Synchronization of Solar and MSEB

In this paper we are providing a overview of recent researches synchronization of solar and MSEB. The term ?smart grid? refers to the use of technologies and tools that help electric utilities better meet consumers? needs reliably and affordably by more effectively monitoring power usage demand and system conditions on a near real-time basis. The smart grid combines digital devices, software applications and two-way communications that allow utilities to track the flow of electricity with great precision, and apply logic to relays according to the situation of input. It can also let utilities record consumer electric use in various time intervals and provide consumers with energy usage data. Considering the problem of generation of the ac supply, we aim to design a system, which can utilize the solar power. Due to the use of solar power for home appliances requirement of grid?s power will be reduced. This system results into the efficient use of renewable energy. This system will be used to overcome the problem of load shedding and reducing the electricity bills. The system consisting of solar dc power can be converted into ac power using the solar micro grid inverter. The synchronized output is given to the microcontroller and the source of supply will be selected automatically according to the requirements of load and status of the sources. Advanced facility like GSM will allow the user to control various appliances just through a message. Daily report of usage of power through individual source will be given to the user by a text message. Various parameters like voltage, current, power consumption will be displayed on a LCD to give the notification of status of the system

Design and implementation of Cell Tracking system and Sync with cloud

In today?s world more than eighty percent people uses the smart phones. As the need increases the misuse of the cell phone also increases. Anyone can distrust or cheat other or suspicious of others activities. There may be loss of an important data in the big organizations due to the employees. Many criminal activities have increased in organization and teenagers are misusing the smart phones. So for the security purpose in the large organizations and to control the activities of the employees and the teenagers, software can be used which keeps the log files in a single mobile with its date and synchronize daily with restricted area in corporate with cloud

Gamma Radiation Induced Formation of Iodine Monochloride in Iodine in Some Aromatic Chlorinated Solvents

Abstract: Gamma radiation induced formation of ICl in aerated and deaerated solutions of iodine in some aromatic chlorinated solvents has been studied for different concentrations and γ dosages. G values for formation of ICl and decomposition of I 2 were determined in gamma radiolysis of aerated and deaerated solutions of I 2 in 1,2-dichlorobenzene(1,2-DCB), 1,3-dichlorobenzene(1,3-DCB), 2-chlorotoluene, 3-chlorotoluene and benzotrichloride. G(ICl) values have been found to decrease in the following order 2-chlorotoluene < 3-chlorotoluene < 1,2-DCB < 1,3-DCB < benzotrichloride. G(ICl) is slightly higher in aerated solutions than in deaerated solutions and is found to be dependent on the structure of the parent organic molecule

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

418-423Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

Effect of lipase from different source on high fat content wastewater of dairy industry

244-250Wastewaters of dairy industry usually present high fat contents. The present study was designed to investigate the effect of different sources of lipase such as lipase contains raw milk, crude lipase from groundnuts extract, fat degrading microorganism from up flow anaerobic sludge blanket reactor (UASBR) culture in nutrient broth and lipase producing microorganism from UASBR culture in a selective media inoculated in holding tank dairy wastewater. Pretreatment of 250 ml dairy wastewater with 10 ml lipase contain sample was optimized under anaerobic condition for 72 hrs at 37ºC. Analytical parameters pH, fat content (FC), chemical oxygen demand (COD) and total solids (TS) were analysed in each treatment process. The results showed that pH of the samples were maintained, the UASBR culture inoculated in selective media degraded the maximum amount of fat; similarly maximum amount of COD and TS were reduced in the selective media treated sample as compared to others treatment process. This study illustrated that application of an enzymatic pretreatment process to hydrolyze and dissolve fats may improve the biological degradation of high fat content in wastewaters. Moreover, pretreatment of wastewater from different lipase sources are new and promising application for lipases. Thus, it is apparent that use of this enzymatic biological treatment can serve an alternative for treatment of Soybean casein digest Soybean casein digest Soybean casein digest Soybean casein digest fat containing wastewater and to provide pollution free environment

Greywater characterization of an Indian household and potential treatment for reuse

Inadequate water supplies recall the environmental values of recovery and reuse of limited resources. One of the exciting opportunities in these circumstances is Grey water. Wastewater generated from household activities like bathing, kitchen sinks, washbasins, and laundry is classified as greywater. Inventing a pilot-scale greywater treatment system that treats in-house generated greywater and makes it reusable by assisting the untapped potential of physical methods of greywater purification was the main aim of this study. The study results from greywater samples' characterization from various sources in an Indian middle-class single household with four residents for six months. Moreover, the designing and analyzing of a treatment system applied to treat this in-house generated greywater was conducted. A filtration system with different filter layers was designed. It was found to have a chemical oxygen demand removal efficiency of 85.98%, biochemical oxygen demand removal efficiency of 86.28%, and total suspended solids removal efficiency of 94.44%. The filter system designed in this study describes improved removal efficiency in all respects and gives an idea of the reusability of in-house treated greywater. The study concludes that greywater can be recycled and reused for toilet flushing, gardening, car washing, and firefighting. This practice can also lead to a significant reduction in the consumption of freshwater

Supervisory Predictive Control Of Standalone Wind/solar Energy Generation Systems

This paper presents a supervisory model predictive control method for the optimalmanagementand operation of hybrid standalone wind-solar energy generation. Thiswork present supervisorycontrol system through modelpredictive control which calculates thepower references for the wind and solar subsystems at each sampling time while minimizing asuitable cost function. Inthis paper wediscuss how toextend the life time of the equipment by reducing the peak values of inrush or surge currents, into the formulation of the model predictive control optimization problem. Wepresentseveral simulation of this system