Büyükada, Musa2021-06-232021-06-2320160960-85241873-2976https://doi.org/10.1016/j.biortech.2016.05.091https://hdl.handle.net/20.500.12491/8715Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R-2 of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305 degrees C, and heating rate of 49 degrees C min (1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5% +/- 0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. (C) 2016 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessPeanut HullCo-CombustionArtificial Neural NetworksParticle Swarm OptimizationMonte CarloArticle10.1016/j.biortech.2016.05.091216280286272436062-s2.0-84969961378Q1WOS:000379555900035Q1