Subset measurement selection for globally self-optimizing control of Tennessee Eastman process

The concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. The TE process contains substantial measurements and had been studied for SOC with controlled variables selected from individual measurements through exhaustive search. This process has been revisited with improved performance recently through a retrofit approach of gSOC. To extend the improvement further, the measurement subset selection problem for gSOC is considered in this work and solved through a modification of an existing partially bidirectional branch and bound (PB3) algorithm originally developed for local SOC. The modified PB3 algorithm efficiently identifies the best measurement candidates among the full set which obtains the globally minimal economic loss. Dynamic simulations are conducted to demonstrate the optimality of proposed results

Retrofit self-optimizing control of Tennessee Eastman process

This paper considers near-optimal operation of the Tennessee Eastman (TE) process by using a retrofit self-optimizing control (SOC) approach. Motivated by the factor that most chemical plants in operation have already been equipped with a workable control system for regulatory control, we propose to improve the economic performance by controlling some self-optimizing controlled variables (CVs). Different from traditional SOC methods, the proposed retrofit SOC approach improves economic optimality of operation through newly added cascaded SOC loops, where carefully selected SOC CVs are maintained at constant by adjusting set-points of the existing regulatory control loops. To demonstrate the effectiveness of the retrofit SOC proposed, we adopted measurement combinations as the CVs for the TE process, so that the economic cost is further reduced comparing to existing studies where single measurements are controlled. The optimality of the designed control architecture is validated through both steady state analysis and dynamic simulations

Analysis on the vibration modes of the electric vehicle motor stator

The lightweight design of the electric vehicle motor brought about more serious vibration and noise problem of the motor. An accurate modal calculation was the basis for the study of the vibration and noise characteristics of the electric vehicle motor. The finite element method was used to perform the modal simulation of the PMSM. Through the reasonable simplification and equivalence of the motor stator model, the first 7 orders natural frequencies and corresponding modes of the motor stator under the free state were calculated. After that, the accuracy of the finite element model was verified by the hammering modal experiment of the prototype. Furthermore, the above results will provide the theoretical basis for the electric vehicle motor’s vibration control and NVH improvement

Latilactobacillus sakei Furu2019 and stachyose as probiotics, prebiotics, and synbiotics alleviate constipation in mice

IntroductionSlow transit constipation (STC) is a common disorder in the digestive system. This study aimed to evaluate the effects of stachyose (ST) and Latilactobacillus sakei Furu 2019 (L. sakei) alone or combined on diphenoxylate-induced constipation and explore the underlying mechanisms using a mouse model.MethodsICR mice were randomly divided into five groups. The normal and constipation model groups were intragastrically administrated with PBS. The ST, L. sakei, and synbiotic groups were intragastrically administrated with ST (1.5 g/kg body weight), alive L. sakei (3 × 109 CFU/mouse), or ST + L. sakei (1.5 g/kg plus 3 × 109 CFU/mouse), respectively. After 21 days of intervention, all mice except the normal mice were intragastrically administrated with diphenoxylate (10 mg/kg body weight). Defecation indexes, constipation-related intestinal factors, serum neurotransmitters, hormone levels, short-chain fatty acids (SCFAs), and intestinal microbiota were measured.ResultsOur results showed that three interventions with ST, L. sakei, and synbiotic combination (ST + L. sakei) all alleviated constipation, and synbiotic intervention was superior to ST or L. sakei alone in some defecation indicators. The RT-PCR and immunohistochemical experiment showed that all three interventions relieved constipation by affecting aquaporins (AQP4 and AQP8), interstitial cells of Cajal (SCF and c-Kit), glial cell-derived neurotrophic factor (GDNF), and Nitric Oxide Synthase (NOS). The three interventions exhibited a different ability to increase the serum excitatory neurotransmitters and hormones (5-hydroxytryptamine, substance P, motilin), and reduce the serum inhibitory neurotransmitters (vasoactive intestinal peptide, endothelin). The result of 16S rDNA sequencing of feces showed that synbiotic intervention significantly increased the relative abundance of beneficial bacteria such as Akkermansia, and regulated the gut microbes of STC mice. In conclusion, oral administration of ST or L. sakei alone or combined are all effective to relieve constipation and the symbiotic use may have a promising preventive effect on STC

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study

Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods

Multivariate Statistical Process Control: Process Monitoring Methods and Applications

  Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas.   Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers

Multi-scale Fusion of Stretched Infrared and Visible Images

Infrared (IR) band sensors can capture digital images under challenging conditions, such as haze, smoke, and fog, while visible (VIS) band sensors seize abundant texture information. It is desired to fuse IR and VIS images to generate a more informative image. In this paper, a novel multi-scale IR and VIS images fusion algorithm is proposed to integrate information from both the images into the fused image and preserve the color of the VIS image. A content-adaptive gamma correction is first introduced to stretch the IR images by using one of the simplest edge-preserving filters, which alleviates excessive luminance shifts and color distortions in the fused images. New contrast and exposedness measures are then introduced for the stretched IR and VIS images to achieve weight matrices that are more in line with their characteristics. The IR and luminance components of the VIS image in grayscale or RGB space are fused by using the Gaussian and Laplacian pyramids. The RGB components of the VIS image are finally expanded to generate the fused image if necessary. Comparisons experimentally demonstrate the effectiveness of the proposed algorithm to 10 different state-of-the-art fusion algorithms in terms of computational cost and quality of the fused images

Recursive Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch Processes

In chemical batch processes with slow responses and a long duration, it is time-consuming and expensive to obtain sufficient normal data for statistical analysis. With the persistent accumulation of the newly evolving data, the modelling becomes adequate gradually and the subsequent batches will change slightly owing to the slow time-varying behavior. To efficiently make use of the small amount of initial data and the newly evolving data sets, an adaptive monitoring scheme based on the recursive Gaussian process (RGP) model is designed in this paper. Based on the initial data, a Gaussian process model and the corresponding SPE statistic are constructed at first. When the new batches of data are included, a strategy based on the RGP model is used to choose the proper data for model updating. The performance of the proposed method is finally demonstrated by a penicillin fermentation batch process and the result indicates that the proposed monitoring scheme is effective for adaptive modelling and online monitoring