caa a22 4500 606159738 CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1766-y doi (NATIONALLICENCE)springer-10.1007/s00521-014-1766-y Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search [Elektronische Daten] [Hadi Fattahi, Amin Gholami, Mohammad Amiribakhtiar, Siyamak Moradi] Precipitation and deposition of asphaltene cause number of severe problems in downstream and upstream of oil industry. Hence, it is essential to present a dependable model for quantitative estimation of asphaltene precipitation. This paper aims to introduce a hybrid support vector regression (SVR) with harmony search (HS) as an intelligence approach to create quantitative formulation between amount of asphaltene precipitation and titration data. Harmony search is combined with SVR for determining the optimal value of its user-defined parameters. The optimization implementation by HS significantly improves the generalization capability of SVR. A dataset that includes 176 data points was employed in the current study, while 141 data points were utilized for constructing the model and the remainder data points (35 data points) were used for assessment of degree of accuracy and robustness. Evaluating the performance of constructed model based on statistical criteria indicates that the hybrid model has acceptable accuracy to estimate the amount of asphaltene precipitation from titration data. This study concludes that optimization of SVR with HS produces a smart hybrid model, which is an appropriate solution for modeling of the asphaltene precipitation. The Natural Computing Applications Forum, 2014 Asphaltene precipitation nationallicence Support vector regression (SVR) nationallicence Harmony search (HS) nationallicence Titration data nationallicence Fattahi Hadi Department of Mining Engineering, Arak University of Technology, Arāk, Iran aut Gholami Amin Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran aut Amiribakhtiar Mohammad Department of Mining Engineering, Arak University of Technology, Arāk, Iran aut Moradi Siyamak Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran aut Neural Computing and Applications Springer London 26/4(2015-05-01), 789-798 0941-0643 26:4<789 2015 26 521 https://doi.org/10.1007/s00521-014-1766-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1766-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fattahi Hadi Department of Mining Engineering, Arak University of Technology, Arāk, Iran aut NATIONALLICENCE 700 1- Gholami Amin Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran aut NATIONALLICENCE 700 1- Amiribakhtiar Mohammad Department of Mining Engineering, Arak University of Technology, Arāk, Iran aut NATIONALLICENCE 700 1- Moradi Siyamak Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 789-798 0941-0643 26:4<789 2015 26 521