Experimental Validation of Mathematical Modeling of MoX Sensors

Controlling air pollution requires a systematic approach, including the establishment of a reliable air quality monitoring system. This work suggests experimenting with the mathematical modeling of metal oxide sensors and its validation to enhance the accuracy of air pollutant measurements, thereby supporting more effective strategies for pollution control and safeguarding human health and the environment. The use of MQ series gas sensors for individual pollutant measurement poses a risk of deviation from actual values due to cross-sensitivity and humidity factors. The mathematical model is derived from fundamental equations, including the Freundlich isotherm, with the aim of predicting the sensor’s characteristics. By utilizing this model, it is expected that the sensor’s performance and its ability to detect changes in pollutant concentrations will be improved. A mathematical model has been created to forecast the reaction of the MQ series sensor. To confirm the effectiveness of the approach, the modeled and original data curves are extensively compared with experimental data from the different experimental setups. Through the experimental setups and the comparison of data with original curves, the model’s ability to predict dynamic changes in gas concentrations based on sensor readings is validated. The versatility of the model was evident in its consistent application across different sensors. Thus, the model proves to be suitable for predicting the response of the MQ series sensor.