Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization
Authors:
- Peiliang YanPYPeiliang Yan
- Weijun FanWFWeijun Fan
- Rongchun ZhangRZRongchun Zhang
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
- National Major Science and Technology Projects of China
This publication has 40 references indexed in Scilit:
- NOx emissions characteristics of the partially premixed combustion of H2/CO/CH4 syngas using artificial neural networksApplied Thermal Engineering, 2015
- Experimental and modeling investigation of the effect of air preheat on the formation of NOx in an RQL combustorHeat and Mass Transfer, 2012
- Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimizationEngineering Applications of Artificial Intelligence, 2012
- Variable selection using random forestsPattern Recognition Letters, 2010
- The random subspace method for constructing decision forestsIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- Bagging predictorsMachine Learning, 1996
- Support-vector networksMachine Learning, 1995
- Interpretation of the Correlation Coefficient: A Basic ReviewJournal of Diagnostic Medical Sonography, 1990
- Learning representations by back-propagating errorsNature, 1986
- Fuel effects on gas turbine combustion-liner temperature, pattern factor, and pollutant emissionsJournal of Aircraft, 1984