Conception and Realization of a Multiparametric Data Acquisition System Based on a Microcontroller for Gas Sensors

Real-time gas detection using a multiparameter data acquisition system has been widely adopted for many gas detection applications. It is attractive for its accurate, non-invasive, and fast determination of critical gas parameters such as concentration, temperature, and humidity. To perform real-time gas detection, data acquisition, and processing must be implemented. The aim is to simultaneously measure the responses of the gas sensor and those of humidity and temperature sensors integrated within the gas test cell to enable the evolution of relative humidity and ambient air temperature to be monitored in parallel with the gas detection process. In this work, a simple and cost-effective up to 16-channel real-time data acquisition, visualization, and storage system with a programmable voltage generator, calibrated sensors, and customized visualization software are designed and developed for gas detection applications. Experimental results show that this data acquisition system can accurately measure a wide range of sensor responses, and is suitable for measuring multiple sensors based on different materials or under different activation conditions. Dedicated software also enables users to easily capture, analyze, and save measurement data. In addition, the framework designed for a multi-parameter system is based on a real-time object-oriented methodology, enabling the implementation of hardware-software functions. The system is similar to the concept of the electronic nose, based on a network of sensors. This is one of the most viable approaches to solving the main problems encountered by most families of gas sensors, namely their lack of selectivity. By simultaneously measuring the response of sensors under different gases and environmental parameters, such as temperature, humidity, and pressure, it is possible to improve the accuracy and reliability of detection. This connection not only enables better analysis of sensor signals but also the identification of environmental influences on measurements, providing a better understanding of evaluation conditions.