Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

Statistical process monitoring of a multiphase flow facility

Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Current multivariate control charts for monitoring large scale industrial processes are typically based on latent variable models, such as principal component analysis (PCA) or its dynamic counterpart when variables present auto-correlation (DPCA). In fact, it is usually considered that, under such conditions, DPCA is capable to effectively deal with both the cross- and auto-correlated nature of data. However, it can easily be verified that the resulting monitoring statistics (T2 and Q, also referred by SPE) still present significant auto-correlation. To handle this issue, a set of multivariate statistics based on DPCA and on the generation of decorrelated residuals were developed, that present low auto-correlation levels, and therefore are better positioned to implement SPC in a more consistent and stable way (DPCA-DR). The monitoring performance of these statistics was compared with that from other alternative methodologies for the well-known Tennessee Eastman process benchmark. From this study, we conclude that the proposed statistics had the highest detection rates on 19 out of the 21 faults, and are statistically superior to their PCA and DPCA counterparts. DPCA-DR statistics also presented lower auto-correlation, which simplifies their implementation and improves their reliability

Indirect monitoring of energy efficiency in a simulated chemical process

Abstract. Energy efficiency is an important part of chemical process sustainability. Wasted energy contributes significantly to process costs and overall emissions. Therefore, contributions to improving energy efficiency in chemical processes are of value. The main objective of this thesis is the exploration of indirect energy efficiency monitoring methods and their compilation into a generalized framework. As part of the proposed framework, data-based modelling methods were explored and used to identify a model for estimating energy efficiency in a simulated process. The proposed framework can act as a potential tool in different practical applications with energy efficiency improvements as an objective. As a simulated test process for this thesis, the Tennessee Eastman process was utilized. This process is widely used in research, especially regarding fault diagnosis and control design. The process includes slow dynamics and nonlinearity, providing interesting challenges for research. Even though the process has been studied extensively, the energy efficiency aspect of the process has not been taken into account in research. The results of the thesis show that data-based models provide sufficient accuracy for real-time estimation of energy efficiency for the Tennessee Eastman process. Parts of the proposed framework were tested with the explored methods, but some areas were beyond the scope of this thesis. As such, further research, for example prediction of the energy efficiency horizon, fault diagnosis and advanced process control, could be beneficial.Energiatehokkuuden epäsuora monitorointi simuloidussa kemiallisessa prosessissa. Tiivistelmä. Energiatehokkuus on tärkeä osa kemiallisen teollisuuden kestävyyttä. Energian käytön tehottomuus näkyy merkittävästi kasvavina prosessikustannuksina ja kokonaispäästöinä. Toimet energiatehokkuuden nostamiseksi ovat siksi merkityksellisiä. Diplomityön päätavoitteena on erilaisten epäsuorien energiatehokkuuden seurantamenetelmien tutkiminen ja niiden kokoaminen yleistettävään menetelmäkehykseen. Datapohjaisia mallinnusmenetelmiä tutkitaan osana esitettyä kehystä, ja niitä hyödynnetään energiatehokkuutta arvioivan mallin muodostuksessa. Esitetty menetelmäkehys voi toimia mahdollisena työkaluna erilaisissa käyttökohteissa, joissa energiatehokkuuden parantaminen on päämääränä. Tutkittavana kohteena diplomityössä käytettiin simuloitua Tennessee Eastman prosessimallia. Vaikka prosessia on tutkittu laajasti, energiatehokkuuden tarkempi tarkastelu on jäänyt vajaaksi. Simuloitua prosessidataa hyödynnettiin tässä työssä prosessin energiatehokkuuden mallipohjaisen arvion muodostuksessa. Työssä analysoitiin myös mallinnuksen luotettavuuteen vaikuttavia tekijöitä, kuten opetusdatan rajallisuutta ja siitä seuraavaa mallin ekstrapolointia. Diplomityön tulokset osoittavat, että Tennessee Eastman prosessin energiatehokkuuden reaaliaikainen arviointi datapohjaisilla menetelmillä onnistuu riittävällä tarkkuudella. Esitetyn menetelmäkehyksen osia testattiin tutkituilla menetelmillä, mutta jotkin alueet jäivät työn ulkopuolelle. Tulevaisuuden mahdollisiin tutkimusalueisiin kuuluukin energiatehokkuuden ennustaminen, vikadiagnostiikka ja niitä yhdistävä kehittynyt prosessisäätö

