Use of data mining in the corrosion classification of pipelines in Naphtha Hydro-Threating Unit (NHT)



Published Aug 31, 2019
Amir Samimi Pathmanathan Rajeev Ali Bagheri Ali Nazari Jay Sanjayan Ahmadreza Amosoltani Mohammad Sadegh Tarkesh Esfahani Soroush Zarinabadi


Nowadays, computational tools for analyzing and collecting data in the operation of petroleum units are essential. One of the methods is the classification or regression to step in the overall process of knowledge extraction. In this paper, a specific type of decision tree algorithm, called the conditional contract arrangement, is Naphtha hydro-threating (NHT) units for 4 factors: Density, pH, total iron ions in vessels (S.FE) and H2S. All of these factors are related to corrosion in NHT units and this paper aims to optimize some conditions to eliminate it. In this regard, using ammonium water with a specific range and pH can be helpful. According to the obtained results the best range of density (in Feed) is less than 0.712 kg/m3, pH (water in vessels) is more than 6.5, S.FE is less than 1.4 ppm and H2S (in recycle gas) is less than 581 ppm. The outcomes also show how this approach can be used to gain insight into some refineries and how to deliver results in a comprehensible and user-friendly way.

How to Cite

Samimi A, Rajeev P, Bagheri A, Nazari A, Sanjayan J, Amosoltani A, Tarkesh Esfahani MS, Zarinabadi S. Use of data mining in the corrosion classification of pipelines in Naphtha Hydro-Threating Unit (NHT). PST [Internet]. 2019Aug.31 [cited 2020May28];3(1):14-1. Available from:


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Ammonium Water, Corrosion, Tree Decision Algorithm, Naphtha Hydro-Treating Unit, Computational Tools

