Online dynamic prediction of potassium concentration in biomass fuels through flame spectroscopic analysis and recurrent neural network modelling

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.