Improving the design stresses of high density polyethylene pipes and vessels used in reverse osmosis desalination plants

Nanocomposite materials have been used on a wide scale in industrial and structural applications. The present work aims at studying the mechanical properties of high density polyethylene (HDPE) grade TR-401 hexene copolymer reinforced by montmorillonite nanoparticles (MMT), used to fabricate pipes and membranes vessels for reverse osmosis desalination plants. Different volume fractions and particle sizes of the MMT clay were used to investigate the effect of this filler on the mechanical properties of the produced composite. Mechanical properties tests were carried out and good improvements of the composite properties were obtained compared to the parent polymer. The test results showed a significant enhancement of the mechanical properties at low filler proportions. Pipe fabricated from these composites had many outstanding and desirable features. For example, by adding 4.75% MMT to the HDPE produced quality pipes and fittings with the highest design stress basis of any polyethylene. A significant increase in the modulus of elasticity observed, together with an unusual increase in the design stress, approved the HDPE/MMT composite for high pressure piping and membrane vessels used in reverse osmosis desalination plants.     

A refined fuzzy time series model for stock market forecasting

Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.