title: Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation.
abstract: Co-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°C, and heating rate of 49°Cmin(-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.
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: D000465:Algorithms; D010367:Arachis / Q000737:chemistry*; D003031:Coal; D008962:Models, Theoretical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D013696:Temperature; D035501:Uncertainty
chemical_list: D003031:Coal
keywords: Artificial neural networks; Co-combustion; Monte Carlo; Particle swarm optimization; Peanut hull