High-precision regression prediction of HCHO concentration based on gas sensors and FPNet

Abstract

The leakage of HCHO gas has direct and irreversible damage on both human health and the environment. However, the current commercial electronic nose (E-nose) has low accuracy in predicting the concentration of HCHO. In this work, a sensor array consisting of 6 gas sensors was fabricated and combined with advanced deep learning algorithms to achieve accurate prediction of HCHO concentration. High-performance SnO2 sensing material with high specific surface area was synthesized by applying the Metal-Organic Frameworks (MOFs) sacrificial template, and the response of the SnO2 gas sensor was as high as 15.098, which is much higher than that of commonly used commercial sensors. In addition, the sensing performance of the SnO2 sensor, including response, selectivity and repeatability etc., were investigated. Afterward, a Formaldehyde Prediction Network (FPNet) model was proposed for processing the HCHO gas dataset and cosine annealing was added to gradually reduce the learning rate during training to help the model converge to the optimal solution. Furthermore, the residual structures with different number of layers were compared, and the 3-layer residual structure expressed the most accurate results of concentration prediction of the HCHO gas, with an MSE of 9.003, MAPE of 0.015, and R2 of 0.998. This study provides a new idea from sensor preparation to pattern recognition algorithm research, effectively solving the problems of the low response of commercial sensors to HCHO and the low accuracy of pattern recognition algorithms for E-nose datasets.