Document Type : Reseach Article

Authors

1 ‎1- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran ‎2- Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran ‎

2 Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Abstract

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 12-‎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 ‎‎0.28249 and an R2 coefficient of determination of 0.99992, 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.‎

Keywords

  • Rivera, G. Herrera, M. Chacón, P. Acosta, and M. Carrillo, “Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors, ” Sensors, Vol. 8, No. 11, pp. 7410-7427, 2008.
  • Rivera, M. Carrillo, M. Chacón, G. Herrera, and G. Bojorquez, “Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks, ” Sensors, Vol. 7, No. 8, pp. 1509-1529, 2007.
  • MA.Zanjani, M. Aalipour, and M. Parvizi. “Design of a Low Power Temperature Sensor Based on Sub-Threshold Performance of Carbon Nanotube Transistors with an Inaccuracy of 1.5 ºC for the range of-30 to 125ºC. ” Journal of Intelligent Procedures in Electrical Technology, Vol. 13, No. 50 pp. 115-127 , 2022.
  • Farias et al., “A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data, Sensors, Vol. 18, No. 3, p. 683, 2018.
  • R. Rivas, F. Lou, H. Harrison, and N. Key, “Measurement and calibration of centrifugal compressor pressure scanning instrumentation, 2015.
  • Sharafat, et al. “Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network”Sensors, Vol. 23, No. 2, p854, 2023.
  • Baranwal, et al. “Electrochemical sensors and their applications: A review, ” Chemosensors, Vol. 10, No. 9, p. 363, 2022.
  • Tangirala, et al. “A study of the CO sensing responses of Cu-, Pt-and Pd-activated SnO2 sensors: effect of precipitation agents, dopants and doping methods, ” Sensors, Vol. 17, No. 5, p. 1717, 2011.
  • Mallampati, S. B., & Seetha, H. “A Review on Recent Approaches of Machine Learning, Deep Learning, and Explainable Artificial Intelligence in Intrusion Detection Systems, ”Majlesi Journal of Electrical Engineering, V 17, No. 1, pp. 29-54, 2023.
  • F. Lyahou, G. van der Horn, and J. H. Huijsing, “A noniterative polynomial 2-D calibration method implemented in a microcontroller, ” IEEE Transactions on Instrumentation and Measurement, Vol. 46, No. 4, pp. 752-757, 1997.
  • D. Pereira, O. Postolache, and P. S. Girao, “Adaptive self-calibration algorithm for smart sensors linearization,” IEEE Instrumentation and Measurement Technology Conference Proceedings, Vol. 1, pp. 648-652, 2005.
  • Abu-Khalaf and J. J. L. Iversen, “Calibration of a sensor array (an electronic tongue) for identification and quantification of odorants from livestock buildings, ” Sensors, Vol. 7, No. 1, pp. 103-128, 2007.
  • Adeli, “Neural networks in civil engineering: 1989–2000,” Computer‐Aided Civil and Infrastructure Engineering, Vol. 16, No. 2, pp. 126-142, 2001.
  • Ghadiri, “Real-time Stability Assessment of Power System using ANN without Requiring Expert Experience, ” Majlesi Journal of Electrical Engineering, Vol. 14, No. 2, pp.  43-49, 2020.
  • B. Mallampati,and H. Seetha, “A Review on Recent Approaches of Machine Learning, Deep Learning, and Explainable Artificial Intelligence in Intrusion Detection Systems,” Majlesi Journal of Electrical Engineering, Vol. 17, No. 1, pp. 29-54, 2023.
  • MA. Zanjani, H. Shahinzadeh, J. Moradi, M. Fayaz-dastgerdi, W. Yaïci, and M. Benbouzid. “Short-term Load Forecasting using the Combined Method of Wavelet Transform and Neural Networks Tuned by the Gray Wolf Optimization Algorithm, Global Energy Conference (GEC), IEEE, pp. 294-299, 2022.
  • Al-Salaymeh, “Optimization of hot-wire thermal flow sensor based on a neural net model, Applied thermal engineering, Vol. 26, No. 8-9, pp. 948-955, 2006.
