工廠異常狀態管理的重要工作之一是失誤即時辨識。一般而言，系統整體的診斷解析度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。由於過去的探討僅限於簡單的物料及能量運送的操作，我們希望能改善既有方法，應用在較為真實且具複雜輸送現象的化工程序上。本研究藉由系統化方法，根據工程知識和ASPEN Plus Dynamics動態模擬數據來建立自動機模型，並據以自動搜尋出最適的診斷測試步驟，最後利用商用軟體ASPEN Plus Dynamics執行動態模擬來驗證所得測試步驟的可行性與正確性。

Although diagnostic tests have been adopted in the past for differentiating the originally inseparable fault origins in simple batch processes, their applicability in realistic systems is still questionable. To address this concern, the dynamic behavior of every processing unit involved in a given sequential operation is modeled in this work by integrating both the generic engineering knowledge and also the ASPEN-generated simulation data into an automaton. The improved test plans can be synthesized according to the system model obtained by assembling all such automata. The validity of this approach has also been verified in dynamic simulation studies in several realistic examples with ASPEN Plus Dynamics. The feasibility of this model building strategy is demonstrated with examples concerning the start-up operations of a flash process and also a distillation column.

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