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ABSTRACT
This paper presents an identification method for types of fuel such as biomass by combining flame spectroscopic monitoring and tree model algorithms. The features of the flame spectra are extracted, including the spectral intensity of flame radicals [OH* (310.85 nm), CN* (390.00 nm), CH* (430.57 nm) and C2* (515.23 nm, 545.59 nm)], flame radiation intensity and flame radiation energy (integration of spectral intensity). The identification models are built using four tree model algorithms, i.e., decision tree, random forest, extremely randomized trees, and gradient boost decision tree. The different type of biomass and spectra features of combustion flames are composed of sample pairs to train identification models. Experiments are carried out on a laboratory-scale biomass-air combustion test rig. Four different biomass fuels, including corncob, willow, peanut shell, and wheat straw are burnt. The results demonstrate that the identification models proposed is capable of identifying types of biomass fuels correctly with the average identification success rate of 98% in 10 trials.
Nomenclature
| = | Data set | |
| = | Subset of left side | |
| = | Subset of right side | |
| F | = | Score |
| FP | = | False positive |
| FN | = | False negative |
| K | = | Number of classifications |
| M | = | Number of attributes |
| N | = | Number of decision trees |
| = | Result belongs to classification K | |
| P | = | Precision |
| r | = | Proportion of training set samples |
| R | = | Recall rate |
| TN | = | True negative |
| TP | = | True positive |
| = | Estimated value of input data |
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.