| 1 |
Predicting the combustion state of rotary kilns using a Convolutional Recurrent Neural Network
Li T, Zhang ZT, Chen H
Journal of Process Control, 84, 207, 2019 |
| 2 |
Predicting the combustion state of rotary kilns using a Convolutional Recurrent Neural Network
Li T, Zhang ZT, Chen H
Journal of Process Control, 84, 207, 2019 |
| 3 |
Deep learning based monitoring of furnace combustion state and measurement of heat release rate
Wang ZY, Song CF, Chen T
Energy, 131, 106, 2017 |
| 4 |
Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques
Bai XJ, Lu G, Hossain MM, Szuhanszki J, Daood SS, Nimmo W, Yan Y, Pourkashanian M
Fuel, 202, 656, 2017 |
| 5 |
Combustion performance, flame, and soot characteristics of gasoline-diesel pre-blended fuel in an optical compression-ignition engine
Jeon J, Lee JT, Kwon SI, Park S
Energy Conversion and Management, 116, 174, 2016 |
| 6 |
Laboratory investigation into fractal characteristics of methane explosion flame
Nie BS, Wang C, Meng JQ, Xue F, Dai LC
Process Safety Progress, 34(3), 244, 2015 |
| 7 |
Principles of optimization of combustion by radiant energy signal and its application in a 660MWe down- and coal-fired boiler
Luo Z, Wang F, Zhou H, Liu R, Li W, Chang G
Korean Journal of Chemical Engineering, 28(12), 2336, 2011 |
| 8 |
Optimization of combustion based on introducing radiant energy signal in pulverized coal-fired boiler
Huang BY, Luo ZX, Zhou HC
Fuel Processing Technology, 91(6), 660, 2010 |
| 9 |
Experimental investigations on temperature distributions of flame sections in a bench-scale opposed multi-burner gasifier
Yan ZY, Liang QF, Guo QH, Yu GS, Yu ZH
Applied Energy, 86(7-8), 1359, 2009 |
| 10 |
Real-time monitoring and characterization of flames by principal-component analysis
Sbarbaro D, Farias O, Zawadsky A
Combustion and Flame, 132(3), 591, 2003 |
