated. The performances of the remaining three RDTs (Falcivax, Onsite Pf and Onsite Pf/Pv) were also evaluated against microscopy and real-time PCR using the stored blood samples.</p> <p>Result</p> <p>In total, 338 febrile patients were enrolled in the study. Malaria parasites were detected in 189 (55.9%) and 188 (55.6%) patients by microscopy and real-time PCR respectively. Among the RDTs, the highest sensitivity for the detection of <it>P. falciparum </it>(including mixed infection) was obtained by Paracheck [98.8%, 95% confidence interval (CI) 95.8-99.9] and Falcivax (97.6%, 95% CI 94.1-99.4) compared to microscopy and real-time PCR respectively. Paracheck and Onsite Pf/Pv gave the highest specificity (98.8%, 95% CI 95.7-99.9) compared to microscopy and Onsite Pf/Pv (98.8, 95% CI 95.8-99.9) compared to real-time PCR respectively for the detection of <it>P. falciparum</it>. On the other hand Falcivax and Onsite Pf/Pv had equal sensitivity (90.5%, 95% CI 69.6-98.8) and almost 100% specificity compared to microscopy for the detection of <it>P. vivax</it>. However, compared to real-time PCR assay RDTs and microscopy gave low sensitivity (76.9%, 95% CI 56.4-91) in detecting of <it>P. vivax </it>although a very high specificity was obtained (99- 100%).</p> <p>Conclusion</p> <p>The results of this study suggest that the SYBR Green-based real-time PCR assay could be used as an alternative gold standard method in a reference setting. Commercially-available RDTs used in the study are quite sensitive and specific in detecting <it>P. falciparum</it>, although their sensitivity in detecting <it>P. vivax </it>was not satisfactory compared to the real-time PCR assay.</p

Effects of pretreatments of Napier Grass with deionized water, sulfuric acid and sodium hydroxide on pyrolysis oil characteristics

The depletion of fossil fuel reserves has led to increasing interest in liquid bio-fuel from renewable biomass. Biomass is a complex organic material consisting of different degrees of cellulose, hemicellulose, lignin, extractives and minerals. Some of the mineral elements tend to retard conversions, yield and selectivity during pyrolysis processing. This study is focused on the extraction of mineral retardants from Napier grass using deionized water, dilute sodium hydroxide and sulfuric acid and subsequent pyrolysis in a fixed bed reactor. The raw biomass was characterized before and after each pretreatment following standard procedure. Pyrolysis study was conducted in a fixed bed reactor at 600 o�C, 30 �C/min and 30 mL/min N2 flow. Pyrolysis oil (bio-oil) collected was analyzed using standard analytic techniques. The bio-oil yield and characteristics from each pretreated sample were compared with oil from the non-pretreated sample. Bio-oil yield from the raw sample was 32.06 wt% compared to 38.71, 33.28 and 29.27 wt% oil yield recorded from the sample pretreated with sulfuric acid, deionized water and sodium hydroxide respectively. GC–MS analysis of the oil samples revealed that the oil from all the pretreated biomass had more value added chemicals and less ketones and aldehydes. Pretreatment with neutral solvent generated valuable leachate, showed significant impact on the ash extraction, pyrolysis oil yield, and its composition and therefore can be regarded as more appropriate for thermochemical conversion of Napier grass

Correlation between physiochemical properties and quality of biodiesel

Biodiesel produced from renewable feedstocks represents a sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Biodiesel offers many benefits over conventional petroleum fuels, including the wide regional distribution of biomass feedstocks, high greenhouse gas reduction potential, biodegradability and a significant contribution to sustainability. 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 when comparing one feedstock to the next in terms of chain length, degree of unsaturation and number of double bonds—all of which determine the fuel properties and quality of biodiesel as a diesel engine fuel. In this chapter, biodiesel feedstocks, production processes, chemical compositions, standards, physicochemical properties and in-use performance are discussed. A correlation study between the properties of biodiesel and its chemical composition is analysed using principal component analysis (PCA). The necessary data regarding the chemical composition and fuel properties of biodiesel were obtained from more than 100 papers published in recognised international journals. The PCA indicated that individual biodiesel properties have a complex correlation with the parameters of chemical composition. The average chain length and average number of double bonds are the most influential parameters that affect all biodiesel properties. The results of this analysis are presented graphically and discussed in this chapter. Therefore, this chapter will provide the reader a clearer understanding of the physicochemical properties of biodiesel

Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine

A neural networks (NN) model has been trained to predict the performance characteristics of a dual fuel internal combustion engine (ICE). In the network, back propagation (BP) neural network with Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms, single hidden-layer and logistic sigmoid transfer function has been used to optimise prediction model performance. The Neural Networks Toolbox of MATLAB 7 was used to train and test the NN model on a personal computer. In this investigation, a multi cylinder diesel engine was modified for duel fuel system to compare the experimental data with the prediction results obtained from NN model. Engine load, speed (rpm) and Diesel-NG ratio have been used as the input layers, while engine thermal efficiency, break specific fuel consumption (BSFC), exhaust temperature and air-fuel ratio have been used at the output layers. It is found that the RMS error values are smaller than 0.015, R2 values are about 0.999 and mean error smaller then 0.01% which indicate the NN model well matches with experimental results. The results of this investigation will be used to optimise the performance of future NG fueled engine

A Review on the Thermochemical Recycling of Waste Tyres to Oil for Automobile Engine Application

Utilising pyrolysis as a waste tyre processing technology has various economic and social advantages, along with the fact that it is an effective conversion method. Despite extensive research and a notable likelihood of success, this technology has not yet seen implementation in industrial and commercial settings. In this review, over 100 recent publications are reviewed and summarised to give attention to the current state of global tyre waste management, pyrolysis technology, and plastic waste conversion into liquid fuel. The study also investigated the suitability of pyrolysis oil for use in diesel engines and provided the results on diesel engine performance and emission characteristics. Most studies show that discarded tyres can yield 40–60% liquid oil with a calorific value of more than 40 MJ/kg, indicating that they are appropriate for direct use as boiler and furnace fuel. It has a low cetane index, as well as high viscosity, density, and aromatic content. According to diesel engine performance and emission studies, the power output and combustion efficiency of tyre pyrolysis oil are equivalent to diesel fuel, but engine emissions (NOX, CO, CO, SOX, and HC) are significantly greater in most circumstances. These findings indicate that tyre pyrolysis oil is not suitable for direct use in commercial automobile engines, but it can be utilised as a fuel additive or combined with other fuels

Thermodynamic Analysis of a Flat Plate Solar Collector with Different Hybrid Nanofluids as Working Medium—A Thermal Modelling Approach

In this study, the performance of hybrid nanofluids in a flat plate solar collector was analysed based on various parameters such as entropy generation, exergy efficiency, heat transfer enhancement, pumping power, and pressure drop. Five different base fluids were used, including water, ethylene glycol, methanol, radiator coolant, and engine oil, to make five types of hybrids nanofluids containing suspended CuO and MWCNT nanoparticles. The nanofluids were evaluated at nanoparticle volume fractions ranging from 1% to 3% and flow rates of 1 to 3.5 L/min. The analytical results revealed that the CuO-MWCNT/water nanofluid performed the best in reducing entropy generation at both volume fractions and volume flow rate when compared to the other nanofluids studied. Although CuO-MWCNT/methanol showed better heat transfer coefficients than CuO-MWCNT/water, it generated more entropy and had lower exergy efficiency. The CuO-MWCNT/water nanofluid not only had higher exergy efficiency and thermal performance but also showed promising results in reducing entropy generation