Review on Deep Learning Driven analysis of Biomedical Waste Incinerator Corrosion

Deep learning mimics how the human brain functions, which is to say, it learns by experience. It is based on neural network theory and applies it to solving difficult issues. Toxic compounds and infectious or noninfectious waste can be found in biological waste, which poses a risk to patients, healthcare workers, and visitors in hospitals. Due to the mixture of infectious and non-infectious waste in biomedical trash, it is designated as hazardous waste. Incineration is the most popular method for disposing of medical waste. Using incineration, it removes the large majority of trash and germs from the environment. Biomedical incinerators have been erected across India, with 149 already operational and eight more under development, according to estimates of their number. Medical waste burning requires a temperature of 900–1200 degrees Celsius for effective combustion. Biomedical waste incinerators may use the heat generated by the combustion of biomedical waste to generate steam or power, depending on the model. An alarming number of boiler tubes that have had their wall thickness reduced due to fireside corrosion in incinerators have been observed by plant operators and design engineers, as well as boiler tube manufacturers. The extremely corrosive environment, which comprises C12, HCl, alkali metals, and heavy metals, exacerbates corrosion in biomedical waste incinerators. The effects of heat corrosion on biomedical incinerators, as well as ways to prevent it, are explored in this study.