Synergizing cybersecurity in healthcare with novel bioprocessing for sustainable energy-centric water remediation

The introduction of several novel chemicals, materials, and processes with varied levels of complexity in recent decades has been a result of the incredibly rapid technological advancement that has characterised those decades. This in turn has led to an increase in the number of pollutants released into the environment, necessitating their effective removal. The results of environmental monitoring reveal that several pollutants are exceeding the limit in ground water, which raises questions about the efficacy of the wastewater treatment methods now in use. This research proposes a novel method for sustainable smart grid (SG)-based energy analysis in water remediation and network cybersecurity analysis for healthcare application. Here the water-caused damages have been analysed based on a healthcare application using SG energy analysis and the network cyber security analysis is carried out using the federated blockchain model (SGEA_FB). Experimental analysis is carried out in terms of network integrity, throughput, scalability, and training accuracy. According to an analysis of the exergy destruction rates in every method component, power generation subsystem has the greatest exergy destruction rate at 15.4 MW. A training accuracy of 96%, throughput of 86%, network integrity of 76%, and scalability of 73% were all achieved by the suggested method.