Aplikasi Metode Spektrofotometri pada Klasifikasi Gas Karbon Monoksida (CO) dan Uap Bahan Bakar Petrodiesel (C14H30)
Abstract
Gas classification techniques are often found in several applied fields such as, detection of leak gas in gas cylinders, monitoring the threshold of harmful pollutant gases in the air, health diagnostics, early detection of fire hazards, and others. This requires measurement techniques that are adaptive and robust that can dynamically capture information on changes in vapor or gas compounds contained in free air. This research has been conducted to analyze and identify the types of gas compounds, namely CO and petrodiesel fuel vapor (C14H30). The design of this tool uses the principle of spectrophotometry and the calculation of Backprogation Neural Networks. The working principle is that light radiation in the Light Emitting Diode (LED) series, which has a wavelength range of 385nm to 1720nm, is absorbed to penetrate CO gas or petrodiesel fuel vapor (C14H30) that you want to identify. Light radiation that has passed through the gas / vapor compound was captured by the photodiode sensor. The emission of LED series light radiation produces different wavelength absorption patterns that will be processed by the Backprogation Neural network as an input signal in the identification and learning process. The results of this experiment show the success rate of the Backpropagation neural network in identifying the type of CO gas and petrodiesel fuel vapor (C14H30) is 80%.
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References
[2] I. Hastuti, dan H, Sulistyarso, "Penyediaan Ruang Terbuka Hijau Berdasarkan Nilai Emisi CO2 di Kawasan Industri Surabaya", Jurnal Teknik POMITS, vol., 1, no. 1, hal. 1-5, 2012.
[3] D. Prabhandhari, "Analisis Kualitas Udara Lima Kota Metropolitan di Indonesia", Institut Pertanian Bogor, Bogor, Indonesia, Jun. 2014.
[4] M. Jamal, M.R. Khan, S.A. Imam, dan A. Jamal, "Artificial Neural Network Based E-Nose and Their Analytical Applications in Various Field", in Int. Conf. Control, Automation, Robotics and Vision ICARCV2010, 2010, hal. 691-698.
[5] M. Rivai, "Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance", Telkomnika, vol. 9, no. 2, hal. 319-326, 2011.
[6] M. Rivai, "Sistim Diagnosa Pernapasan Menggunakan Hidung Elektronik", in Conf.InSINas, Kementerian Riset dan Teknologi, 2012, hal. 205-210.
[7] C. Wang, L. Yin, L. Zhang, D. Xiang, dan R. Gao, "Metal Oxide Gas Sensors: Sensitivity and Influencing Factors", Sensors, vol. 10, hal. 2088-2106, Mar. 2010.
[8] K. Arshak, E. Moore, G.M. Lyons, J. Harris, dan S. Clifford, "A Review of Gas Sensors Employed in Electronic Nose Applications", Sensor Review, vol. 24, hal. 181-198, 2004.
[9] S. Li, "Overview of Odor Detection Instrumentation and the Potential for Human Odor Detection in Air Matrices", MITRE Nanosystems Group and U.S. Government None-enabled Technology Initiative. McLean, Virginia: MITRE.
[10] A. Wego, "Accuracy Simulation of an LED Based Spectrophotometer", Optik, vol. 124, issue 7, hal. 644-649, ¬¬¬¬¬¬¬¬2013.
[11] T. S. Yeh dan S. S. Tseng, "A Low Cost LED Based Spectrometer", Journal of the Chinese Chemical Society, vol. 53, hal. 1067-1072, 2006.
[12] A.D. Wilson, dan M. Baietto, "Applications and Advances in Electronic-Nose Technologies", sensor, vol. 9, hal. 5099-5148, Jun. 2009.
[13] A.G. Triantafyllou, S. Zoras, V. Evagelopoulos, S. Garas, dan C. Diamantopoulos, "DOAS Measurement Above an Urban Street Canyon in a Medium Sized City", Global NEST Journal, vol. 10, no. 2, hal. 151-168, 2008.
[14] I. Morsi, Electronic Nose System and Artificial Intelligent Techniques for Gases Identification, Data Storage, Florin Balasa, Ed. Shanghai, China: InTech, 2010.