以IOHMM為基礎的MPLS模式於線上批次製程監控

On-Line Monitoring of Batch Processes Using IOHMM-Based MPLS

宋哲銘(Che-Ming Song)

指導教授 ： 陳榮輝

中原大學/工學院/化學工程學系/碩士(2009年)

https://doi.org/10.6840/cycu200900050

摘要

在現今多變的市場需求中，具有高產品品質及高附加價值的批次製程在化學、半導體、生物及生化等工業上佔有舉足輕重的角色，而對於批次製程產品品質的線上線上監控亦日趨重要。本研究提出結合MPLS( Multi-way Partial Least Square )及IOHMM( Input-Output Hidden Makov Model )的IOHMM-MPLS模式，在MPLS的結構下將三維的批次數據進行展開後由原始數據空間投射在各個互為獨立的子空間下，降低原來多變數的維度，並且在此簡化的結構中建立各子空間下scores值的條件機率分佈函數，使得IOHMM-MPLS模式簡化了IOHMM模式結構的參數，增加了參數收歛的速度並提高了機率模式的效能及準確度，而且在本文中也針對了非同步的批次數據長度進行探討，並且經由訓練後，以所得到scores值分佈函數建立兩種簡單的管制圖，以監控在每一批次操作中所發生的錯誤情況。最後本研究分別以數學及模擬盤尼西林饋料批次製程進行測試來有效證明本研究所提出的IOHMM-MPLS模式的優點，此外，亦分別針對HMM、IOHMM與本研究所提出的IOHMM-MPLS模式進行機率模式的準確度比較，說明本研究的優點。

關鍵字

機率模式監控 ； 線上批次製程監控

並列摘要

In today’s rapidly changing market, batch processes play an important role for producing value-added and high quality products of chemical, semiconductor, and biological/biochemical industries. On-line monitoring is essential to the product quality of batch processes. In this paper, the integration of MPLS (Multi-way Partial Least Squares) models and IOHMM ( Input-Output Hidden Makov Models ), referred to as the IOHMM-MPLS model, is proposed. This method unfolds the three-dimensional batch process data, projecting the data from the original data space to each independence subspace under the MPLS structure. It reduces the multivariable dimension. Then under the simple structure, the conditional probability distribution function of each score in the subspace is built. The score probability function of the IOHMM-MPLS model with the simplified structure parameters can help accelerate the parameter convergence speed while improving the efficiency and accuracy of the probability model. The issue of the asynchronous data length in the batch monitoring is also discussed. Subsequently, with the trained distribution model, two simple monitoring charts are presented to track the progress of each batch run and monitor the occurrence of the observable upsets. The case studies of a mathematical problem and a simulated fed-batch penicillin cultivation process are used to demonstrate the power and advantages of the proposed method. In addition, the comparison of the probability model accuracy of HMM, IOHMM, and IOHMM-MPLS models is also shown to highlight the good features of the IOHMM-MPLS model.

並列關鍵字

Chemical Process Monitoring ； MPLS ； IOHMM

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