A SOLUTION FOR REDUCING THE TEMPERATURE AND HUMIDITY EFFECTS ON THE ACCURACY OF TGS 2602 SENSOR IN MEASURING NH3 GAS CONCENTRATION

Abstract

This paper presents a solution using artificial neural networks to reduce the effects of the temperature and humidity of the environment on the results of the TGS2602 sensor in measuring NH3 gas concentrations. The TGS2602 in particular and the MOX (Metal Oxide based sensors in general have high sensitivity, fast response, longer service life, wider operating temperature range, low cost, low power consumption but their main disadvantages are the strong affection by humidity level and the environmental temperature. This makes the problem of eliminating (or reducing) the influence factors very important. In this paper, a system with gas sensor, temperature and humidity sensors to measure the environmental conditions and MLP (Multi Layer Perceptron) networks to calibrate the sensor reading will be presented. The simulation results will show the accuracy of the proposed solution.