[1] Arlot, S., Celisse, A., et al., 2010. “A survey of crossvalidation procedures for model Selection”. Stat. Surv. 4, 40–79.
[2] Ting L., Yuannong Z., Chunhua J., Guobin Y., Zhengyu Z, 2018. “Automatic identification of Spread F using decision trees”, Journal of Atmospheric and Solar-Terrestrial Physics, Volume 179, November 2018, Pages 389-395.
[3] Nahla Ben Am., Zeineb El Kh., Hélène Fa., Régis Sabbadin, 2018, “Lexicographic refinements in possibilistic decision trees and finite-horizon Markov decision processes”, Fuzzy Sets and Systems, In press, corrected proof, Available online 21 February 2018.
[4] Kaveh Khalili-Da., Farshid Abdi, Shaghayegh Ab., 2018, “Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries”, Applied Soft Computing, Volume 73, December 2018, Pages 816-828.
[5] Gerdes, M., 2014. “Predictive Health Monitoring for Aircraft Systems using Decision Trees.” Master’s thesis. Linköping University, Fluid and Mechatronic Systems, the Institute of Technology.
[6] Hastie, T., Tibshirani, R., Friedman, J., 2009. “The Elements of Statistical Learning: data Mining, Inference, and Prediction.” Springer, New York, New York, NY, 587–604, (chapter 15.).
[7] Lihong W., Qiang L., Yanwei Y., Jinglei Liu., 2018. “Region compatibility based stability assessment for decision trees.”, Expert Systems with Applications, Volume 105, September 2018, Pages 112-128.
[8] Janitza, S., Tutz, G., Boulesteix, A., 2016. “Random forest for ordinal responses: prediction and variable selection.” Comput. Stat. Data Anal. 96, 57–73.
[9] Shankru Gu., Vijayakumar Ka., V. Umadevi. 2018,“Non-sequential partitioning approaches to decision tree classifier“, Future Computing and Informatics Journal, In press, corrected proof, Available online 18 September 2018.
[10] Xiaoyong G., Dexian H., Yongheng Ji., Tao Chen., 2018. “A decision tree based decomposition method for oil refinery scheduling.”, Chinese Journal of Chemical Engineering, Volume 26, Issue 8, August 2018, Pages 1605-1612.
[11] Marton, I., Sánchezb, A.I., Carlosa, S., Martorella, S., 2013. “Application of data driven methods for condition monitoring maintenance.” Chem. Eng. Trans. 33, 301–306.
[12] Mazloumi, E., Rose, G., Currie, G., Moridpour, S., 2011. “Prediction intervals to account for uncertainties in neural network predictions: methodology and application in bus travel time prediction.” Eng. Appl. Artif. Intell. 24, 534–542.
[13] Aline Sa., Eduardo La., Felipe de A. Mello Pereira. 2017 “Decision tree classification with bounded number of errors”, Information Processing Letters, Volume 127, November 2017, Pages 27-31.
[14] Varga, T., Szeifert, F., Abonyi, J., 2009. “Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting.” Eng. Appl. Artif. Intell. 22, 569–578.
[15] Madhar Ta., 2018, “Investigating the role of socioeconomic factors in comprehension of traffic signs using decision tree algorithm“, Journal of Safety Research, Volume 66, September 2018, Pages 121-129
[16] H.S. OH and W.S. SEO, 2012, “Development of a Decision Tree Analysis model that predicts recovery from acute brain injury“, Japan Journal of Nursing Science. doi: 10.1111/j.1742-7924.2012.00215.x
[17] G. Zhou and L. Wang, 2012, “Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation“, Transportation Research: Part C, 21(1), 287-305.
[18] S. Sohn and J. Kim, 2012. “Decision tree-based technology credit scoring for start-up firms: Korean case “, Expert Systems with Applications, vol. 39(4), 4007-4012.
[19] J. Choand P.U. Kurup, 2011, “Decision tree approach for classification and dimensionality reduction of electronic nose data“, Sensors & Actuators B: Chemical, vol. 160(1), 542-548.
[20] Gembiki Stanley , 2006, “A Biographical Memoir of Veladimir Haensel, 3rd ed.,” The National Academy of Sciences, Washington DC, Vol. 88.
[21] Majid Saidi, Navid Mostoufi, Rahmat Sotudeh, 2011, “Modeling and simulation of continuous catalytic regeneration (CCR) process,” International Journal of Applied Engineering Research, Dindigul, Vol. 2, No. 1.
[22] M. Gyngazova, A.V. Kravtsor, E.D. Ivanchina, M.R. Korolenko, D.D. Uvarkina, 2010, “Kinetic model of the catalytic reforming of gasoline in moving – bed reactors” , Catalysis in Industry , Vol. 2 , No. 4.
[23] HOU Weifeng, SU Hongye, MU Shengjing, CHU Jian, 2007, “Multi objective optimization of the industrial naphtha catalytic reforming process, Chinese Journal of Chemical Engineering ,” Vol. 15 , Issue 1 , p. 75 – 80.
[24] S. Raseev, 2003, “Catalytic Reforming in Thermal and Catalytic Process in Petroleum Reforming,” Science and Technology, Marcel Dekker Inc., New York, U.S.A
[25] Taoufiq Gueddar, Dua Vivek, 2011, “Disaggregation - Aggregation based model reduction for refinery – wide optimization,” Computer & Chemical Engineering , Vol. 35 , Issue 9 , p. 1838-1856.
[26] M.S. Gyngazova, N.V. Chekantsev, M.V. Korolenko, E.D. Ivanchina, A.V. Kravtsov, 2012, “Optimizing the catalyst circulation ratio in a reformer with a moving bed via a combination of real and computational experiments,” Catalysis in Industry, Vol. 4 , p. 284-291
[27] Davood Iranshah, Mohsen Karimi, Shahram Amiri, Mitra Jafari, Razieh Rafiei, Mohamad Reza Rahimpour, 2014, “Modeling of naphtha reforming unit applying detailed description of kinetic in continuous catalytic regeneration process,” Chemical Engineering Research and Design, Vol. 92, Issue 9, p. 1704-1727.
[28] M.Z. Stijepovic, A.V. Ostojic, I. Milenkovic, P. Linke, 2009, “Development of a kinetic model for catalytic reforming of naphtha and parameter estimation using industrial plant data ,” Energy & Fuel Journal, Vol. 23, No. 6, p. 979-983.
[29] H.M. Arani, M. Shirvani, K. Safdarian, E. Dorostkar, 2009, “Lumping procedure for kinetic model of catalytic naphtha reforming , Braz. “, Journal Chemical Engineering, Vol. 26 , No. 4, p. 723-732.
[30] R.E. Palmer, S.H. Kao, C. Tong, D.R. Shipman, 2008, “Consider options to lower benzene levels in gasoline,” Hydrocarbon processing, Houston, Texas, p. 55-66.
Original Work