Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. (September 2016)

• Record Type: Journal Article
• Authors: Buyukada, Musa
• Is Part Of: Bioresource technology. Volume 216(2016)
• Page Start: 280
• Page End: 286
• Publication Date: 2016-09
• Subjects: Peanut hull -- Co-combustion -- Artificial neural networks -- Particle swarm optimization -- Monte Carlo; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2016.05.091
• Languages: English
• ISSNs: 0960-8524

Abstract

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.