The use of artificial neural networks for identifying sustainable biodiesel feedstocks

Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

The influence of fatty acid methyl ester profiles on inter-cycle variability in a heavy duty compression ignition engine

With the advent of alternative fuels, such as biodiesels and related blends, it is important to develop an understanding of their effects on inter-cycle variability which, in turn, influences engine performance as well as its emission. Using four methanol trans-esterified biomass fuels of differing carbon chain length and degree of unsaturation, this paper provides insight into the effect that alternative fuels have on inter-cycle variability. The experiments were conducted with a heavy-duty Cummins, turbo-charged, common-rail compression ignition engine. Combustion performance is reported in terms of the following key in-cylinder parameters: indicated mean effective pressure (IMEP), net heat release rate (NHRR), standard deviation of variability (StDev), coefficient of variation (CoV), peak pressure, peak pressure timing and maximum rate of pressure rise. A link is also established between the cyclic variability and oxygen ratio, which is a good indicator of stoichiometry. The results show that the fatty acid structures did not have a significant effect on injection timing, injection duration, injection pressure, StDev of IMEP, or the timing of peak motoring and combustion pressures. However, a significant effect was noted on the premixed and diffusion combustion proportions, combustion peak pressure and maximum rate of pressure rise. Additionally, the boost pressure, IMEP and combustion peak pressure were found to be directly correlated to the oxygen ratio. The emission of particles positively correlates with oxygen content in the fuel as well as in the air-fuel mixture resulting in a higher total number of particles per unit of mass.

The influence of oxygenated organic aerosols (OOA) on the oxidative potential of diesel and biodiesel particulate matter

Generally, the magnitude of pollutant emissions from diesel engines running on biodiesel fuel is ultimately coupled to the structure of respective molecules that constitutes the fuel. Previous studies demonstrated the relationship between organic fraction of PM and its oxidative potential. Herein, emissions from a diesel engine running on different biofuels were analysed in more detail to explore the role different organic fractions play in the measured oxidative potential. In this work, a more detailed chemical analysis of biofuel PM was undertaken using a compact Time of Flight Aerosol Mass Spectrometer (c-ToF AMS). This enabled a better identification of the different organic fractions that contribute to the overall measured oxidative potentials. The concentration of reactive oxygen species (ROS) was measured using a profluorescent nitroxide molecular probe 9-(1,1,3,3-tetramethylisoindolin-2-yloxyl-5-ethynyl)-10-(phenylethynyl)anthracene (BPEAnit). Therefore the oxidative potential of the PM, measured through the ROS content, although proportional to the total organic content in certain cases shows a much higher correlation with the oxygenated organic fraction as measured by the c-ToF AMS. This highlights the importance of knowing the surface chemistry of particles for assessing their health impacts. It also sheds light onto new aspects of particulate emissions that should be taken into account when establishing relevant metrics for assessing health implications of replacing diesel with alternative fuels.

Effect of atmospheric ageing on volatility and ROS of biodiesel exhaust nano-particles

Generally, the magnitude of pollutant emissions from diesel engines is ultimately coupled to the structure of fuel molecules. The presence of oxygen, level of unsaturation and the carbon chain length of respective molecules influence the combustion chemistry. It is speculated that increased oxygen content in the fuel may lead to the increased oxidative potential (Stevanovic, S. 2013). Also, upon the exposure to UV and ozone in the atmosphere, the chemical composition of the exhaust is changed. The presence of an oxidant and UV is triggering the cascade of photochemical reactions as well as the partitioning of semi-volatile compounds between the gas and particle phase. To gain an insight into the relationship between the molecular structures of the esters, their volatile organic content and the potential toxicity of diesel exhaust particulate matter, measurements were conducted on a modern common rail diesel engine. This research also investigates the contribution of atmospheric conditions on the transfer of semi-volatile fraction of diesel exhaust from the gas phase to the particle phase and the extent to which semi-volatile compounds (SVOCs) are related to the oxidative potential, expressed through the concentration of reactive oxygen species (ROS) (Stevanovic, S. 2013)...

