This research proposed to treat the RO rejected wastewater in a household plant by the integrated treatment system. The possibility of wellhead water treatment by the combined treatment system of coagulation and adsorption for salinity reduction via flexible high recovery RO system was evaluated through analysis of treatment options on a laboratory scale. The naturally available gooseberry seed used as a coagulant in phase-1. It reduced 99.3% of TDS and hardness. It also increases the DO level of RO reject water, at the same time it increases turbidity and color. Turbidity and color removed by surface-modified zeolite in the phase-2. The zeolite material was taken in temperature 400ºC as adsorbent of 6 cm column achieved 8NTU in 150 mmin. The 12 cm column was achieved 7.5NTU in 150 mins. Thomas and Thomson modelling well fitted with an experimental study. The regression correlation reached up to 0.942, 0.9810 and 0.984. It is apparent from the recorded SEM patterns study. This study concludes that the coagulation by Goosperry seed produced the highest removal of TDS and hardness and in the adsorption process, with 400ºC enhances the surface morphology and porous structure indicates that heating with higher-level temperature enhances the adsorption capacity of the adsorbent material. The highest efficiency is observed in hydrothermal hotness.

Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are discarded from households and workplaces. These used e‐wastes are meant to be renovated, reused, recycled, or disposed of, and the processing of these wastes often causes disease and harms the environment. As a result, it is important to handle waste and collect it from the disposal site on a regular basis. Besides, in order to separate precious metals from discarded waste, it is important to identify them by category. Therefore, this article proposes a novel method known as e‐waste management by exploiting the dynamic convolutional neural network (DCNN). This enhances the classification accuracy with the aid of exactly mapping the features of the images. Meanwhile, the collection of waste can be optimized in order to reduce the distance and time. The e‐wastes in the smart garbage bin are frequently monitored by smartphone applications to collect the waste on time. Moreover, it also significantly reduces the training error, classification error, localization error, and validation error on the test images. The experimental depicts that the proposed method hones up the classification accuracy to the great extent.

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Abstract-This research focuses primarily on modelling of an existing building using Revit architecture and importing the model in energy analysis software Green Building studio, applying the energy efficient parameters for light and thermal energy in order to obtain the optimum energy saving and minimise the loss of energy in the building. In this context, subject building refers to the existing building which is individual residential home located in Tambaram IV. INTRODUCTIONRetrofitting an existing building can oftentimes be more costeffective than building a new facility. Since buildings consume a significant amount of energy, particularly for heating and cooling, and because existing buildings comprise the largest segment of the built environment, it is important to initiate energy conservation retrofits to reduce energy consumption and the cost of heating, cooling, and lighting buildings. II. BUILDING INFORMATION MODELLINGBuilding information modelling (BIM) is a new way of approaching the design and documentation of building projects. BIM Models and manages not just graphics, but also information that allows the automatic generation of drawings and reports, design analysis, schedule simulation, facilities management, and more ultimately enabling the building team to make better-informed decisions. A. Revit architectureThe energy analytical model feature in Revit building design software provides tools for fast, flexible creation of models for energy simulation. Autodesk Revit Architecture software provides architects the tools to easily capture and analyse design concepts, and more accurately maintain coordinated and reliable design data through documentation and construction.Energy analytical models are created to suit different design stage needs, workflows and precision preferences. A model to be created directly from architectural building elements and room/space elements, or manually using conceptual massing.

Groundwater samples were collected from the surrounding regions of Pallecheruvu Lake. Pallecheruvu lake is located in the southern part of the Hyderabad city ,between latitudes 17 0 20' N and 17 0 30'N and longitudes 78 0 27'30"E and 78 0 30'E , covering an area of about 16.8 hectares. 15 Groundwater samples from selected bore wells and 4 surface water samples were analysed for important physico-chemical attributes by adopting APHA standard methods. The study aims to understand the distribution of groundwater quality in aforesaid region. The following objectives of the study are to determine groundwater quality parameters such as pH, Chlorides (mg/l), Acidity, Alkalinity, Total dissolved Solids, Electrical Conductivity, Turbidity and to create a database and create various thematic maps and map the spatial distribution of groundwater quality in the study area. Inverse Distance Weighted (IDW) interpolation method was used to create various raster maps which show the spatial distribution. Iso concentration map are prepared by using QGIS software. Iso-concentration maps are very useful for predicting the quality of water and to know the concentrations of different elements at different places.