Machine Learning Based Approaches to Estimation of Quality Variables in Batch Processes

학위논문(석사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2023. 2. 이종민.안전하고 경제적인 배치 공정의 운전 방향을 제시하기 위해서 제품 품질 예측 모델의 개발이 필요하다. 하지만 배치 공정은 비정상상태에서 운전되어 강한 비선형성을 보이기 때문에 물리 법칙 기반 모델의 구축이 어렵다. 또한 각 배치 별로 서로 다른 운전 시간을 가지고 운전 조건에 따라 여러 개의 phase로 구성되는 배치 공정의 특성은 품질 예측 모델의 개발을 더욱 어렵게 만든다. 따라서 본 연구에서는 서로 다른 운전 시간을 갖는 배치 공정에 대해서 공정 운전의 phase를 고려한 데이터 기반 품질 예측 모델을 개발하였다. 공정 데이터의 시계열성을 고려한 phase 분할을 위해서 warped K-means clustering (WKM) 알고리즘을 적용하여 phase를 분할하였다. 분리된 각각의 phase에 대하여 별도의 Recurrent neural network (RNN) 셀을 학습시키는 형태의 품질 예측 모델을 개발하였다. 개발된 품질 예측 모델은 기존의 대표적인 방법론인 Dynamic time warping (DTW)과 Multiway partial least squares (MPLS)를 사용한 경우보다 좋은 성능을 보였다. 또한 phase를 구분하지 않고 하나의 RNN 셀을 사용한 경우보다 phase 별로 서로 다른 RNN 셀을 사용하는 경우에 더 좋은 성능을 보이는 것을 확인하였다. 추가적으로 해당 품질 예측 모델을 온라인으로 활용 가능한 형태로 발전시킴으로써 공정 운전 중 발생하는 이상 진단 및 최적 운전 조건의 탐색에 활용할 수 있을 것이다.It is necessary to develop a product quality estimation model to operate batch processes in a safe and economical way. However, since the batch processes are operated in an unsteady state and show strong nonlinearity, it is difficult to build a first principle model. In addition, the batch processes show uneven operating duration for each batch and consist of several phases according to operating conditions, which makes the development of a quality estimation model more difficult. In this study, a machine learning based approach to estimation of quality variables considering the uneven duration and the multi-phase of batch processes was developed. The phases were divided by applying the warped K-means clustering (WKM) algorithm considering the sequentiality of process data. The quality estimation model in the form of a separate Recurrent Neural Network (RNN) cell for each phase was used. The developed quality estimation model showed better performance than the model using dynamic time warping (DTW) and multiway partial least squares (MPLS). In addition, using different RNN cells for each phase shows better performance than using one RNN cell without distinguishing the phases. By developing the quality estimation model into a form that can be used online, it will be possible to use the developed model for diagnosing abnormalities and searching for optimal operating conditions.제 1 장 서론 5 제 2 장 방법론 12 제 1 절 배치 공정 데이터의 전처리 12 제 2 절 Dynamic Time Warping 18 제 3 절 Multiway Partial Least Squares 20 제 4 절 Phase Partition 21 제 1 항 Sub-PCA 알고리즘 21 제 2 항 Warped K-Means Clustering 알고리즘 23 제 5 절 Recurrent Neural Network 26 제 1 항 Vanilla RNN, LSTM, GRU 26 제 2 항 Multi RNN 모델 31 제 3 장 페니실린 생산 공정 33 제 4 장 결과 및 분석 38 제 1 절 DTW-MPLS 모델 39 제 2 절 Single RNN 모델 45 제 3 절 Multi RNN 모델 52 제 1 항 Phase Partition 52 제 2 항 Multi RNN 모델 59 제 5 장 결론 70석

Data driven process monitoring based on neural networks and classification trees

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem