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Although the Lasso method has been popular for variable selection in regression modeling, it has been known to yield very different model structures with minor perturbations of the training data. A consequence is that, when cross-validation (CV) is used to determine the hyperparameter λ , seemingly heterogeneous model structures among the CV-folds are resulted for the same λ. In this paper, we propose a new stable Lasso method for model structure learning of static and dynamic models. We begin with building consensus Lasso models with a grid of λ values using all training data. Then the CV-fold models are optimized to conform with the consensus model structures with a modified Lasso objective. In addition, we propose a stable criterion that uses CV errors jointly with a stability measure to select the most stable model with near minimum CV errors. The proposed method is applied to inferential modeling of a chemical plant at DOW Chemical and dynamic modeling of an industrial boiler. • A stable Lasso algorithm is proposed to select variables for inferential sensor modeling. • The algorithm enhances consistent structures in the cross-validation step. • A stable selection criterion of MSE jointly the Jaccard stability measure is proposed. • Successful case studies are presented on inferential modeling of two industrial processes. [ABSTRACT FROM AUTHOR]

This paper is concerned with data science and analytics as applied to data from dynamic systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in industrial operation data. Therefore, we focus on latent variable methods that achieve dimension reduction and collinearity removal. We present a new dimension reduction expression of state space framework to unify dynamic latent variable analytics for process data, dynamic factor models for econometrics, subspace identification of multivariate dynamic systems, and machine learning algorithms for dynamic feature analysis. We unify or differentiate them in terms of model structure, objectives with constraints, and parsimony of parameterization. The Kalman filter theory in the latent space is used to give a system theory foundation to some empirical treatments in data analytics. We provide a unifying review of the connections among the dynamic latent variable methods, dynamic factor models, subspace identification methods, dynamic feature extractions, and their uses for prediction and process monitoring. Both unsupervised dynamic latent variable analytics and the supervised counterparts are reviewed. Illustrative examples are presented to show the similarities and differences among the analytics in extracting features for prediction and monitoring. [ABSTRACT FROM AUTHOR]

Foot pad dermatitis (FPD) is a serious problem of the modern poultry industry, negatively affecting birds' welfare and health status, walking and feeding activity, growth performance, carcass quality, and economic performance of meat production. The gut microbiome in poultry with FPD has not been previously investigated. Therefore, we compared the cecal microbiomes of 8 breeding ducks with FPD to 8 control ducks (breeders with apparently healthy feet) by pyrosequencing the bacterial 16S ribosomal RNA gene. The results showed a significant β-diversity (P < 0.05) of cecal microbiota presented between healthy and FPD-affected breeder ducks. The plasma endotoxins, interleukin 1β (IL-1β), IL-17, IL-6, IL-10, and tumor necrosis factor-α concentration, and the abundance of class Clostridia in FPD-affected ducks was markedly higher (P < 0.05), however, the abundance of genus Prevotella, Lactobacillus, Lachnospiraceae UCG-008, and the Firmicutes to Bacteroidetes ratio in FPD-affected ducks was significantly lower (P < 0.05) when compared to healthy ducks. These findings suggest when duck breeders are affected with FPD, ducks show an increased inflammatory response and a difference of structure and composition of the cecal microbiome. [ABSTRACT FROM AUTHOR]

The objective of the present study was to investigate the effect of dietary graded raw potato starch (RPS) levels on growth performance, plasma cytokines concentration, ileal barrier function, and cecal short-chain fatty acids (SCFA) concentration in meat ducks from 1 to 35 D of age. This study included 2 experiments. In experiment (Exp.) 1, sixteen 35-day-old meat ducks were used to evaluate the AME of RPS by orogastric administration. Results showed the AME value of RPS on ducks is 2.76 kcal/g. In Exp. 2, a total of 600 one-day-old ducklings were randomly assigned to 5 isonitrogenous and isoenergetic dietary treatments that included 0 (control), 6, 12, 18, and 24% RPS, respectively. Samples were collected at both of 14 and 35 D. Neither growth performance nor ileal parameters (length, weight, and pH) at both of 14 and 35 D was affected by dietary RPS. However, the mucosal thickness (14 D), villus height (except for 18% RPS at 14 D), and the villus height: crypt depth ratio (14 and 35 D) of the ileum were increased in the 12 and 18% RPS diets when compared to 0% RPS diet. Meanwhile, proinflammatory factors such as plasma interleukin (IL)-1β and IL-6 (14 D) reduced in 12% RPS diet and tumor necrosis factor α decreased in 12% (except for 14 D) and 18% RPS groups. When compared with the control group, diets with 18% RPS significantly increased mucin 2 gene expression at 14 D, and 12% RPS elevated the mRNA expression of tight junction proteins including Zonula occludens-1 and Claudin 1 (except for 14 D) in the ileal mucosa of birds. Furthermore, ducks fed 12% RPS diet had higher concentrations of acetate, propionate, and butyrate in cecal digesta than other groups. These findings indicated that diets with 12 and/or 18% RPS increased the cecal SCFA concentration, which subsequently enhanced the barrier function and improved intestinal health in the ileum for 14 and 35-day-old meat ducks. [ABSTRACT FROM AUTHOR]

