Intelligent Transmitter : Analysis of Effective parameters on Sensor Response of Gas Transmitter to Enhancement Measurment Accuracy by Intelligent Corrective Model Based on Artificial Neural Network

As we may know, H2S is a toxic and dangerous gas that mainly can be found in oil fields, drilling rigs, gas separators, petrochemicals etc. It is so dangerous that if it exceeds a specified amount, it will cause physical and respiratory complications or death in some cases. For the time being, a transmitter is used to detect and measure the concentration of H2S gas which the most significant part of it, is the gas sensor. We used an electrochemical sensor to construct the transmitter. Neural networks have been used to investigate the effect of environmental parameters such as temperature and humidity. The network consists of an input layer, a hidden layer and an output layer. The results show that the output of the neural network is well able to follow the actual output. Therefore, changes in temperature and humidity affect the response of the hydrogen sulfide gas transmitter, and this change reduces the accuracy measured by the device. Furthermore, high-precision hydrogen sulfide detection sensors are generally expensive. This finding has important implications for developing robust gas sensors. By using the achieved relationship and considering the effect of temperature and humidity changes, the accuracy of the low-cos sensors can be greatly increased and the cost of producing a hydrogen sulfide transmitter can be greatly reduced.