Optimization of naphtha purchase price using a price prediction model

被引:10
|
作者
Kwon, Hweeung [1 ]
Lyu, Byeonggil [1 ]
Tak, Kyungjae [1 ]
Lee, Jinsuk [2 ]
Cho, Jae Hyun [3 ]
Moon, Il [1 ]
机构
[1] Yonsei Univ, Dept Chem & Biomol Engn, Seoul 120749, South Korea
[2] Hanwha Total Corp, Daesan Eup Seosan Si 356711, Chungcheongnam, South Korea
[3] Seoul Natl Univ, EDRC, Seoul 151744, South Korea
关键词
Purchase price optimization; Artificial neural network; Forecasting model; System dynamics; Heuristics; ARTIFICIAL NEURAL-NETWORKS; CRUDE-OIL; SYSTEM DYNAMICS; SHORT-TERM; ENERGY; VOLATILITY; MARKET; CONSUMPTION; FORECAST; DEMAND;
D O I
10.1016/j.compchemeng.2015.08.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:226 / 236
页数:11
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