Fuel-type identification using joint probability density arbiter and soft-computing techniques

This paper presents a new method for fuel-type identification by combining the joint probability density arbiter and soft-computing techniques. Extensive flame features were extracted both in the time and frequency domains from each flame oscillation signal and formed an original feature data vector. Orthogonal and dimension-reduced feature data were obtained by using the principal component analysis technique. In order to identify the fuel type, the joint probability density arbiter and soft-computing models were established for each known fuel type by using the orthogonal features. Then, the joint probability density arbiter model was used to determine whether the type of fuel is new or not, and one of the soft-computing models (either a neural network model or a support vector machine model) was used to identify the fuel type if the fuel was one of the known types. Experiments were carried out on an industrial boiler. Four types of coal were tested, and the average success rates of fuel-type identification were higher than 97% in 20 trials. The experimental results demonstrated that the combination of the joint probability density arbiter and one of the two soft-computing techniques was effective in identifying the fuel types (either new or not).