Sulphate-reducing bacteria (SRB) is an anaerobic microorganism that has long been identified as one of the main contributor to the pipeline corrosion problem experienced in gas, petroleum, and water industry. The corrosion issue causes billions of dollars worth of damage each year and may lead to the deterioration of the quality of oil and water under the corroded pipeline. Currently, there are few kits and techniques available in the market targeted for early detection of SRB. Nevertheless, those detection methods have some crucial drawbacks, such as long detection period or have difficulty to conduct field test. Thus, this article proposes a rapid, accurate, and portable embedded-based electronic system to detect the presence of SRB. Preliminary experiment was conducted in lab to evaluate the function and the capability of the system. Based on the findings, the proposed technique was proven to be able to identify whether SRB presence in a presented sample within 1 hour. In addition, the system also includes data logging functionality to help users monitor the growth of SRB from time to time to reduce the damage caused by the corrosion.
Sulphate-Reducing Bacteria, Corrosion, Embedded System, Artificial Neural Network
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