Study on infrared carbon monoxide monitoring system used in mine

Abstract

Carbon monoxide does enormously harm to people and safety production in coal mine and other industries. But because the situation in coal mine is complicated and the interference factors are diversified, at present carbon monoxide detection system has the general problems of low detecting precision, easily poisoning and aging, short service life, narrow measurement range and bed anti-jamming ability. Carbon monoxide concentration is detected by using the infrared absorption principle, and this detection is applied in many fields. A new optics structure was developed with a reference gas cell, dual light sources and dual detectors in this paper, it could compensate to power source anti-jamming, mismatch of the detectors, gas cell material's absorption, and dust's influence. In addition, an infrared carbon monoxide sensor's mathematical model was built by adopting radial basic function's (RBF) neural network model, so as to dispel the influence of temperature, pressure and humidity. A momentum factor's gradient descending method could be applied to adjust the parameters of RBF neural network. The experimental results show that whole system runs very well with a high precision, a strong capacity of anti-jamming, a wide measurement range, a good selectivity, and an online detecting ability.