Deep learning is a recently developed feature representation technique for data with complicated structures, which has great potential for soft sensing of industrial processes. However, most deep ...networks mainly focus on hierarchical feature learning for the raw observed input data. For soft sensor applications, it is important to reduce irrelevant information and extract quality-relevant features from the raw input data for quality prediction. To deal with this problem, a novel deep learning network is proposed for quality-relevant feature representation in this article, which is based on stacked quality-driven autoencoder (SQAE). First, a quality-driven autoencoder (QAE) is designed by exploiting the quality data to guide feature extraction with the constraint that the potential features should largely reconstruct the input layer data and the quality data at the output layer. In this way, quality-relevant features can be captured by QAE. Then, by stacking multiple QAEs to construct the deep SQAE network, SQAE can gradually reduce irrelevant features and learn hierarchical quality-relevant features. Finally, the high-level quality-relevant features can be directly applied for soft sensing of the quality variables. The effectiveness and flexibility of the proposed deep learning model are validated on an industrial debutanizer column process.
Just-in-time learning (JITL) is one of the most widely used strategies for soft sensor modeling in nonlinear processes. However, traditional JITL methods have difficulty in dealing with data samples ...that contain missing values. Meanwhile, data noises and uncertainties have not been taken into consideration for relevant sample selection in existing JITL approaches. To overcome these problems, a new probabilistic JITL (P-JITL) framework is proposed in this brief. In P-JITL, variational Bayesian principal component analysis is first utilized to handle missing values and extract Gaussian posterior distributions of latent variables. Then, symmetric Kullback-Leibler divergence is creatively employed to measure the dissimilarity of two distributions for relevant sample selection in the JITL framework. Finally, a nonlinear regression model, Gaussian process regression, is carried out to model the nonlinear relationship between the output and the extracted latent variables. In this way, the proposed probabilistic JITL (P-JITL) is able to deal with missing data and select relevant samples more accurately. To evaluate the effectiveness and flexibility of P-JITL, comparative studies between P-JITL and traditional deterministic JITL (D-JITL) are carried out on a numerical example and an industrial application example, in which missing data are simulated with percentages from 0% to 50%. The results show that P-JITL can provide more accurate prediction accuracy than D-JITL in each scenario considered.
The principal component regression (PCR) based soft sensor modeling technique has been widely used for process quality prediction in the last decades. While most industrial processes are ...characterized with nonlinearity and time variance, the global linear PCR model is no longer applicable. Thus, its nonlinear and adaptive forms should be adopted. In this paper, a just-in-time learning (JITL) based locally weighted kernel principal component regression (LWKPCR) is proposed to solve the nonlinear and time-variant problems of the process. Soft sensing performance of the proposed method is validated on an industrial debutanizer column and a simulated fermentation process. Compared to the JITL-based PCR, KPCR, and LWPCR soft sensing approaches, the root-mean-square errors (RMSE) of JITL-based LWKPCR are the smallest and the prediction results match the best with the actual outputs, which indicates that the proposed method is more effective for quality prediction in nonlinear time-variant processes.
Industrial process plants are instrumented with a large number of redundant sensors and the measured variables are often contaminated by random noises. Thus, it is significant to discover the general ...trends of data by latent variable models in the probabilistic framework before soft sensor modeling. However, traditional probabilistic latent variable models such as probabilistic principal component analysis are mostly static linear approaches. The process dynamics and nonlinearities have not been well considered. In this paper, a novel weighted linear dynamic system (WLDS) is proposed for nonlinear dynamic feature extraction. In WLDS, two kinds of weights are proposed for local linearization of the nonlinear state evolution and state emission relationships. In this way, a weighted log-likelihood function is designed and expectation-maximization algorithm is then used for parameter estimation. The feasibility and effectiveness of the proposed method is demonstrated with a numerical example and an industrial process application.
