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Renewable energy sources are emerging to replace conventional energy sources in the energy mix as a result of the growing demand for power and the constraints of existing energy sources. With technological advancements, wind energy has become a promising secondary energy source, resulting in the proliferation of wind farms around the globe to bolster traditional energy systems. This has led to a rapid increase in the incorporation of wind power into the power grid, emphasizing the need to understand its effects on the system's parameters. A range of generators, including DFIG, SG, and SCIG, are used to generate wind power. Due to benefits of separate control over true and wattless power, variable speed operation and maximum power tracking, a DFIG based wind turbine became a favourite choice for power utilities.

This paper analyzes the problem of septage management related to opportunities and solution. in accordance with present treatment technologies. The scenario in India is about 41 % of septage sludge is disposed in local area without treatment Still some of the individual housing is not connected to suitable public sewage system. The current scenario of FSM in Warangal city is 77 % of the households have proper access to toilet and 23% insanitary toilet and open defecation were found. The samples were collected from the Warangal city and were tested for the characteristics. BOD and COD were very high above 9800 mg/l. As per CPHEEO norms TSS should be less than 15000 mg/l, but it is observed in all the samples it is in the range of 24,800 mg/l to 82,460 mg/l. Currently the treatment such as sludge drying beds, lime treatment, anaerobic baffled reactor, stabilization pond, constructed wetland, composting with solid waste, Mechanical dewatering,. Neglected septage waste ash was tried for several trial run in the mix design and found 20 % to be optimum replacement of septage ash. Strength enhancement is achieved by adding glass chopped strands and workability is improved by Super plasticizer Polycarboxlate ether. Utilization of residue in septage treatment plant in cost effective and ecofriendly way by replacing cement in concrete was tried in the pilot scale study near Warangal and proven to be effective.

Carpets are the three-dimensional product used as a floor covering in homes, offices, commercial centers, decorative purposes, etc. The average life span of the carpet is four to seven years and after that, it becomes solid waste. The discarded carpets are causing a significant hazardous effect on the environment, climate, soil, and various health issues. To overcome the increasing carpet waste, the re-utilization of carpet is essentially desired. This article focuses on the development of polymer composites developed from discarded nylon carpets for lightweight applications. A modified technique of Vacuum-assisted resin transfer molding (VARTM) was used to fabricate epoxy composites. The tensile and flexural tests evaluated the mechanical performance of the proposed composite. The modified composite is manufactured in two different configurations, namely, face- back-to-back-face (FBBF) and back- face to face- back (BFFB) with the help of the VARTM setup. The result demonstrated that the fabricated BFFB composite has a higher strength. The high-resolution microscopy test of the developed samples shows the feasibility of the composites produced from discarded carpet for lightweight functions. An attempt has been made to resue the waste for the fabrication of cost-effective products.

Carbon nanomaterials reinforced composite materials have been broadly utilized in manufacturing engineering due to improved thermal resistivity, reduced weight, and other improved mechanical properties. This article highlights the drilling experimentation of zero-dimensional (0-D) Carbon nano onion (CNO) reinforced polymer composite. For this, three drilling constraints was considered viz., spindle speed, feed rate, and weight % of nanomaterial reinforced. The objective is to achieve the desired value of generated drilling forces such as Torque (Nm) and Thrust Force (N) during the machining procedure of developed composite samples. The Multiobjective-Particle Swarm Optimization (MOPSO) is utilized to achieve optimal results from the multi-decision criterion for the Machining performance. Exploiting this optimization process, non-dominated solutions were obtained, and the Pareto front was identified. Practical applications for the discovered relationships include using Grey relation analysis (GRA) to extract the most relevant finding from the Pareto Front space of optimal solutions. Using the GRA, the optimum solution was found: Spindle Speed of 1000 RPM, Feed Rate of 100 mm/min, and CNO weight percentage of 0.5. After this, a confirmation test was performed, the expected effects have been confirmed. The findings reveal that the proposed optimization module can be recommended for online quality and productivity control.

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.