Predicting the co-melting temperatures of municipal solid waste incinerator fly ash and sewage sludge ash using grey model and neural network.
A grey model (GM) and an artificial neural network (ANN) were employed to predict co-melting temperature of municipal solid waste incinerator (MSWI) fly ash and sewage sludge ash (SSA) during formation of modified slag. The results indicated that in the aspect of model prediction, the mean absolute percentage error (MAPEs) were between 1.69 and 13.20% when adopting seven different GM (1, N) models. The MAPE were 1.59 and 1.31% when GM (1, 1) and rolling grey model (RGM (1, 1)) were adopted. The MAPEs fell within the range of 0.04 and 0.50% using different types of ANN. In GMs, the MAPE of 1.31% was found to be the lowest when using RGM (1, 1) to predict co-melting temperature. This value was higher than those of ANN2-1 to ANN8-1 by 1.27, 1.25, 1.24, 1.18, 1.16, 1.14 and 0.81%, respectively. GM only required a small amount of data (at least four data). Therefore, GM could be applied successfully in predicting the co-melting temperature of MSWI fly ash and SSA when no sufficient information is available. It also indicates that both the composition of MSWI fly ash and SSA could be applied on the prediction of co-melting temperature.
Pai
Tzu-Yi
TY
Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung, Taiwan, ROC.
Lin
Kae-Long
KL
Shie
Je-Lung
JL
Chang
Tien-Chin
TC
Chen
Bor-Yann
BY
D002244:Carbon / Q000737:chemistry*; D002947:Cities; D060729:Coal Ash; D017745:Incineration / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D008956:Models, Chemical; D016571:Neural Networks, Computer; D052638:Particulate Matter / Q000737:chemistry*; D012722:Sewage / Q000737:chemistry*; D044366:Transition Temperature; D014866:Waste Products / Q000032:analysis* / Q000706:statistics & numerical data
D060729:Coal Ash; D052638:Particulate Matter; D012722:Sewage; D014866:Waste Products; D002244:Carbon