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Artificial Neural Network (ANN) is adopted from the human neural network systems and is among many tools to understand complex model including corrosion. Such predictive model can be developed through corrosion rate. By ANN application, one may update its model iteratively, hence it will be adaptabl...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/17327 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Artificial Neural Network (ANN) is adopted from the human neural network systems and is among many tools to understand complex model including corrosion. Such predictive model can be developed through corrosion rate. By ANN application, one may update its model iteratively, hence it will be adaptable for all decisions. It can be learned from the previous data and recognize patterns of data that always changing and dynamic, like the ones in oil and gas fields. Based on the characteristics of ANN methods, corrosion rate model will update iteratively during the training phase leading to the best (more accurate) model target. Using a number of data input and output characteristics of the fluid, the result of corrosion rate model will be able to predict the corrosion rate in the range of the entered input data. With models that are adaptive and can adjust to changes in the dynamic data, one will be able to obtain predictions with a great degree of accuracy. Research methodology on the subject using crude oil was developed by Hernandez and Nesic(2005). Some crude oil characteristic associated with the corrosion rate are, among others, sulfur, vanadium, nickel contents, total acid number, and API. A correlation between the crude oil characteristics and corrosion rate has been analized with linear regression. Percentage of inhibition in this regard is the number of ratio between the corrosion rate of the fluid-carrying pipe and corrosion rate in the blank pipe. This thesis will describe ANN that creates a program to determine the corrosion rates of the carbon-steel materials containing flowing fluid (gas). <br />
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The characteristics evaluated are gas composition, temperature, pressure, and corrosion inhibitor injection. Only data having a high level of significance will be used.The next step is building program with ANN by using significant input data for corrosion rate prediction in gas pipelines. |
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