Screening of Acetic Acid Producing Microorganisms from Decomposed Fruits for Vinegar Production

Acetic acid bacteria capable of growing at 30&#176C - 37&#176C were collected from various decomposed fruits available in Bangladeshi local markets in order to assess their suitability for vinegar production. Initially, 42 microorganisms were isolated from decomposed fruits like grapes, mangoes, pineapples, oranges, safeda etc. during summer when temperature reaches up to 37&#176C. Then their growths were checked in YPG medium containing various ethanol concentrations at different time point at 37&#176C. From the preliminary screening, 15 Gram negative bacterial isolates have produced halos or yellow zone around the colonies on YPG agar plate at 37&#176C which indicated acetic acid production capability by those bacteria. Furthermore, acetic acid production rates were determined by titration method and about 3 - 6.9 gm/100ml acetic acid were estimated by using 4% ethanol at 37&#176C by shaking culture for 3 days. Several biochemical analysis revealed that our collection contained huge amount of acetic acid producing bacteria and some of them could be potential candidates for vinegar production.

Bio-Geo-Chemical Characterization of Bangladeshi Textile Effluents

Recently industrialization has become one of the most promising contributors for economic development of Bangladesh. However, at the same time, industrial pollution has turned into one of the major problems for human being as well as for the environment. In order to understand the effect of textile effluent (TE) on environmental pollution, TE samples collected from North-west part of the capital of Bangladesh, Dhaka (Savar, Ashulia and Tongi area) were characterized biologically, biochemically and biophysically. Eight potential microorganisms were isolated (3 bacteria and 5 fungi) from the collected TE and two of them were used to de-colorization of TE significantly by bioremediation process. Among the various parameters checked here, some physicochemical properties like TDS, COD, BOD, DO and heavy metals like Cd and Cr were detected in quite high amounts. Altogether, our results indicate that TE is one of the serious pollutants, which could damage environment as well as water body severely.

Online power quality disturbance detection by support vector machine in smart meter

Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.