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During dyeing process, industries consume large quantity of water and subsequently produce large volume of wastewater. This wastewater is rich in color and contains different dyes. Orange II is one of them. In this article, metal-impregnated TiO 2 P-25 catalyst was used to enhance the photocatalytic degradation of Orange II dye. Photodegradation percentage was followed spectrophotometrically by the measurements of absorbance at k max = 483 nm. The effect of copper-impregnated TiO 2 P-25 photocatalyst for the degradation of Orange II has been investigated in terms of percentage removal of color, chemical oxygen demand (COD) and total organic carbon (TOC). As such 98 % color removal efficiency, 97 % percentage removal of COD and 89 % percentage removal of TOC was achieved with TiO 2 P-25/Cu catalysts under typical conditions. Copper-impregnated TiO 2 P-25 photocatalyst showed comparatively higher activity than UV/ H 2 O 2 homogeneous photodegradation. The relative electrical energy consumption for photocatalytic degradation was considerably lower with TiO 2 P-25/Cu photocatalyst than that with homogeneous photodegradation. Transmission electron microscopic analysis was used for catalyst characterization.

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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.

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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