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題名 | 利用貝氏網路優化工場操作:解析低硫燃油之流動點問題=Identifying the Pour Point Anomalies of Low Sulfur Fuel Oil by the Bayesian Network |
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作者姓名(中文) | 呂政芳; 陳彥銘; 趙仁德; 陳彥銘; | 書刊名 | 石油季刊 |
卷期 | 55:4 2019.12[民108.12] |
頁次 | 頁9-27 |
分類號 | 457.5 |
關鍵詞 | 貝氏網路; 數據分析; 重油加氫脫硫工場; 低硫燃油; 流動點; Netica; Bayesian network; Data analysis; RDS; Low sulfur fuel oil; Pour point; |
語文 | 中文(Chinese) |
中文摘要 | 重油加氫脫硫工場(RDS)產出之低硫燃料的高流動點問題,不利於運送,導致客戶投訴。一般在工場操作上都知道觸媒活性會隨使用而降低,因而必須適時增加媒床溫度犧牲觸媒壽命來換取可接受的活性。然而,流動點異常的原因來源廣泛,在一些狀況下,仍然可以藉由不增加成本的條件下,而能解決問題。本研究透過貝氏網絡分析高流動點異常,並依此改善操作過程,以利於產出高品質的低硫燃料油。首先,本文描述RDS工場的生產過程和貝氏網絡分析法。其次,收集大林煉油廠第三RDS工場的現場操作數據,結合專家知識構建流動點貝氏網絡分析模型,並藉由Netica由實際數據中實現其貝氏網路的條件機率數值,以推論RDS流動點異常的原因。最後,透過貝氏網絡的敏感性分析發現,在使用C觸媒情況下,低硫燃油流動點異常時,最有可能的原因可能是媒床溫度設定不足。此外,影響分析發現,在極差操作環境下,使用觸媒C不合格流動點比率高達87.5%。在最佳操作條件下,選用觸媒B不合格流動點比率則降為0.37%。本研究發現,在不影響觸媒壽命的情況下,稍微提升一點媒床溫度,配上本研究所建議的操作條件,適當降低進料流動點,減少輕質成品的取出比例,可以在不增加成本的條件下,穩定低硫燃油的流動點。本研究的成果可以提供油品製程異常監控,並幫助進一步的深入分析與應用。 |
英文摘要 | The transportation problems caused by the high pour point of low-sulfur fuel produced in RDS leads to considerable customers' complaints. However, the reason for the pour point anomaly is still inconclusive. The paper analyzes the anomalies of the pour point by Bayesian Network and optimizes the strategy of operation in order to produce high quality low-sulfur fuels oil. Firstly, the features of RDS process are described. Secondly, we collect recent 4 cycles of operational data from KOR#3 RDS Talin Refinery and build a Bayesian Network Model based on expert knowledge. Next, this paper illustrates the Bayesian network-based methodology and realizes the complete Bayesian Network Analysis by Netica. Then, the preliminary problem of the pour point anomaly in the RDS unit is discussed. Finally, this study tackles sensitivity analysis for pour point anomaly. It is found that the temperature of catalyst bed affects the pour point of low-sulfur fuel most, followed by the catalyst characteristics. In addition, the high pour point generated by the catalyst C (87.5%) is much higher than that done by the catalyst B (0.37%). Research findings help to optimize process which could ensure oil quality and future research. |
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