title: Probabilistic uncertainty analysis based on Monte Carlo simulations of co-combustion of hazelnut hull and coal blends: Data-driven modeling and response surface optimization.
abstract: The aim of present study is to investigate the thermogravimetric behaviour of the co-combustion of hazelnut hull (HH) and coal blends using three approaches: multi non-linear regression (MNLR) modeling based on Box-Behnken design (BBD) (1), optimization based on response surface methodology (RSM) (2), and probabilistic uncertainty analysis based on Monte Carlo simulation as a function of blend ratio, heating rate, and temperature (3). The response variable was predicted by the best-fit MNLR model with a predicted regression coefficient (R2pred) of 99.5%. Blend ratio of 90/10 (HH to coal, %wt), temperature of 405°C, and heating rate of 44°Cmin-1were determined as RSM-optimized conditions with a mass loss of 87.4%. The validation experiments with three replications were performed for justifying the predicted-mass loss percentage and 87.5%±0.2 of mass loss were obtained under RSM-optimized conditions. The probabilistic uncertainty analysis were performed by using Monte Carlo simulations.
authors.affiliation: Department of Environmental Engineering, Abant Izzet Baysal University, 14052 Bolu, Turkey. Electronic address: musabuyukada@hotmail.com.
authors.forename: Musa
authors.initials: M
authors.lastname: Buyukada
mesh_terms: D056804:Biofuels; D003031:Coal; D031211:Corylus / Q000737:chemistry*; D015233:Models, Statistical; D009010:Monte Carlo Method; D009754:Nuts / Q000737:chemistry; D015203:Reproducibility of Results; D013696:Temperature; D013818:Thermogravimetry; D035501:Uncertainty
chemical_list: D056804:Biofuels; D003031:Coal
keywords: Co-combustion; Hazelnut hull; Monte Carlo simulations; Multi non-linear regression; Response surface optimization