To study the feasibility and safety for patients with high-risk (HR) and very high-risk (VHR) prostate cancer treated with adaptive ultra-hypofractionated whole-pelvic radiotherapy (UHF-WPRT) on 1.5 magnetic resonance (MR)-Linac. Sevenpatients with clinical stage T3a-4N0-1M0-1c consecutively treated with UHF-WPRT on a 1.5-T MR-Linac were recruited prospectively in a phase II trial (NCT05183074, ChiCTR2000033382). A 36.25 Gy dose in five fractions was delivered every other day with a boost of 40 Gy to the whole prostate, as well as 25 Gy to whole pelvic nodal area with a concomitant boost of 35 Gy to metastatic regional nodes. To estimate the delivered dose, we collected data by 3D-MR for the following stages: pre-MR, position verification-MR (PV-MR) in the Adapt-To-Shape (ATS) workflow, and 3D-MR during the beam-on phase (Bn-MR) and at the end of RT (post-MR). The target and organ-at-risk contours in the PV-MR, Bn-MR, and post-MR stages were projected from the pre-MR data by deformable image registration and manually adapted by the physician, followed by dose recalculation for the ATS plan. The cumulative acute genitourinary (GU) and gastrointestinal (GI) toxicities were evaluated as per NCI-CTCAE 5.0 criteria. The primary endpoints were acute ≥grade 3 genitourinary (GU) and gastrointestinal (GI) toxicities during the first 3 months. Overall, 133 MR scans were collected (35 pre-MR, 35 PV-MR, 31 Bn-MR and 32 post-MR scans). With a median on-couch time of 61 minutes, the mean prostate and pelvic planning target volume (PTV)-V95% of all scans was 96.98 ± 3.06% and 96.44 ± 2.85%, respectively. The corresponding mean prostate clinical target volume (CTV)-V100% was 99.89 ± 0.32%, 98.71 ± 1.90%, 97.77 ± 2.89%, and 98.56 ± 1.72%, and the mean pelvic CTV-V100% was 97.57% ± 3.70%, 96.54 ± 3.80%, 95.43 ± 4.31%, and 94.39 ± 4.47% on pre-MR, PV-MR, Bn-MR and post-MR scans, respectively. For the 4 patients with positive nodes, the mean V100% of metastatic regional nodes was 99.89 ± 0.81%. The median V29 Gy change in the rectal wall was -1% (-18%–20%). The V29 Gy of the rectal wall increased by >15% was observed in one scan. A slight increase in the high dose of bladder wall was noted due to gradual bladder growth during the workflow. With median follow-up time of 7.3 (4.6-12.2) months, all patients were followed-up for more than 3 months. No patient was observed with acute CTCAE grade 2 or more severe GU or GI toxicities (0%). UHF-RT to prostate and pelvic with ATS workflow is well tolerated by patients with HR and VHR prostate cancer, with only mild GU and GI toxicities. The 3D-MR–based dosimetry analysis demonstrated clinically acceptable estimated dose coverage of target volumes during the beam-on period. [ABSTRACT FROM AUTHOR]

In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement. [ABSTRACT FROM AUTHOR]

