• Login
    View Item 
    •   NM-AIST Home
    • Computational and Communication Science Engineering
    • Research Articles [CoCSE]
    • View Item
    •   NM-AIST Home
    • Computational and Communication Science Engineering
    • Research Articles [CoCSE]
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    IoT-based on boiler fuel monitoring system: A case of Raha Beverages company limited, Arusha-Tanzania

    Thumbnail
    View/Open
    Full text (1.289Mb)
    Date
    2023-07-24
    Author
    Ntambara, Boniface
    Nyambo, Devotha
    Solsbach, Andreas
    Metadata
    Show full item record
    Abstract
    RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilize fuel in steam production in Arusha-Tanzania where fuel data conditions like temperature, pressure, discharge, fuel level, and gas leakage with humidity were a challenge to monitor them which provoked boiler malfunction and plant breakdown. Today, RABEC manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time consuming and gives inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure sensors were used to gather fuel information where GSM module was employed to send fuel data messages to the operator’s phone. An AT mega 328 microcontroller was used to process and analyze the fuel data and send them to the Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the threshold value, an operator was alerted by a refilling message via GSM technology. At 0.1Psi pressure, fuel temperature of 120℃, and 80% humidity, the system notifies the operator by an alert message to check injector pressure and if the fuel-air mixture was perfect. In addition, these data were observed on LCD and ThingSpeak webpage. To conclude, the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and efficiency rate compared to the current monitoring system
    URI
    doi.org/10.21203/rs.3.rs-3167311/v1
    https://dspace.nm-aist.ac.tz/handle/20.500.12479/2412
    Collections
    • Research Articles [CoCSE]

    Nelson Mandela-AIST copyright © 2021  DuraSpace
    Theme by 
    Atmire NV
     

     

    Browse

    All PublicationsCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Nelson Mandela-AIST copyright © 2021  DuraSpace
    Theme by 
    Atmire NV