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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-10-21
摘要
計算 models may help to reduce research cost by predicting properties of alternative blends. Nowadays, most efforts focus on prediction of a few properties for sets of gasoline samples. However, there are no reports of models able for classification of gasoline samples with multiple output properties measured in real life refinery plants. In this work, 資訊 Fusion (IF), Perturbation Theory (PT), and 機器學習 (ML) algorithm (IFPTML) was used to model real production data with >230,000 outcomes gathered from a petroleum refinery plant. IF-pre-processing phase assembled the working dataset with 44 physicochemical output properties vs. 574 input variables of 4 production lines distributed in 26 data blocks including 14 different streams and 23 operations carried out in the plant. PT-calculation phase
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