Nonlinear data driven techniques for process monitoring

The goal of this research is to develop process monitoring technology capable of taking advantage of the large stores of data accumulating in modern chemical plants. There is demand for new techniques for the monitoring of non-linear topology and behavior, and this research presents a topological preservation method for process monitoring using Self Organizing Maps (SOM). The novel architecture presented adapts SOM to a full spectrum of process monitoring tasks including fault detection, fault identification, fault diagnosis, and soft sensing. The key innovation of the new technique is its use of multiple SOM (MSOM) in the data modeling process as well as the use of a Gaussian Mixture Model (GMM) to model the probability density function of classes of data. For comparison, a linear process monitoring technique based on Principal Component Analysis (PCA) is also used to demonstrate the improvements SOM offers. Data for the computational experiments was generated using a simulation of the Tennessee Eastman process (TEP) created in Simulink by (Ricker 1996). Previous studies focus on step changes from normal operations, but this work adds operating regimes with time dependent dynamics not previously considered with a SOM. Results show that MSOM improves upon both linear PCA as well as the standard SOM technique using one map for fault diagnosis, and also shows a superior ability to isolate which variables in the data are responsible for the faulty condition. With respect to soft sensing, SOM and MSOM modeled the compositions equally well, showing that no information was lost in dividing the map representation of process data. Future research will attempt to validate the technique on a real chemical process

Nonlinear dynamic process monitoring using kernel methods

The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis (KPCA) have been reported. Nevertheless, the e ect of combining kernel density estimation (KDE)-based control limits with KPCA for nonlinear process monitoring has not been adequately investi- gated and documented. Therefore, process monitoring using KPCA and KDE-based control limits is carried out in this work. A new KPCA-KDE fault identi cation technique is also proposed. Furthermore, most process systems are complex and data collected from them have more than one characteristic. Therefore, three techniques are developed in this work to capture more than one process behaviour. These include the linear latent variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and kernel latent variable-CVA (KLV-CVA). LLV-CVA captures both linear and dynamic relations in the process variables. On the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro- cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique reported does not address the nonlinear problem directly while the regular kernel CVA approach require regularisation of the constructed kernel data to avoid com- putational instability. However, this compromises process monitoring performance. The results of the work showed that KPCA-KDE is more robust and detected faults higher and earlier than the KPCA technique based on Gaussian assumption of pro- cess data. The nonlinear dynamic methods proposed also performed better than the afore-mentioned existing techniques without employing the ridge-type regulari- sation

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be extended in multiple ways to time series analysis

