- 學位論文
類神經網路之動態估測器於高壓共聚合反應之應用
Dynamic Estimator of Artificial Neural Networks in High-Pressure Copolymerization application
摘要
摘要 本文的目的在研討類神經網路的EVA (Ethylene-Vinyl Acetate) 高壓反應器的共聚合程序之產品性質 (熔融指數) 預測，並利用此項預測探討品別轉換之操作。 本文首先經由甘子威 (2003)、陳柏碩 (2004) 建構EVA高壓共聚合反應器的數學模式及整廠模式的溫度環路控制架構作為模擬之依據及數據之來源。利用產品性質 (熔融指數) 之控制策略與陳柏碩 (2004) 兩階段操作策略比較品別轉換情形下所需的時間；而實際高分子工業中，因無法對聚合物的產品性質 (熔融指數) 作線上量測，以及此共聚合程序的數學模式過於複雜，因此建構一個類神經網路，用以預測產品性質 (熔融指數) 的動態變化，此類神經網路的估測器對新鮮進料±10%改變都有不錯的預測結果；最後並研討共聚合程序模式產生誤差的情形下，如何運用類神經網路的預測以達到控制效果。針對單一品別以及品別轉換下，產品性質 (熔融指數) 皆有控制到所需品別的設定值。
並列摘要
Abstract The main purpose of this research is to construct an Artificial Neural Network (ANN) for the prediction of an important quality variable, Melting Index, in a high-pressure EVA copolymerization reactor, where free radical copolymerization reaction takes place. The ANN is used to explore the feasibility of inferential feedback control and to enhance the grade transition. The modeling building for ANN makes uses of the mathematical models from Gan (2003) and Chen (2004). The model is simulated and excited by PRBS inputs to generate the required dynamic data for modeling. The ANN prediction of MI is corrected every four hours by a measurement data. The corrected ANN prediction is used as feedback to maintain the MI at a specified value. Robustness of the control system to modeling error is also demonstrated. The resulting control of MI is then applied to the case where grade transition is demanded.