周嚴的工廠異常狀態管理(Abnormal Situation Management)可被認為是包含了操作過程中所有失誤之即時辨識與減輕。一般而言，系統整體的診斷解析度是可以藉由增設感測器來提升，但若不希望增加設備投資的預算，則可藉由執行額外的診斷測試步驟來分辨出失誤根源。而在另一方面，失誤診斷後所執行的應變步驟也是相當重要的，包括如何使系統在安全的容許範圍內且產量可接受的情況下繼續維持生產，或是直接緊急停俥。過去文獻通常僅針對失誤診斷或是操作步驟等單一議題作局部性探討，並無涵蓋整個異常狀態管理範圍的通盤研究，因此本研究希望能針對此一缺點發展出建造批次製程中所有設備元件的自動機模型，並標準化緊急應變步驟的搜尋方法，包含診斷測試與應變操作的規劃，最後也利用ASPEN Plus Dynamics執行動態模擬研究來驗證可行性與正確性。

The term “abnormal situation management” (ASM) in general refers to timely identification and mitigation of any significant departure of the chemical process from acceptable normal operating conditions. The diagnostic performance of a given batch process can usually be enhanced with additional online sensors. However, if it is undesirable to increase the capital budget, diagnostic tests may be devised and executed to differentiate fault origins which are originally inseparable. On the other hand, it should be noted that the emergency response operations after fault diagnosis is also important. A set of efficient controller actions may be identified to steer the batch system away from hazardous conditions while still maintain an acceptable production rate or simply to bring it to the shutdown condition.
Although there have been numerous related previous studies, each of them addressed the aforementioned issues only partially. An automata-based modeling strategy is therefore proposed in this thesis to synthesize all operating procedures needed for ASM in any given batch process. The validity of this approach has also been verified in dynamic simulation studies in several realistic examples with ASPEN Plus Dynamics.

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