Online coal calorific value prediction from mutiband coal/air combustion radiation characteristics

A SVR modeling based online coal calorific value prediction method was introduced. Through wavelet transform and PCS process, coal flame radiation characteristics were extracted. Through SVR modeling, relationship model between radiation characteristic variables and coal calorific value was established. Proper SVR construction parameters were chosen through grid computing. Experiment results showed good coherence between SVR model prediction and laboratory results of coal calorific value. Proposed SVR based system relies on low cost multiband photoelectric sensors, which is easy to be installed at production spot, while satisfying system performance could meet practical needs of online operation adjusting to enhance production efficiency and diminish pollutant emissions.

[1]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[2]  D. Basak,et al.  Support Vector Regression , 2008 .

[3]  Michael Gaft,et al.  Laser Induced Breakdown Spectroscopy machine for online ash analyses in coal , 2008 .

[4]  Liu Fu-guo.  REAL TIME IDENTIFICATION TECHNIQUE FOR ULTIMATE ANALYSIS AND CALORIFIC VALUE OF BURNING COAL IN UTILITY BOILER , 2005 .

[5]  B. Sowerby,et al.  An on-belt elemental analyser for the cement industry. , 2001, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[6]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[7]  Doug Body,et al.  Development and Commercial Evaluation of Laser-Induced Breakdown Spectroscopy Chemical Analysis Technology in the Coal Power Generation Industry , 2002 .

[8]  D. R. Brown,et al.  High rate spectroscopy for on-line nuclear coal analyzer (nucoalyzerTM conac) , 1981, IEEE Transactions on Nuclear Science.

[9]  P. Womble,et al.  Pulsed fast/thermal neutron analysis: a technique for explosives detection. , 2001, Talanta.

[10]  Terry Wall,et al.  Fine ash formation during combustion of pulverised coal–coal property impacts , 2006 .

[11]  J. Salgado,et al.  Calibration curves of a PGNAA system for cement raw material analysis using the MCNP code , 1998 .

[12]  Peter M. Fredericks,et al.  Near-Infrared Diffuse Reflectance Spectroscopy of Coal , 1985 .

[13]  M. Yazdi,et al.  Dual-energy gamma-ray technique for quantitative measurement of coal ash in the Shahroud mine, Iran , 2003 .

[14]  Adel F. Sarofim,et al.  A Laboratory Study of the Effect of Coal Selection on the Amount and Composition of Combustion Generated Submicron Particles , 1990 .

[15]  John F. Unsworth,et al.  Coal Quality and Combustion Performance: An International Perspective , 1991 .

[16]  Peter Glarborg,et al.  Influence of coal quality on combustion performance , 1998 .

[17]  Wenzhong Liu,et al.  An evaluation of measurement uncertainties in the on-line measurement of coal ash content by gamma-ray transmission. , 2002, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[18]  RajenderKumar Gupta,et al.  Oxy-fuel combustion technology for coal-fired power generation , 2005 .

[19]  Jean-Louis Pinault,et al.  The optimization of gamma spectra processing in prompt gamma neutron activation analysis (PGNAA) , 2009 .

[20]  B. Kanngießer,et al.  Handbook of practical X-ray fluorescence analysis , 2006 .