The yttria-stabilized zirconia (YSZ)-based mixed potential gas sensor represents a promising platform for portable acetone detection systems, owing to their high sensitivity and selectivity. However, the practical deployment of these systems is hindered by sensor output drift, circuit noise, and signal fluctuations caused by variations in operating temperature and ambient humidity. To address these challenges, we developed GT-KANet, a novel hybrid deep-learning algorithm designed for robust temperature-humidity compensation and accurate acetone concentration prediction. The GT-KANet architecture integrates Gated Recurrent Unit (GRU) networks for initial temporal feature extraction, transformer layers enhanced with ContraNorm to mitigate oversmoothing during deep feature learning, and a Kolmogorov-Arnold Network (KAN) module for final concentration prediction. Trained on a comprehensive dataset acquired from our independently developed YSZ sensor under controlled conditions, the proposed GT-KANet achieved exceptional predictive accuracy (RMSE = 0.0920 ppm, MAE = 0.0542 ppm) across varying operating temperatures, ambient humidity, and gas concentrations, demonstrating excellent stability and adaptability. Furthermore, by leveraging knowledge distillation, we achieved a 55% reduction in the model's parameter size while maintaining exceptional prediction accuracy, significantly enhancing its feasibility for deployment on resource-constrained embedded platforms. A detailed comparative analysis during development systematically validated the efficacy of each module in boosting prediction accuracy. This work offers a competitive approach for temperature and humidity compensation using YSZ-based acetone sensors, demonstrating excellent application potential in rigorous detection scenarios.
Pu et al. (Tue,) studied this question.