<![CDATA[The operation of a fuel (BBM) terminal cannot be separated from the existence of facilities for loading and unloading activities. This loading and unloading activity have the potential risk of oil spills in the waters caused by overϐlow leaks in ships, hoses and fuel transfer pipes. Large oil spills must be dealt with immediately so that they do not result in environmental pollution or ϐire hazards. Digital image processing and radar are methods that are often used to detect oil spills in waters, but these methods have disadvantages if environmental visibility is poor and cannot differentiate the type of oil that is spilled, where some types of oil must be handled using different procedures. This study proposes a low-cost, in-situ vapor-based discriminative detection approach using an MQ-135 resistive gas sensor array and an ESP32 data-acquisition node to distinguish gasoline vs diesel vapors in a submerged-fuel mixing testbed. Compared to conventional methods such as radar imaging [1], [2] and fluorometry, this technique provides localized, near-source detection that is inexpensive and suitable for buoy deployment. The proposed method demonstrates reliable classification using calibrated Rs features. The measurement experiments carried out on diesel fuel and gasoline, data obtained on changes in the internal resistance of the MQ-135 sensor was 45.26kΩ to 72.39kΩ with an average of 55.17kΩ for diesel oil type fuel, and 4.86kΩ to 20.31kΩ with an average -an average of 11.27kΩ for gasoline type fuel.]]>
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