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 R2 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.