Biodiesel with controlled physicochemical properties, a means to further reduce diesel engine particle emissions

The issue of particle emissions from diesel engines is still a matter of concern due its deleterious effects both on human health and environment(Ristovski et al., 2012). Recently, International Agency for Research on Cancer (IARC) inclusion of diesel engine exhaust particles as carcinogenic to human health added a new margin on it. Apart from the use of after treatment technology, biodiesel is also considered as potential way to reduce particle emission alongside with other emissions(Xue, Grift, & Hansen, 2011). Global biodiesel production is still reasonably small compared to its counterpart fossil diesel, but even this small amount comes from a wide variety of feed stocks. Contrary to fossil diesel, the important physicochemical properties of biodiesel vary among different feed stocks(Hoekman, Broch, Robbins, Ceniceros, & Natarajan, 2012).

Zeta-potential and morphology of electrospun nano- and microfibers from biopolymers and their blends used as scaffolds in tissue engineering

Electrostatic spinning or electrospinning is a fiber spinning technique driven by a high-voltage electric field that produces fibers with diameters in a submicrometer to nanometer range.1 Nanofibers are typical one-dimensional colloidal objects with an increased tensile strength, whose length can achieve a few kilometers and the specific surface area can be 100 m2 g–1 or higher.2 Nano- and microfibers from biocompatible polymers and biopolymers have received much attention in medical applications3 including biomedical structural elements (scaffolding used in tissue engineering,2,4–6 wound dressing,7 artificial organs and vascular grafts8), drug and vaccine delivery,9–11 protective shields in speciality fabrics, multifunctional membranes, etc. Other applications concern superhydrophobic coatings,12 encapsulation of solid materials,13 filter media for submicron particles in separation industry, composite reinforcement and structures for nano-electronic machines.

Microalgal species selection for biodiesel production based on fuel properties derived from fatty acid profiles

Physical and chemical properties of biodiesel are influenced by structural features of the fatty acids, such as chain length, degree of unsaturation and branching of the carbon chain. This study investigated if microalgal fatty acid profiles are suitable for biodiesel characterization and species selection through Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA) analysis. Fatty acid methyl ester (FAME) profiles were used to calculate the likely key chemical and physical properties of the biodiesel [cetane number (CN), iodine value (IV), cold filter plugging point, density, kinematic viscosity, higher heating value] of nine microalgal species (this study) and twelve species from the literature, selected for their suitability for cultivation in subtropical climates. An equal-parameter weighted (PROMETHEE-GAIA) ranked Nannochloropsis oculata, Extubocellulus sp. and Biddulphia sp. highest; the only species meeting the EN14214 and ASTM D6751-02 biodiesel standards, except for the double bond limit in the EN14214. Chlorella vulgaris outranked N. oculata when the twelve microalgae were included. Culture growth phase (stationary) and, to a lesser extent, nutrient provision affected CN and IV values of N. oculata due to lower eicosapentaenoic acid (EPA) contents. Application of a polyunsaturated fatty acid (PUFA) weighting to saturation led to a lower ranking of species exceeding the double bond EN14214 thresholds. In summary, CN, IV, C18:3 and double bond limits were the strongest drivers in equal biodiesel parameter-weighted PROMETHEE analysis.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. 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 was 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 absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

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.

Evaluation of a pilot-scale oil extraction from microalgae for biodiesel production

Biodiesel derived from microalgae is one of a suite of potential solutions to meet the increasing demand for a renewable, carbon-neutral energy source. However, there are numerous challenges that must be addressed before algae biodiesel can become commercially viable. These challenges include the economic feasibility of harvesting and dewatering the biomass and the extraction of lipids and their conversion into biodiesel. Therefore, it is essential to find a suitable extraction process given these processes presently contribute significantly to the total production costs which, at this stage, inhibit the ability of biodiesel to compete financially with petroleum diesel. This study focuses on pilot-scale (100 kg dried microalgae) solvent extraction of lipids from microalgae and subsequent transesterification to biodiesel. Three different solvents (hexane, isopropanol (IPA) and hexane + IPA (1:1)) were used with two different extraction methods (static and Soxhlet) at bench-scale to find the most suitable solvent extraction process for the pilot-scale. The Soxhlet method extracted only 4.2% more lipid compared to the static method. However, the fatty acid profiles of different extraction methods with different solvents are similar, suggesting that none of the solvents or extraction processes were biased for extraction of particular fatty acids. Considering the cost and availability of the solvents, hexane was chosen for pilot-scale extraction using static extraction. At pilot-scale the lipid yield was found to be 20.3% of total biomass which is 2.5% less than from bench scale. Extracted fatty acids were dominated by polyunsaturated fatty acids (PUFAs) (68.94±0.17%) including 47.7±0.43 and 17.86±0.42% being docosahexaenoic acid (DHA) (C22:6) and docosapentaenoic acid (DPA) (C22:5, ω-3), respectively. These high amounts of long chain poly unsaturated fatty acids are unique to some marine microalgae and protists and vary with environmental conditions, culture age and nutrient status, as well as with cultivation process. Calculated physical and chemical properties of density, viscosity of transesterified fatty acid methyl esters (FAMEs) were within the limits of the biodiesel standard specifications as per ASTM D6751-2012 and EN 14214. The calculated cetane number was, however, significantly lower (17.8~18.6) compared to ASTM D6751-2012 or EN 14214-specified minimal requirements. We conclude that the obtained microalgal biodiesel would likely only be suitable for blending with petroleum diesel to a maximum of 5 to 20%.

