Online dynamic prediction of potassium concentration in biomass fuels through flame spectroscopic analysis and recurrent neural network modelling
Xinli Li, Changxing Han, Gang Lu, Yong Yan
Abstract
Biomass fuels are widely used as a renewable source for heat and power generation. Alkali metals in a biomass fuel have an significant impact on furnace safety as such metals lead to fouling and slagging in the furnace and corrosion of water pipes. This paper presents a technique for dynamic predicting Potassium (K) concentration in a biomass fuel based on spectroscopic analysis and different recurrent neural networks. A miniature spectrometer is employed to acquire the spectroscopic signals of K in different biomass fuels, including peanut shell, willow, corn cob, corn straw and wheat straw, and their blends. The spectroscopic features of K are extracted. The factors that influence the spectral intensity of K in the biomass fuels are investigated. A basic recurrent neural network (RNN), and its variants, i.e., long short-term memory neural network (LSTM-NN) and deep recurrent neural network (DRNN), are constructed using the spectroscopic signal of K from the spectrometer. The performances of the neural networks for the dynamic prediction of K concentration are compared and analysed theoretically and experimentally. It is found that the relative error in the K concentration prediction through the use of the DRNN model is within 6.34% whilst the LSTM-NN and RNN models give errors slightly greater than this.