Partial least squares (PLS) regression is widely used to capture the latent relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms have been proposed to capture the characteristics of dynamic data. However, none of these algorithms provides an explicit expression for the dynamic inner and outer models. In this paper, a dynamic inner PLS algorithm is proposed for dynamic data modeling. The proposed algorithm provides an explicit dynamic inner model that is ensured in deriving the outer model. Several examples are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Deep learning models such as the long short-term memory (LSTM) network have been applied for dynamic inferential modeling. However, many studies apply LSTM as a black-box approach without examining the necessity and usefulness of the internal LSTM gates for inferential modeling. In this paper, we use LSTM as a state space realization and compare it with linear state space modeling and statistical learning methods, including N4SID, partial least squares, the Lasso, and support vector regression. Two case studies on an industrial 660 MW boiler and a debutanizer column process indicate that LSTM underperforms all other methods. LSTM is shown to be capable of outperforming linear methods for a simulated reactor process with severely excited nonlinearity in the data. By dissecting the sub-components of a simple LSTM model, the effectiveness of the LSTM gates and nonlinear activation functions is scrutinized. • LSTM is implemented as a nonlinear Kalman filter for dynamic inferential modeling. • LSTM is benchmarked with N4SID, PLS, Lasso, and SVR statistical learning methods. • Results on a boiler emission and debutanizer and a CSTR process are reported. • The results show that LSTM underperform N4SID and statistical learning methods. • With severe nonlinearity in the simulated CSTR data, LSTM underperforms SVR. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been widely applied for data modeling and process monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic process, since PCA focuses on variance maximization only and pays no attention to whether the components contain dynamics or not. In this paper, a novel dynamic PCA (DiPCA) algorithm is proposed to extract explicitly a set of dynamic latent variables with which to capture the most dynamic variations in the data. After the dynamic variations are extracted, the residuals are essentially uncorrelated in time and static PCA can be applied. The new models generate a subspace of principal time series that are most predictable from their past data. Geometric properties are explored to give insight into the new dynamic model structure. For the purpose of process monitoring, fault detection indices based on DiPCA are developed based on the proposed model. Case studies on simulation data, data from an industrial boiler process, and the Tennessee Eastman process are presented to illustrate the effectiveness of the proposed dynamic models and fault detection methods. [ABSTRACT FROM AUTHOR]

Traditional multivariate statistical process monitoring techniques usually assume measurements follow a multivariate Gaussian distribution so that T 2 can be used for monitoring. The assumption usually does not hold in practice. Many efforts have been spent on redefining a proper boundary of control region for non-Gaussian distributed processes. These efforts lead to new models such as independent component analysis (ICA), statistical pattern analysis (SPA), and new techniques such as kernel density estimation (KDE), support vector data description (SVDD). However, it has not been stated clearly how a latent structure will affect monitoring performance. In this paper, most of main stream methods for non-Gaussian process monitoring are recalled and categorized. The essential problem formulation of process monitoring is summarized from a general case and then explained in both Gaussian and non-Gaussian distribution, respectively. According to this formulation, KDE and SVDD methods are effective but time-consuming to extract proper control region of non-Gaussian distributed processes. Dimension reduction models are more beneficial to overcome the curse of dimensionality, rather than extracting non-Gaussian data structure. Besides, the monitoring of non-Gaussian processes can be converted into the monitoring of Gaussian processes according to central limitation theorem. [ABSTRACT FROM AUTHOR]

After introducing process data analytics using latent variable methods and machine learning, this paper briefly review the essence and objectives of latent variable methods to distill desirable components from a set of measured variables. These latent variable methods are then extended to modeling high dimensional time series data to extract the most dynamic latent time series, of which the current values are best predicted from the past values of the extracted latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods. The extracted features reveal hidden information in the data that is valuable for understanding process variability. [ABSTRACT FROM AUTHOR]

Background: Arginine depletion is a putative target in hepatocellular carcinoma (HCC). HCC often lacks argininosuccinate synthetase, a citrulline to arginine-repleting enzyme. ADI-PEG 20 is a cloned arginine degrading enzyme--arginine deiminase--conjugated with polyethylene glycol. The goal of this study was to evaluate this agent as a potential novel therapeutic for HCC after first line systemic therapy. Methods and patients: Patients with histologically proven advanced HCC and Child-Pugh up to B7 with prior systemic therapy, were randomized 2: 1 to ADI-PEG 20 18 mg/m2 versus placebo intramuscular injection weekly. The primary end point was overall survival (OS), with 93% power to detect a 4-5.6 months increase in median OS (one-sided α=0.025). Secondary end points included progression-free survival, safety, and arginine correlatives. Results: A total of 635 patients were enrolled: median age 61, 82% male, 60% Asian, 52% hepatitis B, 26% hepatitis C, 76% stage IV, 91% Child-Pugh A, 70% progressed on sorafenib and 16% were intolerant. Median OS was 7.8 months for ADI-PEG 20 versus 7.4 for placebo (P=0.88, HR=1.02) and median progression-free survival 2.6 months versus 2.6 (P=0.07, HR=1.17). Grade 3 fatigue and decreased appetite occurred in <5% of patients. Two patients on ADI-PEG 20 had≤grade 3 anaphylactic reaction. Death rate within 30 days of end of treatment was 15.2% on ADI-PEG 20 versus 10.4% on placebo, none related to therapy. Post hoc analyses of arginine assessment at 4, 8, 12 and 16 weeks, demonstrated a trend of improved OS for those with more prolonged arginine depletion. Conclusion: ADI-PEG 20 monotherapy did not demonstrate an OS benefit in second line setting for HCC. It was well tolerated. Strategies to enhance prolonged arginine depletion and synergize the effect of ADI-PEG 20 are underway. [ABSTRACT FROM AUTHOR]

Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid. [ABSTRACT FROM AUTHOR]

Most existing subspace identification methods use steady-state Kalman filter (SKF) in parameterization, hence, infinite data horizons are implicitly assumed to allow the Kalman gain to reach steady state. However, using infinite horizons requires collecting infinite data which is unrealistic in practice. In this paper, a subspace framework with non-steady state Kalman filter (NKF) parameterization is established to provide exact parameterization for finite data horizon identification problems. Based on this we propose a novel subspace identification method with NKF parameterization which can handle closed-loop data and avoid assumption on infinite horizons. It is shown that with finite data, the proposed parameterization method provides more accurate and consistent solutions than existing SKF based methods. The paper also reveals why it is often beneficial in practice to estimate a bank of ARX models over a single ARX model. [ABSTRACT FROM AUTHOR]

This paper presents an application case study of an economic model predictive control (EMPC) method for optimizing the building demand and energy cost under the time-of-use price policy. The control strategy is comprised of an economic objective function that accounts for the combination of energy and demand costs with a time-of-use rate structure, a dynamic thermal process and power model of the building thermal mass dynamics, and a set of constraints to ensure the building is operated properly. The optimization is a min–max optimization problem and is converted to a linear program. The EMPC method is implemented in a commercial office building located in Milwaukee, Wisconsin, USA. An internet-based control architecture is developed to carry out tests with the EMPC controller at a remote location. The test results show that the EMPC strategy is capable of shifting the peak demand to off-peak hours and reducing energy costs compared to a baseline case for the building. [ABSTRACT FROM AUTHOR]

Abstract: Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper. [Copyright &y& Elsevier]

Highlights: [•] The paper presents a new data-driven time series method for diagnosing the sources of plant-wide oscillations. [•] The proposed method combines the Granger causality, spectral Granger causality, and principal component feature selection. [•] Simulation and industrial case studies demonstrate that the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis–principal component analysis (FKICA–PCA), is developed. In FKICA–PCA, first, a method to calculate the variance of independent component is proposed, which is significant to make Gaussian features and non-Gaussian features comparable and to select dominant components legitimately; second, Genetic Algorithm is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate; furthermore, exponentially weighted moving average (EWMA) scheme is used to filter the monitoring indices of KICA–PCA to improve monitoring performance. In addition, a novel contribution analysis scheme is developed for FKICA–PCA to diagnosis faults. The feasibility and effectiveness of the proposed method are validated on the Tennessee Eastman (TE) process. [Copyright &y& Elsevier]

Abstract: Model quality is a main factor that affects the control performance of model-based controllers. In this paper, a new closed-loop model assessment approach is proposed to assess model deficiency from routine closed-loop data. The proposed model quality index is a minimum variance benchmark for the model residuals obtainable from closed-loop data. From the feedback invariant principle the disturbance innovations are shown to be unaffected by the feedback controller. Then it is shown that the disturbance innovations can be estimated from closed loop data by an orthogonal projection of the current output onto the space spanned by past outputs, inputs or setpoints. With the estimated disturbance innovations as the benchmark, a model quality index is developed by using the ratio of a quadratic form of model residuals and that of the estimated disturbance innovations. The effectiveness of the proposed methods is demonstrated by simulations. [Copyright &y& Elsevier]

