化學工廠平時運作，最重要除了操作安全之外，產品品質及成本將會影響客戶採購的意願。以往確認品質需要取樣至實驗室，使用價格昂貴的分析儀器分析，或安裝線上分析儀器，以便能了解品質狀況，做必要之操作調整。因等候或分析儀器經長年累月運作性能會衰退，錯失調整時機及導致分析失準，因應此等議題已有商業產品以軟體儀表進行量測，並同時與實體分析儀分析來比對，或直接替代實體分析儀，提升操作效率。
本研究以Tennessee Eastman Process資料為基礎，研究正常製程及不正常製程時，使用製程及操作變數，進行軟體儀表之建模。使用統計方法：多變數線性迴歸、二次效應曲面法模型。神經網路方法：神經網路、自動編譯器神經網路、長短記憶模型遞迴神經網路。在各種之數據量下，進行產品品質軟體儀表之建模預測，並與實體分析儀進行比較。
結果顯示，正常製程在數據量少的情況下，模型對訓練組的誤差雖低，但對測試組的結果不好，此種狀況猶如實驗室分析，提供的資料正常但不足，此時統計學習所產生簡單的模型，即有不錯的效果。當數據量多且密集時，使用神經網路方法：長短記憶模型遞迴神經網路建模，對測試組會有比較好的預測效果。不正常製程，普遍因為資料變動大，不論是統計方法或神經網路方法，均有不錯的效果，但應用上仍要注意資料的合理性。

Chemical plants operate in a normal state. The most important issue is safety. Other than this, the customer will determine with quality and cost whether they will buy or not. To check the quality, we need to do the sampling to experiment. Then using expensive instruments or install an online analyzer to analyze. Depend on the purity to do some adjusting. Due to waiting or the analyzer will be decay after a long time operation. The operator will lose the timing to adjust, and the accuracy will be lost. To facing such problems, for business applications, some products get quality from soft sensors. It could check with a rugged analyzer at the same time or replace the hardware to increase operational efficiency.
This research uses the Tennessee Eastman Process database. Study in normal process and abnormal process. To build the models by the process and operation variables will be used. For statics, methods are multiple linear regression, quadratic response surface method. For neural networks, methods are the artificial neural network, autoencoder neural network, and long-short terms memory recurrent neural network. Training the model using different data sizes and then compare it with accurate data.
The results are well for the normal process when the sample data size is small, even for the model error small for the training set. However, the accuracy set is not suitable for the testing set. This situation, like the data, comes from experiments, and the data is insufficient. For this case, the statics model is better. When the data size increases, the neural network method like long-short term memory recurrent neural network will be good. Due to the data variance significance, both the statics and neural network methods will be suitable for abnormal processes. For an actual application, it is must check data sourcing reasonably.

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