Co-combustion of peanut hull and coal blends: artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation
Yükleniyor...
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 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.
Açıklama
Anahtar Kelimeler
Peanut Hull, Co-Combustion, Artificial Neural Networks, Particle Swarm Optimization, Monte Carlo
Kaynak
Bioresource Technology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
216
