Amirhosein Asilian - ‎۱- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran ‎۲- Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran ‎
S. Mohammadali zanjani - Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

The unique properties of carbon monoxide and its high combustibility have led to the creation of various ‎sensors, such as electrochemical sensors and different circuits, to read its output. In this article, a deflection-type ‎Wheatstone bridge is used to measure changes in the sensor resistance, and the output voltage is connected to a ۱۲-‎bit analog-to-digital converter through an adjustable precision amplifier. Next, a new method is proposed for self-calibrating the CO sensor. The Levenberg-Marquardt backpropagation algorithm (LMBP) is utilized in the Artificial ‎Neural Network model to minimize the Mean Squared Error (MSE) and identify the most suitable parameters in the ‎proposed method.‎ The model under consideration has been developed and trained using real-time data.‎ Based on ‎the experimental and evaluation outcomes, it can be concluded that the suggested model has an MSE value of ‎‎۰.۲۸۲۴۹ and an R۲ coefficient of determination of ۰.۹۹۹۹۲, indicating high accuracy and precision. The proposed ‎sensor and calibration method have potential applications in various applications, including industrial and domestic ‎environments where CO monitoring is necessary.‎

Electrochemical sensor, CO monitoring, Levenberg-Marquardt backpropagation algorithm, Mean squared error, ‎Training-Validation and Testing (TVT), coefficient of determination