A Bonnet Probe Microwave Sensor Integrated With Machine Learning Algorithms for Ethanol Concentration Measurement

Ethanol concentration measurement is critical for many industrial applications. This article presents a novel bonnet probe microwave sensor which has a simple, compact dual-probe stainless steel structure. The designed sensor has specific dimensions with an outer diameter of 50 mm and a height of 110 mm. The system operates by extracting multi-frequency scattering parameters (S11and S21) across a broad frequency range of 0.3–6 GHz. To accurately model the nonlinear relationship between microwave signals and concentration, three deep learning algorithms—Long Short-Term Memory (LSTM), Autoencoder, and Gated Recurrent Unit (GRU)—were employed separately. Experimental results demonstrate that the sensor exhibits high sensitivity, particularly within the optimized 2–6 GHz frequency band. Among the models, the LSTM network achieved the best performance with a coefficient of determination (R2) of 0.9976, a root mean squared error (RMSE) of 0.0134, and a mean absolute error (MAE) of 0.0099. These metrics validate the sensor's advantages in terms of high precision, electromagnetic stability, and suitability for real-time detection in industrial settings.