Techno-economics of high ash coal gasification: A machine learning approach using CatBoost model

Abstract

This work explores the techno-economics of high ash coal gasification using circulating fluidized bed (CFB) gasifier technology at the Talaipalli Coal Mining Project, India being developed by NTPC Limited. The research employs the categorical boosting machine learning algorithm to predict syngas composition, yield, and lower heating value offering an innovative approach to optimize the gasification process. The work compares CFB gasification with conventional coal combustion and establishes it as the most suitable gasification technology due to its feedstock flexibility, technological maturity, and cost-effectiveness. A comprehensive financial analysis reveals a favorable net present value (NPV) of ₹ 20150 Million, an internal rate of return (IRR) of 9.54%, and a payback period of 8.3 years for a 65 MW_th CFB gasifier. Sensitivity analysis highlights the influence of key factors such as syngas yield, capital costs, and selling price on financial viability. The environmental benefits, including reduced emissions and waste management, further underscore the value of coal gasification for sustainable energy production. This work aligns with government initiatives like the National Coal Gasification Mission, advocating future research in co-gasification and techno-economic assessments for similar projects.