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Prototype of Fire, Gas, and Steam Pressure Detection Based on Internet of Things (Case Study: PT Agro Sinergi Nusantara) Inzar Salfikar; Faulianur, Rizki; Yhona Syela Inri; Dodi Syahputra
Jurnal Inotera Vol. 10 No. 1 (2025): January-June 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss1.2025.ID429

Abstract

This study is based on a conversation that took place during an industrial vist by Aceh Polytechnic D3 Mechatronics students at PT Agro Sinergi Nusantara (ASN), where the company does not yet have a system in place to monitor gas, steam, and fire pressure in order to reduce hazardous circumstances in the factory. A fire detection system is needed to prevent fires because palm oil factories often have processes that involve flammable materials and have the potential to cause fires. The steam pressure in the boiler of the Steam Power Plant (PLTU) system at PT ASN needs to be monitored to prevent explosions. The PLTU combustion system that utilizes palm oil pulp and empty bunches produces carbon emissions that are harmful to health. Information on carbon gas levels also needs to be known to ensure whether the surrounding conditions are dangerous. The presence of fire, gas and steam pressure needs to be monitored via a smartphone connected to the Internet network. Thus, workers do not have to be close to the machine area because monitoring is sufficient on a smartphone. The research method is a case study at PT ASN, surveys and interviews, literature studies, experiments and simulations on prototypes and data collection in the laboratory. The data used for measurement are fire and gas from matches and air pressure from the compressor. The results obtained, this prototype is able to detect fire, gas and vapor pressure. When fire is detected, pressure and gas that exceed the setting limit will trigger the alarm to activate. The presence of fire and gas and vapor pressure data can be monitored on a smartphone with a delay of 17 seconds. The average accuracy of pressure detection with arduino is 96.20% and smartphone 97.07%.