Optimization of naphtha purchase price using a price prediction model

• Published in: Computers & chemical engineering 2016-01, Vol.84, p.226-236
• Main Authors: Kwon, Hweeung, Lyu, Byeonggil, Tak, Kyungjae, Lee, Jinsuk, Cho, Jae Hyun, Moon, Il
• Format: Article
• Language: English
• Subjects: Artificial neural network; Chemical engineering; Fluctuation; Forecasting; Forecasting model; Heuristics; Mathematical models; Naphtha; Nonlinear programming; Optimization; Purchase price optimization; System dynamics; Trends
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2015.08.012

•The average optimization value is approximately 45.07USD/ton cheaper than the actual purchase price.•Nonlinear programming (NLP) model was developed to optimize the naphtha purchase price.•Proposed SD model give best prediction accuracy for Europe naphtha price (FAP 92.28).•Proposed SD and optimization model would be of great significance for industry decision-makers. 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 45.07USD/ton cheaper than that of the heuristic approach.