Effect of Anaerobic Digestate on the Fatty Acid Profile and Biodiesel Properties of Chlorella sorokiniana Grown Heterotrophically

The growth kinetics and the lipid and protein content of the microalgal species Chlorella sorokiniana (CS) grown heterotrophically in growth media containing glycerol and increasing amounts of anaerobic digestate (AD) equal to 0%, 15%, 30%, and 50% was studied. The effect of the AD on the fatty acid (FA) distribution of the bio-oil extracted from the CS, as well as on the fatty acid methyl ester (FAME) properties such as the saponification number (SN), the iodine value (IV), the cetane number (CN), and the higher heating value (HHV) was also estimated. The percentage of AD in the growth medium affects the rate of carbon uptake. The maximum carbon uptake rate occurs at about 30% AD. Protein and lipid content ranged from 32.3–38.4% and 18.1–23.1%, respectively. Fatty acid distribution ranged from C10 to C26. In all AD percentages the predominant fatty acids were the medium chain FA C16 to C18 constituting up to about 89% of the total FA at 0% AD and 15% AD and up to about 54% of the total FA at 30% AD and 50% AD. With respect to unsaturation, monounsaturated FA (MUFA) were predominant, up to 56%, while significant percentages, up to about 38%, of saturated FA (SFA) were also produced. The SN, IV, CN, and HHV ranged from 198.5–208.3 mg KOH/g FA, 74.5–93.1 g I/100 g FAME, 52.7–56.1, and 39.7–40.0 MJ/kg, respectively. The results showed that with increasing AD percentage, the CN values tend to increase, while decrease in IV leads to biofuel with better ignition quality.

Elastic breakdown via multi-core high frequency discharge for lean-burn ignition

An advanced ignition technique is developed to achieve multi-event breakdown and multi-site ignition using a single coil for ignition quality improvements. The igniter enables a unique elastic breakdown process embracing a series of high-frequency discharge events at the spark gap. The equivalent electric circuits and current/voltage equations are identified and verified for the first time to explain the working principle that governs such an elastic breakdown process. Benchmarking tests are first performed to compare the elastic breakdown ignition with the conventional and advanced multi-electrode ignition systems. The elastic breakdown and spark events are thereafter analyzed through current and voltage measurements and high-speed imaging techniques. Finally, ignition tests in combustion chambers are performed to examine the effects on the ignition process in comparison with conventional coil-based ignition systems. The experiments show that, the elastic breakdown ignition can distribute multiple high-frequency breakdown events at all electrode pairs of a multi-electrode sparkplug while using only one ignition coil, thereby offering significant cost saving advantage and packaging practicability.

A Comprehensive Model for Cetane Number Prediction Using Machine Learning

Machine learning based predictive models are being extensively applied for predicting combustion properties like derived cetane number (DCN), which is the measure of a fuels ignition quality. In the present work, a comprehensive model was developed using artificial neural networks (ANN) that can predict the DCN of fuels containing a large number of chemical classes like paraffins, iso-paraffins, olefins, naphthenes, aromatics, alcohols, ethers, aldehydes, ketones and esters. Experimental DCN’s of 275 fuels was used as a dataset and the composition of the fuels expressed in the form of twelve functional groups and two structural parameters namely, branching index (BI) and molecular weight were used as the input features for the model. A feed forward neural network with two hidden layers with 40 neurons in each layer was developed using Levenberg-Marquardt algorithm. The developed model was validated with 15% of the data points that were randomly generated and kept aside for validation. A regression coefficient (R2) of 0.99 was observed between the predicted and the experimental values along with an average absolute error of 1.1. The results showed that the developed model was successful in predicting the DCN of fuels and can be applied to pure compounds, blends and real fuels containing diverse chemical functionalities.

Varying Ignition Quality of a Fuel for a HCCI Engine Using a Photochemically-Controlled Additive: The Development of a ‘Smart’ Fuel

This study examines the possibility to provide control over ignition timing in a homogeneous charge compression ignition engine (HCCI) using a fuel additive whose molecular structure can be adapted upon exposure to UV light. The UV adapted molecule has a greater influence on retarding ignition than the original molecule, hence the ignition time can be modulated upon expose to UV light. The new fuel is referred to as a ‘smart fuel’. The fuel additive is in the form of 1,3-cyclohexadiene (CHD), upon UV exposure it undergoes electro-cyclic ring opening to form 1,3,5-hexatriene (HT). Various solutions of iso-octane, n-heptane and CHD have been irradiated by UV light for different amounts of time. CHD to HT conversion was examined using gas chromatography coupled with mass spectrometry. A primary reference fuel (PRF) mixture of 90% iso-octane and 10% n-heptane was used as a baseline in an optically accessible combustion chamber in a large bore, single cylinder compression ignition engine. The engine was operated in HCCI mode, using early injection to provide homogeneous mixture and utilized heated and compressed air intake. Following this a PRF with 5% CHD was used in the engine. A PRF with 5% CHD was then irradiated with UV light for 240 min, resulting in a PRF mixture containing 1.72% HT, this was then used in the engine. The HT containing PRF had a much later start of combustion compared with the CHD containing PRF, which in turn had a later start of combustion compared with the PRF baseline. This study has successfully validated the concept of using a photo-chemical ‘smart’ fuel to significantly change the ignition quality of a fuel in HCCI mode combustion and demonstrated the concept of on-board ‘smart fuel’ applications for ICE.

Rational Design and Testing of Anti-Knock Additives

An innovative and informed methodology for the rational design and testing of anti-knock additives is reported. Interaction of the additives with OH● and HO2● is identified as the key reaction pathway by which non-metallic anti-knock additives are proposed to operate. Based on this mechanism, a set of generic design criteria for anti-knock additives is outlined. It is suggested that these additives should contain a weak X-H bond and form stable radical species after hydrogen atom abstraction. A set of molecular structural, thermodynamic, and kinetic quantities that pertain to the propensity of the additive to inhibit knock by this mechanism are identified and determined for a set of 12 phenolic model compounds. The series of structural analogues was carefully selected such that the physical thermodynamic and kinetic quantities could be systematically varied. The efficacy of these molecules as anti-knock additives was demonstrated through the determination of the research octane number (RON) and the derived cetane number(DCN), measured using an ignition quality tester (IQT), of a RON 95 gasoline treated with 1 mole % of the additive. The use of the IQT allows the anti-knock properties of potential additives to be studied on one tenth of the scale, compared to the analogous RON measurement. Using multiple linear regression, the relationship between DCN/RON and the theoretically determined quantities is studied. The overall methodology reported is proposed as an informed alternative to the non-directed experimental screening approach typically adopted in the development of fuel additives.