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The dissimilar combination of 2.25Cr-1Mo (P22) and modified 9Cr-1Mo (P91) obtained using Gas Tungsten Arc Welding (GTAW) process employing the Ni-based superalloy filler ERNiCr-3 (IN82) and ERNiCrMo-3 (IN625) have been investigated for microstructure evolution and mechanical properties. The butt weld joint was produced using single bevel groove geometry. The structural integrity of the welded joint was measured in respect of tensile strength, impact toughness and hardness. The alloying elements’ segregation at the inter-dendritic areas of the weld metal was witnessed while using the IN82 and IN625 filler. The impact test trials showed the mixed mode of fracture with an impact toughness of 82 ± 6 J and 70 ± 5 J for IN82 and IN625 filler, respectively, ensuring that the welded joint was safe for the end boiler application. The tensile test coupons were fractured from the P22 base metal in all the trials and for both the fillers which confirmed the negligible effect of the filler composition on the tensile properties. The hardness plots showed the inhomogeneity in hardness value, which was also supported by the microstructure evolution along the weldments. The average hardness of the IN82 filler was measured lower than the IN625 filler.

The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.