마르코프 랜덤 필드 학습 및 추론과 그래프 라쏘를 활용한 공정 이상 감지 및 진단 방법론

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2019. 2. 이원보.Fault detection and diagnosis (FDD) is an essential part of safe plant operation. Fault detection refers to the process of detecting the occurrence of a fault quickly and accurately, and representative methods include the use of principal component analysis (PCA), and autoencoders (AE). Fault diagnosis is the process of isolating the root cause node of the fault, then determining the fault propagation path to identify the characteristic of the fault. Among the various methods, data-driven methods are the most widely-used, due to their applicability and good performance compared to analytical and knowledge-based methods. Although many studies have been conducted regarding FDD, no methodology for conducting every step of FDD exists, where the fault is effectively detected and diagnosed. Moreover, existing methods have limited applicability and show limited performance. Previous fault detection methods show loss of variable characteristics in dimensionality reduction methods and have large computational loads, leading to poor performance for complex faults. Likewise, preceding fault diagnosis methods show inaccurate fault isolation results, and biased fault propagation path analysis as a consequence of implementing knowledge-based characteristics for construction of digraphs of process variable relationships. Thus a comprehensive methodology for FDD which shows good performance for complex faults and variable relationships, is required. In this study, an efficient and effective comprehensive FDD methodology based on Markov random fields (MRF) modelling is proposed. MRFs provide an effective means for modelling complex variable relationships, and allows efficient computation of marginal probability of the process variables, leading to good performance regarding FDD. First, a fault detection framework for process variables, integrating the MRF modelling and structure learning with iterative graphical lasso is proposed. Graphical lasso is an algorithm for learning the structure of MRFs, and is applicable to large variable sets since it approximates the MRF structure by assuming the relationships between variables to be Gaussian. By iteratively applying the graphical lasso to monitored variables, the variable set is subdivided into smaller groups, and consequently the computational cost of MRF inference is mitigated allowing efficient fault detection. After variable groups are obtained through iterative graphical lasso, they are subject to the MRF monitoring framework that is proposed in this work. The framework obtains the monitoring statistics by calculating the probability density of the variable groups through kernel density estimation, and the monitoring limits are obtained separately for each group by using a false alarm rate of 5%. Second, a fault isolation and propagation path analysis methodology is proposed, where the conditional marginal probability of each variable is computed via inference, then is used to calculate the conditional contribution of individual variables during the occurrence of a fault. Using the kernel belief propagation (KBP) algorithm, which is an algorithm for learning and inferencing MRFs comprising continuous variables, the parameters of MRF are trained using normal process data, then the individual conditional contribution of each variable is calculated for every sample of the fault process data. By analyzing the