Detection and Monitoring of Carbon Monoxide (Co) Toxic Gases in Old Vehicle Cabin (Odometer> 300k Km) and Its Relationship to Vehicle Density Using Fuzzy Method
- DOI
- 10.2991/aer.k.220131.023How to use a DOI?
- Keywords
- Toxic Gases; Fuzzy Method; Odometer; Carbon Monoxide
- Abstract
Carbon monoxide (CO) gas in the cabin of a vehicle affects the health of passengers and can even cause death. This is caused by poor ventilation so that the exhaust that leaks into the car and is slowly inhaled by the person in the car. Carbon monoxide is an odorless, colorless, tasteless and non-irritating gas. Carbon monoxide gas is a material commonly found in industry. This gas is the result of incomplete combustion from motorized vehicles, heating devices, equipment that uses carbon-based fire materials and flames (such as wood stoves), smoke from trains, gas combustion, and tobacco smoke. However, the most common source is engine combustion residue.Based on these problems, it is necessary to do research on the engineering of a control device for carbon monoxic gas levels in a car cabin based on fuzzy logic. In this research, a microcontroller will be used to carry out the process. With this tool, it is hoped that carbon monoxide levels will be obtained in the car so that it can notify drivers. In the end, cases of poisoning by passengers due to CO gas in vehicles can be minimized.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Suzuki Syofian AU - Aji Setiawan AU - Rolan Siregar AU - Muhamad Fathan PY - 2022 DA - 2022/02/01 TI - Detection and Monitoring of Carbon Monoxide (Co) Toxic Gases in Old Vehicle Cabin (Odometer> 300k Km) and Its Relationship to Vehicle Density Using Fuzzy Method BT - Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021) PB - Atlantis Press SP - 142 EP - 145 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.220131.023 DO - 10.2991/aer.k.220131.023 ID - Syofian2022 ER -