Optimization of Waste to Energy Conversion Using AI Based Waste Classification and HHV Prediction

Abstract

The solid waste is primarily composed of trash that has been dumped because it is undesirable and pointless and results from human and animal activity. Commercial, residential, and industrial operations in a given area produce solid waste, which can be managed in a number of ways. As a result, landfills are frequently categorized as being either sanitary, industrial, municipal, or building and demolition waste sites. Plastic, paper, glass, metal, and organic trash are all examples of material-based waste. For example, if an object is radioactive, explosive, infectious, toxic, or non-toxic will affect its potential for harm. No matter where it comes from, what it is made of, or if it raises any issues, solid waste must be managed and organized to ensure environmental best practices. When creating environmental plans, solid waste management must be taken into account because it is essential to environmental cleanliness. newlineAn important global environmental concern is the growing amount of solid garbage produced by human activity. The conventional approaches to managing solid waste, such open dumping and landfilling have shown to be unsustainable and to carry serious threats to human health and the environment. Proper treatment of solid waste can be utilized as a renewable energy source. Waste-to-energy (WTE) technologies reduce the amount of garbage that needs to be disposed of and provide a renewable energy source, making them a viable alternative to traditional solid waste management techniques. For this Waste-to-energy (WTE) conversion process the first thing needed is to newlineix newlineanalyze waste composition. AI based waste classification helps to accurately predict the waste composition. In Waste-to-energy (WTE) conversion process High Heating Value (HHV) plays an important role. Biomass higher heating value (HHV) is the maximum energy released by its complete oxidation. Deep learning methods are used for effective prediction of HHV. Finally, it draws attention to the need for additional study and development.