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

SPH-DEM approach to numerically simulate the deformation of three-dimensional RBCs in non-uniform capillaries

© 2016 The Author(s). Background: Blood continuously flows through the blood vessels in the human body. When blood flows through the smallest blood vessels, red blood cells (RBCs) in the blood exhibit various types of motion and deformed shapes. Computational modelling techniques can be used to successfully predict the behaviour of the RBCs in capillaries. In this study, we report the application of a meshfree particle approach to model and predict the motion and deformation of three-dimensional RBCs in capillaries. Methods: An elastic spring network based on the discrete element method (DEM) is employed to model the three-dimensional RBC membrane. The haemoglobin in the RBC and the plasma in the blood are modelled as smoothed particle hydrodynamics (SPH) particles. For validation purposes, the behaviour of a single RBC in a simple shear flow is examined and compared against experimental results. Then simulations are carried out to predict the behaviour of RBCs in a capillary; (i) the motion of five identical RBCs in a uniform capillary, (ii) the motion of five identical RBCs with different bending stiffness (K b ) values in a stenosed capillary, (iii) the motion of three RBCs in a narrow capillary. Finally five identical RBCs are employed to determine the critical diameter of a stenosed capillary. Results: Validation results showed a good agreement with less than 10% difference. From the above simulations, the following results are obtained; (i) RBCs exhibit different deformation behaviours due to the hydrodynamic interaction between them. (ii) Asymmetrical deformation behaviours of the RBCs are clearly observed when the bending stiffness (K b ) of the RBCs is changed. (iii) The model predicts the ability of the RBCs to squeeze through smaller blood vessels. Finally, from the simulations, the critical diameter of the stenosed section to stop the motion of blood flow is predicted. Conclusions: A three-dimensional spring network model based on DEM in combination with the SPH method is successfully used to model the motion and deformation of RBCs in capillaries. Simulation results reveal that the condition of blood flow stopping depends on the pressure gradient of the capillary and the severity of stenosis of the capillary. In addition, this model is capable of predicting the critical diameter which prevents motion of RBCs for different blood pressures

Composite materials based on rice straw and natural rubber for thermal insulation applications

Thermal properties of composites fabricated with coalescence of two low thermal conducting materials, rice straw and natural latex were investigated. Composites constituting various weight content of oven-dried blended rice straw mixed with a constant volume of natural latex were fabricated with a surface area of 31 cm2 and their thermal properties were compared by pressurizing under a force of 5 tons. Hot Disk Thermal Constants Analyzer Transient Plane Source (TPS) 500S was used to measure the thermal properties such as thermal conductivity, volumetric specific heat capacity, and thermal diffusivity of the aforementioned composites. The lowest thermal conductivity was achieved for both the unpressurized and pressurized composites with 25% of the rice straw’s weight content, which was recorded as 0.0636 and 0.1526 Wm-1K-1 respectively. High Specific heat capacity and less thermal diffusivity were also seen in the pressurized samples with 25% of rice straw, whereas this behaviour was the opposite in the unpressurized sample. The effect of the applied pressure on the thermal properties of the composite is also studied and it was observed that the thermal conductivity increases up to 8 tons on 31 cm2 in the composite with increasing pressure and then decreases while it continues to increase in the sample made of rice straw alone. Since high specific heat and low thermal diffusivity are the desired features of a thermally insulating material other than the low thermal conductivity, this economical and eco-friendly composite pressurized up to a certain limit could be used by further processing with preservatives toward efficient energy management as a good material for thermal insulation of building applications

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

Fluidisation behaviour of cylindrical green bean particulates in drying

Fluidisation behaviour of cylindrical bean particulates were studies and empirical models were presente

Change of physical properties of green beans during drying and its influence on fluidization

Cylindrical green beans physical property was studied. Their effect on fluidization characteristics were described

A coupled SPH-DEM model for fluid and solid mechanics of apple parenchyma cells during drying

A coupled SPH-DEM based two-dimensional (2-D) micro-scale single cell model is developed to predict basic cell-level shrinkage effects of apple parenchyma cells during air drying. In this newly developed drying model, Smoothed Particle Hydrodynamics (SPH) is used to model the low Reynolds Number fluid motions of the cell protoplasm, and a Discrete Element Method (DEM) is employed to simulate the polymer-like cell wall. Simulations results reasonably agree with published experimental drying results on cellular shrinkage properties such as cellular area, diameter and perimeter. These preliminary results indicate that the model is effective for the modelling and simulation of apple parenchyma cells during air drying

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