Abstract: This paper provides a state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades. The scope of the problem is described with reference to the scale and complexity of industrial process operations, where multi-level hierarchical optimization and control are necessary for efficient operation, but are also prone to hard failure and soft operational faults that lead to economic losses. Commonly used multivariate statistical tools are introduced to characterize normal variations and detect abnormal changes. Further, diagnosis methods are surveyed and analyzed, with fault detectability and fault identifiability for rigorous analysis. Challenges, opportunities, and extensions are summarized with the intent to draw attention from the systems and control community and the process control community. [Copyright &y& Elsevier]

Purpose: To compare different imaging characteristics between hepatic benign regenerative nodules and hepatocellular carcinomas (HCCs) associated with Budd-Chiari syndrome (BCS) by contrast enhanced ultrasound (CEUS). Materials and Methods: A total of 32 chronic BCS patients (mean age, 42 years; age range, 18-59 years) with hepatic nodules who underwent CEUS were retrospectively studied. All patients had no the history of viral hepatitis. There were 23 patients with benign regenerative nodules (22±9 mm; range, 8-42 mm) and 9 patients with HCCs (63±21 mm; range, 26-90 mm). Lesion characteristics, including number, size, vascularization on color Doppler flow imaging, echogenicity, peripheral hypoechoic rim, and enhancement patterns in arterial, portal, and late phases on CEUS, were analyzed. Results: There were significant differences in number and size of the lesions between two groups. No significant differences were observed in vascularity, echogenicity, and peripheral hypoechoic rim. Overall, there were significant differences in enhancement patterns in arterial, portal, and late phases between them on CEUS. Of 23 patients with benign regenerative nodules, 16 (70%) were center-to-periphery hyperenhanced and 7 patients (30%) were homogeneously hyperenhanced in arterial phase; the majority were homogeneously hyperenhanced in portal and late phases. Of 9 patients with HCCs, 8 (89%) were heterogeneously hyperenhanced in arterial phase and most of them were hypoenhanced in portal and late phases. Conclusion: CEUS imaging characteristics of benign regenerative nodules radically differ from that of HCCs in BCS patients. [ABSTRACT FROM AUTHOR]

Abstract: The problem of accurately forecasting the amount and content of a landfill gas has remained an active area of research, due to the safety and environmental concerns, as well as use of the gas as an energy source. The key obstacle to the forecasting is that only limited measured data, such as the gas flux and composition, are usually available. In this part of the series, we propose a novel approach, based on a combination of the genetic algorithm (GA) and the ensemble Kalman filter (EnKF), to the problem of generation and dynamic updating of a landfill model. First, the GA, whose use in landfill problems was demonstrated in a previous part of this series, is used to generate the initial spatial distribution of the permeability in a landfill by assimilating the available measured data. Then, the EnKF is employed to continuously update the model using the data measured in real time. The effectiveness of the combination of the GA and EnKF is demonstrated with the simulation of a model landfill and gas generation and transport therein, developed previously in this series, using synthetic data and a parallel computational strategy. [Copyright &y& Elsevier]

Abstract: Monitoring of extracellular brain glutamate concentrations by intracerebral biosensors is a promising approach to further investigate the role of this important neurotransmitter. However, amperometric biosensors are typically hampered by Faradaic interference caused by the presence of other electroactive species in the brain, such as ascorbic acid, dopamine, and uric acid. Various permselective membranes are often used on biosensors to prevent this. In this study we evaluated the most commonly used membranes, i.e. nafion, polyphenylenediamine, polypyrrole, polyaniline, and polynaphthol using a novel silica-based platinum electrode. First we selected the membranes with the highest sensitivity for hydrogen peroxide in vitro and an optimal selectivity against electrochemical interferents. Then we evaluated the performances of these membranes in a short lasting (3–4h) in vivo experiment. We found that best in vitro performance was accomplished with biosensors that were protected by a poly(m-phenylenediamine) membrane deposited onto the platinum electrode by cyclic voltammetry. However, post-implantation evaluation of these membranes showed poor selectivity against dopamine. Combination with a previously applied nafion layer did not protect the sensors against acute biofouling; indeed it was even counter effective. Finally, we investigated the ability of our biosensors to monitor the effect of glutamate transport blocker DL-TBOA on modulating glutamate concentrations in the prefrontal cortex of anaesthetized rats. The optimized biosensors recorded a rapid 35-fold increase in extracellular glutamate, and are considered suitable for further exploration in vivo [Copyright &y& Elsevier]