magnitude and reaction speed of the conditional contribution of individual variables, the root fault node can be isolated and the fault propagation path can be determined effectively. Finally, the proposed methodology is verified by applying it to the well-known Tennessee Eastman process (TEP) model. Since the TEP has been used as a benchmark process over the past years for verifying various FDD methods, it serves the purpose of performance comparison. Also, since it consists of multiple units and has complex variable relationships such as recycle loops, it is suitable for verifying the performance of the proposed methodology. Application results show that the proposed methodology performs better compared to state-of-the-art FDD algorithms, in terms of both fault detection and diagnosis. Fault detection results showed that all 28 faults designed inside the TEP model were detected with a fault detection accuracy of over 95%, which is higher than any other previously proposed fault detection method. Also, the method showed good fault isolation and propagation path analysis results, where the root-cause node for every fault was detected correctly, and the characteristics of the initiated faults were identified through fault propagation path analysis.공정 이상의 감지 및 진단 시스템은 안전한 공정 운영에 필수적인 부분이다. 이상 감지는 이상이 발생했을 경우 즉각적으로 이를 정확하게 감지하는 프로세스를 의미하며, 대표적인 방법으로는 주성분 분석 및 오토인코더를 활용한 감지 방법론이 있다. 이상 진단은 결함의 근본 원인이 되는 노드를 격리하고, 이상의 전파 경로를 탐지하여 이상의 특성을 식별하는 프로세스이다. 공정 이상의 감지 및 진단 방법론에는 모델 분석 방법론, 지식 기반 방법론 등의 다양한 방법론이 있지만, 공정에 대한 적용 가능성과 성능 측면에서 가장 유용하다고 알려져 있는 데이터 기반 방법론이 널리 활용되고 있다. 공정 이상의 감지 및 진단에 대한 데이터 기반 방법론은 다방면으로 연구되어 왔지만, 이상 감지 및 진단을 모두 효과적으로 수행할 수 있는 방법론은 소수에 불과하며, 존재하고 있는 방법론들 역시 두 분야 모두에서 좋은 성능을 보여주고 있는 경우는 없다. 이는 기존 방법론들의 적용 가능성이 제한되어 있으며 공정에 적용시 제한된 성능을 보여주기 때문이다. 이상 감지의 경우, 대용량의 데이터를 처리할 때 발생하는 과부하로 인한 감지 능력의 저하, 차원 축소 방법론들을 사용할 시 이에 따른 변수 특성 반영의 부정확성, 그리고 축소된 차원에서의 계산으로 인하여 복합적인 형태의 이상을 감지해 내지 못하는 문제 등이 있다. 이상 진단의 경우 이상의 원인이 되는 노드의 격리 및 이상 전파 경로에 대한 분석이 부정확한 경우가 많은데, 이는 차원 축소로 인하여 공정 변수의 특성이 소실되는 성질이 있고, 방향성 그래프를 활용할 시 공정에 대한 선행 지식을 적용함으로써 편향된 이상 진단 결과가 나타나는 경우들이 발생하기 때문이다. 기존 방법론들에 대한 이러한 한계점들을 고려해 봤을때, 변수 각각의 특성이 소실되지 않도록하여 효과적으로 이상에 대한 감지와 진단을 모두 수행해 낼 수 있으면서도, 계산상의 효율성을 갖춘, 이상 감지 및 진단에 대한 통합된 방법론의 개발이 시급하다고 할 수 있다. 본 연구에서는 마르코프 랜덤 필드 모델링과 그래프 라쏘를 기반으로하여, 이상에 대한 감지 및 진단을 모두 수행해 낼 수 있는 통합적인 공정 모니터링 방법론을 제안한다. 마르코프 랜덤 필드는 비선형적이고 비정규적인 변수 관계를 효과적으로 모델링할 수 있게 해주고, 이상 발생 상황에서의 모니터링 통계값 계산시에 각 변수의 특성을 반영하여 확률 계산을 해 낼 수 있기 때문에 효과적인 이상 감지 및 진단 수단이 된다. 기본적으로 마르코프 랜덤 필드는 확률값 계산시의 부하가 크지만, 본 연구에서는 그래프 라쏘 방법론을 추가적으로 함께 활용하여 계산 상의 부하를 줄이고 효율적으로 이상 감지 및 진단을 해낼 수 있도록 하였다. 본 연구에서 제안된 내용들은 다음과 같다. 첫째, 공정 변수를 마르코프 랜덤 필드 형태로 모델링하고, 그래프 라쏘를 활용해 마르코프 랜덤 필드의 구조를 얻을 수 있는 방법론을 제시하였다. 그래프 라쏘는 마르코프 랜덤 필드의 구조를 파악하기 위한 방법론인데, 변수 간의 관계를 가우스 함수의 형태로 가정하기 때문에 다변수 시스템에서도 효율적으로 그래프 구조를 파악할 수 있도록 해준다. 본 연구에서는 반복적 그래프 라쏘를 제안하여 모든 공정 변수들이 상관관계가 높은 변수 집단으로 묶일 수 있도록 하였다. 이를 활용하면 전체 공정 변수 집단을 다수의 소집단으로 분류하고 각각에 대한 그래프 구조를 파악할 수 있게 되는데, 크게 두 가지의 효과를 얻을 수 있다. 우선적으로 마르코프 랜덤 필드 확률 계산의 대상이 되는 변수의 개수를 줄여줌으로써 계산 부하를 줄이고 효율적인 이상 감지가 이루어질 수 있도록 한다. 또한 상관관계가 높은 집단끼리 묶여서 모델링 된 그래프를 활용하여 이상의 진단 과정에서 공정 변수 간의 관계 파악 및 전파 경로 분석을 용이하도록 해준다. 두 번째로, 마르코프 랜덤 필드의 확률 추론을 기반으로 하여 효과적으로 이상 감지가 이루어질 수 있도록 하는 방법론을 제안하였다. 반복적 그래프 라쏘를 통해 얻어진 다수의 변수 소집단에 대하여 각각 확률 추론을 적용하여 이상 감지를 진행하게 되는데, 제안된 방법론에서는 커널 밀도 추정 방법론을 활용하였다. 정상 데이터를 활용하여 각 변수들에 대한 커널 밀도의 대역폭을 학습하고, 이상 데이터가 발생할 시 이를 활용한 커널 밀도 추정법을 사용하여 이상감시 통계치를 계산하게 된다. 이때 허위 진단율을 5%로 가정하여 각각의 소집단에 대한 공정 감지 기준선을 설정하였고, 이상감시 통계치가 공정 감시 기준선보다 낮게 될 경우 이상이 감지된다. 