Data-driven Modelling of an Indirect Photoacoustic Carbon dioxide Sensor

This study aims to analyze a novel indirect photoacoustic sensor (PAS) using Machine Learning techniques. The studies focus on understanding the sensor’s repeatability, the influence of temperature and humidity on microphone output voltage, and the applicability of Machine Learning models to accurately describe the sensor’s behavior. To describe the sensor behavior, two studies are carried out in a controlled setting. With a R2 score of 0.964 between the microphone voltage and the gas concentration in ppm, the first study illustrates the sensor’s repeatability for concentration measurements. The second study looks at how temperature and humidity affect microphone output voltage. For this study, R2 score of 0.948 is obtained. The studies underscore the necessity for further investigation of the sensor under diverse testing conditions. The findings demonstrate that the sensor exhibits consistent behavior and can be effectively modeled using Machine Learning techniques.