Rapid detection of loss on ignition for unburned carbon powder in fly ash triboelectric separation based on image recognition and machine learning

Loss on ignition (LOI) is an essential evaluation index for fly ash triboelectric separation to recover unburned carbon products, and an important basis to regulate the technology parameters of triboelectric separation. Due to the problems of the traditional cauterization method, such as complex process, time-consuming, and error interference, the purpose of the paper is to introduce a new method for rapid detection of fly ash LOI based on image recognition and machine learning. An image recognition technique was applied to extract image grayscale features of unburned carbon particles recycled by triboelectric separation. The extreme learning machine (ELM) and genetic algorithm-optimized extreme learning machine (GA-ELM) neural networks established the relationship between grayscale feature parameters and LOI. Fly ash original samples and electrowinning experimental samples were used to evaluate the feasibility of 2 neural networks. The results showed that the evaluation indexes of GA-ELM network consisted of R2 is 95.218%, MAE is 0.0041546%, RMSE is 0.006709% and MAPE is 0.04213% at different voltages in the triboelectric separation experiments. The detection accuracy and detection ability of GA-ELM network was higher than ELM model. Therefore, the method can quickly and accurately detect the LOI of unburned carbon recovered from fly ash triboelectric separation.