Optimization of naphtha purchase price using a price prediction model

Hweeung Kwon, Byeonggil Lyu, Kyungjae Tak, Jinsuk Lee, Jae Hyun Cho, il Moon

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In order to meet company needs, various models of naphtha price forecasting and optimization models of average naphtha purchase price have been developed. However, these general models are limited in their ability to predict future trends as they only include quantitative data. Furthermore, naphtha price predictions based on fluctuation trends have not been published in the literature. Thus, we developed a system dynamics (SD) model considering time-series data, mathematical formulations, and qualitative factors. The results obtained from our model were compared with the published literature. The best result of the SD is the European naphtha forecasting price model, and the forecasting accuracy percentage shows 92.82%. Furthermore, a nonlinear programming (NLP) model was developed to optimize the purchase price by considering the naphtha price of the forecasting models. In addition, the average optimization value was approximately 45.07. USD/ton cheaper than that of the heuristic approach.

Original languageEnglish
Pages (from-to)226-236
Number of pages11
JournalComputers and Chemical Engineering
Volume84
DOIs
Publication statusPublished - 2016 Jan 4

Fingerprint

Naphthas
Nonlinear programming
naphtha
Time series
Dynamic models
Dynamical systems
Industry

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Kwon, Hweeung ; Lyu, Byeonggil ; Tak, Kyungjae ; Lee, Jinsuk ; Cho, Jae Hyun ; Moon, il. / Optimization of naphtha purchase price using a price prediction model. In: Computers and Chemical Engineering. 2016 ; Vol. 84. pp. 226-236.
@article{0574b3ef0a1a4b2fb161353810d46fa2,
title = "Optimization of naphtha purchase price using a price prediction model",
abstract = "In order to meet company needs, various models of naphtha price forecasting and optimization models of average naphtha purchase price have been developed. However, these general models are limited in their ability to predict future trends as they only include quantitative data. Furthermore, naphtha price predictions based on fluctuation trends have not been published in the literature. Thus, we developed a system dynamics (SD) model considering time-series data, mathematical formulations, and qualitative factors. The results obtained from our model were compared with the published literature. The best result of the SD is the European naphtha forecasting price model, and the forecasting accuracy percentage shows 92.82{\%}. Furthermore, a nonlinear programming (NLP) model was developed to optimize the purchase price by considering the naphtha price of the forecasting models. In addition, the average optimization value was approximately 45.07. USD/ton cheaper than that of the heuristic approach.",
author = "Hweeung Kwon and Byeonggil Lyu and Kyungjae Tak and Jinsuk Lee and Cho, {Jae Hyun} and il Moon",
year = "2016",
month = "1",
day = "4",
doi = "10.1016/j.compchemeng.2015.08.012",
language = "English",
volume = "84",
pages = "226--236",
journal = "Computers and Chemical Engineering",
issn = "0098-1354",
publisher = "Elsevier BV",

}

Optimization of naphtha purchase price using a price prediction model. / Kwon, Hweeung; Lyu, Byeonggil; Tak, Kyungjae; Lee, Jinsuk; Cho, Jae Hyun; Moon, il.

In: Computers and Chemical Engineering, Vol. 84, 04.01.2016, p. 226-236.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimization of naphtha purchase price using a price prediction model

AU - Kwon, Hweeung

AU - Lyu, Byeonggil

AU - Tak, Kyungjae

AU - Lee, Jinsuk

AU - Cho, Jae Hyun

AU - Moon, il

PY - 2016/1/4

Y1 - 2016/1/4

N2 - In order to meet company needs, various models of naphtha price forecasting and optimization models of average naphtha purchase price have been developed. However, these general models are limited in their ability to predict future trends as they only include quantitative data. Furthermore, naphtha price predictions based on fluctuation trends have not been published in the literature. Thus, we developed a system dynamics (SD) model considering time-series data, mathematical formulations, and qualitative factors. The results obtained from our model were compared with the published literature. The best result of the SD is the European naphtha forecasting price model, and the forecasting accuracy percentage shows 92.82%. Furthermore, a nonlinear programming (NLP) model was developed to optimize the purchase price by considering the naphtha price of the forecasting models. In addition, the average optimization value was approximately 45.07. USD/ton cheaper than that of the heuristic approach.

AB - In order to meet company needs, various models of naphtha price forecasting and optimization models of average naphtha purchase price have been developed. However, these general models are limited in their ability to predict future trends as they only include quantitative data. Furthermore, naphtha price predictions based on fluctuation trends have not been published in the literature. Thus, we developed a system dynamics (SD) model considering time-series data, mathematical formulations, and qualitative factors. The results obtained from our model were compared with the published literature. The best result of the SD is the European naphtha forecasting price model, and the forecasting accuracy percentage shows 92.82%. Furthermore, a nonlinear programming (NLP) model was developed to optimize the purchase price by considering the naphtha price of the forecasting models. In addition, the average optimization value was approximately 45.07. USD/ton cheaper than that of the heuristic approach.

UR - http://www.scopus.com/inward/record.url?scp=84941975865&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941975865&partnerID=8YFLogxK

U2 - 10.1016/j.compchemeng.2015.08.012

DO - 10.1016/j.compchemeng.2015.08.012

M3 - Article

AN - SCOPUS:84941975865

VL - 84

SP - 226

EP - 236

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

SN - 0098-1354

ER -