Rapid estimation of concentration of aromatic classes in middistillate fuels by high-performance liquid chromatography

An high performance liquid chromatography (HPLC) method to estimate four aromatic classes in middistillate fuels is presented. Average refractive indices are used in a correlation to obtain the concentrations of each of the aromatic classes from HPLC data. The aromatic class concentrations can be obtained in about 15 min when the concentration of the aromatic group is known. Seven fuels with a wide range of compositions were used to test the method. Relative errors in the concentration of the two major aromatic classes were not over 10 percent. Absolute errors of the minor classes were all less than 0.3 percent. The data show that errors in group-type analyses using sulfuric acid derived standards are greater for fuels containing high concentrations of polycyclic aromatics. Corrections are based on the change in refractive index of the aromatic fraction which can occur when sulfuric acid and the fuel react. These corrections improved both the precision and the accuracy of the group-type results

Group-type hydrocarbon standards for high-performance liquid chromatographic analysis of middistillate fuels

A new high-performance liquid chromatographic (HPLC) method for group-type analysis of middistillate fuels is described. It uses a refractive index detector and standards that are prepared by reacting a portion of the fuel sample with sulfuric acid. A complete analysis of a middistillate fuel for saturates and aromatics (including the preparation of the standard) requires about 15 min if standards for several fuels are prepared simultaneously. From model fuel studies, the method was found to be accurate to within 0.4 vol% saturates or aromatics, and provides a precision of + or - 0.4 vol%. Olefin determinations require an additional 15 min of analysis time. However, this determination is needed only for those fuels displaying a significant olefin response at 200 nm (obtained routinely during the saturated/aromatics analysis procedure). The olefin determination uses the responses of the olefins and the corresponding saturates, as well as the average value of their refractive index sensitivity ratios (1.1). Studied indicated that, although the relative error in the olefins result could reach 10 percent by using this average sensitivity ratio, it was 5 percent for the fuels used in this study. Olefin concentrations as low as 0.1 vol% have been determined using this method

High performance liquid chromatographic hydrocarbon group-type analyses of mid-distillates employing fuel-derived fractions as standards

Two high performance liquid chromatographic (HPLC) methods have been developed for the determination of saturates, olefins and aromatics in petroleum and shale derived mid-distillate fuels. In one method the fuel to be analyzed is reacted with sulfuric acid, to remove a substantial portion of the aromatics, which provides a reacted fuel fraction for use in group type quantitation. The second involves the removal of a substantial portion of the saturates fraction from the HPLC system to permit the determination of olefin concentrations as low as 0.3 volume percent, and to improve the accuracy and precision of olefins determinations. Each method was evaluated using model compound mixtures and real fuel samples

Research on aviation fuel instability

The underlying causes of fuel thermal degradation are discussed. Topics covered include: nature of fuel instability and its temperature dependence, methods of measuring the instability, chemical mechanisms involved in deposit formation, and instrumental methods for characterizing fuel deposits. Finally, some preliminary thoughts on design approaches for minimizing the effects of lowered thermal stability are briefly discussed

Voice Control in Calorie Tracker Application Using Levenshtein Distance Algorithm

Each food consumed by people contains a number of calories needed by the body to perform an activity. Calories can be described as fuel of engine to move and carry out tasks. Public\u27s indifference to the food consumed can cause some negative effects on health like, too skinny, obesity, and emergence of various diseases. Therefore, it is necessary to have an application which can provide some information about the calorie needs, so that user can control the calorie intake. This paper describes the development of an application to assist user in controlling calorie intake according to the calorie needs. This application supports voice control feature to improve user comfort in performing certain commands and inputting food consumed. In addition, this application also uses Levenshtein Distance algorithm to correct food recognition errors which is spoken by user. This application is developed using Java programming language for Android with SQLite database

IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process