Determination of biodiesel quality parameters for optimization of production process conditions

According to many scientists third industrial revolution has already began and this primarily means the transition to renewable energy sources. Energy requirements are increasing rapidly due to fast industrialization and the increased number of vehicles on the roads. Massive consumption of fossil fuels leads to environmental pollution, therefore, biofuels are offered as an alternative. For example, the application of biodiesel in diesel engines instead of diesel results in the proven reduction of harmful exhaust emissions. One of the most important technologies, which has been already explored at the commercial level, is the production of a liquid biofuel applicable in compression-ignition engines (or diesel engines), from biomass rich in fats and oils. This biofuel is generically referred as biodiesel, and consists essentially of a mixture of FAME's (fatty acid methyl esters). This current work describes modern approaches of biodiesel production from vegetable oil and subsequent analysis of produced biodiesel main characteristics such as density, acidity, iodine value and FAME content.

Determination of Signature Volatile Odor Chemicals Emanating from Novel Biological Specimens by Non-invasive Analytical Techniques for the Potential use in Forensic Identifications

Human scent, or the volatile organic compounds (VOCs) produced by an individual, has been recognized as a biometric measurement because of the distinct variations in both the presence and abundance of these VOCs between individuals. In forensic science, human scent has been used as a form of associative evidence by linking a suspect to a scene/object through the use of human scent discriminating canines. The scent most often collected and used with these specially trained canines is from the hands because a majority of the evidence collected is likely to have been handled by the suspect. However, the scents from other biological specimens, especially those that are likely to be present at scenes of violent crimes, have yet to be explored. Hair, fingernails and saliva are examples of these types of specimens. In this work, a headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technique was used for the identification of VOCs from hand odor, hair, fingernails and saliva. Sixty individuals were sampled and the profiles of the extracted VOCs were evaluated to assess whether they could be used for distinguishing individuals. Preliminary analysis of the biological specimens collected from an individual (intra-subject) showed that, though these materials have some VOCs in common, their overall chemical profile is different for each specimen type. Pair-wise comparisons, using Spearman Rank correlations, were made between the chemical profiles obtained from each subject, per a specimen type. Greater than 98.8% of the collected samples were distinguished from the subjects for all of the specimen types, demonstrating that these specimens can be used for distinguishing individuals. Additionally, field trials were performed to determine the utility of these specimens as scent sources for human scent discriminating canines. Three trials were conducted to evaluate hair, fingernails and saliva in comparison to hand odor, which was considered the standard source of human odor. It was revealed that canines perform similarly to these alternative human scent sources as they do to hand odor implying that, though there are differences in the chemical profiles released by these specimens, they can still be used for the discrimination of individuals by trained canines.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.