  • S. Ciminski, “Neural network based adaptable control method for linearization of high power amplifiers,” AEU-International Journal of Electronics and Communications, Vol. 59, No. 4, pp. 239-243, 2005.
  • Ramadan Suleiman and M. L. Nehdi, “Modeling self-healing of concrete using hybrid genetic algorithm–artificial neural network,” Materials, Vol. 10, No. 2, p. 135, 2017.
  • H. Hu and J.-N. Hwang, “Introduction to neural networks for signal processing,” in Handbook of neural network signal processing: CRC press, p. 1, 2018.
  • Erhan, C. Oral, and E. Ufuk Ergül. “Classification of arithmetic mental task performances using EEG and ECG signals,The Journal of Supercomputing, pp. 1-13, 2023.
  • Depari, A. Flammini, D. Marioli, and A. Taroni, “Application of an ANFIS algorithm to sensor data processing,” IEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 1, pp. 75-79, 2007.
  • Depari et al., “Digital signal processing for biaxial position measurement with a pyroelectric sensor array,” IEEE transactions on instrumentation and measurement, Vol. 55, No. 2, pp. 501-506, 2006.
  • M. Almassri, W. Z. Wan Hasan, S. A. Ahmad, S. Shafie, C. Wada, and K. Horio, “Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network,” Sensors, Vol. 18, No. 8, p. 2561, 2018.
  • Asilian, S. MA .Zanjani. “Design and fabrication of an amperometric CO gas sensor and a readout circuit using a low-noise transimpedance amplifier to achieve standard analog outputs,”AEU-International Journal of Electronics and Communications, Vol. 171, p. 154864, 2023.
  • Wozniak, L., Kalinowski, P., Jasinski, G., & Jasinski, P. (2018). “FFT analysis of temperature modulated semiconductor gas sensor response for the prediction of ammonia concentration under humidity interference,”Microelectronics Reliability, V 84, pp. 163-169, 2018.
  • Burgués, J., Esclapez, M. D., Doñate, S., & Marco, S. “ RHINOS: A lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants, ”. IScience, V 24, No. 12, 2021.
  • Wang, J., Lian, S., Lei, B., Li, B., & Lei, S. “Co-training neural network-based infrared sensor array for natural gas monitoring,” Sensors and Actuators A: Physical, V 335, pp. 113392, 2022.
  • Chen, K., Liu, L., Nie, B., Lu, B., Fu, L., He, Z., . & Liu, H. “Recognizing lung cancer and stages using a self-developed electronic nose system,”Computers in Biology and Medicine, V 131, p. 104294, 2021.
  • Srinivasan, P., Robinson, J., Geevaretnam, J., & Rayappan, J. B. B. (2020). “Development of electronic nose (Shrimp-Nose) for the determination of perishable quality and shelf-life of cultured Pacific white shrimp (Litopenaeus Vannamei),” Sensors and Actuators B: Chemical, V 317, pp. 128192, 2020.
  • Bax, C., Prudenza, S., Gutierrez-Galvez, A., & Capelli, L. “Drift compensation on electronic nose data relevant to the monitoring of odorous emissions from a landfill by opls,” Chemical Engineering Transactions, V 85, pp. 13-18, 2021.
  • Valcárcel, M., Ibáñez, G., Martí, R., Beltrán, J., Cebolla-Cornejo, J., & Roselló, S. “Optimization of electronic nose drift correction applied to tomato volatile profiling, ”Analytical and Bioanalytical Chemistry, V 413, No. 15, pp. 3893-3907 , 2021.
  • Liang, Z., Xue, Q., Tian, F., Xu, C., Wang, C., Yang, L., & Guo, T. “A sparse reconstruction domain transfer method for interference suppression in artificial olfactory system,”IEEE Sensors Journal, V 22, No. 7, pp. 6717-6730 , 2022.
  • Zhu, X., Liu, T., Chen, J., Cao, J., & Wang, H. “One-Class Drift Compensation for an Electronic Nose,”Chemosensors, V 9, No. 8, p. 208, 2021.
  • Yi, Z., Shang, W., Xu, T., & Wu, X. “Neighborhood preserving and weighted subspace learning method for drift compensation in gas sensor,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, V 52, No. 6, pp. 3530-3541, 2021.