Researcher:
Serfidan, Ahmet Can

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Master Student

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Ahmet Can

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Serfidan

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Serfidan, Ahmet Can

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2020 - 20214

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Researcher Of Publications: search.filters.isAuthorOfPublication.841456fb-9a19-4f22-913e-1d616c49b299

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    Automated Box-Jenkins methodology to forecast the prices of crude oil and its derivatives
    (Elsevier B.V., 2021) Ozkan, Gurkan; Department of Industrial Engineering; N/A; Türkay, Metin; Serfidan, Ahmet Can; Faculty Member; Master Student; Department of Industrial Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A
    Developing forecasting models that incorporate that external parameters in addition to past data for crude oil and derivatives are a challenging task since it is highly dependent on economic, geographical, and political issues. However, forecasting the prices is very important for strategic planning and oil refineries’ operational decisions. This paper presents an automated tool to predict crude oil prices and their main products by applying Box-Jenkins methodology for the next two months at the beginning of each month in a rolling horizon manner. The resulting forecast is shared with related departments to develop their production plans accordingly. We show that improved accuracy with this forecasting approach is beneficial in any planning and decision-making process and increases profit.
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    Explanatory and predictive analysis of naphtha splitter products
    (Elsevier B.V., 2021) Department of Industrial Engineering; N/A; Türkay, Metin; Serfidan, Ahmet Can; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A
    Refinery operations are always prone to optimization, and due to the increasingly adverse effects of COVID-19 on energy sectors, its importance increased significantly. This work aims to predict the naphtha column KPI parameters with high accuracy so that operators make corrective actions efficiently. Although linear regression provides acceptable results for prediction, this is not the case for top and bottom product C7 and C6 prediction in the central Naphtha Splitter column. First, we did gather all the available data to overcome this problem, which can affect the top and bottom products. Including upstream units that feed the column. Instead of one common technique (linear regression), we used five additional machine learning methods: Adaboost, support vectors, kNN, random forest, XGboosting. Since there are many measurements, however, very few samples need to reduce dimensions before modeling. We used BorutaSharp to select the essential features. We also use classification machine learning methods to categorize bottom products since there is no need to predict the value instead of whether the value is higher or lower than a constant. Overall, we achieved 30% higher accuracy than the traditional ways for the top product, and we reached to predict C6 content in the bottom with higher accuracy than 80%. Xgboost provides the best regression model, and stochastic gradient boosting yields the best classification model. After our implementation, the energy consumption is decreased significantly, and 100k$/month is saved since we can monitor top and bottom products simultaneously.
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    Octane optimization with a combined machine learning and optimization approach
    (Elsevier B.V., 2021) Department of Industrial Engineering; N/A; Türkay, Metin; Serfidan, Ahmet Can; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A
    Refinery operations are always sensitive to optimization, and due to the increasingly adverse effects of COVID-19 on energy sectors, its importance had increased significantly. This thesis aims to analyze the reactor temperature that yields a higher RON (octane measurement) value in isomerate product using all available information in the isomerate production network. The main explanatory variables that can affect the RON value can be divided into three categories: feed impurities, isomerization reactor operations, deizohexanizer column operations. Isomerate feed network is quite complex and fed by different crude distillation units and cracker units. Various reactions occur in the isomerization reactors, and depending on the feed content, the reaction mechanism changes. This thesis applies machine learning algorithms to build a model that can capture the relationship between RON and reactor temperature with the other explanatory variables. We implemented a number of machine learning algorithms to assess their performance on the problem, specifically Linear Regression, Decision Tree, Random Forest, XGBoost, Support Vector Regression, and KNN. Comparing with the linear regression, we achieved 0.82 decreases in the mean absolute error. The mean absolute error of the XGBoost model is 0.08 RON. We find a temperature value with the selected model that yields a higher RON number by trying different temperature values while keeping the same values for the other variables. If we used the suggested temperature by our model, we predict that we could obtain a 0.2 RON increase in the validation zone resulting in an annual profit increase of 528 000 USD Dollar.
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    Optimal estimation of physical properties of the products of an atmospheric distillation column using support vector regression
    (Pergamon-Elsevier Science Ltd, 2020) N/A; N/A; Department of Industrial Engineering; Serfidan, Ahmet Can; Uzman, Fırat; Türkay, Metin; Master Student; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 24956
    Atmospheric distillation column is one of the most important units in an oil refinery where crude oil is fractioned into its more valuable constituents. Almost all of the state-of-the art online equipment has a time lag to complete the physical property analysis in real time due to complexity of the analyses. Therefore, estimation of the physical properties from online plant data with a soft sensor has significant benefits. In this paper, we estimate the physical properties of the hydrocarbon products of an atmospheric distillation column by support vector regression using Linear, Polynomial and Gaussian Radial Basis Function kernels and SVR parameters are optimized by using a variety of algorithms including genetic algorithm, grid search and non-linear programming. The optimization-based data analytics approach is shown to produce superior results compared to linear regression, the mean testing error of estimation is improved by 5% with SVR 4.01 degrees C to 3.8 degrees C.