In modern industrial processes, soft sensors have played increasingly important roles for effective process monitoring, control and optimization. Deep learning has shown excellent ability for ...hierarchical nonlinear feature representation in soft sensors. However, the existing deep learning based soft sensors are mostly trained offline and applied online without updating mechanism. This may cause their performance degradation in time-varying processes. To deal with this problem, an adaptive updating framework is proposed for deep learning, which is based on just-in-time fine-tuning of stacked autoencoder (JIT-SAE). In JIT-SAE, an offline SAE model is first trained with layer-wise unsupervised pre-training and supervised fine tuning. For online prediction, the network is dynamically fine-tuned upon the query sample. For each query sample, the most relevant labeled samples are selected to form a fine-tuning dataset from the historical labeled database, which is regularly augmented once new labeled samples are available from laboratory analysis. Moreover, each relevant sample is assigned with a weight according to its similarity with the query sample. Then, the deep network is fine-tuned with these relevant labeled samples by designing a weighted loss function. Thus, JIT-SAE is able to track the newest process running state timely and match the data pattern accurately. Case study on an industrial hydrocracking process is provided to demonstrate the effectiveness of the JIT-SAE framework.
Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. However, traditional JITL approaches mainly focus on equal sample sizes between process ...(input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised weighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and weight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding weights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results.
Polyetheretherketone (PEEK) is an important material applied in orthopedic applications, as it posses favorable properties for orthopedic implants, e.g., radiolucency and suitable elastic modulus. ...However, PEEK exhibits insufficient osteogenesis and osteointegration that limits its clinical applications. In this study, we aimed to enhance the osteogenisis of PEEK by using a surface coating approach. Nanocomposite coating composed of albumin/lithium containing bioactive glass nanospheres was fabricated on PEEK through dip-coating method. The presence of nanocomposite coating on PEEK was confirmed by SEM, FTIR, and XRD techniques. Nanocomposite coatings significantly enhanced hydrophilicity and roughness of PEEK. The nanocomposite coatings also enhanced adhesion, proliferation, and osteogenic differentiation of bone mesenchymal stem cells due to the presence of bioactive glass nanospheres and the BSA substrate film. The results indicate the great potential of the nanocomposite coating in enhancing osteogenesis and osteointegration of PEEK implants.
Probabilistic principal component regression (PPCR) has been introduced for soft sensor modeling as a probabilistic projection regression method, which is effective in handling data collinearity and ...random noises. However, the linear limitation of data relationships may cause its performance deterioration when applied to nonlinear processes. Therefore, a novel weighted PPCR (WPPCR) algorithm is proposed in this paper for soft sensing of nonlinear processes. In WPPCR, by including the most relevant samples for local modeling, different weights will be assigned to these samples according to their similarities with the testing sample. Then, a weighted log-likelihood function is constructed, and expectation-maximization algorithm can be carried out iteratively to obtain the optimal model parameters. In this way, the nonlinear data relationship can be locally approximated by WPPCR. The effectiveness and flexibility of the proposed method are validated on a numerical example and an industrial process.
Small RNAs (sRNAs) encoded by plant genomes have received widespread attention because they can affect multiple biological processes. Different sRNAs that are synthesized in plant cells can move ...throughout the plants, transport to plant pathogens
extracellular vesicles (EVs), and transfer to mammals
food. Small RNAs function at the target sites through DNA methylation, RNA interference, and translational repression. In this article, we reviewed the systematic processes of sRNA biogenesis, trafficking, and the underlying mechanisms of its functions.
3-nitro-1,2,4-triazol-5-one (NTO)–based polymer-bonded explosives (PBXs) have been widely used in insensitive munitions, but the main properties of NTO-based PBXs such as compatibility, safety ...performance, and mechanical properties are rarely reported. In this work, molecular dynamics simulation was carried out to study interface interactions of NTO-based PBXs, in which hydroxy-terminated polybutadiene (HTPB), ethylene–vinyl acetate copolymer (EVA), glycidyl azide polymer (GAP), poly-3-nitratomethyl-3-methyl oxetane (Poly-NIMMO), and ester urethane (Estane5703) are selected as binders. The binding energy analysis indicates that the order of compatibility is NTO/GAP > NTO/Estane5703 > NTO/HTPB > NTO/Poly-NIMMO > NTO/EVA. Radial distribution function analysis results show that the interface interaction is mainly the hydrogen bond between H atoms of NTO and O atoms of Estane5703, HTPB, EVA, and Poly-NIMMO or N atoms of GAP. The values of cohesive energy density verify that the safety is NTO/GAP > NTO/Poly-NIMMO > NTO/HTPB > NTO/EVA > NTO/Estane5703. Mechanical properties results show that GAP and EVA would improve the plasticity of the systems effectively. Furthermore, it can be found that the most favorable interactions occur between the NTO (1 0 0) crystal face and binders.
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