| 1 |
Maximum burning rate and fixed carbon burnout efficiency of power coal blends predicted with back-propagation neural network models
Cheng J, Wang X, Si TT, Zhou F, Wang ZH, Zhou JH, Cen KF
Fuel, 172, 170, 2016 |
| 2 |
Deep-staged, oxygen enriched combustion of coal
Daood SS, Nimmo W, Edge P, Gibbs BM
Fuel, 101, 187, 2012 |
| 3 |
Combining support vector regression and cellular genetic algorithm for multi-objective optimization of coal-fired utility boilers
Wu F, Zhou H, Ren T, Zheng L, Cen KF
Fuel, 88(10), 1864, 2009 |
| 4 |
Experimental investigation on NOx emission and carbon burnout from a radially biased pulverized coal whirl burner
Xue S, Hui SE, Liu TS, Zhou QL, Xu TM, Hu HL
Fuel Processing Technology, 90(9), 1142, 2009 |
| 5 |
Influence of rank and macerals on the burnout behaviour of pulverized Indian coal
Choudhury N, Biswas S, Sarkar P, Kumar M, Ghosal S, Mitra T, Mukherjee A, Choudhury A
International Journal of Coal Geology, 74(2), 145, 2008 |
| 6 |
Computer modelling of the combined effects of plant conditions and coal quality on burnout in utility furnaces
Stephenson P
Fuel, 86(14), 2026, 2007 |
| 7 |
Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms
Zhou H, Cen KF, Fan JR
International Journal of Energy Research, 29(6), 499, 2005 |
| 8 |
Optimizing pulverized coal combustion performance based on ANN and GA
Hao Z, Qian XP, Cen KF, Fan JR
Fuel Processing Technology, 85(2-3), 113, 2004 |
| 9 |
Techniques to determine ignition, flame stability and burnout of blended coals in p.f. power station boilers
Su S, Pohl JH, Holcombe D, Hart JA
Progress in Energy and Combustion Science, 27(1), 75, 2001 |
