Liu, JingyongHuang, LimaoBüyükada, MusaEvrendilek, Fatih2021-06-232021-06-2320171359-4311https://doi.org/10.1016/j.applthermaleng.2017.07.008https://hdl.handle.net/20.500.12491/9136The present study aims at quantifying mass loss percentage (MLP, %) predictions and their stochastic uncertainty when co-combustion of sewage sludge (SS) and water hyacinth (WH) are applied as alternative biomass, materials under different blend ratios (BR), heating rates (HR, degrees C/min) and temperatures (T, degrees C). Optimization and validation of experimental data through Box-Behnken design pointed to 630.9 degrees C for T, 60.1% SS for BR, and 29.9 degrees C/min for HR as the optimal co-combustion parameters to achieve the maximum MLP of 92.4%. Monte Carlo (MC) simulations were used to quantify uncertainty in MLP predictions of the best-fit multiple non-linear regression (MNLR) model derived from the entire experimental data as a function of MC-generated T as the only continuous predictor of the MNLR. Mean MLP value of the MNLR predictions was higher by 19% than that of the MC-simulated T whose mean was higher by only 1% than mean measured T. Incorporating the uncertainty estimation based on Monte Carlo simulations with response surface approach for co-combustion of SS and WH was one of the main novel contributors of the present study to related literature. (C) 2017 Elsevier'Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessWater HyacinthSewage SludgeBox-Behnken DesignData-Driven ModelingMonte Carlo SimulationArticle10.1016/j.applthermaleng.2017.07.0081253283352-s2.0-85022224054Q1WOS:000410011200030Q1