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Pressure swing adsorption (PSA) is a widely used technology to separate a gas product from impurities in a variety of fields. Due to the complexity of PSA operations, process and instrument faults can occur at different parts and/or steps of the process. Thus, effective process monitoring is critical for ensuring efficient and safe operations of PSA systems. However, multi-bed PSA processes present several major challenges to process monitoring. First, a PSA process is operated in a periodic or cyclic fashion and never reaches a steady state; Second, the duration of different operation cycles is dynamically controlled in response to various disturbances, which results in a wide range of normal operation trajectories. Third, there is limited data for process monitoring, and bed pressure is usually the only measured variable for process monitoring. These key characteristics of the PSA operation make process monitoring, especially early fault detection, significantly more challenging than that for a continuous process operated at a steady state. To address these challenges, we propose a feature-based statistical process monitoring (SPM) framework for PSA processes, namely feature space monitoring (FSM). Through feature engineering and feature selection, we show that FSM can naturally handle the key challenges in PSA process monitoring and achieve early detection of subtle faults from a wide range of normal operating conditions. The performance of FSM is compared to the conventional SPM methods using both simulated and real faults from an industrial PSA process. The results demonstrate FSM’s superior performance in fault detection and fault diagnosis compared to the traditional SPM methods. In particular, the robust monitoring performance from FSM is achieved without any data preprocessing, trajectory alignment or synchronization required by the conventional SPM methods. 

Rotating machines, such as pumps and compressors, are critical components in refineries and chemical plants used to transport fluids between processing units. Bearings are often the critical parts of rotating machinery, and their failure could result in economic loss and/or safety issues. Therefore, estimation of the remaining useful life (RUL) of a bearing plays an important role in reducing production losses and avoiding machine damage. Because bearing failure mechanisms tend to be complex and stochastic, data-driven RUL estimation approaches have found more applications. This work proposes a novel RUL estimation method based on systematic feature engineering and extreme learning machine (ELM). The PRONOSTIA dataset is used to demonstrate the effectiveness of the proposed method. 

For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry. 

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics. 

For the pulping process in a pulp & paper plant that uses wood as a raw material, it is important to have real-time knowledge about the moisture content of the woodchips so that the process can be optimized and/or controlled correspondingly to achieve satisfactory product quality while minimizing the consumption of energy and chemicals. Both destructive and non-destructive methods have been developed for estimating moisture content in woodchips, but these methods are often lab-based that cannot be implemented online, or too fragile to stand the harsh manufacturing environment. To address these limitations, we propose a non-destructive and economic approach based on 5 GHz Wi-Fi and use channel state information (CSI) to estimate the moisture content in woodchips. In addition, we propose to use statistics pattern analysis (SPA) to extract features from raw CSI data of amplitude and phase difference. The extracted features are then used for classification model building using linear discriminant analysis (LDA) and subspace discriminant (SD) classification. The woodchip moisture classification results are validated using the oven drying method. 

In the last few decades, various spectroscopic soft sensors that predict sample properties from its spectroscopic readings have been reported. To improve prediction performance, variable selection that aims to eliminate irrelevant wavelengths is often performed prior to soft sensor model building. However, due to the data-driven nature of many variable selection methods, they can be sensitive to the choice of the training data, and oftentimes the selected wavelengths show little connection to the underlying chemical bonds or function groups that determine the property of the sample. To address these limitations, we proposed a new variable selection method, namely consistency enhanced evolution for variable selection (CEEVS), which focuses on identifying the variables that are consistently selected from different training dataset. To demonstrate the effectiveness and robustness of CEEVS, we compared it with three representative variable selection methods using two published NIR datasets. We show that by identifying variables with high selection consistency, CEEVS not only achieves improved soft sensor performance, but also identifies key chemical information from spectroscopic data.