Sriram Devanathan

Intelligent selection of hypothesis tests to enhance gross error identification

Abstract The objective of this study was to evaluate the ability of a new technique to identify systematic measurement errors (i.e. biases) in process variables. This technique obtains high identification accuracy and computational speed by efficiently selecting a small subset of statistical hypothesis tests from a very large set using new selection criteria developed in this work. In this article the proposed technique is also evaluated and compared to a well known method in a fairly extenisve Monte Carlo simulation study. The proposed technique was found to be computationally faster and, as the variances of measurement errors decreased, significantly more accurate in identifying systematic errors.

Synthesis of micrometer-sized hierarchical rutile TiO2 flowers and their application in dye sensitized solar cells

Cactus-like hierarchical rutile TiO2 flowers and three dimensional (3D) highly branched rutile TiO2 nanorods with sizes measuring up to 5 microns were synthesized on conductive substrates by a facile hydrothermal route without the presence of a surfactant or template. These samples with different morphologies and microstructures were studied by X-ray powder diffraction (XRD), field emission-scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM). We also studied the photovoltaic performances of these samples by using them as photoanodes in dye-sensitized solar cells (DSSCs). The highly branched TiO2 nanorod based photoanode in DSSCs showed a power conversion efficiency of 3.07% which was significantly higher than that of the cactus TiO2 flower based (2.66%) photoanode. The electrochemical impedance spectroscopy (EIS) analysis of the interfacial charge transfer kinetics in these photoanodes in DSSCs showed higher recombination resistance (R2) and longer electron lifetime in highly branched nanorods. The enhancement of the efficiency of the highly branched TiO2 nanorod photoanode based DSSC compared to that of cactus TiO2 flower DSSC is mainly attributed to the superior light scattering capability, fast electron transfer and longer electron lifetime with suppressed recombination.

Measurement bias detection in linear dynamic systems

A new method to detect the existence of biased measured variables in dynamic processes is presented. Hence, this work presents a new Dynamic Global Test (DGT) and test procedure for dynamic gross error detection (GED) that brings to light certain of its attributes which have not hitherto (to our knowledge) been presented in GED literature. Recognition of these attributes leads to a scheme that enables identification of the type of biased measurement (e.g. flow or level). This approach is not computationally intensive and is applicable in the case of process leaks and multiple biased variables. Simulation results for the identification of the type of biased measurement (e.g. flow or level) and the estimation of the time of occurrence (ETOC) are given. The performance study in this work specifically varied the size of measurement bias (i), the bias location (i ), the bias true time of occurrence (TTOC), the significance level (), and the sample size (N). This study shows the proposed approach to be accurate in identifying the type of biased variable and its TTOC. The performance of the proposed scheme improves as N and i increase.

A new approach for improved identification of measurement bias

Abstract This work presents a technique that can completely and accurately identify measurement bias in cases where it is not possible to use the method of Rollins and Davis (1992, 1993) and where the method of Narasimhan and Mah (1987) fail to perform accurately. This technique makes use of information contained in the relationship between individual measurements and the corresponding nodal imbalance. The performance of this method is demonstrated on a problem from the literature that has been difficult for other methods to handle. In addition, this article discusses how the new technique can be used as a visual monitoring tool for identifying biased measured variables.

Solar based lemon grass essential oil distillation for sustainability and livelihood in tribal community

Lemon grass essential oil is used in manufacture of soaps, beauty products, and mosquito repellents. Nearly, 40% of world lemon grass oil is produced in India and Kerala state plays a major role. Valaramkunnu is a village in Wayanad district of Kerala, situated on the top of a hill with 300 inhabitants. The villagers walk for 8 km down the hill for daily wages as there is no other source of income. 25 acres of land is available, where lemongrass is grown naturally. The village had a history of extracting lemon grass oil, has two problems a) Batch operation — oil extraction is limited by equilibrium and b) Fire wood is used as energy source — this results in deforestation and therefore not a sustainable energy source. To address these challenges, a semi-continuous lab scale distillation was set up to overcome the limitation of equilibrium and was tested. The proposed system was initially tested at lab scale. A prototype lemon grass essential oil is setup in Valaramkunnu. For the prototype, a solar steam generation system is chosen. Effects of process parameters such as drying time, steam temperature, distillation time and packing density on oil yield were studied. It was found that 8 times more essential oil can be extracted, when compared to existing commercial methodologies. It is also estimated that, nearly 55 litres of oil per acre per annum may be produced in Valaramkunnu. Based on the current price of lemon grass oil, the villagers are estimated to earn INR 800,000 per annum. The report will describe the socioeconomic disadvantages faced by the study population, limitations of their attempts to address these disadvantages, and the analysis to rationalize projected benefits from the proposed technological and social interventions.