Experimental and Molecular Level Analysis of the Tribological and Oxidative Properties of Chaulmoogra Oil

This study introduces chaulmoogra oil as a base stock for lubricant formulation. The tribological properties of chaulmoogra oil are evaluated by quantitative structure-property relation (QSPR) technique using the molecular modelling package Spartan 18. The quantum chemical calculations were performed on a typical molecule of chaulmoogra oil and its constituent fatty acids. The orbital energy gap of the constituent fatty acids in chaulmoogra oil is 7.37 eV and that of chaulmoogra oil molecule is 6.8 eV, which is less than that of the lauric acid, the main constituent of coconut oil (7.78 eV). Orbital energy gap predicts a better tribological performance for chaulmoogra oil, and the four ball test result is in agreement with this prediction. Oxidative property of chaulmoogra oil is tested by isothermal thermogravimetric/differential thermal analysis (TGA/DTA) and compared with different oils. Weight gain in oxygen is only 0.02% for chaulmoogra oil and showed better oxidative stability among all other tested oils

Forecasting of a Short Life Baked Product Using Exponential Smoothing and Markov Method

Abstract The objective of this paper is to develop a demand forecast model for a short life baked product. The initial forecast is obtained by using exponential smoothing and the error corresponding to each day is estimated for this forecast. A control chart is plotted for these errors after determining its upper control limit and lower control limit. A generalized Markov algorithm is applied to these errors and the demand of different states are determined. The demand corresponding to the state with maximum probability is taken as optimal demand. The obtained results can act as a basis for better planning of demand of short life baked products in India. Keywords: Demand; Exponential smoothing; Forecasting; Markov algorithm; Random; State; Planning. Introduction Almost all organizations analyses past sales data and predict the future sales based on this past data. An attempt has been done to predict future sales based on the sales data of two successive months collected from a reputed firm. Various statistical techniques are available for forecasting. Nice properties of a weighted moving average would be one where the weights not only decrease as older and older data are used, but one where the differences between the weights are -smooth‖. Obviously the desire would be for the weight on the most recent data to be the largest. The weights should then get progressively smaller the more periods one considers into the past. The exponentially decreasing weights of the basic exponential smoothing forecast fit this bill nicely. The forecast equation is given by