세 번째로, 이상 발생 시 원인이 되는 변수의 격리 및 이상 전파 경로 분석을 효과적으로 수행할 수 있는 방법론을 제시하였다. 제시된 방법론에서는 마르코프 랜덤 필드의 확률 추론 과정을 활용하여 이상 발생 시 각 변수의 조건부 한계 확률을 계산하고, 이를 활용해 새롭게 정의된 조건부 기여도 값을 계산하여, 이상에 대한 각 변수의 기여도를 파악할 수 있도록 한다. 이 과정에서는 커널 신뢰도 전파 방법론이 사용되는데, 이는 연속 변수를 가지는 마르코프 랜덤 필드에 대하여 확률 추론을 수행할 수 있도록 하는 방법론이다. 커널 신뢰도 전파법을 사용하면 정상 상태의 공정 데이터를 활용하여 마르코프 랜덤 필드를 구성하는 파라미터 값들을 학습하고, 이상 발생시 이상 데이터에 대하여 각 변수의 조건부 기여도 값을 계산할 수 있게 된다. 이 때 계산된 조건부 기여도 값의 크기와, 이상 발생 이후 각 변수의 조건부 기여도 값의 변화 반응 속도를 종합적으로 판단하여, 이상의 원인 변수에 대한 격리와 이상 전파 경로 분석을 효과적으로 수행할 수 있게 된다. 본 연구에서는 제안된 이상 감지 및 진단 방법론의 성능을 검증하기 위하여 테네시 이스트만 공정 모델에 이를 적용하고 결과를 분석하였다. 테네시 이스트만 공정은 수년간 공정 감시 방법론을 검증하기 위한 벤치마크 공정으로 널리 사용되어 왔기 때문에, 제시된 방법론을 이에 적용해 봄으로써 다른 공정 감시 방법론들과의 성능을 비교해 볼 수 있었다. 또한 다수의 단위 공정을 포함하고 있고, 순환적인 변수 관계 역시 포함하고 있기 때문에 제시된 방법론의 성능을 시험해 보기에 적합했다. 테네시 이스트만 공정 내부에는 28개 종류의 이상이 프로그램 상에 내장되어 있는데, 제시된 공정 감지 방법론을 적용한 결과 모든 이상에 대하여 96% 이상의 높은 이상 감지율을 나타내었다. 이는 기존에 제시된 공정 감시 방법론들에 비하여 월등히 높은 수치였다. 또한 이상 진단 성능을 분석해 본 결과, 모든 이상에 대하여 원인이 되는 노드를 효과적으로 파악할 수 있었고, 이상 전파 경로 역시 정확하게 탐지하여 기존 방법론들과는 차별화된 성능을 나타내었다. 제시된 방법론을 테네시 이스트만 공정에 적용해 봄으로써, 본 연구 내용이 공정 이상의 감지 및 진단에 대한 통합적인 방법론 중에서 가장 우수한 성능을 나타내는 것을 확인할 수 있었다.Contents Abstract i Contents iv List of Tables vii List of Figures ix 1 Introduction 1 1.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Markov Random Fields Modelling, Graphical Lasso, and Optimal Structure Learning 10 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Graphical Lasso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 MRF Modelling & Structure Learning . . . . . . . . . . . . . . . . . 19 2.4.1 MRF modelling in process systems . . . . . . . . . . . . . . 19 2.4.2 Structure learning using iterative graphical lasso . . . . . . . 20 2.5 Application of Iterative Graphical Lasso on the TEP . . . . . . . . . . 24 3 Efficient Process Monitoring via the Integrated Use of Markov Random Fields Learning and the Graphical Lasso 31 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 MRF Monitoring Integrated with Graphical Lasso . . . . . . . . . . . 35 3.2.1 Step 1: Iterative graphical lasso . . . . . . . . . . . . . . . . 36 3.2.2 Step 2: MRF monitoring . . . . . . . . . . . . . . . . . . . . 36 3.3 Implementation of Glasso-MRF monitoring to the Tennessee Eastman process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1 Tennessee Eastman process . . . . . . . . . . . . . . . . . . 41 3.3.2 Glasso-MRF monitoring on TEP . . . . . . . . . . . . . . . . 48 3.3.3 Fault detection accuracy comparison with other monitoring techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.3.4 Fault detection speed & fault propagation . . . . . . . . . . . 95 4 Process Fault Diagnosis via Markov Random Fields Learning and Inference 101 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.1 Probabilistic graphical models & Markov random fields . . . 106 4.2.2 Kernel belief propagation . . . . . . . . . . . . . . . . . . . . 107 4.3 Fault Diagnosis via MRF Modeling . . . . . . . . . . . . . . . . . . 113 4.3.1 MRF structure learning via graphical lasso . . . . . . . . . . 116 4.3.2 Kernel belief propagation - bandwidth selection . . . . . . . . 116 4.3.3 Conditional contribution evaluation . . . . . . . . . . . . . . 117 4.4 Application Results & Discussion . . . . . . . . . . . . . . . . . . . 118 4.4.1 Two tank process . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4.2 Tennessee Eastman process . . . . . . . . . . . . . . . . . . 137 5 Concluding Remarks 152 Bibliography 157 Nomenclature 169 Abstract (In Korean) 170Docto