Abstract: In process monitoring, several diagnosis methods have been used for fault diagnosis. These methods have been developed from different backgrounds and considerations. In this paper, five existing diagnosis methods are analyzed and generalized. It is shown that they can be unified into three general methods, making the original diagnosis methods special cases of the general ones. Also, a new form of relative contributions is proposed. An analysis of the diagnosability shows that some diagnosis methods do not guarantee correct diagnosis even for simple sensor faults with large magnitudes. For faults with modest fault magnitudes, Monte Carlo simulation is applied to compare the performance of the diagnosis methods. [Copyright &y& Elsevier]

Abstract: In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector AR model with wavelet based denoising. Given the fault direction, a new index is proposed to detect the fault, which integrates fault detection and prognosis together. Case studies on a continuous stirred tank reactor and the Tennessee Eastman process demonstrate the effectiveness of the proposed approaches. [Copyright &y& Elsevier]

Abstract: Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input X, while principal component analysis (PCA) produces unsupervised decomposition of input X. In this paper, the effect of output Y on the X-space decomposition in PLS is analyzed and geometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric way for the purpose of process monitoring. A numerical example and a case study are given to illustrate the analysis results. [Copyright &y& Elsevier]

Abstract: A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process. [Copyright &y& Elsevier]

Abstract: Although the minimum variance control (MVC) benchmark is popular for control performance monitoring, the requirement of a general interactor for multivariable processes is equivalent to knowing the Markov parameters, which is inconvenient in practice. To reduce the model requirement of a general interactor, a right diagonal interactor matrix is first used for a class of MIMO processes. Then a solution to the MIMO MVC benchmark is developed using the right diagonal interactor. Next, both the left and right diagonal interactors are integrated to characterize the complex time-delay structure of an extended class of multivariate processes. The factorization of the combined left/right diagonal interactors and the corresponding MVC benchmark estimation are also presented. The advantages of the new approach lie in the reduced a priori process knowledge and the simplified numerical procedures. A number of simulated examples are provided to illustrate the validity and effectiveness of the proposed performance monitoring approach. [Copyright &y& Elsevier]

Abstract: This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, and a combined index . Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes. [Copyright &y& Elsevier]

Abstract: A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. [Copyright &y& Elsevier]

Abstract: The problem of process systems subject to actuator faults (partial loss of actuator effectiveness) is considered. An active fault compensation control law is designed that utilizes compensation in a way that accounts for matching and unmatching uncertainties and the occurrence of actuator faults. The main idea is designing the robust compensation controller to guarantee closed-loop stability in the presence of faults, based on a neural network representation of the fault dynamics. Changes in the system due to faults are modeled as unknown nonlinear functions. The updating control law is derived such that all the parameters of the closed-loop system are bounded. An output feedback controller is used to the “healthy” system and the adaptive feedback controller is used to compensate for the effect of the dynamics caused by the fault. The advantage of fault compensation is the dynamics caused by faults can be accommodated online. The proposed design method is illustrated on a three-tank system. [Copyright &y& Elsevier]

Abstract: Selective serotonin reuptake inhibitors (SSRIs), such as Prozac®, are used to treat mood disorders. SSRIs attenuate (i.e. desensitize) serotonin 1A (5-HT1A) receptor signaling, as demonstrated in rats through decreased release of oxytocin and adrenocorticotropin hormone (ACTH) following 5-HT1A receptor stimulation. Maximal therapeutic effects of SSRIs for treatment of mood disorders, as well as effects on hypothalamic 5-HT1A receptor signaling in animals, take 1 to 2 weeks to develop. Estradiol also attenuates 5-HT1A receptor signaling, but, in rats, these effects occur within 2 days; thus, estrogens or selective estrogen receptor modulators may serve as useful short-term tools to accelerate desensitization of 5-HT1A receptors in response to SSRIs if candidate estrogen receptor targets in the hypothalamus are identified. We found high levels of GPR30, which has been identified recently as a pertussis-toxin (PTX) sensitive G-protein-coupled estrogen receptor, in the hypothalamic paraventricular nucleus (PVN) of rats. Double-label immunohistochemistry revealed that GPR30 co-localizes with 5-HT1A receptors, corticotrophin releasing factor (CRF) and oxytocin in neurons in the PVN. Pretreatment with PTX to the PVN before peripheral injections of 17-β-estradiol 3-benzoate completely prevented the reduction of the oxytocin response to the 5-HT1A receptor agonist, (+)-8-hydroxy-2-dipropylaminotetralin (DPAT). Treatment with the selective GRP30 agonist, G-1, attenuated 5-HT1A receptor signaling in the PVN as measured by an attenuated oxytocin (by 29%) and ACTH (by 31%) response to DPAT. This study indicates that a putative extra-nuclear estrogen receptor, GPR30, may play a role in estradiol-mediated attenuation of 5-HT1A receptor signaling, and potentially in accelerating the effects of SSRIs in treatment of mood disorders. [Copyright &y& Elsevier]