Biodiesel production from non-edible beauty leaf (Calophyllum Inophyllum) oil : process optimization using response surface methodology (RSM)

In recent years, the beauty leaf plant (Calophyllum Inophyllum) is being considered as a potential 2nd generation biodiesel source due to high seed oil content, high fruit production rate, simple cultivation and ability to grow in a wide range of climate conditions. However, however, due to the high free fatty acid (FFA) content in this oil, the potential of this biodiesel feedstock is still unrealized, and little research has been undertaken on it. In this study, transesterification of beauty leaf oil to produce biodiesel has been investigated. A two-step biodiesel conversion method consisting of acid catalysed pre-esterification and alkali catalysed transesterification has been utilized. The three main factors that drive the biodiesel (fatty acid methyl ester (FAME)) conversion from vegetable oil (triglycerides) were studied using response surface methodology (RSM) based on a Box-Behnken experimental design. The factors considered in this study were catalyst concentration, methanol to oil molar ratio and reaction temperature. Linear and full quadratic regression models were developed to predict FFA and FAME concentration and to optimize the reaction conditions. The significance of these factors and their interaction in both stages was determined using analysis of variance (ANOVA). The reaction conditions for the largest reduction in FFA concentration for acid catalysed pre-esterification was 30:1 methanol to oil molar ratio, 10% (w/w) sulfuric acid catalyst loading and 75 °C reaction temperature. In the alkali catalysed transesterification process 7.5:1 methanol to oil molar ratio, 1% (w/w) sodium methoxide catalyst loading and 55 °C reaction temperature were found to result in the highest FAME conversion. The good agreement between model outputs and experimental results demonstrated that this methodology may be useful for industrial process optimization for biodiesel production from beauty leaf oil and possibly other industrial processes as well.

Microalgae: An alternative source of biodiesel for the compression ignition (CI) engine

This thesis is a comprehensive study of microalgae biodiesel for the compression ignition engine. It examines microalgae growing conditions, the extraction process and physiochemical properties with a wide range of microalgae species. It also evaluates microalgae biodiesel with regards to engine performance and emission characteristics and explains the difficulties and potentiality of microalgae as a biodiesel. In doing so, an extensive analysis of different extraction methods and engine testing was conducted and a comprehensive study on microalgae biodiesel is presented.

Technoeconomic analysis of an integrated microalgae photobioreactor, biodiesel and biogas production facility

As fossil fuel prices increase and environmental concerns gain prominence, the development of alternative fuels from biomass has become more important. Biodiesel produced from microalgae is becoming an attractive alternative to share the role of petroleum. Currently it appears that the production of microalgal biodiesel is not economically viable in current environment because it costs more than conventional fuels. Therefore, a new concept is introduced in this article as an option to reduce the total production cost of microalgal biodiesel. The integration of biodiesel production system with methane production via anaerobic digestion is proved in improving the economics and sustainability of overall biodiesel stages. Anaerobic digestion of microalgae produces methane and further be converted to generate electricity. The generated electricity can surrogate the consumption of energy that require in microalgal cultivation, dewatering, extraction and transesterification process. From theoretical calculations, the electricity generated from methane is able to power all of the biodiesel production stages and will substantially reduce the cost of biodiesel production (33% reduction). The carbon emissions of biodiesel production systems are also reduced by approximately 75% when utilizing biogas electricity compared to when the electricity is otherwise purchased from the Victorian grid. The overall findings from this study indicate that the approach of digesting microalgal waste to produce biogas will make the production of biodiesel from algae more viable by reducing the overall cost of production per unit of biodiesel and hence enable biodiesel to be more competitive with existing fuels.