空氣分離製程最經濟操作點與最適操作策略之研究

Optimal Operating Point and Optimal Operating Strategy for Air Separation Process

李明龍(Ming-Lung Li)

指導教授 ： 錢義隆

國立臺灣大學/工學院/化學工程學研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.10519

摘要

空氣主要成分為氮氣、氧氣與氬氣，此三成分皆被廣泛地應用於各種產業，如化學、鋼鐵、電力、醫療、半導體等。而氧氣生產與鋼鐵製造業密切相關，但由於空氣分離製程為高度熱整合程序，當產能負載需求變化時各塔之變負載控制環路設定值應有其對應變化。目前中鋼氧氣工場是根據現場人員之經驗，透過手動方式進行變負載時的調控，因此無法精確得知是否操作於最經濟操作點，此方法之穩定性及變負載的速度也都有再提昇的空間。

本研究與中鋼氧氣工場合作，利用現場數據建立穩態與動態模型。模擬範圍為空氣出分子篩後的流程，主要包括主熱交換器、下塔、上塔與粗氬塔，以中鋼公司所提供之氧氣工場數據平均值進行模擬，再藉由模擬結果與實際操作值進行修正，完成穩態模型。在完成穩態模型後，根據現場設計圖於穩態模型中填入桶槽、再沸器、冷凝器之設計數據，以完成動態所需之各項資訊。過程採壓力驅動(pressure-driven)之方式轉換至動態，並以現場變負載資料進行動態模型的驗證。所挑選之變負載範圍包含空氣進料與氧氣產物流量之升降，模擬結果與現場趨勢大致吻合。

在擁有動態模型後，利用動態模型解決在僅有穩態模型的情況下，無法尋找空氣分離製程最經濟操作點之阻礙。最終結果顯示最經濟操作點相較原操作點可節省7.45%的電力消耗。同時為解決現場人員以經驗方式操作空氣分離製程，而對於控制環路設定值無法有系統調整之問題，在參考中鋼不同機組之自動變負載調整方式後，以現場資料回歸出關係公式，之後進行穩態模擬測試。穩態模擬測試結果則顯示此關係公式估算之誤差值小於2%，因此具有一定之準確性。最後利用建立之動態模型，找尋變負載時最適動態軌跡，建立最適操作策略，結果顯示能迅速且穩定地變負載。

關鍵字

空氣分離製程 ； 自動變負載技術 ； 最經濟操作點 ； 最適操作策略

並列摘要

Air is mainly composed of nitrogen, oxygen and argon, and these three gases are widely used in many industries. Cryogenic air separation process is operated at extremely low temperatures (-170 to -195 ℃) and high degree of energy integration, which makes it difficult to operate. In China Steel Corporation’s (CSC) ASU, the set point changes are manipulated by operator’s experience. Hence the goal of this research is to build up the optimal operating point and optimal operating strategy for this air separation unit hoping to improve the operation.

The steady-state and dynamic model of CSC’s ASU are based on real plant data. This research simulated the part after molecular sieves up to the crude argon column and does not include the final purified argon column. The steady-state model used the average real plant data. Pressure-driven simulation in Aspen Plus Dynamics is used in the dynamics model and holdups of all columns and flashers are calculated by the real plant design. The simulation results fit well to the real plant data.

The optimal operating point could be found by using dynamic model and could save 7.45% electricity consumption. To find out the formula among gaseous product demands and set point values, the approach refers to the ALC control strategy and operator’s experience. Regression formula fits well to the steady state model and can be used to predict the set point values when gaseous products demand changes. Combined the fegression formula and dynamic model, the optimal operating strategy was built.

並列關鍵字

Air Separation Process ； Automatic Load Control ； Optimal Operating Point ； Optimal Operating Strategy

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