• A statistical learning procedure for Dow data challenge problem (Braun et al., 2020) is presented that integrates process knowledge in all steps from pre-processing and model interpretation. • An accurate inferential sensor model is built with online bias learning based on new data to predict the impurity in the product stream with apparent drifts. • Least angle regression solution (LARS) is shown to select only one variable among a set of collinear variables. • We report the detection of an equipment-switching operation in the data and interpolations found in the impurity data, which leads to unique data pre-processing measures. • Using a softplus function, we propose a method to deal with non-negative physical property modeling. In this paper, we propose a statistical learning procedure that integrates process knowledge for the Dow data challenge problem presented in Braun et al. (2020). The task is to build an accurate inferential sensor model to predict the impurity in the product stream with apparent drifts. The proposed method consists of i) process data exploratory analysis, ii) a method for variable selection, iii) a method to deal with non-negative physical property modeling using a softplus function; and iv) a method for online bias updating based on known data. We make use of process operation knowledge in all steps of data analytics, including exploratory analysis and feature selection. We report the detection of equipment-switching operations in the data and interpolations found in the impurity data. Partial least squares (PLS) and least angle regression solution (LARS) are adopted to model the data with strong collinearity. Pros and cons of LARS and PLS are given with practical implications. [ABSTRACT FROM AUTHOR]

• A novel procedure for diagnosing and troubleshooting plant-wide anomalies is proposed using dynamic embedded latent feature analysis. • Composite loadings are proposed for root cause analysis of an interested feature that improves the traditional bi-plots of loadings. • A dynamic inner canonical analysis algorithm with exogenous variables is proposed, which removes the impact of uninterested features. • A thorough application to an industrial plant with datasets before and after maintenance is presented to illustrate the proposed troubleshooting method. Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of external disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latent variables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamic latent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latent dynamic features are powerful for troubleshooting of process anomalies and diagnosing their causes in a plant-wide setting. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a data-based covariance benchmark is proposed for control performance monitoring. Within the covariance monitoring scheme, generalized eigenvalue analysis is used to extract the directions with the degraded or improved control performance against the benchmark. It is shown that the generalized eigenvalues and the covariance-based performance index are invariant to scaling of the data. A statistical inference method is further developed for the generalized eigenvalues and the corresponding confidence intervals are derived from asymptotic statistics. This procedure can be used to determine the directions or subspaces with significantly worse or better performance versus the benchmark. The covariance-based performance indices within the isolated worse and better performance subspaces are then derived to assess the performance degradation and improvement. Two simulated examples, a multiloop control and a multivariable MPC system, are provided to illustrate the utility of the proposed approach. Then an industrial wood waste burning power boiler unit is used to demonstrate the effectiveness of the method. [Copyright &y& Elsevier]

Abstract: In this paper, the data-driven control performance monitoring framework [J. Yu, S.J. Qin, Statistical MIMO controller performance monitoring. Part I: data driven covariance benchmark, J. Proc. Cont., in press] is extended to the performance diagnosis aspect with focus on variable identification. To identify the control loops or controlled variables responsible for performance degradation or improvement, two types of multivariate contribution methods are proposed. One of the diagnostic methods is a loading based contribution chart to evaluate the significance of contribution of the corresponding loop/variable. The bootstrap resampling procedure is conducted to estimate the probability distribution and statistics of the relevant eigenvector loadings. Then confidence intervals are derived for the loadings. The other approach is to examine the angle between each individual loop/variable and the worse/better performance subspace. The cosine of the angle is defined as the contribution index and shown to be the canonical correlation coefficient between a unit vector and the worse/better performance subspace. The asymptotic statistics of canonical correlation is then utilized to derive the confidence limits for the angle based contributions. Two simulated examples (a multiloop control and a multivariable MPC system) are provided to illustrate the effectiveness of the proposed performance diagnosis approaches. An industrial example from a power boiler unit is further used to show the validity of the methods. The performance diagnosis results and the numerical features of these two approaches are compared and discussed. [Copyright &y& Elsevier]