To overcome the water pollution problems, and to meet stringent environmental regulations, scientist and researchers have been focusing on the development of new water purification processes. One such group of new technologies is advanced oxidation processes (AOPs). Among the AOPs, titanium dioxide photocatalysis has been widely studied on lab scale by the researchers for decontamination of drinking water. In the present chapter, a conceptual as well as state-of-art review of titanium dioxide photocatalysis for water purification has been discussed.

ABSTRACT:The heavy metals or trace elements play an important role in the metabolic pathways during the growth and development of plants, when available in required concentration. The heavy metal concentration of Cadmium (Cd), Cobalt (Co), Copper (Cu), Iron (Fe), Nickel (Ni), Lead (Pb) and Zinc (Zn) was analyzed using Inductive Coupled Plasma Analyzer (ICPA) (Perkin-Elmer ICP Optima 3300 RL, USA) in 21 vegetables collected from Vegetable Market of Anand town, Gujarat. The vegetables are Lady's Finger (Abelmoschus esculentus), Onion (Alium sepa), Cauliflower (Brassica oleracea var.

The experimental determination of various properties of diesel-biodiesel mixtures is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of diesel-biodiesel blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the flash point, fire point, density and viscosity of diesel and biodiesel mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of dieselbiodiesel mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures of diesel and biodiesel. Key words: Biodiesel; Artificial Neural Network; Principle of least squares; Diesel; Linear Regression. DOI: 10.3126/kuset.v6i2.4017Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.98-103

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