Soft computing techniques for the assessment of energy content from waste: A mini review

The growing global demand for energy, coupled with the pressing challenges of waste management and environmental sustainability has intensified interest in waste-to-energy (WTE) technology. Among emerging solutions, soft computing techniques have proven its viability in addressing the inherent complexities and uncertainties of waste and biomass data. This mini-review explores the role of soft computing approaches, namely artificial neural networks, fuzzy logic, and evolutionary algorithms in the prediction and optimization of the energy content of waste materials. It highlights recent advancements, key applications areas, and the challenges associated with implementing these methods in WTE systems. The finding reveals the effectiveness of soft computing techniques in enhancing energy recovery, thereby supporting sustainable waste management and cleaner energy production. This review offers valuable insights for researchers, engineers, and policymakers seeking innovative and efficient solutions in the WTE space.