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.
Bibliographical noteFunding Information:
Financial support from Samsung Total Petrochemicals Co. Ltd. and the BK 21 Program funded by the Ministry of Education (MOE) of Korea are gratefully acknowledged. Also, this research was supported by the Engineering Development Research Center (EDRC) funded by the Ministry of Trade, Industry, and Energy (MOTIE) .
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Computer Science Applications