Özbayoğlu, GülhanOzbayoglu,A.M.Ozbayoglu,M.E.Ozbayoglu,G.Energy Systems Engineering2024-10-062024-10-06201218190171437978-819017143-4[SCOPUS-DOI-BELIRLENECEK-226]2-s2.0-84879950552https://hdl.handle.net/20.500.14411/9324Metso; Vale; Tata Steel; ESSAR STEEL; TATA CONSULTANCY SERVICESGross calorific value (GCV) of coals was estimated using artificial neural networks, linear and non-linear regression techniques. Proximate and ultimate analysis results were collected for 187 different coal samples. Different input data sets were compared, such as both proximate and ultimate analysis data, and only proximate analysis data and only ultimate analysis data. It was observed that the best results were obtained when both proximate analysis and ultimate analysis results were used for estimating the gross calorific value. When the performance of artificial neural networks and regression analysis techniques were compared, it was observed that both artificial neural networks and regression techniques were promisingly accurate in estimating gross calorific values. In general, most of the models estimated the gross calorific value within ±3% of the expected value.eninfo:eu-repo/semantics/closedAccessGross calorific valueLignitesNeural networksProximate analysisRegressionUltimate analysisConference ObjectN/AN/A40114023