Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

Abstract Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models the LHV of various blends. Coal two different ranks types biomass used in study. One was South African imported bituminous coal, other lignite thar coal extracted from Thar Block‐2 mine by Sind Engro Mining Company, Pakistan. Two biomass, that is, sugarcane bagasse rice husk, were obtained locally sugar mill located vicinity Sahiwal, Punjab. Bituminous mixture with both are 10%, 20%, 30%, 40%, 50% weight fractions, respectively. The calculation model development procedure resulted 91 AI‐based models. best Ridge Regressor, high‐level end‐to‐end approach fitting model. can predict under vast share