Malliprediktiivinen säädin Tennessee Eastman prosessille

This thesis aims to design a multivariable Model Predictive Control (MPC) scheme for a complex industrial process. The focus of the thesis is on the implementation and testing of a linear MPC control strategy combined with fault detection and diagnosis methods. The studied control methodology is based on a linear time invariant state-space model and the quadratic programming optimization procedure. The control scheme is realized as a supervisory one, where the MPC is used to calculate the optimal set point trajectories for the lower level PI controllers, thus aiming to decrease the fluctuations in the end product flows. The Tennessee Eastman (TE) process is used as the testing environment. The TE process is a benchmark based on a real process modified for testing. It has five units, four reactants, an inert, two products and a byproduct. The control objective is to maintain the production rate and the product quality at the desired level. To achieve this, the MPC implemented in this thesis gives setpoints to three stabilizing PI control loops around the reactor and the product stripper. The performance of the designed control systems is evaluated by inducing process disturbances, setpoint changes, and faults for two operational regimes. The obtained results show the efficiency of the adopted approach in handling disturbances and flexibility in control of different operational regimes without the need of retuning. To suppress the effects caused by faults, an additional level that provides fault detection and controller reconfiguration should be developed as further research.Tämän diplomityön tavoite on suunnitella monimuuttujainen-malliprediktiivinen säädin (MPC) teolliselle prosessille. Diplomityö keskittyy toteuttamaan ja testaamaan lineaarisen MPC strategian, joka yhdistettynä vikojen havainnointiin ja tunnistukseen sekä uudelleen konfigurointiin voidaan laajentaa vikasietoiseksi. Tutkittu säätöstrategia perustuu lineaariseen ajan suhteen muuttumattomaan tilataso-malliin ja neliöllisen ohjelmoinnin optimointimenetelmään. Säätö on toteutettu nk. ylemmän tason järjestelmänä, eli MPC:tä käytetään laskemaan optimaaliset asetusarvot alemman säätötason PI säätimille, tavoitteena vähentää vaihtelua lopputuotteen virroissa. Tennessee Eastman (TE) prosessia käytetään testiympäristönä. TE on testiprosessi, joka perustuu todelliseen teollisuuden prosessiin ja jota on muokattu testauskäyttöön sopivaksi. Prosessissa on viisi yksikköä, neljä lähtöainetta, inertti, kaksi tuotetta ja yksi sivutuote. Säätötavoite on ylläpitää haluttu taso tuotannon määrässä ja laadussa. Tämän saavuttamiseksi tässä diplomityössä toteutettu MPC antaa asetusarvoja kolmelle stabiloivalle PI-säätimelle reaktorin ja stripperin hallinnassa. Säätösysteemin suorituskykyä arvioitiin aiheuttamalla prosessiin häiriöitä, asetusarvon muutoksia ja vikoja eri operatiivisissa olosuhteissa. Saavutetut tulokset osoittavat valitun menetelmän tehokkuuden häiriöiden käsittelyyn ja joustavaan säätöön eri olosuhteissa. Tutkimuksen jatkokehityksenä vikojen vaikutuksen vaimentamiseksi säätöön tulisi lisätä taso, joka havaitsee viat ja uudelleen konfiguroi säätimen sen mukaisesti

Data-driven, mechanistic and hybrid modelling for statistical fault detection and diagnosis in chemical processes

Research and applications of multivariate statistical process monitoring and fault diagnostic techniques for performance monitoring of continuous and batch processes continue to be a very active area of research. Investigations into new statistical and mathematical methods and there applicability to chemical process modelling and performance monitoring is ongoing. Successive researchers have proposed new techniques and models to address the identified limitations and shortcomings of previously applied linear statistical methods such as principal component analysis and partial least squares. This thesis contributes to this volume of research and investigation into alternative approaches and their suitability for continuous and batch process applications. In particular, the thesis proposes a modified canonical variate analysis state space model based monitoring scheme and compares the proposed scheme with several existing statistical process monitoring approaches using a common benchmark simulator – Tennessee Eastman benchmark process. A hybrid data driven and mechanistic model based process monitoring approach is also investigated. The proposed hybrid scheme gives more specific considerations to the implementation and application of the technique for dynamic systems with existing control structures. A nonmechanistic hybrid approach involving the combination of nonlinear and linear data based statistical models to create a pseudo time-variant model for monitoring of large complex plants is also proposed. The hybrid schemes are shown to provide distinct advantages in terms of improved fault detection and reliability. The demonstration of the hybrid schemes were carried out on two separate simulated processes: a CSTR with recycle through a heat exchanger and a CHEMCAD simulated distillation column. Finally, a batch process monitoring schemed based on a proposed implementation of interval partial least squares (IPLS) technique is demonstrated using a benchmark simulated fed-batch penicillin production process. The IPLS strategy employs data unfolding methods and a proposed algorithm for segmentation of the batch duration into optimal intervals to give a unique implementation of a Multiway-IPLS model. Application results show that the proposed method gives better model prediction and monitoring performance than the conventional IPLS approach.EThOS - Electronic Theses Online ServiceGBUnited Kingdo