Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. (September 2016) - Buyukada, Musa
Graphical abstract: Highlights: Thermal behavior of coal and peanut hull were investigated. Mass loss percent of co-combustion process were predicted by ANN. Input parameters of co-combustion process were optimized by PSO. Stochastic variability and uncertainty were analyzed by 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 °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.