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On the prediction of CO2 corrosion in petroleum industry

Hatami, S ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1016/j.supflu.2016.05.047
  3. Publisher: Elsevier B.V , 2016
  4. Abstract:
  5. In this communication, a hybrid model based on Least Square Support Vector Machine (LSSVM) was constructed to predict CO2 corrosion rate. The input parameters of the model are temperature, CO2 partial pressure, flow velocity and pH. The data used for training and testing of the developed model are 612 and 109 data, respectively. In order to benefit LSSVM from Kernel learning, we compared three kernel functions to select the most efficient one. Furthermore, Coupled Simulated Annealing (CSA) optimization technique was adapted to choose the best optimal values of the model parameters. The results elucidate that Gaussian Kernel functions is the desired function which can afford high accuracy for predicting CO2 corrosion in oil and gas industries
  6. Keywords:
  7. CO2 corrosion ; Coupled simulated annealing ; Optimal value ; Support vector machine ; Corrosion ; Corrosion rate ; Flow velocity ; Forecasting ; Optimal systems ; Petroleum industry ; Simulated annealing ; Support vector machines ; Gaussian kernel functions ; Kernel function ; Least square support vector machines ; Model parameters ; Oil and gas industry ; Optimal values ; Optimization techniques ; Training and testing ; Carbon dioxide
  8. Source: Journal of Supercritical Fluids ; Volume 117 , 2016 , Pages 108-112 ; 08968446 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0896844616301474